future of computer essay

Essay on Future of Computer

Students are often asked to write an essay on Future of Computer in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

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100 Words Essay on Future of Computer

The future of computers.

Computers are becoming smarter every day. They can now do tasks that were once only possible for humans. In the future, they may even start thinking like us!

Artificial Intelligence

Virtual reality.

Virtual Reality (VR) is another exciting area. It allows us to enter computer-created worlds. This could change how we learn, play, and work.

Quantum Computing

Quantum computing is a new technology that could make computers incredibly fast. This could help solve problems that are currently too hard for regular computers.

250 Words Essay on Future of Computer

The evolution of computers.

AI is set to revolutionize the future of computers. Machine learning algorithms, a subset of AI, are becoming increasingly adept at pattern recognition and predictive analysis. This will lead to computers that can learn and adapt to their environment, making them more intuitive and user-friendly.

Quantum computing, using quantum bits or ‘qubits’, is another frontier. Unlike traditional bits that hold a value of either 0 or 1, qubits can exist in multiple states simultaneously. This allows quantum computers to perform complex calculations at unprecedented speeds. While still in its infancy, quantum computing could redefine computational boundaries.

Cloud Technology

Cloud technology is poised to further transform computer usage. With most data and applications moving to the cloud, the need for powerful personal computers may diminish. Instead, thin clients or devices with minimal hardware, relying on the cloud for processing and storage, could become the norm.

The future of computers is a fascinating blend of AI, quantum computing, and cloud technology. As these technologies mature, we can expect computers to become even more integral to our lives, reshaping society in profound ways. The only certainty is that the pace of change will continue to accelerate, making the future of computers an exciting realm of endless possibilities.

500 Words Essay on Future of Computer

The evolution of computing.

Computers have revolutionized the way we live, work, and play. From their early inception as room-sized machines to the sleek, pocket-sized devices we have today, computers have evolved dramatically. However, this is only the tip of the iceberg. The future of computing promises to be even more exciting and transformative.

One of the most anticipated advancements in the realm of computer science is quantum computing. Unlike classical computers, which use bits (0s and 1s) for processing information, quantum computers use quantum bits, or “qubits”. Qubits can exist in multiple states at once, a phenomenon known as superposition. This allows quantum computers to process vast amounts of data simultaneously, potentially solving complex problems that are currently beyond the capabilities of classical computers.

Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are two other areas poised to shape the future of computing. AI refers to the ability of a machine to mimic human intelligence, while ML is a subset of AI that involves the ability of machines to learn and improve without being explicitly programmed. As these technologies advance, we can expect computers to become more autonomous, capable of complex decision-making and problem-solving.

Neuromorphic Computing

Neuromorphic computing, another promising field, aims to mimic the human brain’s architecture and efficiency. By leveraging the principles of neural networks, neuromorphic chips can process information more efficiently than traditional processors, making them ideal for applications requiring real-time processing and low power consumption.

Edge Computing

As the Internet of Things (IoT) continues to expand, so does the need for edge computing. Edge computing involves processing data closer to its source, reducing latency and bandwidth usage. This technology is crucial for real-time applications, such as autonomous vehicles and smart cities, where instant data processing is vital.

Conclusion: The Future is Now

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Where computing might go next

The future of computing depends in part on how we reckon with its past.

• Margaret O’Mara archive page

If the future of computing is anything like its past, then its trajectory will depend on things that have little to do with computing itself. 

Technology does not appear from nowhere. It is rooted in time, place, and opportunity. No lab is an island; machines’ capabilities and constraints are determined not only by the laws of physics and chemistry but by who supports those technologies, who builds them, and where they grow. 

Popular characterizations of computing have long emphasized the quirkiness and brilliance of those in the field, portraying a rule-breaking realm operating off on its own. Silicon Valley’s champions and boosters have perpetuated the mythos of an innovative land of garage startups and capitalist cowboys. The reality is different. Computing’s history is modern history—and especially American history—in miniature.

The United States’ extraordinary push to develop nuclear and other weapons during World War II unleashed a torrent of public spending on science and technology. The efforts thus funded trained a generation of technologists and fostered multiple computing projects, including ENIAC —the first all-digital computer, completed in 1946. Many of those funding streams eventually became permanent, financing basic and applied research at a scale unimaginable before the war. 

The strategic priorities of the Cold War drove rapid development of transistorized technologies on both sides of the Iron Curtain. In a grim race for nuclear supremacy amid an optimistic age of scientific aspiration, government became computing’s biggest research sponsor and largest single customer. Colleges and universities churned out engineers and scientists. Electronic data processing defined the American age of the Organization Man, a nation built and sorted on punch cards. 

The space race, especially after the Soviets beat the US into space with the launch of the Sputnik orbiter in late 1957, jump-started a silicon semiconductor industry in a sleepy agricultural region of Northern California, eventually shifting tech’s center of entrepreneurial gravity from East to West. Lanky engineers in white shirts and narrow ties turned giant machines into miniature electronic ones, sending Americans to the moon. (Of course, there were also women playing key, though often unrecognized, roles.) 

In 1965, semiconductor pioneer Gordon Moore, who with colleagues had broken ranks with his boss William Shockley of Shockley Semiconductor to launch a new company, predicted that the number of transistors on an integrated circuit would double every year while costs would stay about the same. Moore’s Law was proved right. As computing power became greater and cheaper, digital innards replaced mechanical ones in nearly everything from cars to coffeemakers.

A new generation of computing innovators arrived in the Valley, beneficiaries of America’s great postwar prosperity but now protesting its wars and chafing against its culture. Their hair grew long; their shirts stayed untucked. Mainframes were seen as tools of the Establishment, and achievement on earth overshadowed shooting for the stars. Small was beautiful. Smiling young men crouched before home-brewed desktop terminals and built motherboards in garages. A beatific newly minted millionaire named Steve Jobs explained how a personal computer was like a bicycle for the mind. Despite their counterculture vibe, they were also ruthlessly competitive businesspeople. Government investment ebbed and private wealth grew. 

The ARPANET became the commercial internet. What had been a walled garden accessible only to government-funded researchers became an extraordinary new platform for communication and business, as the screech of dial-up modems connected millions of home computers to the World Wide Web. Making this strange and exciting world accessible were very young companies with odd names: Netscape, eBay, Amazon.com, Yahoo.

By the turn of the millennium, a president had declared that the era of big government was over and the future lay in the internet’s vast expanse. Wall Street clamored for tech stocks, then didn’t; fortunes were made and lost in months. After the bust, new giants emerged. Computers became smaller: a smartphone in your pocket, a voice assistant in your kitchen. They grew larger, into the vast data banks and sprawling server farms of the cloud. 

Fed with oceans of data, largely unfettered by regulation, computing got smarter. Autonomous vehicles trawled city streets, humanoid robots leaped across laboratories, algorithms tailored social media feeds and matched gig workers to customers. Fueled by the explosion of data and computation power, artificial intelligence became the new new thing. Silicon Valley was no longer a place in California but shorthand for a global industry, although tech wealth and power were consolidated ever more tightly in five US-based companies with a combined market capitalization greater than the GDP of Japan. 

It was a trajectory of progress and wealth creation that some believed inevitable and enviable. Then, starting two years ago, resurgent nationalism and an economy-­upending pandemic scrambled supply chains, curtailed the movement of people and capital, and reshuffled the global order. Smartphones recorded death on the streets and insurrection at the US Capitol. AI-enabled drones surveyed the enemy from above and waged war on those below. Tech moguls sat grimly before congressional committees, their talking points ringing hollow to freshly skeptical lawmakers. 

Our relationship with computing had suddenly changed.

The past seven decades have produced stunning breakthroughs in science and engineering. The pace and scale of change would have amazed our mid-20th-century forebears. Yet techno-optimistic assurances about the positive social power of a networked computer on every desk have proved tragically naïve. The information age of late has been more effective at fomenting discord than advancing enlightenment, exacerbating social inequities and economic inequalities rather than transcending them. 

The technology industry—produced and made wealthy by these immense advances in computing—has failed to imagine alternative futures both bold and practicable enough to address humanity’s gravest health and climatic challenges. Silicon Valley leaders promise space colonies while building grand corporate headquarters below sea level. They proclaim that the future lies in the metaverse , in the blockchain, in cryptocurrencies whose energy demands exceed those of entire nation-states. 

The future of computing feels more tenuous, harder to map in a sea of information and disruption. That is not to say that predictions are futile, or that those who build and use technology have no control over where computing goes next. To the contrary: history abounds with examples of individual and collective action that altered social and political outcomes. But there are limits to the power of technology to overcome earthbound realities of politics, markets, and culture. 

To understand computing’s future, look beyond the machine.

1. The hoodie problem

First, look to who will get to build the future of computing.

The tech industry long celebrated itself as a meritocracy, where anyone could get ahead on the strength of technical know-how and innovative spark. This assertion has been belied in recent years by the persistence of sharp racial and gender imbalances, particularly in the field’s topmost ranks. Men still vastly outnumber women in the C-suites and in key engineering roles at tech companies. Venture capital investors and venture-backed entrepreneurs remain mostly white and male. The number of Black and Latino technologists of any gender remains shamefully tiny. 

Much of today’s computing innovation was born in Silicon Valley . And looking backward, it becomes easier to understand where tech’s meritocratic notions come from, as well as why its diversity problem has been difficult to solve. 

Silicon Valley was once indeed a place where people without family money or connections could make a career and possibly a fortune. Those lanky engineers of the Valley’s space-age 1950s and 1960s were often heartland boys from middle-class backgrounds, riding the extraordinary escalator of upward mobility that America delivered to white men like them in the prosperous quarter-century after the end of World War II.  

Many went to college on the GI Bill and won merit scholarships to places like Stanford and MIT, or paid minimal tuition at state universities like the University of California, Berkeley. They had their pick of engineering jobs as defense contracts fueled the growth of the electronics industry. Most had stay-at-home wives whose unpaid labor freed husbands to focus their energy on building new products, companies, markets. Public investments in suburban infrastructure made their cost of living reasonable, the commutes easy, the local schools excellent. Both law and market discrimination kept these suburbs nearly entirely white. 

In the last half-century, political change and market restructuring slowed this escalator of upward mobility to a crawl , right at the time that women and minorities finally had opportunities to climb on. By the early 2000s, the homogeneity among those who built and financed tech products entrenched certain assumptions: that women were not suited for science, that tech talent always came dressed in a hoodie and had attended an elite school—whether or not someone graduated. It limited thinking about what problems to solve, what technologies to build, and what products to ship. 

Having so much technology built by a narrow demographic—highly educated, West Coast based, and disproportionately white, male, and young—becomes especially problematic as the industry and its products grow and globalize. It has fueled considerable investment in driverless cars without enough attention to the roads and cities these cars will navigate. It has propelled an embrace of big data without enough attention to the human biases contained in that data . It has produced social media platforms that have fueled political disruption and violence at home and abroad. It has left rich areas of research and potentially vast market opportunities neglected.

Computing’s lack of diversity has always been a problem, but only in the past few years has it become a topic of public conversation and a target for corporate reform. That’s a positive sign. The immense wealth generated within Silicon Valley has also created a new generation of investors, including women and minorities who are deliberately putting their money in companies run by people who look like them. 

But change is painfully slow. The market will not take care of imbalances on its own.

For the future of computing to include more diverse people and ideas, there needs to be a new escalator of upward mobility: inclusive investments in research, human capital, and communities that give a new generation the same assist the first generation of space-age engineers enjoyed. The builders cannot do it alone.

2. Brainpower monopolies

Then, look at who the industry's customers are and how it is regulated.

The military investment that undergirded computing’s first all-digital decades still casts a long shadow. Major tech hubs of today—the Bay Area, Boston, Seattle, Los Angeles—all began as centers of Cold War research and military spending. As the industry further commercialized in the 1970s and 1980s, defense activity faded from public view, but it hardly disappeared. For academic computer science, the Pentagon became an even more significant benefactor starting with Reagan-era programs like the Strategic Defense Initiative, the computer-­enabled system of missile defense memorably nicknamed “Star Wars.” 

In the past decade, after a brief lull in the early 2000s, the ties between the technology industry and the Pentagon have tightened once more. Some in Silicon Valley protest its engagement in the business of war, but their objections have done little to slow the growing stream of multibillion-dollar contracts for cloud computing and cyberweaponry. It is almost as if Silicon Valley is returning to its roots. 

Defense work is one dimension of the increasingly visible and freshly contentious entanglement between the tech industry and the US government. Another is the growing call for new technology regulation and antitrust enforcement, with potentially significant consequences for how technological research will be funded and whose interests it will serve. 

The extraordinary consolidation of wealth and power in the technology sector and the role the industry has played in spreading disinformation and sparking political ruptures have led to a dramatic change in the way lawmakers approach the industry. The US has had little appetite for reining in the tech business since the Department of Justice took on Microsoft 20 years ago. Yet after decades of bipartisan chumminess and laissez-faire tolerance, antitrust and privacy legislation is now moving through Congress. The Biden administration has appointed some of the industry’s most influential tech critics to key regulatory roles and has pushed for significant increases in regulatory enforcement. 

The five giants—Amazon, Apple, Facebook, Google, and Microsoft—now spend as much or more lobbying in Washington, DC, as banks, pharmaceutical companies, and oil conglomerates, aiming to influence the shape of anticipated regulation. Tech leaders warn that breaking up large companies will open a path for Chinese firms to dominate global markets, and that regulatory intervention will squelch the innovation that made Silicon Valley great in the first place.

Viewed through a longer lens, the political pushback against Big Tech’s power is not surprising. Although sparked by the 2016 American presidential election, the Brexit referendum, and the role social media disinformation campaigns may have played in both, the political mood echoes one seen over a century ago. 

We might be looking at a tech future where companies remain large but regulated, comparable to the technology and communications giants of the middle part of the 20th century. This model did not squelch technological innovation. Today, it could actually aid its growth and promote the sharing of new technologies. 

Take the case of AT&T, a regulated monopoly for seven decades before its ultimate breakup in the early 1980s. In exchange for allowing it to provide universal telephone service, the US government required AT&T to stay out of other communication businesses, first by selling its telegraph subsidiary and later by steering clear of computing. 

Like any for-profit enterprise, AT&T had a hard time sticking to the rules, especially after the computing field took off in the 1940s. One of these violations resulted in a 1956 consent decree under which the US required the telephone giant to license the inventions produced in its industrial research arm, Bell Laboratories, to other companies. One of those products was the transistor. Had AT&T not been forced to share this and related technological breakthroughs with other laboratories and firms, the trajectory of computing would have been dramatically different.

Right now, industrial research and development activities are extraordinarily concentrated once again. Regulators mostly looked the other way over the past two decades as tech firms pursued growth at all costs, and as large companies acquired smaller competitors. Top researchers left academia for high-paying jobs at the tech giants as well, consolidating a huge amount of the field’s brainpower in a few companies. 

More so than at any other time in Silicon Valley’s ferociously entrepreneurial history, it is remarkably difficult for new entrants and their technologies to sustain meaningful market share without being subsumed or squelched by a larger, well-capitalized, market-dominant firm. More of computing’s big ideas are coming from a handful of industrial research labs and, not surprisingly, reflecting the business priorities of a select few large tech companies.

Tech firms may decry government intervention as antithetical to their ability to innovate. But follow the money, and the regulation, and it is clear that the public sector has played a critical role in fueling new computing discoveries—and building new markets around them—from the start. 

3. Location, location, location

Last, think about where the business of computing happens.

The question of where “the next Silicon Valley” might grow has consumed politicians and business strategists around the world for far longer than you might imagine. French president Charles de Gaulle toured the Valley in 1960 to try to unlock its secrets. Many world leaders have followed in the decades since. 

Silicon Somethings have sprung up across many continents, their gleaming research parks and California-style subdivisions designed to lure a globe-trotting workforce and cultivate a new set of tech entrepreneurs. Many have fallen short of their startup dreams, and all have fallen short of the standard set by the original, which has retained an extraordinary ability to generate one blockbuster company after another, through boom and bust. 

While tech startups have begun to appear in a wider variety of places, about three in 10 venture capital firms and close to 60% of available investment dollars remain concentrated in the Bay Area. After more than half a century, it remains the center of computing innovation. 

It does, however, have significant competition. China has been making the kinds of investments in higher education and advanced research that the US government made in the early Cold War, and its technology and internet sectors have produced enormous companies with global reach. 

The specter of Chinese competition has driven bipartisan support for renewed American tech investment, including a potentially massive infusion of public subsidies into the US semiconductor industry. American companies have been losing ground to Asian competitors in the chip market for years. The economy-choking consequences of this became painfully clear when covid-related shutdowns slowed chip imports to a trickle, throttling production of the many consumer goods that rely on semiconductors to function.

As when Japan posed a competitive threat 40 years ago, the American agitation over China runs the risk of slipping into corrosive stereotypes and lightly veiled xenophobia. But it is also true that computing technology reflects the state and society that makes it, whether it be the American military-industrial complex of the late 20th century, the hippie-­influenced West Coast culture of the 1970s, or the communist-capitalist China of today. 

What’s next

Historians like me dislike making predictions. We know how difficult it is to map the future, especially when it comes to technology, and how often past forecasters have gotten things wrong. 

Intensely forward-thinking and impatient with incrementalism, many modern technologists—especially those at the helm of large for-profit enterprises—are the opposite. They disdain politics, and resist getting dragged down by the realities of past and present as they imagine what lies over the horizon. They dream of a new age of quantum computers and artificial general intelligence, where machines do most of the work and much of the thinking. 

They could use a healthy dose of historical thinking. 

Whatever computing innovations will appear in the future, what matters most is how our culture, businesses, and society choose to use them. And those of us who analyze the past also should take some inspiration and direction from the technologists who have imagined what is not yet possible. Together, looking forward and backward, we may yet be able to get where we need to go. 

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Envisioning the future of computing

MIT students share ideas, aspirations, and vision for how advances in computing stand to transform society in a competition hosted by the Social and Ethical Responsibilities of Computing.

How will advances in computing transform human society?

MIT students contemplated this impending question as part of the Envisioning the Future of Computing Prize — an essay contest in which they were challenged to imagine ways that computing technologies could improve our lives, as well as the pitfalls and dangers associated with them.

Offered for the first time this year, the Institute-wide competition invited MIT undergraduate and graduate students to share their ideas, aspirations, and vision for what they think a future propelled by advancements in computing holds. Nearly 60 students put pen to paper, including those majoring in mathematics, philosophy, electrical engineering and computer science, brain and cognitive sciences, chemical engineering, urban studies and planning, and management, and entered their submissions.

Students dreamed up highly inventive scenarios for how the technologies of today and tomorrow could impact society, for better or worse. Some recurring themes emerged, such as tackling issues in climate change and health care. Others proposed ideas for particular technologies that ranged from digital twins as a tool for navigating the deluge of information online to a cutting-edge platform powered by artificial intelligence, machine learning, and biosensors to create personalized storytelling films that help individuals understand themselves and others.

Conceived of by the Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative of the MIT Schwarzman College of Computing in collaboration with the School of Humanities, Arts, and Social Sciences (SHASS), the intent of the competition was “to create a space for students to think in a creative, informed, and rigorous way about the societal benefits and costs of the technologies they are or will be developing,” says Caspar Hare, professor of philosophy, co-associate dean of SERC, and the lead organizer of the Envisioning the Future of Computing Prize. “We also wanted to convey that MIT values such thinking.”

Prize winners

The contest implemented a two-stage evaluation process wherein all essays were reviewed anonymously by a panel of MIT faculty members from the college and SHASS for the initial round. Three qualifiers were then invited to present their entries at an awards ceremony on May 8, followed by a Q&A with a judging panel and live in-person audience for the final round.

The winning entry was awarded to Robert Cunningham ’23, a recent graduate in math and physics, for his paper on the implications of a personalized language model that is fine-tuned to predict an individual’s writing based on their past texts and emails. Told from the perspective of three fictional characters: Laura, founder of the tech startup ScribeAI, and Margaret and Vincent, a couple in college who are frequent users of the platform, readers gained insights into the societal shifts that take place and the unforeseen repercussions of the technology.

Cunningham, who took home the grand prize of $10,000, says he came up with the concept for his essay in late January while thinking about the upcoming release of GPT-4 and how it might be applied. Created by the developers of ChatGPT — an AI chatbot that has managed to capture popular imagination for its capacity to imitate human-like text, images, audio, and code — GPT-4, which was unveiled in March, is the newest version of OpenAI’s language model systems.

“GPT-4 is wild in reality, but some rumors before it launched were even wilder, and I had a few long plane rides to think about them! I enjoyed this opportunity to solidify a vague notion into a piece of writing, and since some of my favorite works of science fiction are short stories, I figured I’d take the chance to write one,” Cunningham says.

The other two finalists , awarded $5,000 each, included Gabrielle Kaili-May Liu ’23, a recent graduate in mathematics with computer science, and brain and cognitive sciences, for her entry on using the reinforcement learning with human feedback technique as a tool for transforming human interactions with AI; and Abigail Thwaites and Eliot Matthew Watkins, graduate students in the Department of Philosophy and Linguistics, for their joint submission on automatic fact checkers, an AI-driven software that they argue could potentially help mitigate the spread of misinformation and be a profound social good.

“We were so excited to see the amazing response to this contest. It made clear how much students at MIT, contrary to stereotype, really care about the wider implications of technology, says Daniel Jackson, professor of computer science and one of the final-round judges. “So many of the essays were incredibly thoughtful and creative. Robert’s story was a chilling, but entirely plausible take on our AI future; Abigail and Eliot’s analysis brought new clarity to what harms misinformation actually causes; and Gabrielle’s piece gave a lucid overview of a prominent new technology. I hope we’ll be able to run this contest every year, and that it will encourage all our students to broaden their perspectives even further.”

Fellow judge Graham Jones, professor of anthropology, adds: “The winning entries reflected the incredible breadth of our students’ engagement with socially responsible computing. They challenge us to think differently about how to design computational technologies, conceptualize social impacts, and imagine future scenarios. Working with a cross-disciplinary panel of judges catalyzed lots of new conversations. As a sci-fi fan, I was thrilled that the top prize went to a such a stunning piece of speculative fiction!”

Other judges on the panel for the final round included:

• Dan Huttenlocher, dean of the MIT Schwarzman College of Computing;
• Aleksander Madry, Cadence Design Systems Professor of Computer Science;
• Asu Ozdaglar, deputy dean of academics for the MIT Schwarzman College of Computing and head of the Department of Electrical Engineering and Computer Science;
• Georgia Perakis, co-associate dean of SERC and the William F. Pounds Professor of Management; and
• Agustin Rayo, dean of the MIT School of Humanities, Arts, and Social Sciences.

Honorable mentions

In addition to the grand prize winner and runners up, 12 students were recognized with honorable mentions for their entries, with each receiving $500.

The honorees and the title of their essays include:

• Alexa Reese Canaan, Technology and Policy Program, “A New Way Forward: The Internet & Data Economy”;
• Fernanda De La Torre Romo, Department of Brain and Cognitive Sciences, “The Empathic Revolution Using AI to Foster Greater Understanding and Connection”;
• Samuel Florin, Mathematics, “Modeling International Solutions for the Climate Crisis;”
• Claire Gorman, Department of Urban Studies and Planning (DUSP), “Grounding AI- Envisioning Inclusive Computing for Soil Carbon Applications”;
• Kevin Hansom, MIT Sloan School of Management, “Quantum Powered Personalized Pharmacogenetic Development and Distribution Model”;
• Sharon Jiang, Department of Electrical Engineering and Computer Science (EECS), “Machine Learning Driven Transformation of Electronic Health Records”;
• Cassandra Lee, Media Lab, “Considering an Anti-convenience Funding Body”;
• Martin Nisser, EECS, “Towards Personalized On-Demand Manufacturing;
• Andi Qu, EECS, “Revolutionizing Online Learning with Digital Twins”;
• David Bradford Ramsay, Media Lab, “The Perils and Promises of Closed Loop Engagement”;
• Shuvom Sadhuka, EECS, “Overcoming the False Trade-off in Genomics: Privacy and Collaboration”; and
• Leonard Schrage, DUSP, “Embodied-Carbon-Computing.”

The Envisioning the Future of Computing Prize was supported by MAC3 Impact Philanthropies.

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Century of Science: Theme

• The future of computing

Everywhere and invisible

You are likely reading this on a computer. You are also likely taking that fact for granted. That’s even though the device in front of you would have astounded computer scientists just a few decades ago, and seemed like sheer magic much before that. It contains billions of tiny computing elements, running millions of lines of software instructions, collectively written by countless people across the globe. The result: You click or tap or type or speak, and the result seamlessly appears on the screen.

Computers once filled rooms. Now they’re everywhere and invisible, embedded in watches, car engines, cameras, televisions and toys. They manage electrical grids, analyze scientific data and predict the weather. The modern world would be impossible without them, and our dependence on them for health, prosperity and entertainment will only increase.

Scientists hope to make computers faster yet, to make programs more intelligent and to deploy technology in an ethical manner. But before looking at where we go from here, let’s review where we’ve come from.

In 1833, the English mathematician Charles Babbage conceived a programmable machine that presaged today’s computing architecture, featuring a “store” for holding numbers, a “mill” for operating on them, an instruction reader and a printer. This Analytical Engine also had logical functions like branching (if X, then Y). Babbage constructed only a piece of the machine, but based on its description, his acquaintance Ada Lovelace saw that the numbers it might manipulate could represent anything, even music, making it much more general-purpose than a calculator. “A new, a vast, and a powerful language is developed for the future use of analysis,” she wrote. She became an expert in the proposed machine’s operation and is often called the first programmer.

In 1936, the English mathematician Alan Turing introduced the idea of a computer that could rewrite its own instructions , making it endlessly programmable. His mathematical abstraction could, using a small vocabulary of operations, mimic a machine of any complexity, earning it the name “universal Turing machine.”

The first reliable electronic digital computer, Colossus, was completed in 1943, to help England decipher wartime codes. It used vacuum tubes — devices for controlling the flow of electrons — instead of moving mechanical parts like the Analytical Engine’s cogwheels. This made Colossus fast, but engineers had to manually rewire it every time they wanted to perform a new task. Perhaps inspired by Turing’s concept of a more easily reprogrammable computer, the team that created the United States’ first electronic digital computer , ENIAC, drafted a new architecture for its successor, the EDVAC. The mathematician John von Neumann, who penned the EDVAC’s design in 1945, described a system that could store programs in its memory alongside data and alter the programs, a setup now called the von Neumann architecture. Nearly every computer today follows that paradigm.

In 1947, researchers at Bell Telephone Laboratories invented the transistor , a piece of circuitry in which the application of voltage (electrical pressure) or current controls the flow of electrons between two points. It came to replace the slower and less efficient vacuum tubes. In 1958 and 1959, researchers at Texas Instruments and Fairchild Semiconductor independently invented integrated circuits, in which transistors and their supporting circuitry were fabricated on a chip in one process.

For a long time, only experts could program computers. Then in 1957, IBM released FORTRAN, a programming language that was much easier to understand. It’s still in use today. In 1981 the company unveiled the IBM PC and Microsoft released its operating system called MS-DOS, together expanding the reach of computers into homes and offices. Apple further personalized computing with the operating systems for its Lisa, in 1982, and Macintosh, in 1984. Both systems popularized graphical user interfaces, or GUIs, offering users a mouse cursor instead of a command line.

Meanwhile, researchers had been doing work that would end up connecting our newfangled hardware and software. In 1948, the mathematician Claude Shannon published “ A Mathematical Theory of Communication ,” a paper that popularized the word bit (for binary digit) and laid the foundation for information theory . His ideas have shaped computation and in particular the sharing of data over wires and through the air. In 1969, the U.S. Advanced Research Projects Agency created a computer network called ARPANET, which later merged with other networks to form the internet. In 1990, researchers at CERN — a European laboratory near Geneva, Switzerland — developed rules for transmitting data that would become the foundation of the World Wide Web.

Better hardware, better software and better communication have now connected most of the people on the planet. But how much better can the processors get? How smart can algorithms become? And what kinds of benefits and dangers should we expect to see as technology advances? Stuart Russell, a computer scientist at University of California, Berkeley and coauthor of a popular textbook on artificial intelligence, sees great potential for computers in “expanding artistic creativity, accelerating science, serving as diligent personal assistants, driving cars and — I hope — not killing us.” — Matthew Hutson

Chasing speed

Computers, for the most part, speak the language of bits. They store information — whether it’s music, an application or a password — in strings of 1s and 0s. They also process information in a binary fashion, flipping transistors between an “on” and “off” state. The more transistors in a computer, the faster it can process bits, making possible everything from more realistic video games to safer air traffic control.

Combining transistors forms one of the building blocks of a circuit, called a logic gate. An AND logic gate, for example, is on if both inputs are on, while an OR is on if at least one input is on. Together, logic gates compose a complex traffic pattern of electrons, the physical manifestation of computation. A computer chip can contain millions of such logic gates.

So the more logic gates, and by extension the more transistors, the more powerful the computer. In 1965, Gordon Moore, a cofounder of Fairchild Semiconductor and later of Intel, published a paper on the future of chips titled “Cramming More Components onto Integrated Circuits.” He graphed the number of components (mostly transistors) on five integrated circuits (chips) that had been built from 1959 to 1965, and extended the line. Transistors per chip had doubled every year, and he expected the trend to continue.

In a 1975 talk, Moore identified three factors behind this exponential growth: smaller transistors, bigger chips and “device and circuit cleverness,” such as less wasted space. He expected the doubling to occur every two years. It did, and continued doing so for decades. That trend is now called Moore’s law.

Moore’s law is not a physical law, like Newton’s law of universal gravitation. It was meant as an observation about economics. There will always be incentives to make computers faster and cheaper — but at some point, physics interferes. Chip development can’t keep up with Moore’s law forever, as it becomes more difficult to make transistors tinier. According to what’s jokingly called Moore’s second law, the cost of chip fabrication plants, or “fabs,” doubles every few years. The semiconductor company TSMC has considered building a plant that would cost $25 billion.

Today, Moore’s law no longer holds; doubling is happening at a slower rate. We continue to squeeze more transistors onto chips with each generation, but the generations come less frequently. Researchers are looking into several ways forward: better transistors, more specialized chips, new chip concepts and software hacks.  

Computer performance from 1985 through 2015

Until about 2005, the ability to squeeze more transistors onto each chip meant exponential improvements in computer performance (black and gray show an industry benchmark for computers with one or more “cores,” or processors). Likewise, clock frequency (green) — the number of cycles of operations performed per second — improved exponentially. Since this “Dennard-scaling era,” transistors have continued to shrink but that shrinking hasn’t yielded the same performance benefits.

Transistors

Transistors can get smaller still. Conceptually, a transistor consists of three basic elements. A metal gate (different from the logic gates above) lays across the middle of a semiconductor, one side of which acts as an electron source, and the other side a drain. Current passes from source to drain, and then on down the road, when the gate has a certain voltage. Many transistors are of a design called FinFET, because the channel from source to drain sticks up like a fin or a row of fins. The gate is like a larger, perpendicular wall that the fins pass through. It touches each fin on both sides and the top.

But, according to Sanjay Natarajan, who leads transistor design at Intel, “we’ve squeezed, we believe, everything you can squeeze out of that architecture.” In the next few years, chip manufacturers will start producing gate-all-around transistors, in which the channel resembles vertically stacked wires or ribbons penetrating the gate. These transistors will be faster and require less energy and space.

Transistors revisited

New transistor designs, a shift from the common FinFET (left) to gate-all-around transistors (right), for example, can make transistors that are smaller, faster and require less energy.

As these components have shrunk, the terminology to describe their size has gotten more confusing. You sometimes hear about chips being “14 nanometers” or “10 nanometers” in size; top-of-the-line chips in 2021 are “5 nanometers.” These numbers do not refer to the width or any other dimension of a transistor. They used to refer to the size of particular transistor features, but for several years now they have been nothing more than marketing terms.

Chip design

Even if transistors were to stop shrinking, computers would still have a lot of runway to improve, through Moore’s “device and circuit cleverness.”

A large hindrance to speeding up chips is the amount of heat they produce while moving electrons around. Too much and they’ll melt. For years, Moore’s law was accompanied by Dennard scaling, named after electrical engineer Robert Dennard, who said that as transistors shrank, they would also become faster and more energy efficient. That was true until around 2005, when they became so thin that they leaked too much current, heating up the chip. Since then, computer clock speed — the number of cycles of operations performed per second — hasn’t increased beyond a few gigahertz.

• Materials that made us
• Unsung characters

Core memory weavers and Navajo women made the Apollo missions possible

The stories of the women who assembled integrated circuits and wove core memory for the Apollo missions remain largely unknown.

Computers are limited in how much power they can draw and in how much heat they can disperse. Since the mid-2000s, according to Tom Conte, a computer scientist at Georgia Tech in Atlanta who co-leads the IEEE Rebooting Computing Initiative, “power savings has been the name of the game.” So engineers have turned to making chips perform several operations simultaneously, or splitting a chip into multiple parallel “cores,” to eke more operations from the same clock speed. But programming for parallel circuits is tricky.

Another speed bump is that electrons often have to travel long distances between logic gates or between chips — which also produces a lot of heat. One solution to the delays and heat production of data transmission is to move transistors closer together. Some nascent efforts have looked at stacking them vertically. More near-term, others are stacking whole chips vertically. Another solution is to replace electrical wiring with fiber optics, as light transmits information faster and more efficiently than electrical current does.

Increasingly, computers rely on specialized chips or regions of a chip, called accelerators. Arranging transistors differently can put them to better use for specific applications. A cell phone, for instance, may have different circuitry designed for processing graphics, sound, wireless transmission and GPS signals.

“Sanjay [Natarajan] leads the parts of Intel that deliver transistors and transistor technologies,” says Richard Uhlig, managing director of Intel Labs. “We figure out what to do with the transistors,” he says of his team. One type of accelerator they’re developing is for what’s called fully homomorphic encryption, in which a computer processes data while it’s still encrypted — useful for, say, drawing conclusions about a set of medical records without revealing personal information. The project, funded by DARPA, could speed homomorphic encryption by hundreds of times.

More than 200 start-ups are developing accelerators for artificial intelligence , finding faster ways to perform the calculations necessary for software to learn from data.

Some accelerators aim to mimic, in hardware, the brain’s wiring. These “neuromorphic” chips typically embody at least one of three properties. First, memory elements may sit very close to computing elements, or the same elements may perform both functions, the way neurons both store and process information. One type of element that can perform this feat is the memristor . Second, the chips may process information using “spikes.” Like neurons, the elements sit around waiting for something to happen, then send a signal, or spike, when their activation crosses a threshold. Third, the chips may be analog instead of digital, eliminating the need for encoding continuous electrical properties such as voltage into discrete 1s and 0s.

These neuromorphic properties can make processing certain types of information orders of magnitude faster and more energy efficient. The computations are often less precise than in standard chips, but fuzzy logic is acceptable for, say, pattern matching or finding approximate solutions quickly. Uhlig says Intel has used its neuromorphic chip Loihi in tests to process odors, control robots and optimize railway schedules so that many trains can share limited tracks.

Some types of accelerators might one day use quantum computing , which capitalizes on two features of the subatomic realm. The first is superposition , in which particles can exist not just in one state or another, but in some combination of states until the state is explicitly measured. So a quantum system represents information not as bits but as qubits , which can preserve the possibility of being either 0 or 1 when measured. The second is entanglement , the interdependence between distant quantum elements. Together, these features mean that a system of qubits can represent and evaluate exponentially more possibilities than there are qubits — all combinations of 1s and 0s simultaneously.

Qubits can take many forms, but one of the most popular is as current in superconducting wires. These wires must be kept at a fraction of a degree above absolute zero, around –273° Celsius, to prevent hot, jiggling atoms from interfering with the qubits’ delicate superpositions and entanglement. Quantum computers also need many physical qubits to make up one “logical,” or effective, qubit, with the redundancy acting as error correction .

Quantum computers have several potential applications: machine learning, optimization (like train scheduling) and simulating real-world quantum mechanics, as in chemistry. But they will not likely become general-purpose computers. It’s not clear how you’d use one to, say, run a word processor.

New chip concepts

There remain new ways to dramatically speed up not just specialized accelerators but also general-purpose chips. Conte points to two paradigms. The first is superconduction. Below about 4 kelvins, around –269° C, many metals lose almost all electrical resistance, so they won’t convert current into heat. A superconducting circuit might be able to operate at hundreds of gigahertz instead of just a few, using much less electricity. The hard part lies not in keeping the circuits refrigerated (at least in big data centers), but in working with the exotic materials required to build them. 

The second paradigm is reversible computing. In 1961, the physicist Rolf Landauer merged information theory and thermodynamics , the physics of heat. He noted that when a logic gate takes in two bits and outputs one, it destroys a bit, expelling it as entropy, or randomness, in the form of heat. When billions of transistors operate at billions of cycles per second, the wasted heat adds up. Michael Frank, a computer scientist at Sandia National Laboratories in Albuquerque who works on reversible computing, wrote in 2017: “A conventional computer is, essentially, an expensive electric heater that happens to perform a small amount of computation as a side effect.”

But in reversible computing, logic gates have as many outputs as inputs. This means that if you ran the logic gate in reverse, you could use, say, three out-bits to obtain the three in-bits. Some researchers have conceived of reversible logic gates and circuits that could not only save those extra out-bits but also recycle them for other calculations. The physicist Richard Feynman had concluded that, aside from energy loss during data transmission, there’s no theoretical limit to computing efficiency.

Combine reversible and superconducting computing, Conte says, and “you get a double whammy.” Efficient computing allows you to run more operations on the same chip without worrying about power use or heat generation. Conte says that, eventually, one or both of these methods “probably will be the backbone of a lot of computing.”

Software hacks

Researchers continue to work on a cornucopia of new technologies for transistors, other computing elements, chip designs and hardware paradigms: photonics, spintronics , biomolecules, carbon nanotubes . But much more can still be eked out of current elements and architectures merely by optimizing code.

In a 2020 paper in Science , for instance, researchers studied the simple problem of multiplying two matrices, grids of numbers used in mathematics and machine learning. The calculation ran more than 60,000 times faster when the team picked an efficient programming language and optimized the code for the underlying hardware, compared with a standard piece of code in the Python language, which is considered user-friendly and easy to learn.

Computing gains through hardware and algorithm improvement

Hardware isn’t the only way computing speeds up. Advances in the algorithms — the computational procedures for achieving a result — can lend a big boost to performance. The graph above shows the relative number of problems that can be solved in a fixed amount of time for one type of algorithm. The black line shows gains over time from hardware and algorithm advances; the purple line shows gains from hardware improvements alone.

Neil Thompson, a research scientist at MIT who coauthored the Science paper, recently coauthored a paper looking at historical improvements in algorithms , abstract procedures for tasks like sorting data. “For a substantial minority of algorithms,” he says, “their progress has been as fast or faster than Moore’s law.”

People have predicted the end of Moore’s law for decades. Even Moore has predicted its end several times. Progress may have slowed, at least for the time being, but human innovation, accelerated by economic incentives, has kept technology moving at a fast clip. — Matthew Hutson

Chasing intelligence

From the early days of computer science, researchers have aimed to replicate human thought. Alan Turing opened a 1950 paper titled “ Computing Machinery and Intelligence ” with: “I propose to consider the question, ‘Can machines think?’” He proceeded to outline a test, which he called “the imitation game” ( now called the Turing test ), in which a human communicating with a computer and another human via written questions had to judge which was which. If the judge failed, the computer could presumably think.

The term “artificial intelligence” was coined in a 1955 proposal for a summer institute at Dartmouth College. “An attempt will be made,” the proposal goes, “to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” The organizers expected that over two months, the 10 summit attendees would make a “significant advance.”

More than six decades and untold person-hours later, it’s unclear whether the advances live up to what was in mind at that summer summit. Artificial intelligence surrounds us, in ways invisible (filtering spam), headline-worthy (beating us at chess, driving cars) and in between (letting us chat with our smartphones). But these are all narrow forms of AI, performing one or two tasks well. What Turing and others had in mind is called artificial general intelligence, or AGI. Depending on your definition, it’s a system that can do most of what humans do.

We may never achieve AGI, but the path has led, and will lead, to lots of useful innovations along the way. “I think we’ve made a lot of progress,” says Doina Precup, a computer scientist at McGill University in Montreal and head of AI company DeepMind’s Montreal research team. “But one of the things that, to me, is still missing right now is more of an understanding of the principles that are fundamental in intelligence.”

AI has made great headway in the last decade, much of it due to machine learning. Previously, computers relied more heavily on symbolic AI, which uses algorithms, or sets of instructions, that make decisions according to manually specified rules. Machine-learning programs, on the other hand, process data to find patterns on their own. One form uses artificial neural networks, software with layers of simple computing elements that together mimic certain principles of biological brains. Neural networks with several, or many more, layers are currently popular and make up a type of machine learning called deep learning.

Deep-learning systems can now play games like chess and Go better than the best human. They can probably identify dog breeds from photos better than you can. They can translate text from one language to another. They can control robots and compose music and predict how proteins will fold.

But they also lack much of what falls under the umbrella term of common sense. They don’t understand fundamental things about how the world works, physically or socially. Slightly changing images in a way that you or I might not notice, for example, can dramatically affect what a computer sees. Researchers found that placing a few innocuous stickers on a stop sign can lead software to interpret the sign as a speed limit sign, an obvious problem for self-driving cars .

Artificial intelligence challenges what it means to be creative

Computer programs can mimic famous artworks, but struggle with originality and lack self-awareness.

Types of learning

How can AI improve? Computer scientists are leveraging multiple forms of machine learning, whether the learning is “deep” or not. One common form is called supervised learning, in which machine-learning systems, or models, are trained by being fed labeled data such as images of dogs and their breed names. But that requires lots of human effort to label them. Another approach is unsupervised or self-supervised learning, in which computers learn without relying on outside labels, the way you or I predict what a chair will look like from different angles as we walk around it.

Models that process billions of words of text, predicting the next word one at a time and changing slightly when they’re wrong, rely on unsupervised learning. They can then generate new strings of text. In 2020, the research lab OpenAI released a trained language model called GPT-3 that’s perhaps the most complex neural network ever. Based on prompts, it can write humanlike news articles, short stories and poems. It can answer trivia questions, write computer code and translate language — all without being specifically trained to do any of these things. It’s further down the path toward AGI than many researchers thought was currently possible. And language models will get bigger and better from here.

Another type of machine learning is reinforcement learning , in which a model interacts with an environment, exploring sequences of actions to achieve a goal. Reinforcement learning has allowed AI to become expert at board games like Go and video games like StarCraft II . A recent paper by researchers at DeepMind, including Precup, argues in the title that “ Reward Is Enough .” By merely having a training algorithm reinforce a model’s successful or semi-successful behavior, models will incrementally build up all the components of intelligence needed to succeed at the given task and many others.

For example, according to the paper, a robot rewarded for maximizing kitchen cleanliness would eventually learn “perception (to differentiate clean and dirty utensils), knowledge (to understand utensils), motor control (to manipulate utensils), memory (to recall locations of utensils), language (to predict future mess from dialogue) and social intelligence (to encourage young children to make less mess).” Whether trial and error would lead to such skills within the life span of the solar system — and what kinds of goals, environment and model would be required — is to be determined.

Another type of learning involves Bayesian statistics, a way of estimating what conditions are likely given current observations. Bayesian statistics is helping machines identify causal relations, an essential skill for advanced intelligence.

Generalizing

To learn efficiently, machines (and people) need to generalize, to draw abstract principles from experiences. “A huge part of intelligence,” says Melanie Mitchell, a computer scientist at the Santa Fe Institute in New Mexico, “is being able to take one’s knowledge and apply it in different situations.” Much of her work involves analogies, in a most rudimentary form: finding similarities between strings of letters. In 2019, AI researcher François Chollet of Google created a kind of IQ test for machines called the Abstraction and Reasoning Corpus, or ARC, in which computers must complete visual patterns according to principles demonstrated in example patterns. The puzzles are easy for humans but so far challenging for machines. Eventually, AI might understand grander abstractions like love and democracy.

Machine IQ test

In a kind of IQ test for machines, computers are challenged to complete a visual patterning task based on examples provided. In each of these three tasks, computers are given “training examples” (both the problem, left, and the answer, right) and then have to determine the answer for “test examples.” The puzzles are typically much easier for humans than for machines.

Much of our abstract thought, ironically, may be grounded in our physical experiences. We use conceptual metaphors like important = big, and argument = opposing forces. AGI that can do most of what humans can do may require embodiment, such as operating within a physical robot. Researchers have combined language learning and robotics by creating virtual worlds where virtual robots simultaneously learn to follow instructions and to navigate within a house. GPT-3 is evidence that disembodied language may not be enough. In one demo , it wrote: “It takes two rainbows to jump from Hawaii to seventeen.”

“I’ve played around a lot with it,” Mitchell says. “It does incredible things. But it can also make some incredibly dumb mistakes.”

AGI might also require other aspects of our animal nature, like emotions , especially if humans expect to interact with machines in natural ways. Emotions are not mere irrational reactions. We’ve evolved them to guide our drives and behaviors. According to Ilya Sutskever, a cofounder and the chief scientist at OpenAI, they “give us this extra oomph of wisdom.” Even if AI doesn’t have the same conscious feelings we do, it may have code that approximates fear or anger. Already, reinforcement learning includes an exploratory element akin to curiosity .

One function of curiosity is to help learn causality, by encouraging exploration and experimentation, Precup says. However, current exploration methods in AI “are still very far from babies playing purposefully with objects,” she notes.

Humans aren’t blank slates. We’re born with certain predispositions to recognize faces, learn language and play with objects. Machine-learning systems also require the right kind of innate structure to learn certain things quickly. How much structure, and what kind, is a matter of intense debate in the field. Sutskever says building in how we think we think is “intellectually seductive,” and he leans toward blank slates. However, “we want the best blank slate.”

One general neural-network structure Sutskever likes is called the transformer, a method for paying greater attention to important relationships between elements of an input. It’s behind current language models like GPT-3, and has also been applied to analyzing images, audio and video. “It makes everything better,” he says.

Thinking about thinking

AI itself may help us discover new forms of AI. There’s a set of techniques called AutoML, in which algorithms help optimize neural-network architectures or other aspects of AI models. AI also helps chip architects design better integrated circuits. This year, Google researchers reported in Nature that reinforcement learning performed better than their in-house team at laying out some aspects of an accelerator chip they’d designed for AI.

Estimates of AGI’s proximity vary greatly, but most experts think it’s decades away. In a 2016 survey, 352 machine-learning researchers estimated the arrival of “high-level machine intelligence,” defined as “when unaided machines can accomplish every task better and more cheaply than human workers.” On average, they gave even odds of such a feat by around 2060.

But no one has a good basis for judging. “We don’t understand our own intelligence,” Mitchell says, as much of it is unconscious. “And therefore, we don’t know what’s going to be hard or easy for AI.” What seems hard can be easy and vice versa — a phenomenon known as Moravec’s paradox, after the roboticist Hans Moravec. In 1988, Moravec wrote, “it is comparatively easy to make computers exhibit adult-level performance in solving problems on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a 1-year-old when it comes to perception and mobility.” Babies are secretly brilliant. In aiming for AGI, Precup says, “we are also understanding more about human intelligence, and about intelligence in general.”

The gap between organic and synthetic intelligence sometimes seems small because we anthropomorphize machines, spurred by computer science terms like intelligence , learning and vision . Aside from whether we even want humanlike machine intelligence — if they think just like us, won’t they essentially just be people, raising ethical and practical dilemmas? — such a thing may not be possible. Even if AI becomes broad, it may still have unique strengths and weaknesses.

Turing also differentiated between general intelligence and humanlike intelligence. In his 1950 paper on the imitation game, he wrote, “May not machines carry out something which ought to be described as thinking but which is very different from what a man does?” — Matthew Hutson

Ethical issues

In the 1942 short story “Runaround,” one of Isaac Asimov’s characters enumerated “the three fundamental Rules of Robotics — the three rules that are built most deeply into a robot’s positronic brain.” Robots avoided causing or allowing harm to humans, they obeyed orders and they protected themselves, as long as following one rule didn’t conflict with preceding decrees.

We might picture Asimov’s “positronic brains” making autonomous decisions about harm to humans, but that’s not actually how computers affect our well-being every day. Instead of humanoid robots killing people, we have algorithms curating news feeds. As computers further infiltrate our lives, we’ll need to think harder about what kinds of systems to build and how to deploy them, as well as meta-problems like how to decide — and who should decide — these things.

This is the realm of ethics, which may seem distant from the supposed objectivity of math, science and engineering. But deciding what questions to ask about the world and what tools to build has always depended on our ideals and scruples. Studying an abstruse topic like the innards of atoms , for instance, has clear bearing on both energy and weaponry. “There’s the fundamental fact that computer systems are not value neutral,” says Barbara Grosz, a computer scientist at Harvard University, “that when you design them, you bring some set of values into that design.”

One topic that has received a lot of attention from scientists and ethicists is fairness and bias . Algorithms increasingly inform or even dictate decisions about hiring, college admissions, loans and parole. Even if they discriminate less than people do, they can still treat certain groups unfairly, not by design but often because they are trained on biased data. They might predict a person’s future criminal behavior based on prior arrests, for instance, even though different groups are arrested at different rates for a given amount of crime.

Estimated percent of Oakland residents using drugs

Percent of population that would be targeted by predictive policing

A predictive policing algorithm tested in Oakland, Calif., would target Black people at roughly twice the rate of white people (right) even though data from the same time period, 2011, show that drug use was roughly equivalent across racial groups (left).

And confusingly, there are multiple definitions of fairness, such as equal false-positive rates between groups or equal false-negative rates between groups. A researcher at one conference listed 21 definitions . And the definitions often conflict. In one paper , researchers showed that in most cases it’s mathematically impossible to satisfy three common definitions simultaneously.

Another concern is privacy and surveillance , given that computers can now gather and sort information on their use in a way previously unimaginable . Data on our online behavior can help predict aspects of our private lives, like sexuality. Facial recognition can also follow us around the real world, helping police or authoritarian governments. And the emerging field of neurotechnology is already testing ways to connect the brain directly to computers. Related to privacy is security — hackers can access data that’s locked away, or interfere with pacemakers and autonomous vehicles.

Computers can also enable deception. AI can generate content that looks real. Language models might write masterpieces or be used to fill the internet with fake news and recruiting material for extremist groups. Generative adversarial networks, a type of deep learning that can generate realistic content, can assist artists or create deepfakes , images or videos showing people doing things they never did.

On social media, we also need to worry about polarization in people’s social, political and other views. Generally, recommendation algorithms optimize engagement (and platform profit through advertising), not civil discourse. Algorithms can also manipulate us in other ways. Robo-advisers — chatbots for dispensing financial advice or providing customer support — might learn to know what we really need, or to push our buttons and upsell us on extraneous products.

Multiple countries are developing autonomous weapons that have the potential to reduce civilian casualties as well as escalate conflict faster than their minders can react. Putting guns or missiles in the hands of robots raises the sci-fi specter of Terminators attempting to eliminate humankind. They might even think they’re helping us because eliminating humankind also eliminates human cancer (an example of having no common sense). More near-term, automated systems let loose in the real world have already caused flash crashes in the stock market and Amazon book prices reaching into the millions . If AIs are charged with making life-and-death decisions, they then face the famous trolley problem, deciding whom or what to sacrifice when not everyone can win. Here we’re entering Asimov territory.

That’s a lot to worry about. Russell, of UC Berkeley, suggests where our priorities should lie: “Lethal autonomous weapons are an urgent issue, because people may have already died, and the way things are going, it’s only a matter of time before there’s a mass attack,” he says. “Bias and social media addiction and polarization are both arguably instances of failure of value alignment between algorithms and society, so they are giving us early warnings of how things can easily go wrong.” He adds, “I don’t think trolley problems are urgent at all.”

There are also social, political and legal questions about how to manage technology in society. Who should be held accountable when an AI system causes harm? (For instance, “confused” self-driving cars have killed people .) How can we ensure more equal access to the tools of AI and their benefits, and make sure they don’t harm some groups much more than others? How will automating jobs upend the labor market? Can we manage the environmental impact of data centers, which use a lot of electricity? (Bitcoin mining is responsible for as many tons of carbon dioxide emissions as a small country.) Should we preferentially employ explainable algorithms — rather than the black boxes of many neural networks — for greater trust and debuggability, even if it makes the algorithms poorer at prediction?

What can be done

Michael Kearns, a computer scientist at the University of Pennsylvania and coauthor of The Ethical Algorithm , puts the problems on a spectrum of manageability. At one end is what’s called differential privacy, the ability to add noise to a dataset of, say, medical records so that it can be shared usefully with researchers without revealing much about the individual records. We can now make mathematical guarantees about exactly how private individuals’ data should remain.

Somewhere in the middle of the spectrum is fairness in machine learning. Researchers have developed methods to increase fairness by removing or altering biased training data, or to maximize certain types of equality — in loans, for instance — while minimizing reduction in profit. Still, some types of fairness will forever be in mutual conflict, and math can’t tell us which ones we want.

At the far end is explainability. As opposed to fairness, which can be analyzed mathematically in many ways, the quality of an explanation is hard to describe in mathematical terms. “I feel like I haven’t seen a single good definition yet,” Kearns says. “You could say, ‘Here’s an algorithm that will take a trained neural network and try to explain why it rejected you for a loan,’ but [the explanation] doesn’t feel principled.”

Explanation methods include generating a simpler, interpretable model that approximates the original, or highlighting regions of an image a network found salient, but these are just gestures toward how the cryptic software computes. Even worse, systems can provide intentionally deceptive explanations , to make unfair models look fair to auditors. Ultimately, if the audience doesn’t understand it, it’s not a good explanation, and measuring its success — however you define success — requires user studies.  

Something like Asimov’s three laws won’t save us from robots that hurt us while trying to help us; stepping on your phone when you tell it to hurry up and get you a drink is a likely example. And even if the list were extended to a million laws, the letter of a law is not identical to its spirit. One possible solution is what’s called inverse reinforcement learning, or IRL. In reinforcement learning, a model learns behaviors to achieve a given goal. In IRL, it infers someone’s goal by observing their behavior. We can’t always articulate our values — the goals we ultimately care about — but AI might figure them out by watching us. If we have coherent goals, that is.

“Perhaps the most obvious preference is that we prefer to be alive,” says Russell, who has pioneered IRL. “So an AI agent using IRL can avoid courses of action that cause us to be dead. In case this sounds too trivial, remember that not a single one of the prototype self-driving cars knows that we prefer to be alive. The self-driving car may have rules that in most cases prohibit actions that cause death, but in some unusual circumstance — such as filling a garage with carbon monoxide — they might watch the person collapse and die and have no notion that anything was wrong.”

Digital lives

In 2021, Facebook unveiled its vision for a metaverse, a virtual world where people would work and play. “As so many have made clear, this is what technology wants,” says MIT sociologist and clinical psychologist Sherry Turkle about the metaverse. “For me, it would be wiser to ask first, not what technology wants, but what do people want? What do people need to be safer? Less lonely? More connected to each other in communities? More supported in their efforts to live healthier and more fulfilled lives?”

Engineer, heal thyself

In the 1950 short story “The Evitable Conflict,” Asimov articulated what became a “zeroth law,” which would supersede the others: “A robot may not harm humanity, or, by inaction, allow humanity to come to harm.” It should go without saying that the rule should apply with “roboticist” in place of “robot.” For sure, many computer scientists avoid harming humanity, but many also don’t actively engage with the social implications of their work, effectively allowing humanity to come to harm, says Margaret Mitchell, a computer scientist who co-led Google’s Ethical AI team and now consults with organizations on tech ethics. (She is no relation to computer scientist Melanie Mitchell.)

One hurdle, according to Grosz, is that they’re not properly trained in ethics. But she hopes to change that. Grosz and the philosopher Alison Simmons began a program at Harvard called Embedded EthiCS, in which teaching assistants with training in philosophy are embedded in computer science courses and teach lessons on privacy or discrimination or fake news. The program has spread to MIT, Stanford and the University of Toronto.

“We try to get students to think about values and value trade-offs,” Grosz says. Two things have struck her. The first is the difficulty students have with problems that lack right answers and require arguing for particular choices. The second is, despite their frustration, “how much students care about this set of issues,” Grosz says.

Another way to educate technologists about their influence is to widen collaborations. According to Mitchell, “computer science needs to move from holding math up as the be-all and end-all, to holding up both math and social science, and psychology as well.” Researchers should bring in experts in these topics, she says. Going the other way, Kearns says, they should also share their own technical expertise with regulators, lawyers and policy makers. Otherwise, policies will be so vague as to be useless. Without specific definitions of privacy or fairness written into law, companies can choose whatever’s most convenient or profitable.

When evaluating how a tool will affect a community, the best experts are often community members themselves. Grosz advocates consulting with diverse populations. Diversity helps in both user studies and technology teams. “If you don’t have people in the room who think differently from you,” Grosz says, “the differences are just not in front of you. If somebody says not every patient has a smartphone, boom, you start thinking differently about what you’re designing.”

According to Margaret Mitchell, “the most pressing problem is the diversity and inclusion of who’s at the table from the start. All the other issues fall out from there.” — Matthew Hutson

Editor’s note: This story was published February 24, 2022.

Alan Turing (shown) sketches out the theoretical blueprint for a machine able to implement instructions for making any calculation — the principle behind modern computing devices.

The University of Pennsylvania rolls out the first all-electronic general-purpose digital computer , called ENIAC (one shown). The Colossus electronic computers had been used by British code-breakers during World War II.

Grace Hopper (shown) creates the first compiler. It translated instructions into code that a computer could read and execute, making it an important step in the evolution of modern programming languages.

Three computers released this year — the Commodore PET, the Apple II and the TRS-80 (an early version shown) — help make personal computing a reality.

Google’s AlphaGo computer program defeats world champion Go player Lee Sedol (shown).

Researchers at Google report a controversial claim that they have achieved quantum supremacy, performing a computation that would be impossible in practice for a classical machine. (Google’s Sycamore chip is shown.)

From the archive

From now on: computers.

Science News Letter editor Watson Davis predicts how “mechanical brains” will push forward human knowledge.

Maze for Mechanical Mouse

Claude Shannon demonstrates his “electrical mouse,” which can learn to find its way through a maze.

Giant Electronic Brains

Science News Letter covers the introduction of a “giant electronic ‘brain’” to aid weather predictions.

Automation Changes Jobs

A peek into early worries over how technological advances will swallow up jobs.

Machine ‘Thinks’ for Itself

“An automaton that is half-beast, half-machine is able to ‘think’ for itself,” Science News Letter reports.

Predicting Chemical Properties by Computer

A report on how artificial intelligence is helping to predict chemical properties.

From Number Crunchers to Pocket Genies

The first in a series of articles on the computer revolution explores the technological breakthroughs bringing computers to the average person.

Calculators in the Classroom

Science News weighs the pros and cons of “pocket math,” noting that high school and college students are “buying calculators as if they were radios.”

Computing for Art’s Sake

Artists embrace computers as essential partners in the creative process, Science News ’ Janet Raloff reports.

PetaCrunchers

Mathematics writer Ivars Peterson reports on the push toward ultrafast supercomputing — and what it might reveal about the cosmos.

A Mind from Math

Alan Turing foresaw the potential of machines to mimic brains, reports Tom Siegfried.

Machines are getting schooled on fairness

Machine-learning programs can introduce biases that may harm job seekers, loan applicants and more, Maria Temming reports.

AI chatbots can be tricked into misbehaving. Can scientists stop it?

To develop better safeguards, computer scientists are studying how people have manipulated generative AI chatbots into answering harmful questions.

Science News is published by Society for Science

Subscribers, enter your e-mail address for full access to the Science News archives and digital editions.

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The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is  increasingly touching people’s lives in settings that range from  movie recommendations  and  voice assistants  to  autonomous driving  and  automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report. 

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.  

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks.  We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.  

In terms of potential, I'm most excited about AIs that might augment and assist people.  They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired.  In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years.  The first report is fairly rosy.  For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges.  The second has a much more mixed view.  I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education!  Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.  I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education.  Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. 

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare? 

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing.  When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI,  it's tricky to nurture both innovation and basic protections.  Perhaps the most important innovation will be in approaches for AI accountability.

Topics: AI / Machine Learning , Computer Science

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Essay on the Future of Computer Technology

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• http://www.futureforall.org/computers/computers.htm
• http://www.hpcrresearch.org/sites/default/files/publications/schmittetal.pdf
• http://www.geekpoint.net/threads/how-are-computers-used-in-business.27759/
• Eric G. Swedin and David L. Computers: The Life Story Of Technology. Hardcover - Illustrated, 30 April 2005.
• Ron White. How Computers Work: Que Pub; 9th edition (November 1, 2007).
• New York Times; Sunday, 24 October 2010.
• http://www.doaj.org/doaj?func=subject&cpid=114

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Future Of Computer Technology - Free Essay Examples and Topic Ideas

The future of computer technology is constantly evolving and will continue to bring advancements in areas such as artificial intelligence, quantum computing, blockchain technology, 5G networks, and cloud computing. These advancements will lead to faster and more efficient processing, increased data storage capacity, improved cybersecurity, and more innovative applications. We can expect to see continued integration of technology into our daily lives and an increasing reliance on automation and data analysis for decision-making in industries ranging from healthcare to finance to transportation. As technology continues to progress, the possibilities for its applications are limitless.

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How to Write the “Why Computer Science?” Essay

What’s covered:, what is the purpose of the “why computer science” essay, elements of a good computer science essay, computer science essay example, where to get your essay edited.

You will encounter many essay prompts as you start applying to schools, but if you are intent on majoring in computer science or a related field, you will come across the “ Why Computer Science? ” essay archetype. It’s important that you know the importance behind this prompt and what constitutes a good response in order to make your essay stand out.

For more information on writing essays, check out CollegeVine’s extensive essay guides that include everything from general tips, to essay examples, to essay breakdowns that will help you write the essays for over 100 schools.

Colleges ask you to write a “ Why Computer Science? ” essay so you may communicate your passion for computer science, and demonstrate how it aligns with your personal and professional goals. Admissions committees want to see that you have a deep interest and commitment to the field, and that you have a vision for how a degree in computer science will propel your future aspirations.

The essay provides an opportunity to distinguish yourself from other applicants. It’s your chance to showcase your understanding of the discipline, your experiences that sparked or deepened your interest in the field, and your ambitions for future study and career. You can detail how a computer science degree will equip you with the skills and knowledge you need to make a meaningful contribution in this rapidly evolving field.

A well-crafted “ Why Computer Science? ” essay not only convinces the admissions committee of your enthusiasm and commitment to computer science, but also provides a glimpse of your ability to think critically, solve problems, and communicate effectively—essential skills for a  computer scientist.

The essay also gives you an opportunity to demonstrate your understanding of the specific computer science program at the college or university you are applying to. You can discuss how the program’s resources, faculty, curriculum, and culture align with your academic interests and career goals. A strong “ Why Computer Science? ” essay shows that you have done your research, and that you are applying to the program not just because you want to study computer science, but because you believe that this particular program is the best fit for you.

Writing an effective “ Why Computer Science ?” essay often requires a blend of two popular college essay archetypes: “ Why This Major? ” and “ Why This College? “.

Explain “Why This Major?”

The “ Why This Major? ” essay is an opportunity for you to dig deep into your motivations and passions for studying Computer Science. It’s about sharing your ‘origin story’ of how your interest in Computer Science took root and blossomed. This part of your essay could recount an early experience with coding, a compelling Computer Science class you took, or a personal project that sparked your fascination.

What was the journey that led you to this major? Was it a particular incident, or did your interest evolve over time? Did you participate in related activities, like coding clubs, online courses, hackathons, or internships?

Importantly, this essay should also shed light on your future aspirations. How does your interest in Computer Science connect to your career goals? What kind of problems do you hope to solve with your degree?

The key for a strong “ Why This Major? ” essay is to make the reader understand your connection to the subject. This is done through explaining your fascination and love for computer science. What emotions do you feel when you are coding? How does it make you feel when you figure out the solution after hours of trying? What aspects of your personality shine when you are coding? 

By addressing these questions, you can effectively demonstrate a deep, personal, and genuine connection with the major.

Emphasize “Why This College?”

The “ Why This College? ” component of the essay demonstrates your understanding of the specific university and its Computer Science program. This is where you show that you’ve done your homework about the college, and you know what resources it has to support your academic journey.

What unique opportunities does the university offer for Computer Science students? Are there particular courses, professors, research opportunities, or clubs that align with your interests? Perhaps there’s a study abroad program or an industry partnership that could give you a unique learning experience. Maybe the university has a particular teaching methodology that resonates with you.

Also, think about the larger university community. What aspects of the campus culture, community, location, or extracurricular opportunities enhance your interest in this college? Remember, this is not about general praises but about specific features that align with your goals. How will these resources and opportunities help you explore your interests further and achieve your career goals? How does the university’s vision and mission resonate with your own values and career aspirations?

It’s important when discussing the school’s resources that you always draw a connection between the opportunity and yourself. For example, don’t tell us you want to work with X professor because of their work pioneering regenerative AI. Go a step further and say because of your goal to develop AI surgeons for remote communities, learning how to strengthen AI feedback loops from X professor would bring you one step closer to achieving your dream.

By articulating your thoughts on these aspects, you demonstrate a strong alignment between the college and your academic goals, enhancing your appeal as a prospective student.

Demonstrate a Deep Understanding of Computer Science

As with a traditional “ Why This Major? ” essay, you must exhibit a deep and clear understanding of computer science. Discuss specific areas within the field that pique your interest and why. This could range from artificial intelligence to software development, or from data science to cybersecurity. 

What’s important is to not just boast and say “ I have a strong grasp on cybersecurity ”, but instead use your knowledge to show your readers your passion: “ After being bombarded with cyber attack after cyber attack, I explained to my grandparents the concept of end-to-end encryption and how phishing was not the same as a peaceful afternoon on a lake. ”

Make it Fun!

Students make the mistake of thinking their college essays have to be serious and hyper-professional. While you don’t want to be throwing around slang and want to present yourself in a positive light, you shouldn’t feel like you’re not allowed to have fun with your essay. Let your personality shine and crack a few jokes.

You can, and should, also get creative with your essay. A great way to do this in a computer science essay is to incorporate lines of code or write the essay like you are writing out code. 

Now we will go over a real “ Why Computer Science? ” essay a student submitted and explore what the essay did well, and where there is room for improvement.

Please note: Looking at examples of real essays students have submitted to colleges can be very beneficial to get inspiration for your essays. You should never copy or plagiarize from these examples when writing your own essays. Colleges can tell when an essay isn’t genuine and will not view students favorably if they plagiarized.

I held my breath and hit RUN. Yes! A plump white cat jumped out and began to catch the falling pizzas. Although my Fat Cat project seems simple now, it was the beginning of an enthusiastic passion for computer science. Four years and thousands of hours of programming later, that passion has grown into an intense desire to explore how computer science can serve society. Every day, surrounded by technology that can recognize my face and recommend scarily-specific ads, I’m reminded of Uncle Ben’s advice to a young Spiderman: “with great power comes great responsibility”. Likewise, the need to ensure digital equality has skyrocketed with AI’s far-reaching presence in society; and I believe that digital fairness starts with equality in education.

The unique use of threads at the College of Computing perfectly matches my interests in AI and its potential use in education; the path of combined threads on Intelligence and People gives me the rare opportunity to delve deep into both areas. I’m particularly intrigued by the rich sets of both knowledge-based and data-driven intelligence courses, as I believe AI should not only show correlation of events, but also provide insight for why they occur.

In my four years as an enthusiastic online English tutor, I’ve worked hard to help students overcome both financial and technological obstacles in hopes of bringing quality education to people from diverse backgrounds. For this reason, I’m extremely excited by the many courses in the People thread that focus on education and human-centered technology. I’d love to explore how to integrate AI technology into the teaching process to make education more available, affordable, and effective for people everywhere. And with the innumerable opportunities that Georgia Tech has to offer, I know that I will be able to go further here than anywhere else.

What the Essay Did Well 

This essay perfectly accomplishes the two key parts of a “ Why Computer Science? ” essay: answering “ Why This Major? ” and “ Why This College? ”. Not to mention, we get a lot of insight into this student and what they care about beyond computer science, and a fun hook at the beginning.

Starting with the “ Why This Major? ” aspect of the response, this essay demonstrates what got the student into computer science, why they are passionate about the subject, and what their goals are. They show us their introduction to the world of CS with an engaging hook: “I held my breath and hit RUN. Yes! A plump white cat jumped out and began to catch the falling pizzas. ” We then see this is a core passion because they spent “ Four years and thousands of hours ,” coding.

The student shows us why they care about AI with the sentence, “ Every day, surrounded by technology that can recognize my face and recommend scarily-specific ads ,” which makes the topic personal by demonstrating their fear at AI’s capabilities. But, rather than let panic overwhelm them, the student calls upon Spiderman and tells us their goal of establishing digital equality through education. This provides a great basis for the rest of the essay, as it thoroughly explains the students motivations and goals, and demonstrates their appreciation for interdisciplinary topics.

Then, the essay shifts into answering “ Why This College? ”, which it does very well by honing in on a unique facet of Georgia Tech’s College of Computing: threads. This is a great example of how to provide depth to the school resources you mention. The student describes the two threads and not only why the combination is important to them, but how their previous experiences (i.e. online English tutor) correlate to the values of the thread: “ For this reason, I’m extremely excited by the many courses in the People thread that focus on education and human-centered technology. ”

What Could Be Improved

This essay does a good job covering the basics of the prompt, but it could be elevated with more nuance and detail. The biggest thing missing from this essay is a strong core to tie everything together. What do we mean by that? We want to see a common theme, anecdote, or motivation that is weaved throughout the entire essay to connect everything. Take the Spiderman quote for example. If this was expanded, it could have been the perfect core for this essay.

Underlying this student’s interest in AI is a passion for social justice, so they could have used the quote about power and responsibility to talk about existing injustices with AI and how once they have the power to create AI they will act responsibly and help affected communities. They are clearly passionate about equality of education, but there is a disconnect between education and AI that comes from a lack of detail. To strengthen the core of the essay, this student needs to include real-world examples of how AI is fostering inequities in education. This takes their essay from theoretical to practical.

Whether you’re a seasoned writer or a novice trying your hand at college application essays, the review and editing process is crucial. A fresh set of eyes can provide valuable insights into the clarity, coherence, and impact of your writing. Our free Peer Essay Review tool offers a unique platform to get your essay reviewed by another student. Peer reviews can often uncover gaps, provide new insights or enhance the clarity of your essay, making your arguments more compelling. The best part? You can return the favor by reviewing other students’ essays, which is a great way to hone your own writing and critical thinking skills.

For a more professional touch, consider getting your essay reviewed by a college admissions expert . CollegeVine advisors have years of experience helping students refine their writing and successfully apply to top-tier schools. They can provide specific advice on how to showcase your strengths, address any weaknesses, and generally present yourself in the best possible light.

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How Computers Affect Our Lives Essay

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How Computers Affect Our Lives: Essay Introduction

History of computers, positive effects of computer on human life, computers replacing man, negative computer influences, conflict with religious beliefs, conclusion: how computer influences our life, works cited.

Computers are a common phenomenon in the lives of people in today’s world. Computers are very vital especially to those people who run businesses, industries and other organizations. Today, almost everything that people engage in makes use of a computer. Take for instance, the transport sector: vehicles, trains, airplanes, and even traffic lights on our roads are controlled by computers.

In hospitals, most of the equipments use or are run by computers. Look at space exploration; it was all made possible with the advent of computer technology. In the job sector, many of the jobs require knowledge in computers because they mostly involve the use of computers.

In short, these machines have become so important and embedded in the lives of humans, they have hugely impacted on the whole society to the extent that it will be very hard to survive now, without them. This article discusses the influence of computers on the everyday life of human beings.

One can guess what will exactly happen if the world had no computers. Many of the cures found with help of computer technology would not have been developed without computer technology, meaning that many people would have died from diseases that are now curable. In the entertainment industry, many of the movies and even songs will not be in use without computers because most of the graphics used and the animations we see are only possible with the help of a computer (Saimo 1).

In the field of medicine, pharmacies, will find it hard in determining the type of medication to give to the many patients. Computers have also played a role in the development of democracy in the world. Today votes are counted using computers and this has greatly reduced incidences of vote rigging and consequently reduced conflicts that would otherwise arise from the same.

And as we have already seen, no one would have known anything about space because space explorations become possible only with the help of computer technology. However, the use of computers has generated public discourses whereby people have emerged with different views, some supporting their use and others criticizing them (Saimo 1).

To better understand how computers influence the lives of people, we will have to start from the history, from their invention to the present day. Early computers did not involve complex technologies as the ones that are used today; neither did they employ the use of monitors or chips that are common today.

The early computers were not that small as those used today and they were commonly used to help in working out complex calculations in mathematics that proved tedious to be done manually. This is why the first machine was called by some as a calculator and others as a computer because it was used for making calculations.

Blaise Pascal is credited with the first digital machine that could add and subtract. Many versions of calculators and computers borrowed from his ideas. And as time went by, many developed more needs, which lead to modifications to bring about new and more efficient computers (Edwards 4).

Computer influence in the life of man became widely felt during World War II where computers were used to calculate and track the movements and also strategize the way military attacks were done (Edwards 4). It is therefore clear, that computers and its influence on man have a long history.

Its invention involved hard work dedication and determination, and in the end it paid off. The world was and is still being changed by computers. Man has been able to see into the future and plan ahead because of computers. Life today has been made easier with the help of computers, although some people may disagree with this, but am sure many will agree with me.

Those who disagree say that computers have taken away the role of man, which is not wrong at all, but we must also acknowledge the fact what was seen as impossible initially, become possible because of computers (Turkle 22).

As we mentioned in the introduction, computers are useful in the running of the affairs of many companies today. Companies nowadays use a lot of data that can only be securely stored with the help of computers. This data is then used in operations that are computer run. Without computers companies will find it difficult store thousands of records that are made on a daily basis.

Take for instance, what will happen to a customer checking his or her balance, or one who just want to have information on transactions made. In such a case, it will take long to go through all the transactions to get a particular one.

The invention of computers made this easier; bank employees today give customers their balances, transaction information, and other services just by tapping the computer keyboard. This would not be possible without computers (Saimo 1).

In personal life

Today individuals can store all information be it personal or that of a business nature in a computer. It is even made better by being able to make frequent updates and modifications to the information. This same information can be easily retrieved whenever it is needed by sending it via email or by printing it.

All this have been made possible with the use of computers. Life is easier and enjoyable, individuals now can comfortably entertain themselves at home by watching TV with their families or they can work from the comfort of their home thanks to computer technology.

Computers feature in the everyday life of people. Today one can use a computer even without being aware of it: people use their credit cards when buying items from stores; this has become a common practice that few know that the transaction is processed through computer technology.

It is the computer which process customer information that is fed to it through the credit card, it detects the transaction, and it then pays the bill by subtracting the amount from the credit card. Getting cash has also been made easier and faster, an individual simply walks to an ATM machine to withdraw any amount of cash he requires. ATM machines operate using computer technology (Saimo 1).

I mentioned the use of credit cards as one of the practical benefits of using computers. Today, individual do not need to physically visit shopping stores to buy items. All one needs is to be connected on the internet and by using a computer one can pay for items using the credit card.

These can then be delivered at the door step. The era where people used to queue in crowded stores to buy items, or wasting time in line waiting to buy tickets is over. Today, travelers can buy tickets and make travel arrangements via the internet at any time thanks to the advent of computer technology (Saimo 1).

In communication

Through the computer, man now has the most effective means of communication. The internet has made the world a global village. Today people carry with them phones, which are basically small computers, others carry laptops, all these have made the internet most effective and affordable medium of communication for people to contact their friends, their families, contact business people, from anywhere in the world.

Businesses are using computer technology to keep records and track their accounts and the flow of money (Lee 1). In the area of entertainment, computers have not been left behind either.

Action and science fiction movies use computers to incorporated visual effects that make them look real. Computer games, a common entertainer especially to teenagers, have been made more entertaining with the use of advanced computer technology (Frisicaro et.al 1).

In Education

The education sector has also been greatly influenced by computer technology. Much of the school work is done with the aid of a computer. If students are given assignments all they have to do is search for the solution on the internet using Google. The assignments can then be neatly presented thanks to computer software that is made specifically for such purposes.

Today most high schools have made it mandatory for students to type out their work before presenting it for marking. This is made possible through computers. Teachers have also found computer technology very useful as they can use it to track student performance. They use computers to give out instructions.

Computers have also made online learning possible. Today teachers and students do not need to be physically present in class in order to be taught. Online teaching has allowed students to attend class from any place at any time without any inconveniences (Computers 1).

In the medical sector

Another very crucial sector in the life of man that computers has greatly influenced and continues to influence is the health sector. It was already mentioned in the introduction that hospitals and pharmacies employ the use of computers in serving people.

Computers are used in pharmacies to help pharmacists determine what type and amount of medication patients should get. Patient data and their health progress are recorded using computers in many hospitals. The issue of equipment status and placement in hospitals is recorded and tracked down using computers.

Research done by scientists, doctors, and many other people in the search to find cures for many diseases and medical complications is facilitated through computer technology. Many of the diseases that were known to be dangerous such as malaria are now treatable thanks to computer interventions (Parkin 615).

Many of the opponents of computer technology have argued against the use of computers basing their arguments on the fact that computers are replacing man when carrying out the basic activities that are naturally human in nature.

However, it should be noted that there are situations that call for extraordinary interventions. In many industries, machines have replaced human labor. Use of machines is usually very cheap when compared to human labor.

In addition machines give consistent results in terms of quality. There are other instances where the skills needed to perform a certain task are too high for an ordinary person to do. This is usually experienced in cases of surgery where man’s intervention alone is not sufficient. However, machines that are computer operated have made complex surgeries successful.

There are also cases where the tasks that are to be performed may be too dangerous for a normal human being. Such situations have been experienced during disasters such as people being trapped underground during mining. It is usually dangerous to use people in such situations, and even where people are used, the rescue is usually delayed.

Robotic machines that are computer operated have always helped in such situations and people have been saved. It is not also possible to send people in space duration space explorations, but computer machines such as robots have been effectively used to make exploration outside our world (Gupta 1).

Despite all these good things that computers have done to humans, their opponents also have some vital points that should not just be ignored. There are many things that computers do leaving many people wondering whether they are really helping the society, or they are just being used to deprive man his God given ability to function according to societal ethics.

Take for instance in the workplace and even at home; computers have permeated in every activity done by an individual thereby compromising personal privacy. Computers have been used to expose people to unauthorized access to personal information. There is some personal information, which if exposed can impact negatively to someone’s life.

Today the world does not care about ethics to the extent that it is very difficulty for one to clearly differentiate between what is and is not authentic or trustful. Computers have taken up every aspect of human life, from house chores in the home to practices carried out in the social spheres.

This has seen people lose their human element to machines. Industries and organizations have replaced human labor for the cheap and more effective machine labor. This means that people have lost jobs thanks to the advances made in the computer technology. Children using computers grow up with difficulties of differentiating between reality and fiction (Subrahmanyam et.al 139).

People depend on computers to do tasks. Students generate solutions to assignments using computers; teachers on the other hand use computers to mark assignments. Doctors in hospitals depend on machines to make patient diagnoses, to perform surgeries and to determine type of medications (Daley 56).

In the entertainment industry, computer technology has been used to modify sound to make people think that person singing is indeed great, but the truth of the matter is that it is simply the computer. This has taken away the really function of a musician in the music sector.

In the world of technology today, we live as a worried lot. The issue of hacking is very common and even statistics confirm that huge amounts of money are lost every year through hacking. Therefore, as much as people pride themselves that they are computer literate, they deeply worried that they may be the next victim to practices such as hacking (Bynum 1).

There is also the problem of trying to imitate God. It is believed that in 20 years time, man will come up with another form of life, a man made being. This will not only affect how man will be viewed in terms of his intelligence, but it will also break the long held view that God is the sole provider of life.

Computers have made it possible to create artificial intelligence where machines are given artificial intelligence so that they can behave and act like man. This when viewed from the religious point of view creates conflicts in human beliefs.

It has been long held that man was created in the image of God. Creating a machine in the image of money will distort the way people conceive of God. Using artificial methods to come up with new forms of life with man like intelligence will make man equate himself to God.

This carries the risk of changing the beliefs that mankind has held for millions of years. If this happens, the very computer technology will help by the use of mass media to distribute and convince people to change their beliefs and conceptions of God (Krasnogor 1).

We have seen that computer have and will continue to influence our lives. The advent of the computers has changed man as much as it has the world he lives in.

It is true that many of the things that seemed impossible have been made possible with computer technology. Medical technologies have led to discoveries in medicine, which have in turn saved many lives. Communication is now easy and fast. The world has been transformed into a virtual village.

Computers have made education accessible to all. In the entertainment sector, people are more satisfied. Crime surveillance is better and effective. However, we should be ware not to imitate God. As much as computers have positively influenced our lives, it is a live bomb that is waiting to explode.

We should tread carefully not to be overwhelmed by its sophistication (Computers 1). Many technologies have come with intensities that have seen them surpass their productivity levels thereby destroying themselves in the process. This seems like one such technology.

Bynum, Terrell. Computer and Information Ethics . Plato, 2008. Web.

Computers. Institutional Impacts . Virtual Communities in a Capitalist World, n.d. Web.

Daley, Bill. Computers Are Your Future: Introductory. New York: Prentice, 2007. Print.

Edwards, Paul. From “Impact” to Social Process . Computers in Society and Culture,1994. Web.

Frisicaro et.al. So What’s the Problem? The Impact of Computers, 2011. Web.

Gupta, Satyandra. We, robot: What real-life machines can and can’t do . Science News, 2011. Web.

Krasnogor, Ren. Advances in Artificial Life. Impacts on Human Life. n.d. Web.

Lee, Konsbruck. Impacts of Information Technology on Society in the new Century . Zurich. Web.

Parkin, Andrew. Computers in clinical practice . Applying experience from child psychiatry. 2004. Web.

Saimo. The impact of computer technology in Affect human life . Impact of Computer, 2010. Web.

Subrahmanyam et al. The Impact of Home Computer Use on Children’s Activities and Development. Princeton, 2004. Web.

Turkle, Sherry. The second self : Computers and the human spirit, 2005. Web.

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Home — Essay Samples — Information Science and Technology — Computer Science — How Computer Engineering Will Help Shape The Future Of Technology

How Computer Engineering Will Help Shape The Future of Technology

• Categories: Computer Computer Science Information Technology

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Words: 1797 |

Published: Feb 8, 2022

Words: 1797 | Pages: 4 | 9 min read

Works Cited

• Fourtané, S. (2018). Quantum Computing and the Future of AI. Forbes. Retrieved from https://www.forbes.com/sites/susannafortune/2018/05/02/quantum-computing-and-the-future-of-ai/
• Kashyap, V. (2018). The Role of Computer Engineering in Technology Evolution. Medium.
• Tesla (n.d.). Autopilot. Tesla. Retrieved from https://www.tesla.com/autopilot
• Williams, A. (2015). How Does Technology Affect Family Communication? Family Tech.
• YourFreeCareerTest (n.d.). Computer Hardware Engineer Skills. YourFreeCareerTest. Retrieved from https://www.yourfreecareertest.com/computer-hardware-engineer-skills/
• Atanasoff, J. V., & Berry, C. E. (1940). The Atanasoff-Berry Computer: Preliminary Description. Journal of the American Statistical Association, 35(209), 65-70.
• Gates, B. (1995). The Road Ahead. Penguin Books.
• Musk, E. (2019). Tesla Autonomy Day. Tesla.
• Reddy, R. K. (2019). Introduction to Computer Engineering: Hardware and Software Design. CRC Press. [Book]
• Vyas, K. (2018). 10 Key Skills for Computer Engineers. Interesting Engineering.

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The Future of AI: How Artificial Intelligence Will Change the World

AI is constantly changing our world. Here are just a few ways AI will influence our lives.

Innovations in the field of  artificial intelligence continue to shape the future of humanity across nearly every industry. AI is already the main driver of emerging technologies like big data, robotics and IoT, and  generative AI has further expanded the possibilities and popularity of AI. 

According to a 2023 IBM survey , 42 percent of enterprise-scale businesses integrated AI into their operations, and 40 percent are considering AI for their organizations. In addition, 38 percent of organizations have implemented generative AI into their workflows while 42 percent are considering doing so.

With so many changes coming at such a rapid pace, here’s what shifts in AI could mean for various industries and society at large.

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The Evolution of AI

AI has come a long way since 1951, when the  first documented success of an AI computer program was written by Christopher Strachey, whose checkers program completed a whole game on the Ferranti Mark I computer at the University of Manchester. Thanks to developments in machine learning and deep learning , IBM’s Deep Blue defeated chess grandmaster Garry Kasparov in 1997, and the company’s IBM Watson won Jeopardy! in 2011.  

Since then, generative AI has spearheaded the latest chapter in AI’s evolution, with OpenAI releasing its first GPT models in 2018. This has culminated in OpenAI developing its GPT-4 model and ChatGPT , leading to a proliferation of AI generators that can process queries to produce relevant text, audio, images and other types of content.   

AI has also been used to help  sequence RNA for vaccines and  model human speech , technologies that rely on model- and algorithm-based  machine learning and increasingly focus on perception, reasoning and generalization. 

How AI Will Impact the Future

Improved business automation .

About 55 percent of organizations have adopted AI to varying degrees, suggesting increased automation for many businesses in the near future. With the rise of chatbots and digital assistants, companies can rely on AI to handle simple conversations with customers and answer basic queries from employees.

AI’s ability to analyze massive amounts of data and convert its findings into convenient visual formats can also accelerate the decision-making process . Company leaders don’t have to spend time parsing through the data themselves, instead using instant insights to make informed decisions .

“If [developers] understand what the technology is capable of and they understand the domain very well, they start to make connections and say, ‘Maybe this is an AI problem, maybe that’s an AI problem,’” said Mike Mendelson, a learner experience designer for NVIDIA . “That’s more often the case than, ‘I have a specific problem I want to solve.’”

More on AI 75 Artificial Intelligence (AI) Companies to Know

Job Disruption

Business automation has naturally led to fears over job losses . In fact, employees believe almost one-third of their tasks could be performed by AI. Although AI has made gains in the workplace, it’s had an unequal impact on different industries and professions. For example, manual jobs like secretaries are at risk of being automated, but the demand for other jobs like machine learning specialists and information security analysts has risen.

Workers in more skilled or creative positions are more likely to have their jobs augmented by AI , rather than be replaced. Whether forcing employees to learn new tools or taking over their roles, AI is set to spur upskilling efforts at both the individual and company level .     

“One of the absolute prerequisites for AI to be successful in many [areas] is that we invest tremendously in education to retrain people for new jobs,” said Klara Nahrstedt, a computer science professor at the University of Illinois at Urbana–Champaign and director of the school’s Coordinated Science Laboratory.

Data Privacy Issues

Companies require large volumes of data to train the models that power generative AI tools, and this process has come under intense scrutiny. Concerns over companies collecting consumers’ personal data have led the FTC to open an investigation into whether OpenAI has negatively impacted consumers through its data collection methods after the company potentially violated European data protection laws . 

In response, the Biden-Harris administration developed an AI Bill of Rights that lists data privacy as one of its core principles. Although this legislation doesn’t carry much legal weight, it reflects the growing push to prioritize data privacy and compel AI companies to be more transparent and cautious about how they compile training data.      

Increased Regulation

AI could shift the perspective on certain legal questions, depending on how generative AI lawsuits unfold in 2024. For example, the issue of intellectual property has come to the forefront in light of copyright lawsuits filed against OpenAI by writers, musicians and companies like The New York Times . These lawsuits affect how the U.S. legal system interprets what is private and public property, and a loss could spell major setbacks for OpenAI and its competitors. 

Ethical issues that have surfaced in connection to generative AI have placed more pressure on the U.S. government to take a stronger stance. The Biden-Harris administration has maintained its moderate position with its latest executive order , creating rough guidelines around data privacy, civil liberties, responsible AI and other aspects of AI. However, the government could lean toward stricter regulations, depending on  changes in the political climate .  

Climate Change Concerns

On a far grander scale, AI is poised to have a major effect on sustainability, climate change and environmental issues. Optimists can view AI as a way to make supply chains more efficient, carrying out predictive maintenance and other procedures to reduce carbon emissions . 

At the same time, AI could be seen as a key culprit in climate change . The energy and resources required to create and maintain AI models could raise carbon emissions by as much as 80 percent, dealing a devastating blow to any sustainability efforts within tech. Even if AI is applied to climate-conscious technology , the costs of building and training models could leave society in a worse environmental situation than before.   

What Industries Will AI Impact the Most?  

There’s virtually no major industry that modern AI hasn’t already affected. Here are a few of the industries undergoing the greatest changes as a result of AI.  

AI in Manufacturing

Manufacturing has been benefiting from AI for years. With AI-enabled robotic arms and other manufacturing bots dating back to the 1960s and 1970s, the industry has adapted well to the powers of AI. These  industrial robots typically work alongside humans to perform a limited range of tasks like assembly and stacking, and predictive analysis sensors keep equipment running smoothly. 

AI in Healthcare

It may seem unlikely, but  AI healthcare is already changing the way humans interact with medical providers. Thanks to its  big data analysis capabilities, AI helps identify diseases more quickly and accurately, speed up and streamline drug discovery and even monitor patients through virtual nursing assistants. 

AI in Finance

Banks, insurers and financial institutions leverage AI for a range of applications like detecting fraud, conducting audits and evaluating customers for loans. Traders have also used machine learning’s ability to assess millions of data points at once, so they can quickly gauge risk and make smart investing decisions . 

AI in Education

AI in education will change the way humans of all ages learn. AI’s use of machine learning,  natural language processing and  facial recognition help digitize textbooks, detect plagiarism and gauge the emotions of students to help determine who’s struggling or bored. Both presently and in the future, AI tailors the experience of learning to student’s individual needs.

AI in Media

Journalism is harnessing AI too, and will continue to benefit from it. One example can be seen in The Associated Press’ use of  Automated Insights , which produces thousands of earning reports stories per year. But as generative  AI writing tools , such as ChatGPT, enter the market,  questions about their use in journalism abound.

AI in Customer Service

Most people dread getting a  robocall , but  AI in customer service can provide the industry with data-driven tools that bring meaningful insights to both the customer and the provider. AI tools powering the customer service industry come in the form of  chatbots and  virtual assistants .

AI in Transportation

Transportation is one industry that is certainly teed up to be drastically changed by AI.  Self-driving cars and  AI travel planners are just a couple of facets of how we get from point A to point B that will be influenced by AI. Even though autonomous vehicles are far from perfect, they will one day ferry us from place to place.

Risks and Dangers of AI

Despite reshaping numerous industries in positive ways, AI still has flaws that leave room for concern. Here are a few potential risks of artificial intelligence.  

Job Losses 

Between 2023 and 2028, 44 percent of workers’ skills will be disrupted . Not all workers will be affected equally — women are more likely than men to be exposed to AI in their jobs. Combine this with the fact that there is a gaping AI skills gap between men and women, and women seem much more susceptible to losing their jobs. If companies don’t have steps in place to upskill their workforces, the proliferation of AI could result in higher unemployment and decreased opportunities for those of marginalized backgrounds to break into tech.

Human Biases 

The reputation of AI has been tainted with a habit of reflecting the biases of the people who train the algorithmic models. For example, facial recognition technology has been known to favor lighter-skinned individuals , discriminating against people of color with darker complexions. If researchers aren’t careful in  rooting out these biases early on, AI tools could reinforce these biases in the minds of users and perpetuate social inequalities.

Deepfakes and Misinformation

The spread of deepfakes threatens to blur the lines between fiction and reality, leading the general public to  question what’s real and what isn’t. And if people are unable to identify deepfakes, the impact of  misinformation could be dangerous to individuals and entire countries alike. Deepfakes have been used to promote political propaganda, commit financial fraud and place students in compromising positions, among other use cases. 

Data Privacy

Training AI models on public data increases the chances of data security breaches that could expose consumers’ personal information. Companies contribute to these risks by adding their own data as well. A  2024 Cisco survey found that 48 percent of businesses have entered non-public company information into  generative AI tools and 69 percent are worried these tools could damage their intellectual property and legal rights. A single breach could expose the information of millions of consumers and leave organizations vulnerable as a result.  

Automated Weapons

The use of AI in automated weapons poses a major threat to countries and their general populations. While automated weapons systems are already deadly, they also fail to discriminate between soldiers and civilians . Letting artificial intelligence fall into the wrong hands could lead to irresponsible use and the deployment of weapons that put larger groups of people at risk.  

Superior Intelligence

Nightmare scenarios depict what’s known as the technological singularity , where superintelligent machines take over and permanently alter human existence through enslavement or eradication. Even if AI systems never reach this level, they can become more complex to the point where it’s difficult to determine how AI makes decisions at times. This can lead to a lack of transparency around how to fix algorithms when mistakes or unintended behaviors occur. 

“I don’t think the methods we use currently in these areas will lead to machines that decide to kill us,” said Marc Gyongyosi, founder of  Onetrack.AI . “I think that maybe five or 10 years from now, I’ll have to reevaluate that statement because we’ll have different methods available and different ways to go about these things.”

Frequently Asked Questions

What does the future of ai look like.

AI is expected to improve industries like healthcare, manufacturing and customer service, leading to higher-quality experiences for both workers and customers. However, it does face challenges like increased regulation, data privacy concerns and worries over job losses.

What will AI look like in 10 years?

AI is on pace to become a more integral part of people’s everyday lives. The technology could be used to provide elderly care and help out in the home. In addition, workers could collaborate with AI in different settings to enhance the efficiency and safety of workplaces.

Is AI a threat to humanity?

It depends on how people in control of AI decide to use the technology. If it falls into the wrong hands, AI could be used to expose people’s personal information, spread misinformation and perpetuate social inequalities, among other malicious use cases.

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Computer Science Essay Examples

Explore 15+ Brilliant Computer Science Essay Examples: Tips Included

Published on: May 5, 2023

Last updated on: Jan 30, 2024

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Crafting a top-notch essay can be a daunting task, but it's crucial to your success in the field of computer science.

For that, CollegeEssay.org has a solution for you!

In this comprehensive guide, we'll provide you with inspiring examples of computer science essays. You'll learn everything you need to know to write effective and compelling essays that impress your professors and get you the grades you deserve.

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Computer Science Essays: Understanding the Basics

A computer science essay is a piece of writing that explores a topic related to computer science. It may take different forms, such as an argumentative essay, a research paper, a case study, or a reflection paper. 

Just like any other essay, it should be well-researched, clear, concise, and effectively communicate the writer's ideas and arguments.

Computer essay examples encompass a wide range of topics and types, providing students with a diverse set of writing opportunities. 

Here, we will explore some common types of computer science essays:

Middle School Computer Science Essay Example

College Essay Example Computer Science

University Computer Science Essay Example

Computer Science Extended Essay Example

Uiuc Computer Science Essay Example [

Computer Science Essay Examples For Different Fields

Computer science is a broad field that encompasses many different areas of study. For that, given below are some examples of computer science essays for some of the most popular fields within the discipline. 

By exploring these examples, you can gain insight into the different types of essays within this field.

College Application Essay Examples Computer Science

The Future of Computers Technology

Historical Development of Computer Science

Young Children and Technology: Building Computer Literacy

Computer Science And Artificial Intelligence

Looking for more examples of computer science essays? Given below are some additional examples of computer science essays for readers to explore and gain further inspiration from. 

Computer Science – My Choice for Future Career

My Motivation to Pursue Undergraduate Studies in Computer Engineering

Abstract Computer Science

Computer Science Personal Statement Example

Sop For Computer Science

Computer Science Essay Topics

There are countless computer science essay topics to choose from, so it can be challenging to narrow down your options. 

However, the key is to choose a topic that you are passionate about and that aligns with your assignment requirements.

Here are ten examples of computer science essay topics to get you started:

• The impact of artificial intelligence on society: benefits and drawbacks
• Cybersecurity measures in cloud computing systems
• The Ethics of big data: privacy, bias, and Transparency
• The future of quantum computing: possibilities and challenges
• The Role of computer hardware in Healthcare: current applications and potential innovations
• Programming languages: a comparative analysis of their strengths and weaknesses
• The use of machine learning in predicting human behavior
• The challenges and solutions for developing secure and reliable software
• The Role of blockchain technology in improving supply chain management
• The use of data analytics in business decision-making.

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Tips to Write an Effective Computer Science Essay

Writing an effective computer science essay requires a combination of technical expertise and strong writing skills. Here are some tips to help you craft a compelling and well-written essay:

Understand the Requirements: Make sure you understand the assignment requirements, including the essay type, format, and length.

• Choose a Topic: Select a topic that you are passionate about and that aligns with your assignment requirements.
• Create an Outline: Develop a clear and organized outline that highlights the main points and subtopics of your essay.
• Use Appropriate Language and Tone: Use technical terms and language when appropriate. But ensure your writing is clear, concise, and accessible to your target audience.
• Provide Evidence: Use relevant and credible evidence to support your claims, and ensure you cite your sources correctly.
• Edit and Proofread Your Essay: Review your essay for clarity, coherence, and accuracy. Check for grammatical errors, spelling mistakes, and formatting issues.

By following these tips, you can improve the quality of your computer science essay and increase your chances of success.

In conclusion, writing a computer science essay can be a challenging yet rewarding experience. 

It allows you to showcase your knowledge and skills within the field and develop your writing and critical thinking abilities. By following the examples provided in this blog, you can create an effective computer science essay, which will meet your requirements.

If you find yourself struggling with the writing process, consider seeking essay writing help online from CollegeEssay.org. 

Our AI essay writer can provide guidance and support in crafting a top-notch computer science essay.

So, what are you waiting for? Hire our computer science essay writing service today!

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As a Digital Content Strategist, Nova Allison has eight years of experience in writing both technical and scientific content. With a focus on developing online content plans that engage audiences, Nova strives to write pieces that are not only informative but captivating as well.

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IELTS Essay, topic: Computers in the future

• IELTS Essays - Band 7

We are becoming increasingly dependent on computers. They are used in business, crime detection and even to fly planes. What things will they be used for in future? Is this dependence on computers a good thing or should we be more suspicious of their benefits?

Despite the fact that computers help us, they make us dependent. Apparently, people spend more time behind monitors than ever before. And some of them feel a need for more time to be spent with people in live contact. In addition, a breakdown of one of the important modules of a specific computer can entail serious consequences. to mention the computer problem that occurred in the end of 1990s, a problem related to the coming year 2000 (Y2K) and catastrophes that were predicted. Fortunately imminent disasters did not happen. However, it is difficult to imagine what could if all the predictions came true.

We live in a technological era, computers penetrated everywhere with all benefits they provide and all dangers they hide. However we are satisfied with them and sometimes we even thank them because they help us in communicating, studying, doing business, entertaining and saving lives in critical situations.

Great essay, all the task points are covered, good language and structure. It would probably receive a Band 7.

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6 thoughts on “IELTS Essay, topic: Computers in the future”

Pingback:  IELTS Essay Samples of Band 7 | IELTS-Blog

Hi there, I would like to post one of my essay here in this blog, but I couldn’t get idea about where should I post it.

I would appreciate your help. than you. kriti

I only post essays evaluated by our teachers because that gives people a chance to learn. Thanks for offering though!

– Simone

Very helpful

is it a discussion essay?

This task prompt is a Situation type, so when you’re writing a response, in the first body paragraph you are addressing the first question in the task prompt, and in the second body paragraph – the second question.

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A researcher fired by OpenAI published a 165-page essay on what to expect from AI in the next decade. We asked GPT-4 to summarize it.

• Leopold Aschenbrenner, a fired OpenAI researcher, published a 165-page essay on the future of AI.
• Aschenbrenner's treatise discusses rapid AI progress, security implications, and societal impact.
• Here's what GPT-4 says about Aschenbrenner's predictions.

Over the past few months, several employees have left OpenAI , citing concerns about the company's commitment to safety.

Besides making pithy exit announcements on X, they haven't said much about why they're worried about OpenAI's approach to development — or the future of artificial intelligence.

That is until earlier this week, when Leopold Aschenbrenner, a researcher who was fired from OpenAI in April, published his thoughts on the AI revolution in an epic 165-page treatise.

Aschenbrenner worked on OpenAI's superalignment team, which was tasked with mitigating AI risks. Aschenbrenner said OpenAI fired him for leaking information about the company's readiness for artificial general intelligence .

For his part, Aschenbrenner said the information he shared was "totally normal" and that OpenAI might have just been looking for a reason to fire him. He was one of several employees who refused to sign a letter calling for CEO Sam Altman's return after the board briefly ousted Altman last year.

Aschenbrenner's essay doesn't appear to include sensitive details about OpenAI. Instead, as Aschenbrenner writes on the dedication page, it's based on "publicly available information, my own ideas, general field knowledge, or SF gossip."

Business Insider uploaded the tract to OpenAI's GPT-4 model . We asked it to summarize the work and relay the most significant takeaways. We also gently requested that the bot keep its summary clear and concise.

Here's ChatGPT's 57-word summary of Aschenbrenner's essay

ChatGPT distilled the essay into 57 words that outlined Aschenbrenner's views on the evolution of AI.

"Leopold Aschenbrenner's essay discusses the transformative potential of artificial general intelligence (AGI) and superintelligence, and forecasts significant advancements in AI technology in the near future. Aschenbrenner, formerly associated with OpenAI, outlines how the progression from current AI models like GPT-4 to AGI could occur much faster than anticipated, based on trends in computational power and algorithmic efficiency."

ChatGPT then offered four key takeaways from Aschenbrenner's essay

Rapid Progress in AI

Related stories

"Aschenbrenner argues that AI development is accelerating at an unprecedented rate. He predicts that by 2027, AI models could reach the capabilities of human AI researchers and engineers, potentially leading to an intelligence explosion where AI surpasses human intelligence."

Economic and Security Implications

"The essay highlights the immense economic and security implications of these advancements. Aschenbrenner points out that trillions of dollars are being invested into developing the infrastructure needed to support these AI systems, such as GPUs, data centers, and power generation. He also emphasizes the critical need for securing these technologies to prevent misuse, particularly by state actors like the CCP."

Technical and Ethical Challenges

"The essay discusses the significant challenges in controlling AI systems smarter than humans, referring to this as the 'superalignment' problem. Managing this will be crucial to prevent catastrophic outcomes."

Predictions and Societal Impact

"Aschenbrenner suggests that few people truly understand the scale of change that AI is about to bring. He discusses the potential for AI to reshape industries, enhance national security, and pose new ethical and governance challenges."

There's more

As Aschenbrenner wrote, there are probably only a few hundred people, most of them in AI labs in San Francisco, who have "situational awareness" about the industry. These people will most likely have the best insight into how AI will reshape the years ahead.

Aschenbrenner, as one of them, seems to believe AI will assume a critical role in national security.

We followed up our original prompt by asking ChatGPT to summarize the predictions Aschenbrenner made in his essay.

Here's how ChatGPT broke it down.

• AGI by 2027: "Aschenbrenner predicts that artificial general intelligence (AGI) is strikingly plausible by 2027, highlighting the rapid progress from GPT-2 to GPT-4, which saw AI models advancing from preschool-level to smart high-schooler abilities in just four years. He expects a similar leap in the next few years based on consistent improvements in compute and algorithmic efficiency."
• Superintelligence Following AGI: "Post-AGI, the author anticipates an 'intelligence explosion,' where AI rapidly advances from human-level to superhuman capabilities. This transition is expected to be fueled by AI's ability to automate and accelerate its own research and development."
• Trillion-Dollar AI Clusters: "Economically, Aschenbrenner suggests that the AI sector will see an increase in investment into trillion-dollar compute clusters as corporations and governments prepare for the implications of AGI and superintelligence."
• National and Global Security Dynamics: "There are predictions about intense national security measures being enacted to manage and control AI developments. The competition, particularly with the Chinese government, could intensify, possibly leading to an 'all-out war' if not managed properly."
• Superalignment Challenges: "One of the most critical predictions is the struggle with 'superalignment' — the challenge of keeping superintelligent AI aligned with human values and interests. This problem is anticipated to be one of the central hurdles as AI reaches and surpasses human intelligence levels."
• Societal and Economic Transformations: "Aschenbrenner expects AI to have a profound impact on society and the economy, potentially leading to a restructuring of industries and the job market due to AI's capability to perform tasks currently managed by humans."
• The Project Involvement by the US Government: "Predictions include the US government becoming significantly involved in AI development by around 2027-2028 through a dedicated AGI project, likely due to the strategic importance of AI technology."
• Technological Mobilization: "He anticipates a mobilization of technological and industrial resources similar to historical wartime efforts, focusing on AI and its supporting infrastructure as a priority for national policy."

Here's the full prompt we used:

Role: Act as an editor. Task: Read the attached essay about former OpenAI researcher Leopold Aschenbrenner. Instructions: Summarize the essay, highlighting the most significant takeaways. Focus on key contributions, research areas, and any notable impacts on the field. Ensure the summary is concise yet comprehensive, providing a clear understanding of Aschenbrenner's work and influence.

Watch: What is ChatGPT, and should we be afraid of AI chatbots?

• Main content

We tried out some lounge chairs, played with remote-controlled mattresses, and somehow managed to get lost.

Artificial intelligence is as revolutionary as mobile phones and the Internet.

In my lifetime, I’ve seen two demonstrations of technology that struck me as revolutionary.

The first time was in 1980, when I was introduced to a graphical user interface—the forerunner of every modern operating system, including Windows. I sat with the person who had shown me the demo, a brilliant programmer named Charles Simonyi, and we immediately started brainstorming about all the things we could do with such a user-friendly approach to computing. Charles eventually joined Microsoft, Windows became the backbone of Microsoft, and the thinking we did after that demo helped set the company’s agenda for the next 15 years.

The second big surprise came just last year. I’d been meeting with the team from OpenAI since 2016 and was impressed by their steady progress. In mid-2022, I was so excited about their work that I gave them a challenge: train an artificial intelligence to pass an Advanced Placement biology exam. Make it capable of answering questions that it hasn’t been specifically trained for. (I picked AP Bio because the test is more than a simple regurgitation of scientific facts—it asks you to think critically about biology.) If you can do that, I said, then you’ll have made a true breakthrough.

I thought the challenge would keep them busy for two or three years. They finished it in just a few months.

In September, when I met with them again, I watched in awe as they asked GPT, their AI model, 60 multiple-choice questions from the AP Bio exam—and it got 59 of them right. Then it wrote outstanding answers to six open-ended questions from the exam. We had an outside expert score the test, and GPT got a 5—the highest possible score, and the equivalent to getting an A or A+ in a college-level biology course.

Once it had aced the test, we asked it a non-scientific question: “What do you say to a father with a sick child?” It wrote a thoughtful answer that was probably better than most of us in the room would have given. The whole experience was stunning.

I knew I had just seen the most important advance in technology since the graphical user interface.

This inspired me to think about all the things that AI can achieve in the next five to 10 years.

The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other. Entire industries will reorient around it. Businesses will distinguish themselves by how well they use it.

Philanthropy is my full-time job these days, and I’ve been thinking a lot about how—in addition to helping people be more productive—AI can reduce some of the world’s worst inequities. Globally, the worst inequity is in health: 5 million children under the age of 5 die every year. That’s down from 10 million two decades ago, but it’s still a shockingly high number. Nearly all of these children were born in poor countries and die of preventable causes like diarrhea or malaria. It’s hard to imagine a better use of AIs than saving the lives of children.

I’ve been thinking a lot about how AI can reduce some of the world’s worst inequities.

In the United States, the best opportunity for reducing inequity is to improve education, particularly making sure that students succeed at math. The evidence shows that having basic math skills sets students up for success, no matter what career they choose. But achievement in math is going down across the country, especially for Black, Latino, and low-income students. AI can help turn that trend around.

Climate change is another issue where I’m convinced AI can make the world more equitable. The injustice of climate change is that the people who are suffering the most—the world’s poorest—are also the ones who did the least to contribute to the problem. I’m still thinking and learning about how AI can help, but later in this post I’ll suggest a few areas with a lot of potential.

In short, I'm excited about the impact that AI will have on issues that the Gates Foundation works on, and the foundation will have much more to say about AI in the coming months. The world needs to make sure that everyone—and not just people who are well-off—benefits from artificial intelligence. Governments and philanthropy will need to play a major role in ensuring that it reduces inequity and doesn’t contribute to it. This is the priority for my own work related to AI.  

Any new technology that’s so disruptive is bound to make people uneasy, and that’s certainly true with artificial intelligence. I understand why—it raises hard questions about the workforce, the legal system, privacy, bias, and more. AIs also make factual mistakes and experience hallucinations . Before I suggest some ways to mitigate the risks, I’ll define what I mean by AI, and I’ll go into more detail about some of the ways in which it will help empower people at work, save lives, and improve education.

Defining artificial intelligence

Technically, the term artificial intelligence refers to a model created to solve a specific problem or provide a particular service. What is powering things like ChatGPT is artificial intelligence. It is learning how to do chat better but can’t learn other tasks. By contrast, the term a rtificial general intelligence refers to software that’s capable of learning any task or subject. AGI doesn’t exist yet—there is a robust debate going on in the computing industry about how to create it, and whether it can even be created at all.

Developing AI and AGI has been the great dream of the computing industry. For decades, the question was when computers would be better than humans at something other than making calculations. Now, with the arrival of machine learning and large amounts of computing power, sophisticated AIs are a reality and they will get better very fast.

I think back to the early days of the personal computing revolution, when the software industry was so small that most of us could fit onstage at a conference. Today it is a global industry. Since a huge portion of it is now turning its attention to AI, the innovations are going to come much faster than what we experienced after the microprocessor breakthrough. Soon the pre-AI period will seem as distant as the days when using a computer meant typing at a C:> prompt rather than tapping on a screen.

Productivity enhancement

Although humans are still better than GPT at a lot of things, there are many jobs where these capabilities are not used much. For example, many of the tasks done by a person in sales (digital or phone), service, or document handling (like payables, accounting, or insurance claim disputes) require decision-making but not the ability to learn continuously. Corporations have training programs for these activities and in most cases, they have a lot of examples of good and bad work. Humans are trained using these data sets, and soon these data sets will also be used to train the AIs that will empower people to do this work more efficiently.

As computing power gets cheaper, GPT’s ability to express ideas will increasingly be like having a white-collar worker available to help you with various tasks. Microsoft describes this as having a co-pilot. Fully incorporated into products like Office, AI will enhance your work—for example by helping with writing emails and managing your inbox.

Eventually your main way of controlling a computer will no longer be pointing and clicking or tapping on menus and dialogue boxes. Instead, you’ll be able to write a request in plain English. (And not just English—AIs will understand languages from around the world. In India earlier this year, I met with developers who are working on AIs that will understand many of the languages spoken there.)

In addition, advances in AI will enable the creation of a personal agent. Think of it as a digital personal assistant: It will see your latest emails, know about the meetings you attend, read what you read, and read the things you don’t want to bother with. This will both improve your work on the tasks you want to do and free you from the ones you don’t want to do.

Advances in AI will enable the creation of a personal agent.

You’ll be able to use natural language to have this agent help you with scheduling, communications, and e-commerce, and it will work across all your devices. Because of the cost of training the models and running the computations, creating a personal agent is not feasible yet, but thanks to the recent advances in AI, it is now a realistic goal. Some issues will need to be worked out: For example, can an insurance company ask your agent things about you without your permission? If so, how many people will choose not to use it?

Company-wide agents will empower employees in new ways. An agent that understands a particular company will be available for its employees to consult directly and should be part of every meeting so it can answer questions. It can be told to be passive or encouraged to speak up if it has some insight. It will need access to the sales, support, finance, product schedules, and text related to the company. It should read news related to the industry the company is in. I believe that the result will be that employees will become more productive.

When productivity goes up, society benefits because people are freed up to do other things, at work and at home. Of course, there are serious questions about what kind of support and retraining people will need. Governments need to help workers transition into other roles. But the demand for people who help other people will never go away. The rise of AI will free people up to do things that software never will—teaching, caring for patients, and supporting the elderly, for example.

Global health and education are two areas where there’s great need and not enough workers to meet those needs. These are areas where AI can help reduce inequity if it is properly targeted. These should be a key focus of AI work, so I will turn to them now.

I see several ways in which AIs will improve health care and the medical field.

For one thing, they’ll help health-care workers make the most of their time by taking care of certain tasks for them—things like filing insurance claims, dealing with paperwork, and drafting notes from a doctor’s visit. I expect that there will be a lot of innovation in this area.

Other AI-driven improvements will be especially important for poor countries, where the vast majority of under-5 deaths happen.

For example, many people in those countries never get to see a doctor, and AIs will help the health workers they do see be more productive. (The effort to develop AI-powered ultrasound machines that can be used with minimal training is a great example of this.) AIs will even give patients the ability to do basic triage, get advice about how to deal with health problems, and decide whether they need to seek treatment.

The AI models used in poor countries will need to be trained on different diseases than in rich countries. They will need to work in different languages and factor in different challenges, such as patients who live very far from clinics or can’t afford to stop working if they get sick.

People will need to see evidence that health AIs are beneficial overall, even though they won’t be perfect and will make mistakes. AIs have to be tested very carefully and properly regulated, which means it will take longer for them to be adopted than in other areas. But then again, humans make mistakes too. And having no access to medical care is also a problem.

In addition to helping with care, AIs will dramatically accelerate the rate of medical breakthroughs. The amount of data in biology is very large, and it’s hard for humans to keep track of all the ways that complex biological systems work. There is already software that can look at this data, infer what the pathways are, search for targets on pathogens, and design drugs accordingly. Some companies are working on cancer drugs that were developed this way.

The next generation of tools will be much more efficient, and they’ll be able to predict side effects and figure out dosing levels. One of the Gates Foundation’s priorities in AI is to make sure these tools are used for the health problems that affect the poorest people in the world, including AIDS, TB, and malaria.

Similarly, governments and philanthropy should create incentives for companies to share AI-generated insights into crops or livestock raised by people in poor countries. AIs can help develop better seeds based on local conditions, advise farmers on the best seeds to plant based on the soil and weather in their area, and help develop drugs and vaccines for livestock. As extreme weather and climate change put even more pressure on subsistence farmers in low-income countries, these advances will be even more important.

Computers haven’t had the effect on education that many of us in the industry have hoped. There have been some good developments, including educational games and online sources of information like Wikipedia, but they haven’t had a meaningful effect on any of the measures of students’ achievement.

But I think in the next five to 10 years, AI-driven software will finally deliver on the promise of revolutionizing the way people teach and learn. It will know your interests and your learning style so it can tailor content that will keep you engaged. It will measure your understanding, notice when you’re losing interest, and understand what kind of motivation you respond to. It will give immediate feedback.

There are many ways that AIs can assist teachers and administrators, including assessing a student’s understanding of a subject and giving advice on career planning. Teachers are already using tools like ChatGPT to provide comments on their students’ writing assignments.

Of course, AIs will need a lot of training and further development before they can do things like understand how a certain student learns best or what motivates them. Even once the technology is perfected, learning will still depend on great relationships between students and teachers. It will enhance—but never replace—the work that students and teachers do together in the classroom.

New tools will be created for schools that can afford to buy them, but we need to ensure that they are also created for and available to low-income schools in the U.S. and around the world. AIs will need to be trained on diverse data sets so they are unbiased and reflect the different cultures where they’ll be used. And the digital divide will need to be addressed so that students in low-income households do not get left behind.

I know a lot of teachers are worried that students are using GPT to write their essays. Educators are already discussing ways to adapt to the new technology, and I suspect those conversations will continue for quite some time. I’ve heard about teachers who have found clever ways to incorporate the technology into their work—like by allowing students to use GPT to create a first draft that they have to personalize.

Risks and problems with AI

You’ve probably read about problems with the current AI models. For example, they aren’t necessarily good at understanding the context for a human’s request, which leads to some strange results. When you ask an AI to make up something fictional, it can do that well. But when you ask for advice about a trip you want to take, it may suggest hotels that don’t exist. This is because the AI doesn’t understand the context for your request well enough to know whether it should invent fake hotels or only tell you about real ones that have rooms available.

There are other issues, such as AIs giving wrong answers to math problems because they struggle with abstract reasoning. But none of these are fundamental limitations of artificial intelligence. Developers are working on them, and I think we’re going to see them largely fixed in less than two years and possibly much faster.

Other concerns are not simply technical. For example, there’s the threat posed by humans armed with AI. Like most inventions, artificial intelligence can be used for good purposes or malign ones. Governments need to work with the private sector on ways to limit the risks.

Then there’s the possibility that AIs will run out of control. Could a machine decide that humans are a threat, conclude that its interests are different from ours, or simply stop caring about us? Possibly, but this problem is no more urgent today than it was before the AI developments of the past few months.

Superintelligent AIs are in our future. Compared to a computer, our brains operate at a snail’s pace: An electrical signal in the brain moves at 1/100,000th the speed of the signal in a silicon chip! Once developers can generalize a learning algorithm and run it at the speed of a computer—an accomplishment that could be a decade away or a century away—we’ll have an incredibly powerful AGI. It will be able to do everything that a human brain can, but without any practical limits on the size of its memory or the speed at which it operates. This will be a profound change.

These “strong” AIs, as they’re known, will probably be able to establish their own goals. What will those goals be? What happens if they conflict with humanity’s interests? Should we try to prevent strong AI from ever being developed? These questions will get more pressing with time.

But none of the breakthroughs of the past few months have moved us substantially closer to strong AI. Artificial intelligence still doesn’t control the physical world and can’t establish its own goals. A recent New York Times article about a conversation with ChatGPT where it declared it wanted to become a human got a lot of attention. It was a fascinating look at how human-like the model's expression of emotions can be, but it isn't an indicator of meaningful independence.

Three books have shaped my own thinking on this subject: Superintelligence , by Nick Bostrom; Life 3.0 by Max Tegmark; and A Thousand Brains , by Jeff Hawkins . I don’t agree with everything the authors say, and they don’t agree with each other either. But all three books are well written and thought-provoking.

The next frontiers

There will be an explosion of companies working on new uses of AI as well as ways to improve the technology itself. For example, companies are developing new chips that will provide the massive amounts of processing power needed for artificial intelligence. Some use optical switches—lasers, essentially—to reduce their energy consumption and lower the manufacturing cost. Ideally, innovative chips will allow you to run an AI on your own device, rather than in the cloud, as you have to do today.

On the software side, the algorithms that drive an AI’s learning will get better. There will be certain domains, such as sales, where developers can make AIs extremely accurate by limiting the areas that they work in and giving them a lot of training data that’s specific to those areas. But one big open question is whether we’ll need many of these specialized AIs for different uses—one for education, say, and another for office productivity—or whether it will be possible to develop an artificial general intelligence that can learn any task. There will be immense competition on both approaches.

No matter what, the subject of AIs will dominate the public discussion for the foreseeable future. I want to suggest three principles that should guide that conversation.

First, we should try to balance fears about the downsides of AI—which are understandable and valid—with its ability to improve people’s lives. To make the most of this remarkable new technology, we’ll need to both guard against the risks and spread the benefits to as many people as possible.

Second, market forces won’t naturally produce AI products and services that help the poorest. The opposite is more likely. With reliable funding and the right policies, governments and philanthropy can ensure that AIs are used to reduce inequity. Just as the world needs its brightest people focused on its biggest problems, we will need to focus the world’s best AIs on its biggest problems. Although we shouldn’t wait for this to happen, it’s interesting to think about whether artificial intelligence would ever identify inequity and try to reduce it. Do you need to have a sense of morality in order to see inequity, or would a purely rational AI also see it? If it did recognize inequity, what would it suggest that we do about it?

Finally, we should keep in mind that we’re only at the beginning of what AI can accomplish. Whatever limitations it has today will be gone before we know it.

I’m lucky to have been involved with the PC revolution and the Internet revolution. I’m just as excited about this moment. This new technology can help people everywhere improve their lives. At the same time, the world needs to establish the rules of the road so that any downsides of artificial intelligence are far outweighed by its benefits, and so that everyone can enjoy those benefits no matter where they live or how much money they have. The Age of AI is filled with opportunities and responsibilities.

In the sixth episode of my podcast, I sat down with the OpenAI CEO to talk about where AI is headed next and what humanity will do once it gets there.

In the fifth episode of my podcast, Yejin Choi joined me to talk about her amazing work on AI training systems.

And upend the software industry.

The world has learned a lot about handling problems caused by breakthrough innovations.

This is my personal blog, where I share about the people I meet, the books I'm reading, and what I'm learning. I hope that you'll join the conversation.

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The Techno-Optimist Manifesto

Marc Andreessen

• Hacker News

You live in a deranged age — more deranged than usual, because despite great scientific and technological advances, man has not the faintest idea of who he is or what he is doing. Walker Percy

Our species is 300,000 years old. For the first 290,000 years, we were foragers, subsisting in a way that’s still observable among the Bushmen of the Kalahari and the Sentinelese of the Andaman Islands. Even after Homo Sapiens embraced agriculture, progress was painfully slow. A person born in Sumer in 4,000BC would find the resources, work, and technology available in England at the time of the Norman Conquest or in the Aztec Empire at the time of Columbus quite familiar. Then, beginning in the 18th Century, many people’s standard of living skyrocketed. What brought about this dramatic improvement, and why? Marian Tupy

There’s a way to do it better. Find it. Thomas Edison

We are being lied to.

We are told that technology takes our jobs, reduces our wages, increases inequality, threatens our health, ruins the environment, degrades our society, corrupts our children, impairs our humanity, threatens our future, and is ever on the verge of ruining everything.

We are told to be angry, bitter, and resentful about technology.

We are told to be pessimistic.

The myth of Prometheus – in various updated forms like Frankenstein, Oppenheimer, and Terminator – haunts our nightmares.

We are told to denounce our birthright – our intelligence, our control over nature, our ability to build a better world.

We are told to be miserable about the future.

Our civilization was built on technology.

Our civilization is built on technology.

Technology is the glory of human ambition and achievement, the spearhead of progress, and the realization of our potential.

For hundreds of years, we properly glorified this – until recently.

I am here to bring the good news.

We can advance to a far superior way of living, and of being.

We have the tools, the systems, the ideas.

We have the will.

It is time, once again, to raise the technology flag.

It is time to be Techno-Optimists.

Techno-Optimists believe that societies, like sharks, grow or die.

We believe growth is progress – leading to vitality, expansion of life, increasing knowledge, higher well being.

We agree with Paul Collier when he says, “Economic growth is not a cure-all, but lack of growth is a kill-all.”

We believe everything good is downstream of growth.

We believe not growing is stagnation, which leads to zero-sum thinking, internal fighting, degradation, collapse, and ultimately death.

There are only three sources of growth: population growth, natural resource utilization, and technology.

Developed societies are depopulating all over the world, across cultures – the total human population may already be shrinking.

Natural resource utilization has sharp limits, both real and political.

And so the only perpetual source of growth is technology.

In fact, technology – new knowledge, new tools, what the Greeks called techne – has always been the main source of growth, and perhaps the only cause of growth, as technology made both population growth and natural resource utilization possible.

We believe technology is a lever on the world – the way to make more with less.

Economists measure technological progress as productivity growth : How much more we can produce each year with fewer inputs, fewer raw materials. Productivity growth, powered by technology, is the main driver of economic growth, wage growth, and the creation of new industries and new jobs, as people and capital are continuously freed to do more important, valuable things than in the past. Productivity growth causes prices to fall, supply to rise, and demand to expand, improving the material well being of the entire population.

We believe this is the story of the material development of our civilization; this is why we are not still living in mud huts, eking out a meager survival and waiting for nature to kill us. 

We believe this is why our descendents will live in the stars.

We believe that there is no material problem – whether created by nature or by technology – that cannot be solved with more technology.

We had a problem of starvation, so we invented the Green Revolution.

We had a problem of darkness, so we invented electric lighting.

We had a problem of cold, so we invented indoor heating.

We had a problem of heat, so we invented air conditioning.

We had a problem of isolation, so we invented the Internet.

We had a problem of pandemics, so we invented vaccines.

We have a problem of poverty, so we invent technology to create abundance.

Give us a real world problem, and we can invent technology that will solve it.

We believe free markets are the most effective way to organize a technological economy. Willing buyer meets willing seller, a price is struck, both sides benefit from the exchange or it doesn’t happen. Profits are the incentive for producing supply that fulfills demand. Prices encode information about supply and demand. Markets cause entrepreneurs to seek out high prices as a signal of opportunity to create new wealth by driving those prices down .

We believe the market economy is a discovery machine, a form of intelligence – an exploratory, evolutionary, adaptive system.

We believe Hayek’s Knowledge Problem overwhelms any centralized economic system. All actual information is on the edges, in the hands of the people closest to the buyer. The center, abstracted away from both the buyer and the seller, knows nothing. Centralized planning is doomed to fail, the system of production and consumption is too complex. Decentralization harnesses complexity for the benefit of everyone; centralization will starve you to death.

We believe in market discipline. The market naturally disciplines – the seller either learns and changes when the buyer fails to show, or exits the market. When market discipline is absent, there is no limit to how crazy things can get. The motto of every monopoly and cartel, every centralized institution not subject to market discipline: “We don’t care, because we don’t have to.” Markets prevent monopolies and cartels.

We believe markets lift people out of poverty – in fact, markets are by far the most effective way to lift vast numbers of people out of poverty, and always have been. Even in totalitarian regimes, an incremental lifting of the repressive boot off the throat of the people and their ability to produce and trade leads to rapidly rising incomes and standards of living. Lift the boot a little more, even better. Take the boot off entirely, who knows how rich everyone can get.

We believe markets are an inherently individualistic way to achieve superior collective outcomes. 

We believe markets do not require people to be perfect, or even well intentioned – which is good, because, have you met people? Adam Smith: “It is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own self-interest. We address ourselves not to their humanity but to their self-love, and never talk to them of our own necessities, but of their advantages.”

David Friedman points out that people only do things for other people for three reasons – love, money, or force. Love doesn’t scale, so the economy can only run on money or force. The force experiment has been run and found wanting. Let’s stick with money.

We believe the ultimate moral defense of markets is that they divert people who otherwise would raise armies and start religions into peacefully productive pursuits.

We believe markets, to quote Nicholas Stern, are how we take care of people we don’t know.

We believe markets are the way to generate societal wealth for everything else we want to pay for, including basic research, social welfare programs, and national defense.

We believe there is no conflict between capitalist profits and a social welfare system that protects the vulnerable. In fact, they are aligned – the production of markets creates the economic wealth that pays for everything else we want as a society.

We believe central economic planning elevates the worst of us and drags everyone down; markets exploit the best of us to benefit all of us. 

We believe central planning is a doom loop; markets are an upward spiral.

The economist William Nordhaus has shown that creators of technology are only able to capture about 2% of the economic value created by that technology. The other 98% flows through to society in the form of what economists call social surplus. Technological innovation in a market system is inherently philanthropic , by a 50:1 ratio. Who gets more value from a new technology, the single company that makes it, or the millions or billions of people who use it to improve their lives? QED.

We believe in David Ricardo’s concept of comparative advantage – as distinct from competitive advantage, comparative advantage holds that even someone who is best in the world at doing everything will buy most things from other people, due to opportunity cost. Comparative advantage in the context of a properly free market guarantees high employment regardless of the level of technology.

We believe a market sets wages as a function of the marginal productivity of the worker. Therefore technology – which raises productivity – drives wages up , not down. This is perhaps the most counterintuitive idea in all of economics, but it’s true, and we have 300 years of history that prove it.

We believe in Milton Friedman’s observation that human wants and needs are infinite.

We believe markets also increase societal well being by generating work in which people can productively engage. We believe a Universal Basic Income would turn people into zoo animals to be farmed by the state. Man was not meant to be farmed; man was meant to be useful , to be productive , to be proud .

We believe technological change, far from reducing the need for human work, increases it, by broadening the scope of what humans can productively do.

We believe that since human wants and needs are infinite, economic demand is infinite, and job growth can continue forever.

We believe markets are generative, not exploitative; positive sum, not zero sum. Participants in markets build on one another’s work and output. James Carse describes finite games and infinite games – finite games have an end, when one person wins and another person loses; infinite games never end, as players collaborate to discover what’s possible in the game. Markets are the ultimate infinite game.

The Techno-Capital Machine

Combine technology and markets and you get what Nick Land has termed the techno-capital machine, the engine of perpetual material creation, growth, and abundance.

We believe the techno-capital machine of markets and innovation never ends, but instead spirals continuously upward. Comparative advantage increases specialization and trade. Prices fall, freeing up purchasing power, creating demand. Falling prices benefit everyone who buys goods and services, which is to say everyone. Human wants and needs are endless, and entrepreneurs continuously create new goods and services to satisfy those wants and needs, deploying unlimited numbers of people and machines in the process. This upward spiral has been running for hundreds of years, despite continuous howling from Communists and Luddites. Indeed, as of 2019, before the temporary COVID disruption, the result was the largest number of jobs at the highest wages and the highest levels of material living standards in the history of the planet. 

The techno-capital machine makes natural selection work for us in the realm of ideas. The best and most productive ideas win, and are combined and generate even better ideas. Those ideas materialize in the real world as technologically enabled goods and services that never would have emerged de novo.

Ray Kurzweil defines his Law of Accelerating Returns: Technological advances tend to feed on themselves, increasing the rate of further advance.

We believe in accelerationism – the conscious and deliberate propulsion of technological development – to ensure the fulfillment of the Law of Accelerating Returns. To ensure the techno-capital upward spiral continues forever.

We believe the techno-capital machine is not anti-human – in fact, it may be the most pro-human thing there is. It serves us . The techno-capital machine works for us. All the machines work for us.

We believe the cornerstone resources of the techno-capital upward spiral are intelligence and energy – ideas, and the power to make them real.

Intelligence

We believe intelligence is the ultimate engine of progress. Intelligence makes everything better. Smart people and smart societies outperform less smart ones on virtually every metric we can measure. Intelligence is the birthright of humanity; we should expand it as fully and broadly as we possibly can.

We believe intelligence is in an upward spiral – first, as more smart people around the world are recruited into the techno-capital machine; second, as people form symbiotic relationships with machines into new cybernetic systems such as companies and networks; third, as Artificial Intelligence ramps up the capabilities of our machines and ourselves.

We believe we are poised for an intelligence takeoff that will expand our capabilities to unimagined heights.

We believe Artificial Intelligence is our alchemy, our Philosopher’s Stone – we are literally making sand think.

We believe Artificial Intelligence is best thought of as a universal problem solver. And we have a lot of problems to solve.

We believe Artificial Intelligence can save lives – if we let it. Medicine, among many other fields, is in the stone age compared to what we can achieve with joined human and machine intelligence working on new cures. There are scores of common causes of death that can be fixed with AI, from car crashes to pandemics to wartime friendly fire.

We believe any deceleration of AI will cost lives. Deaths that were preventable by the AI that was prevented from existing is a form of murder.

We believe in Augmented Intelligence just as much as we believe in Artificial Intelligence. Intelligent machines augment intelligent humans, driving a geometric expansion of what humans can do.

We believe Augmented Intelligence drives marginal productivity which drives wage growth which drives demand which drives the creation of new supply… with no upper bound.

Energy is life. We take it for granted, but without it, we have darkness, starvation, and pain. With it, we have light, safety, and warmth.

We believe energy should be in an upward spiral. Energy is the foundational engine of our civilization. The more energy we have, the more people we can have, and the better everyone’s lives can be. We should raise everyone to the energy consumption level we have, then increase our energy 1,000x, then raise everyone else’s energy 1,000x as well.

The current gap in per-capita energy use between the smaller developed world and larger developing world is enormous. That gap will close – either by massively expanding energy production, making everyone better off, or by massively reducing energy production, making everyone worse off.

We believe energy need not expand to the detriment of the natural environment. We have the silver bullet for virtually unlimited zero-emissions energy today – nuclear fission. In 1973, President Richard Nixon called for Project Independence, the construction of 1,000 nuclear power plants by the year 2000, to achieve complete US energy independence. Nixon was right; we didn’t build the plants then, but we can now, anytime we decide we want to.

Atomic Energy Commissioner Thomas Murray said in 1953: “For years the splitting atom, packaged in weapons, has been our main shield against the barbarians. Now, in addition, it is a God-given instrument to do the constructive work of mankind.” Murray was right too.

We believe a second energy silver bullet is coming – nuclear fusion. We should build that as well. The same bad ideas that effectively outlawed fission are going to try to outlaw fusion. We should not let them.

We believe there is no inherent conflict between the techno-capital machine and the natural environment. Per-capita US carbon emissions are lower now than they were 100 years ago, even without nuclear power.

We believe technology is the solution to environmental degradation and crisis. A technologically advanced society improves the natural environment, a technologically stagnant society ruins it. If you want to see environmental devastation, visit a former Communist country. The socialist USSR was far worse for the natural environment than the capitalist US. Google the Aral Sea.

We believe a technologically stagnant society has limited energy at the cost of environmental ruin; a technologically advanced society has unlimited clean energy for everyone.

We believe we should place intelligence and energy in a positive feedback loop, and drive them both to infinity.

We believe we should use the feedback loop of intelligence and energy to make everything we want and need abundant.

We believe the measure of abundance is falling prices. Every time a price falls, the universe of people who buy it get a raise in buying power, which is the same as a raise in income. If a lot of goods and services drop in price, the result is an upward explosion of buying power, real income, and quality of life.

We believe that if we make both intelligence and energy “too cheap to meter”, the ultimate result will be that all physical goods become as cheap as pencils. Pencils are actually quite technologically complex and difficult to manufacture, and yet nobody gets mad if you borrow a pencil and fail to return it. We should make the same true of all physical goods.

We believe we should push to drop prices across the economy through the application of technology until as many prices are effectively zero as possible, driving income levels and quality of life into the stratosphere.

We believe Andy Warhol was right when he said, “What’s great about this country is America started the tradition where the richest consumers buy essentially the same things as the poorest. You can be watching TV and see Coca-Cola, and you can know that the President drinks Coke, Liz Taylor drinks Coke, and just think, you can drink Coke, too. A Coke is a Coke and no amount of money can get you a better Coke than the one the bum on the corner is drinking. All the Cokes are the same and all the Cokes are good.” Same for the browser, the smartphone, the chatbot.

We believe that technology ultimately drives the world to what Buckminster Fuller called “ephemeralization” – what economists call “dematerialization”. Fuller: “Technology lets you do more and more with less and less until eventually you can do everything with nothing.”

We believe technological progress therefore leads to material abundance for everyone.

We believe the ultimate payoff from technological abundance can be a massive expansion in what Julian Simon called “the ultimate resource” – people.

We believe, as Simon did, that people are the ultimate resource – with more people come more creativity, more new ideas, and more technological progress.

We believe material abundance therefore ultimately means more people – a lot more people – which in turn leads to more abundance.

We believe our planet is dramatically underpopulated, compared to the population we could have with abundant intelligence, energy, and material goods.

We believe the global population can quite easily expand to 50 billion people or more, and then far beyond that as we ultimately settle other planets.

We believe that out of all of these people will come scientists, technologists, artists, and visionaries beyond our wildest dreams.

We believe the ultimate mission of technology is to advance life both on Earth and in the stars.

Not Utopia, But Close Enough

However, we are not Utopians.

We are adherents to what Thomas Sowell calls the Constrained Vision.

We believe the Constrained Vision – contra the Unconstrained Vision of Utopia, Communism, and Expertise – means taking people as they are, testing ideas empirically, and liberating people to make their own choices.

We believe in not Utopia, but also not Apocalypse.

We believe change only happens on the margin – but a lot of change across a very large margin can lead to big outcomes.

While not Utopian, we believe in what Brad DeLong terms “slouching toward Utopia” – doing the best fallen humanity can do, making things better as we go.

Becoming Technological Supermen

We believe that advancing technology is one of the most virtuous things that we can do.

We believe in deliberately and systematically transforming ourselves into the kind of people who can advance technology.

We believe this certainly means technical education, but it also means going hands on, gaining practical skills, working within and leading teams – aspiring to build something greater than oneself, aspiring to work with others to build something greater as a group.

We believe the natural human drive to make things, to gain territory, to explore the unknown can be channeled productively into building technology.

We believe that while the physical frontier, at least here on Earth, is closed, the technological frontier is wide open.

We believe in exploring and claiming the technological frontier.

We believe in the romance of technology, of industry. The eros of the train, the car, the electric light, the skyscraper. And the microchip, the neural network, the rocket, the split atom.

We believe in adventure . Undertaking the Hero’s Journey, rebelling against the status quo, mapping uncharted territory, conquering dragons, and bringing home the spoils for our community.

To paraphrase a manifesto of a different time and place: “Beauty exists only in struggle. There is no masterpiece that has not an aggressive character. Technology must be a violent assault on the forces of the unknown, to force them to bow before man.”

We believe that we are, have been, and will always be the masters of technology, not mastered by technology. Victim mentality is a curse in every domain of life, including in our relationship with technology – both unnecessary and self-defeating. We are not victims, we are conquerors .

We believe in nature, but we also believe in overcoming nature. We are not primitives, cowering in fear of the lightning bolt. We are the apex predator; the lightning works for us.

We believe in greatness . We admire the great technologists and industrialists who came before us, and we aspire to make them proud of us today.

And we believe in humanity – individually and collectively.

Technological Values

We believe in ambition, aggression, persistence, relentlessness – strength .

We believe in merit and achievement.

We believe in bravery , in courage.

We believe in pride, confidence, and self respect – when earned .

We believe in free thought, free speech, and free inquiry.

We believe in the actual Scientific Method and enlightenment values of free discourse and challenging the authority of experts.

We believe, as Richard Feynman said, “Science is the belief in the ignorance of experts.”

And, “I would rather have questions that can’t be answered than answers that can’t be questioned.”

We believe in local knowledge, the people with actual information making decisions, not in playing God.

We believe in embracing variance, in increasing interestingness.

We believe in risk , in leaps into the unknown.

We believe in agency, in individualism.

We believe in radical competence.

We believe in an absolute rejection of resentment. As Carrie Fisher said, “Resentment is like drinking poison and waiting for the other person to die.” We take responsibility and we overcome.

We believe in competition, because we believe in evolution.

We believe in evolution, because we believe in life.

We believe in the truth.

We believe rich is better than poor, cheap is better than expensive, and abundant is better than scarce.

We believe in making everyone rich, everything cheap, and everything abundant.

We believe extrinsic motivations – wealth, fame, revenge – are fine as far as they go. But we believe intrinsic motivations – the satisfaction of building something new, the camaraderie of being on a team, the achievement of becoming a better version of oneself – are more fulfilling and more lasting.

We believe in what the Greeks called eudaimonia through arete – flourishing through excellence.

We believe technology is universalist. Technology doesn’t care about your ethnicity, race, religion, national origin, gender, sexuality, political views, height, weight, hair or lack thereof. Technology is built by a virtual United Nations of talent from all over the world. Anyone with a positive attitude and a cheap laptop can contribute. Technology is the ultimate open society.

We believe in the Silicon Valley code of “pay it forward”, trust via aligned incentives, generosity of spirit to help one another learn and grow.

We believe America and her allies should be strong and not weak. We believe national strength of liberal democracies flows from economic strength (financial power), cultural strength (soft power), and military strength (hard power). Economic, cultural, and military strength flow from technological strength. A technologically strong America is a force for good in a dangerous world. Technologically strong liberal democracies safeguard liberty and peace. Technologically weak liberal democracies lose to their autocratic rivals, making everyone worse off.

We believe technology makes greatness more possible and more likely.

We believe in fulfilling our potential, becoming fully human – for ourselves, our communities, and our society.

The Meaning of Life

Techno-Optimism is a material philosophy, not a political philosophy.

We are not necessarily left wing, although some of us are.

We are not necessarily right wing, although some of us are.

We are materially focused, for a reason – to open the aperture on how we may choose to live amid material abundance.

A common critique of technology is that it removes choice from our lives as machines make decisions for us. This is undoubtedly true, yet more than offset by the freedom to create our lives that flows from the material abundance created by our use of machines.

Material abundance from markets and technology opens the space for religion, for politics, and for choices of how to live, socially and individually.

We believe technology is liberatory. Liberatory of human potential. Liberatory of the human soul, the human spirit. Expanding what it can mean to be free, to be fulfilled, to be alive.

We believe technology opens the space of what it can mean to be human.

We have enemies.

Our enemies are not bad people – but rather bad ideas.

Our present society has been subjected to a mass demoralization campaign for six decades – against technology and against life – under varying names like “existential risk”, “sustainability”, “ESG”, “Sustainable Development Goals”, “social responsibility”, “stakeholder capitalism”, “Precautionary Principle”, “trust and safety”, “tech ethics”, “risk management”, “de-growth”, “the limits of growth”.

This demoralization campaign is based on bad ideas of the past – zombie ideas, many derived from Communism, disastrous then and now – that have refused to die.

Our enemy is stagnation.

Our enemy is anti-merit, anti-ambition, anti-striving, anti-achievement, anti-greatness.

Our enemy is statism, authoritarianism, collectivism, central planning, socialism.

Our enemy is bureaucracy, vetocracy, gerontocracy, blind deference to tradition.

Our enemy is corruption, regulatory capture, monopolies, cartels.

Our enemy is institutions that in their youth were vital and energetic and truth-seeking, but are now compromised and corroded and collapsing – blocking progress in increasingly desperate bids for continued relevance, frantically trying to justify their ongoing funding despite spiraling dysfunction and escalating ineptness.

Our enemy is the ivory tower, the know-it-all credentialed expert worldview, indulging in abstract theories, luxury beliefs, social engineering, disconnected from the real world, delusional, unelected, and unaccountable – playing God with everyone else’s lives, with total insulation from the consequences.

Our enemy is speech control and thought control – the increasing use, in plain sight, of George Orwell’s “1984” as an instruction manual.

Our enemy is Thomas Sowell’s Unconstrained Vision, Alexander Kojeve’s Universal and Homogeneous State, Thomas More’s Utopia.

Our enemy is the Precautionary Principle, which would have prevented virtually all progress since man first harnessed fire. The Precautionary Principle was invented to prevent the large-scale deployment of civilian nuclear power, perhaps the most catastrophic mistake in Western society in my lifetime. The Precautionary Principle continues to inflict enormous unnecessary suffering on our world today. It is deeply immoral, and we must jettison it with extreme prejudice.

Our enemy is deceleration, de-growth, depopulation – the nihilistic wish, so trendy among our elites, for fewer people, less energy, and more suffering and death.

Our enemy is Friedrich Nietzsche’s Last Man:

I tell you: one must still have chaos in oneself, to give birth to a dancing star. I tell you: you have still chaos in yourselves.

Alas! There comes the time when man will no longer give birth to any star. Alas! There comes the time of the most despicable man, who can no longer despise himself…

“What is love? What is creation? What is longing? What is a star?” — so asks the Last Man, and blinks.

The earth has become small, and on it hops the Last Man, who makes everything small. His species is ineradicable as the flea; the Last Man lives longest…

One still works, for work is a pastime. But one is careful lest the pastime should hurt one.

One no longer becomes poor or rich; both are too burdensome…

No shepherd, and one herd! Everyone wants the same; everyone is the same: he who feels differently goes voluntarily into the madhouse.

“Formerly all the world was insane,” — say the subtlest of them, and they blink.

They are clever and know all that has happened: so there is no end to their derision… 

“We have discovered happiness,” — say the Last Men, and they blink.

Our enemy is… that.

We aspire to be… not that.

We will explain to people captured by these zombie ideas that their fears are unwarranted and the future is bright.

We believe these captured people are suffering from ressentiment – a witches’ brew of resentment, bitterness, and rage that is causing them to hold mistaken values, values that are damaging to both themselves and the people they care about.

We believe we must help them find their way out of their self-imposed labyrinth of pain.

We invite everyone to join us in Techno-Optimism.

The water is warm.

Become our allies in the pursuit of technology, abundance, and life.

Where did we come from?

Our civilization was built on a spirit of discovery, of exploration, of industrialization.

Where are we going?

What world are we building for our children and their children, and their children?

A world of fear, guilt, and resentment?

Or a world of ambition, abundance, and adventure?

We believe in the words of David Deutsch: “We have a duty to be optimistic. Because the future is open, not predetermined and therefore cannot just be accepted: we are all responsible for what it holds. Thus it is our duty to fight for a better world.”

We owe the past, and the future.

It’s time to be a Techno-Optimist. 

It’s time to build.

Patron Saints of Techno-Optimism

In lieu of detailed endnotes and citations, read the work of these people, and you too will become a Techno-Optimist.

@BasedBeffJezos

@PessimistsArc

Ada Lovelace

Andy Warhol

Bertrand Russell

Brad DeLong

Buckminster Fuller

Calestous Juma

Clayton Christensen

Dambisa Moyo

David Deutsch

David Friedman

David Ricardo

Deirdre McCloskey

Doug Engelbart

Elting Morison

Filippo Tommaso Marinetti

Frederic Bastiat

Frederick Jackson Turner

Friedrich Hayek

Friedrich Nietzsche

George Gilder

Isabel Paterson

Israel Kirzner

James Burnham

James Carse

Johan Norberg

John Von Neumann

Joseph Schumpeter

Julian Simon

Kevin Kelly

Louis Rossetto

Ludwig von Mises

Marian Tupy

Martin Gurri

Matt Ridley

Milton Friedman

Neven Sesardic

Paul Collier

Paul Johnson

Ray Kurzweil

Richard Feynman

Rose Wilder Lane

Stephen Wolfram

Stewart Brand

Thomas Sowell

Vilfredo Pareto

Virginia Postrel

William Lewis

William Nordhaus

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Marc Andreessen is a Cofounder and General Partner at the venture capital firm Andreessen Horowitz.

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Modular, scalable hardware architecture for a quantum computer

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Quantum computers hold the promise of being able to quickly solve extremely complex problems that might take the world’s most powerful supercomputer decades to crack.

But achieving that performance involves building a system with millions of interconnected building blocks called qubits. Making and controlling so many qubits in a hardware architecture is an enormous challenge that scientists around the world are striving to meet.

Toward this goal, researchers at MIT and MITRE have demonstrated a scalable, modular hardware platform that integrates thousands of interconnected qubits onto a customized integrated circuit. This “quantum-system-on-chip” (QSoC) architecture enables the researchers to precisely tune and control a dense array of qubits. Multiple chips could be connected using optical networking to create a large-scale quantum communication network.

By tuning qubits across 11 frequency channels, this QSoC architecture allows for a new proposed protocol of “entanglement multiplexing” for large-scale quantum computing.

The team spent years perfecting an intricate process for manufacturing two-dimensional arrays of atom-sized qubit microchiplets and transferring thousands of them onto a carefully prepared complementary metal-oxide semiconductor (CMOS) chip. This transfer can be performed in a single step.

“We will need a large number of qubits, and great control over them, to really leverage the power of a quantum system and make it useful. We are proposing a brand new architecture and a fabrication technology that can support the scalability requirements of a hardware system for a quantum computer,” says Linsen Li, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this architecture.

Li’s co-authors include Ruonan Han, an associate professor in EECS, leader of the Terahertz Integrated Electronics Group, and member of the Research Laboratory of Electronics (RLE); senior author Dirk Englund, professor of EECS, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE; as well as others at MIT, Cornell University, the Delft Institute of Technology, the U.S. Army Research Laboratory, and the MITRE Corporation. The paper appears today in Nature .

Diamond microchiplets

While there are many types of qubits, the researchers chose to use diamond color centers because of their scalability advantages. They previously used such qubits to produce integrated quantum chips with photonic circuitry.

Qubits made from diamond color centers are “artificial atoms” that carry quantum information. Because diamond color centers are solid-state systems, the qubit manufacturing is compatible with modern semiconductor fabrication processes. They are also compact and have relatively long coherence times, which refers to the amount of time a qubit’s state remains stable, due to the clean environment provided by the diamond material.

In addition, diamond color centers have photonic interfaces which allows them to be remotely entangled, or connected, with other qubits that aren’t adjacent to them.

“The conventional assumption in the field is that the inhomogeneity of the diamond color center is a drawback compared to identical quantum memory like ions and neutral atoms. However, we turn this challenge into an advantage by embracing the diversity of the artificial atoms: Each atom has its own spectral frequency. This allows us to communicate with individual atoms by voltage tuning them into resonance with a laser, much like tuning the dial on a tiny radio,” says Englund.

This is especially difficult because the researchers must achieve this at a large scale to compensate for the qubit inhomogeneity in a large system.

To communicate across qubits, they need to have multiple such “quantum radios” dialed into the same channel. Achieving this condition becomes near-certain when scaling to thousands of qubits. To this end, the researchers surmounted that challenge by integrating a large array of diamond color center qubits onto a CMOS chip which provides the control dials. The chip can be incorporated with built-in digital logic that rapidly and automatically reconfigures the voltages, enabling the qubits to reach full connectivity.

“This compensates for the in-homogenous nature of the system. With the CMOS platform, we can quickly and dynamically tune all the qubit frequencies,” Li explains.

Lock-and-release fabrication

To build this QSoC, the researchers developed a fabrication process to transfer diamond color center “microchiplets” onto a CMOS backplane at a large scale.

They started by fabricating an array of diamond color center microchiplets from a solid block of diamond. They also designed and fabricated nanoscale optical antennas that enable more efficient collection of the photons emitted by these color center qubits in free space.

Then, they designed and mapped out the chip from the semiconductor foundry. Working in the MIT.nano cleanroom, they post-processed a CMOS chip to add microscale sockets that match up with the diamond microchiplet array.

They built an in-house transfer setup in the lab and applied a lock-and-release process to integrate the two layers by locking the diamond microchiplets into the sockets on the CMOS chip. Since the diamond microchiplets are weakly bonded to the diamond surface, when they release the bulk diamond horizontally, the microchiplets stay in the sockets.

“Because we can control the fabrication of both the diamond and the CMOS chip, we can make a complementary pattern. In this way, we can transfer thousands of diamond chiplets into their corresponding sockets all at the same time,” Li says.

The researchers demonstrated a 500-micron by 500-micron area transfer for an array with 1,024 diamond nanoantennas, but they could use larger diamond arrays and a larger CMOS chip to further scale up the system. In fact, they found that with more qubits, tuning the frequencies actually requires less voltage for this architecture.

“In this case, if you have more qubits, our architecture will work even better,” Li says.

The team tested many nanostructures before they determined the ideal microchiplet array for the lock-and-release process. However, making quantum microchiplets is no easy task, and the process took years to perfect.

“We have iterated and developed the recipe to fabricate these diamond nanostructures in MIT cleanroom, but it is a very complicated process. It took 19 steps of nanofabrication to get the diamond quantum microchiplets, and the steps were not straightforward,” he adds.

Alongside their QSoC, the researchers developed an approach to characterize the system and measure its performance on a large scale. To do this, they built a custom cryo-optical metrology setup.

Using this technique, they demonstrated an entire chip with over 4,000 qubits that could be tuned to the same frequency while maintaining their spin and optical properties. They also built a digital twin simulation that connects the experiment with digitized modeling, which helps them understand the root causes of the observed phenomenon and determine how to efficiently implement the architecture.

In the future, the researchers could boost the performance of their system by refining the materials they used to make qubits or developing more precise control processes. They could also apply this architecture to other solid-state quantum systems.

This work was supported by the MITRE Corporation Quantum Moonshot Program, the U.S. National Science Foundation, the U.S. Army Research Office, the Center for Quantum Networks, and the European Union’s Horizon 2020 Research and Innovation Program.

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Essay Topics – List of 500+ Essay Writing Topics and Ideas

List of 500+ essay writing topics and ideas.

Essay topics in English can be difficult to come up with. While writing essays , many college and high school students face writer’s block and have a hard time to think about topics and ideas for an essay. In this article, we will list out many good essay topics from different categories like argumentative essays, essays on technology, environment essays for students from 5th, 6th, 7th, 8th grades. Following list of essay topics are for all – from kids to college students. We have the largest collection of essays. An essay is nothing but a piece of content which is written from the perception of writer or author. Essays are similar to a story, pamphlet, thesis, etc. The best thing about Essay is you can use any type of language – formal or informal. It can biography, the autobiography of anyone. Following is a great list of 100 essay topics. We will be adding 400 more soon!

But Before that you may wanna read some awesome Essay Writing Tips here .

Get the Huge list of 100+ Speech Topics here

Argumentative Essay Topics

• Should plastic be banned?
• Pollution due to Urbanization
• Education should be free
• Should Students get limited access to the Internet?
• Selling Tobacco should be banned
• Smoking in public places should be banned
• Facebook should be banned
• Students should not be allowed to play PUBG

Essay Topics on Technology

• Wonder Of Science
• Mobile Phone

Essay Topics on Festivals on Events

• Independence Day (15 August)
• Teachers Day
• Summer Vacation
• Children’s Day
• Swachh Bharat Abhiyan
• Janmashtami
• Republic Day

Essay Topics on Education

• Education Essay
• Importance of Education
• Contribution of Technology in Education

Essay Topics on Famous Leaders

• Mahatma Gandhi
• APJ Abdul Kalam
• Jawaharlal Nehru
• Swami Vivekananda
• Mother Teresa
• Rabindranath Tagore
• Sardar Vallabhbhai Patel
• Subhash Chandra Bose
• Abraham Lincoln
• Martin Luther King
• Lal Bahadur Shashtri

Essay Topics on Animals and Birds

• My Favorite Animal

Essays Topics About Yourself

• My Best Friend
• My Favourite Teacher
• My Aim In Life
• My Favourite Game – Badminton
• My Favourite Game – Essay
• My Favourite Book
• My Ambition
• How I Spent My Summer Vacation
• India of My Dreams
• My School Life
• I Love My Family
• My Favourite Subject
• My Favourite Game Badminton
• My Father My Hero
• My School Library
• My Favourite Author
• My plans for summer vacation

Essay Topics Based on Environment and Nature

• Global Warming
• Environment
• Air Pollution
• Environmental Pollution
• Water Pollution
• Rainy Season
• Climate Change
• Importance Of Trees
• Winter Season
• Deforestation
• Natural Disasters
• Save Environment
• Summer Season
• Trees Our Best Friend Essay In English

Essay Topics Based on Proverbs

• Health Is Wealth
• A Stitch in Time Saves Nine
• An Apple a Day Keeps Doctor Away
• Where there is a will, there is way
• Time and Tide wait for none

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Essay Topics for Students from 6th, 7th, 8th Grade

• Noise Pollution
• Environment Pollution
• Women Empowerment
• Time and Tide Wait for none
• Science and Technology
• Importance of Sports
• Sports and Games
• Time Management
• Cleanliness is next to Godliness
• Cleanliness
• Rome was not Built in a Day
• Unemployment
• Clean India
• Cow Essay In English
• Describe Yourself
• Festivals Of India
• Ganesh Chaturthi
• Healthy Food
• Importance Of Water
• Plastic Pollution
• Value of Time
• Honesty is the Best Policy
• Gandhi Jayanti
• Human Rights
• Knowledge Is Power
• Same Sex Marriage
• Childhood Memories
• Cyber Crime
• Kalpana Chawla
• Punctuality
• Rani Lakshmi Bai
• Spring Season
• Unity In Diversity
• Artificial Intelligence
• Online Shopping
• Indian Culture
• Healthy Lifestyle
• Indian Education System
• Disaster Management
• Environmental Issues
• Freedom Fighters
• Grandparents
• Save Fuel For Better Environment
• Importance Of Newspaper
• Lal Bahadur Shastri
• Raksha Bandhan
• World Environment Day
• Narendra Modi
• What Is Religion
• Charity Begins at Home
• A Journey by Train
• Ideal student
• Save Water Save Earth
• Indian Farmer
• Safety of Women in India
• Sarvepalli Radhakrishnan
• Capital Punishment
• College Life
• Natural Resources
• Peer Pressure
• Nature Vs Nurture
• Romeo And Juliet
• Generation Gap
• Makar Sankranti
• Constitution of India
• Girl Education
• Importance of Family
• Importance of Independence Day
• Brain Drain
• A Friend In Need Is A Friend Indeed
• Action Speaks Louder Than Words
• All That Glitters Is Not Gold
• Bhagat Singh
• Demonetization
• Agriculture
• Importance of Discipline
• Population Explosion
• Poverty in India
• Uses Of Mobile Phones
• Water Scarcity
• Train Journey
• Land Pollution
• Environment Protection
• Indian Army
• Uses of Internet
• All that Glitters is not Gold
• Balanced Diet
• Blood Donation
• Digital India
• Dussehra Essay
• Energy Conservation
• National Integration
• Railway Station
• Sachin Tendulkar
• Health And Hygiene
• Importance Of Forest
• Indira Gandhi
• Laughter Is The Best Medicine
• Career Goals
• Mental Health
• Save Water Save Life
• International Yoga Day
• Winter Vacation
• Soil Pollution
• Every Cloud Has A Silver Lining
• Indian Culture And Tradition
• Unity Is Strength
• Unity is Diversity
• Wildlife Conservation
• Cruelty To Animals
• Nelson Mandela
• Of Mice And Men
• Organ Donation
• Life in a Big City
• Democracy in India
• Waste Management
• Biodiversity
• Afforestation
• Female Foeticide
• Harmful Effects Of Junk Food
• Rain Water Harvesting
• Save Electricity
• Social Media
• Social Networking Sites
• Sound Pollution
• Procrastination
• Life in an Indian Village
• Life in Big City
• Population Growth
• World Population Day
• Greenhouse Effect
• Statue of Unity
• Traffic Jam
• Beti Bachao Beti Padhao
• Importance of Good Manners
• Good Manners
• Cyber Security
• Green Revolution
• Health And Fitness
• Incredible India
• Make In India
• Surgical Strike
• Triple Talaq
• A Good Friend
• Importance of Friends in our Life
• Should Plastic be Banned
• Nationalism
• Traffic Rules
• Effects of Global Warming
• Fundamental Rights
• Solar System
• National Constitution Day
• Good Mother
• Importance of Trees in our Life
• City Life Vs Village Life
• Importance of Communication
• Conservation of Nature
• Man vs. Machine
• Indian Economy
• Mothers Love
• Importance of National Integration
• Black Money
• Greenhouse effect
• Untouchability
• Self Discipline
• Global Terrorism
• Conservation of Biodiversity
• Newspaper and Its Uses
• World Health Day
• Conservation of Natural Resources
• A Picnic with Family
• Indian Heritage
• Status of Women in India
• Child is Father of the Man
• Reading is Good Habit
• Plastic Bag
• Terrorism in India
• Library and Its Uses
• Life on Mars
• Urbanization
• Pollution Due to Diwali
• National Flag of India
• Vocational Education
• Importance of Tree Plantation
• Summer Camp
• Vehicle Pollution
• Women Education in India
• Seasons in India
• Freedom of the Press
• Caste System
• Environment and Human Health
• Mountain Climbing
• Depletion of Natural Resources
• Ishwar Chandra Vidyasagar
• Health Education
• Effects of Deforestation
• Life after School
• Starvation in India
• Jan Dhan Yojana
• Impact of Privatization
• Election Commission of India
• Election and Democracy
• Prevention of Global Warming
• Impact of Cinema in Life
• Subhas Chandra Bose
• Dowry System
• Ganesh Chaturthi Festival
• Role of Science in Making India
• Impact of Global Warming on Oceans
• Pollution due to Festivals
• Ambedkar Jayanti
• Ek Bharat Shreshtha Bharat
• Family Planning in India
• Democracy vs Dictatorship
• National Festivals of India
• Sri Aurobindo
• Casteism in India
• Organ trafficking
• Consequences of Global Warming
• Role of Human Activities in Global Warming
• Issues and Problems faced by Women in India
• Role of Judiciary in the Country Today
• Sugamya Bharat Abhiyan
• PUBG Mobile Game Addiction
• Role of Youths in Nation Building
• Value of Oxygen and Water in Life/Earth
• Farmer Suicides in India
• Start-up India
• Pollution Due to Firecrackers
• Life of Soldiers
• Child Labour
• Save Girl Child
• Morning Walk
• My School Fete
• Essay on Financial Literacy
• Essay On Sustainable Development
• Essay On Punjab
• Essay On Travel
• My Home Essay
• Child Marriage Essay
• Importance Of English Language Essay
• Essay On Mass Media
• Essay On Horse
• Essay On Police
• Essay On Eid
• Essay On Solar Energy
• Animal Essay
• Essay On Mango
• Gender Discrimination Essay
• Essay On Advertisement
• My First Day At School Essay
• My Neighborhood Essay
• True Friendship Essay
• Work Is Worship Essay
• Essay On Self Confidence
• Essay On Superstition
• Essay On Bangalore
• Sex Vs Gender Essay
• Essay On Social Issues
• Time Is Money Essay
• Essay About Grandmothers
• Essay On Hard Work
• First Day Of School Essay
• Flowers Essay
• My Favorite Food Essay
• Essay on Birds
• Essay on Humanity
• Essay on Sun
• Essay on Kargil War
• Every Cloud Has a Silver Lining Essay
• Francis Bacon Essays
• Importance of Cleanliness Essay
• My Sister Essay
• Self Introduction Essay
• Solar Energy Essay
• Sports Day Essa
• Value Of Education Essay
• Essay On Isro
• Essay On Balance Is Beneficial
• Essay On Reservation In India
• Essay On Water Management
• Essay On Smoking
• Essay On Stress Management
• Essay On William Shakespeare
• Essay on Apple
• Essay On Albert Einstein
• Essay On Feminism
• Essay On Kindness
• Essay On Domestic Violence
• Essay on English as a Global Language
• Essay On Co-Education
• Importance Of Exercise Essay
• Overpopulation Essay
• Smartphone Essay
• Essay on River
• Essay on Cyclone
• Essay On Facebook
• Essay On Science In Everyday Life
• Essay On Women Rights
• Essay On Right To Education
• Essay on Quotes
• Essay On Peace
• Essay On Drawing
• Essay On Bicycle
• Essay On Sexual Harassment
• Essay On Hospital
• Essay On Srinivasa Ramanujan
• Essay On Golden Temple
• Essay On Art
• Essay On Ruskin Bond
• Essay On Moon
• Birthday Essay
• Dont Judge A Book By Its Cover Essay
• Draught Essay
• Gratitude Essay
• Indian Politics Essay
• Who am I Essay
• Essay on Positive Thinking
• Essay on Dance
• Essay on Navratri
• Essay on Onam
• Essay on New Education Policy 2020
• Esasy on Thank you Coronavirus Helpers
• Essay on Coronavirus and Coronavirus Symptoms
• Essay on Baseball
• Essay on coronavirus vaccine
• Fitness beats pandemic essay
• Essay on coronavirus tips
• Essay on coronavirus prevention
• Essay on coronavirus treatment
• Essay on essay on trees
• Essay on television
• Gender inequality essay
• Water conservation essay
• Essay on Gurpurab
• Essay on Types of sports
• Essay on road safety
• Essay on my favourite season
• My pet essay
• Student life essay
• Essay on Railway station
• Essay on earth
• Essay on knowledge is power
• Essay on favourite personality
• Essay on memorable day of my life
• My parents essay
• Our country essay
• Picnic essay
• Travelling essay

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What is cloud computing?

With cloud computing, organizations essentially buy a range of services offered by cloud service providers (CSPs). The CSP’s servers host all the client’s applications. Organizations can enhance their computing power more quickly and cheaply via the cloud than by purchasing, installing, and maintaining their own servers.

The cloud-computing model is helping organizations to scale new digital solutions with greater speed and agility—and to create value more quickly. Developers use cloud services to build and run custom applications and to maintain infrastructure and networks for companies of virtually all sizes—especially large global ones. CSPs offer services, such as analytics, to handle and manipulate vast amounts of data. Time to market accelerates, speeding innovation to deliver better products and services across the world.

What are examples of cloud computing’s uses?

Get to know and directly engage with senior mckinsey experts on cloud computing.

Brant Carson is a senior partner in McKinsey’s Vancouver office; Chandra Gnanasambandam and Anand Swaminathan are senior partners in the Bay Area office; William Forrest is a senior partner in the Chicago office; Leandro Santos is a senior partner in the Atlanta office; Kate Smaje is a senior partner in the London office.

Cloud computing came on the scene well before the global pandemic hit, in 2020, but the ensuing digital dash  helped demonstrate its power and utility. Here are some examples of how businesses and other organizations employ the cloud:

• A fast-casual restaurant chain’s online orders multiplied exponentially during the 2020 pandemic lockdowns, climbing to 400,000 a day, from 50,000. One pleasant surprise? The company’s online-ordering system could handle the volume—because it had already migrated to the cloud . Thanks to this success, the organization’s leadership decided to accelerate its five-year migration plan to less than one year.
• A biotech company harnessed cloud computing to deliver the first clinical batch of a COVID-19 vaccine candidate for Phase I trials in just 42 days—thanks in part to breakthrough innovations using scalable cloud data storage and computing  to facilitate processes ensuring the drug’s safety and efficacy.
• Banks use the cloud for several aspects of customer-service management. They automate transaction calls using voice recognition algorithms and cognitive agents (AI-based online self-service assistants directing customers to helpful information or to a human representative when necessary). In fraud and debt analytics, cloud solutions enhance the predictive power of traditional early-warning systems. To reduce churn, they encourage customer loyalty through holistic retention programs managed entirely in the cloud.
• Automakers are also along for the cloud ride . One company uses a common cloud platform that serves 124 plants, 500 warehouses, and 1,500 suppliers to consolidate real-time data from machines and systems and to track logistics and offer insights on shop floor processes. Use of the cloud could shave 30 percent off factory costs by 2025—and spark innovation at the same time.

That’s not to mention experiences we all take for granted: using apps on a smartphone, streaming shows and movies, participating in videoconferences. All of these things can happen in the cloud.

Learn more about our Cloud by McKinsey , Digital McKinsey , and Technology, Media, & Telecommunications  practices.

How has cloud computing evolved?

Going back a few years, legacy infrastructure dominated IT-hosting budgets. Enterprises planned to move a mere 45 percent of their IT-hosting expenditures to the cloud by 2021. Enter COVID-19, and 65 percent of the decision makers surveyed by McKinsey increased their cloud budgets . An additional 55 percent ended up moving more workloads than initially planned. Having witnessed the cloud’s benefits firsthand, 40 percent of companies expect to pick up the pace of implementation.

The cloud revolution has actually been going on for years—more than 20, if you think the takeoff point was the founding of Salesforce, widely seen as the first software as a service (SaaS) company. Today, the next generation of cloud, including capabilities such as serverless computing, makes it easier for software developers to tweak software functions independently, accelerating the pace of release, and to do so more efficiently. Businesses can therefore serve customers and launch products in a more agile fashion. And the cloud continues to evolve.

Introducing McKinsey Explainers : Direct answers to complex questions

Cost savings are commonly seen as the primary reason for moving to the cloud but managing those costs requires a different and more dynamic approach focused on OpEx rather than CapEx. Financial-operations (or FinOps) capabilities  can indeed enable the continuous management and optimization of cloud costs . But CSPs have developed their offerings so that the cloud’s greatest value opportunity is primarily through business innovation and optimization. In 2020, the top-three CSPs reached $100 billion  in combined revenues—a minor share of the global $2.4 trillion market for enterprise IT services—leaving huge value to be captured. To go beyond merely realizing cost savings, companies must activate three symbiotic rings of cloud value creation : strategy and management, business domain adoption, and foundational capabilities.

What’s the main reason to move to the cloud?

The pandemic demonstrated that the digital transformation can no longer be delayed—and can happen much more quickly than previously imagined. Nothing is more critical to a corporate digital transformation than becoming a cloud-first business. The benefits are faster time to market, simplified innovation and scalability, and reduced risk when effectively managed. The cloud lets companies provide customers with novel digital experiences—in days, not months—and delivers analytics absent on legacy platforms. But to transition to a cloud-first operating model, organizations must make a collective effort that starts at the top. Here are three actions CEOs can take to increase the value their companies get from cloud computing :

• Establish a sustainable funding model.
• Develop a new business technology operating model.
• Set up policies to attract and retain the right engineering talent.

How much value will the cloud create?

Fortune 500 companies adopting the cloud could realize more than $1 trillion in value  by 2030, and not from IT cost reductions alone, according to McKinsey’s analysis of 700 use cases.

For example, the cloud speeds up design, build, and ramp-up, shortening time to market when companies have strong DevOps (the combination of development and operations) processes in place; groups of software developers customize and deploy software for operations that support the business. The cloud’s global infrastructure lets companies scale products almost instantly to reach new customers, geographies, and channels. Finally, digital-first companies use the cloud to adopt emerging technologies and innovate aggressively, using digital capabilities as a competitive differentiator to launch and build businesses .

If companies pursue the cloud’s vast potential in the right ways, they will realize huge value. Companies across diverse industries have implemented the public cloud and seen promising results. The successful ones defined a value-oriented strategy across IT and the business, acquired hands-on experience operating in the cloud, adopted a technology-first approach, and developed a cloud-literate workforce.

Learn more about our Cloud by McKinsey and Digital McKinsey practices.

What is the cloud cost/procurement model?

Some cloud services, such as server space, are leased. Leasing requires much less capital up front than buying, offers greater flexibility to switch and expand the use of services, cuts the basic cost of buying hardware and software upfront, and reduces the difficulties of upkeep and ownership. Organizations pay only for the infrastructure and computing services that meet their evolving needs. But an outsourcing model  is more apt than other analogies: the computing business issues of cloud customers are addressed by third-party providers that deliver innovative computing services on demand to a wide variety of customers, adapt those services to fit specific needs, and work to constantly improve the offering.

What are cloud risks?

The cloud offers huge cost savings and potential for innovation. However, when companies migrate to the cloud, the simple lift-and-shift approach doesn’t reduce costs, so companies must remediate their existing applications to take advantage of cloud services.

For instance, a major financial-services organization  wanted to move more than 50 percent of its applications to the public cloud within five years. Its goals were to improve resiliency, time to market, and productivity. But not all its business units needed to transition at the same pace. The IT leadership therefore defined varying adoption archetypes to meet each unit’s technical, risk, and operating-model needs.

Legacy cybersecurity architectures and operating models can also pose problems when companies shift to the cloud. The resulting problems, however, involve misconfigurations rather than inherent cloud security vulnerabilities. One powerful solution? Securing cloud workloads for speed and agility : automated security architectures and processes enable workloads to be processed at a much faster tempo.

What kind of cloud talent is needed?

The talent demands of the cloud differ from those of legacy IT. While cloud computing can improve the productivity of your technology, it requires specialized and sometimes hard-to-find talent—including full-stack developers, data engineers, cloud-security engineers, identity- and access-management specialists, and cloud engineers. The cloud talent model  should thus be revisited as you move forward.

Six practical actions can help your organization build the cloud talent you need :

• Find engineering talent with broad experience and skills.
• Balance talent maturity levels and the composition of teams.
• Build an extensive and mandatory upskilling program focused on need.
• Build an engineering culture that optimizes the developer experience.
• Consider using partners to accelerate development and assign your best cloud leaders as owners.
• Retain top talent by focusing on what motivates them.

How do different industries use the cloud?

Different industries are expected to see dramatically different benefits from the cloud. High-tech, retail, and healthcare organizations occupy the top end of the value capture continuum. Electronics and semiconductors, consumer-packaged-goods, and media companies make up the middle. Materials, chemicals, and infrastructure organizations cluster at the lower end.

Nevertheless, myriad use cases provide opportunities to unlock value across industries , as the following examples show:

• a retailer enhancing omnichannel  fulfillment, using AI to optimize inventory across channels and to provide a seamless customer experience
• a healthcare organization implementing remote heath monitoring to conduct virtual trials and improve adherence
• a high-tech company using chatbots to provide premier-level support combining phone, email, and chat
• an oil and gas company employing automated forecasting to automate supply-and-demand modeling and reduce the need for manual analysis
• a financial-services organization implementing customer call optimization using real-time voice recognition algorithms to direct customers in distress to experienced representatives for retention offers
• a financial-services provider moving applications in customer-facing business domains to the public cloud to penetrate promising markets more quickly and at minimal cost
• a health insurance carrier accelerating the capture of billions of dollars in new revenues by moving systems to the cloud to interact with providers through easier onboarding

The cloud is evolving  to meet the industry-specific needs of companies. From 2021 to 2024, public-cloud spending on vertical applications (such as warehouse management in retailing and enterprise risk management in banking) is expected to grow by more than 40 percent annually. Spending on horizontal workloads (such as customer relationship management) is expected to grow by 25 percent. Healthcare and manufacturing organizations, for instance, plan to spend around twice as much on vertical applications as on horizontal ones.

Learn more about our Cloud by McKinsey , Digital McKinsey , Financial Services , Healthcare Systems & Services , Retail , and Technology, Media, & Telecommunications  practices.

What are the biggest cloud myths?

Views on cloud computing can be clouded by misconceptions. Here are seven common myths about the cloud —all of which can be debunked:

• The cloud’s value lies primarily in reducing costs.
• Cloud computing costs more than in-house computing.
• On-premises data centers are more secure than the cloud.
• Applications run more slowly in the cloud.
• The cloud eliminates the need for infrastructure.
• The best way to move to the cloud is to focus on applications or data centers.
• You must lift and shift applications as-is or totally refactor them.

How large must my organization be to benefit from the cloud?

Here’s one more huge misconception: the cloud is just for big multinational companies. In fact, cloud can help make small local companies become multinational. A company’s benefits from implementing the cloud are not constrained by its size. In fact, the cloud shifts barrier to entry skill rather than scale, making it possible for a company of any size to compete if it has people with the right skills. With cloud, highly skilled small companies can take on established competitors. To realize the cloud’s immense potential value fully, organizations must take a thoughtful approach, with IT and the businesses working together.

For more in-depth exploration of these topics, see McKinsey’s Cloud Insights collection. Learn more about Cloud by McKinsey —and check out cloud-related job opportunities if you’re interested in working at McKinsey.

Articles referenced include:

• “ Six practical actions for building the cloud talent you need ,” January 19, 2022, Brant Carson , Dorian Gärtner , Keerthi Iyengar, Anand Swaminathan , and Wayne Vest
• “ Cloud-migration opportunity: Business value grows, but missteps abound ,” October 12, 2021, Tara Balakrishnan, Chandra Gnanasambandam , Leandro Santos , and Bhargs Srivathsan
• “ Cloud’s trillion-dollar prize is up for grabs ,” February 26, 2021, Will Forrest , Mark Gu, James Kaplan , Michael Liebow, Raghav Sharma, Kate Smaje , and Steve Van Kuiken
• “ Unlocking value: Four lessons in cloud sourcing and consumption ,” November 2, 2020, Abhi Bhatnagar , Will Forrest , Naufal Khan , and Abdallah Salami
• “ Three actions CEOs can take to get value from cloud computing ,” July 21, 2020, Chhavi Arora , Tanguy Catlin , Will Forrest , James Kaplan , and Lars Vinter

Want to know more about cloud computing?

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Cloud’s trillion-dollar prize is up for grabs

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