2022‑23
Revised
2023‑24
Revised
2024‑25
Proposed
Prior‑year fund balance
$61,737
$42,078
$8,030
Revenues and transfers
180,416
196,859
214,699
Expenditures
200,075
230,908
208,718
Ending fund balance
$42,078
$8,030
$14,010
Encumbrances
10,569
10,569
10,569
SFEU balance
31,509
‑2,539
3,441
Reserves
BSA
$21,708
$23,132
$11,106
SFEU
31,509
‑2,539
3,441
Safety net
900
900
—
Total Reserves
$54,117
$21,493
$14,547
BSA = Budget Stabilization Account and SFEU = Special Fund for Economic Uncertainties.
Under Governor’s Budget, Reserves Would Total $14.5 Billion by End of 2024‑25. Under the Governor’s budget, general purpose reserves would total $14.5 billion by the end of 2024‑25. (In addition, the state would have $3.9 billion in the Proposition 98 Reserve, available only for school and community college programs.) The remaining balance of the BSA—$11 billion—would likely be available to address a budget problem next year in the very likely event that it occurs.
Administration Plans for Significant Future Budget Deficits. The Governor’s budget includes estimates of multiyear revenues and spending. Under the administration’s projections, the state faces operating deficits of $37 billion in 2025‑26, $30 billion in 2026‑27, and $28 billion in 2027‑28. (As shown in Figure 4 , these deficits are very similar to our December projections of the budget’s position—although our estimates were based on current law and policy, not the Governor’s budget proposals.) Although these future deficits are smaller than the current one, they are still quite significant. Moreover, the state is likely to face these deficits with fewer options—such as one‑time spending reductions and reserves. As such, future deficits are likely to require more difficult decisions, like ongoing spending cuts and revenue increases.
Funding for Schools and Community Colleges Down $14.3 Billion Over Budget Window. Compared with the estimates included in the June 2023 budget plan, the administration estimates the constitutional minimum funding level for schools and community colleges is down $14.3 billion over the 2022‑23 through 2024‑25 period. This downward revision consists of a $15.2 billion reduction in required General Fund spending, partially offset by a $903 million increase in local property tax revenue. Most of the reduction—$9.1 billion—is attributable to 2022‑23, with the remainder divided about evenly between 2023‑24 and 2024‑25. The Governor proposes to reduce funding to the lower constitutional level through a combination of spending reductions and discretionary withdrawals from the Proposition 98 Reserve. These reductions also free up funding for a few smaller augmentations.
Assumes $8 Billion in Lower Spending in 2022‑23. The budget proposes to reduce General Fund spending on school and community college programs in 2022‑23 by $8 billion. The budget does not specify how the state will implement this reduction, but indicates the state will make the reduction in a way that avoids impacting school and community college budgets. We also understand that as part of this action, the state would make supplemental payments totaling $8 billion over a five‑year period (from 2025‑26 through 2029‑30). (Separate from this proposal, the budget scores $1.1 billion in lower baseline spending in 2022‑23.)
Proposes Discretionary Withdrawal From Proposition 98 Reserve. The Proposition 98 Reserve is a statewide reserve account for school and community college funding. The Governor proposes to make a discretionary withdrawal of $5.7 billion from this account to help cover costs for existing school and community college programs in 2023‑24 and 2024‑25. After accounting for the discretionary withdrawal and a few other automatic adjustments, the remaining balance in the reserve would be $3.9 billion.
Funds Augmentations in a Few Areas. The most notable ongoing augmentation is a 0.76 percent statutory cost‑of‑living adjustment (COLA) for existing school and community college programs. The most notable one‑time proposal is $500 million for a second round of grants funding zero‑emission school buses. The budget also proposes smaller increases related to the educator workforce, education technology, and community college nursing programs.
Revenues Optimistic but Plausible. California entered a revenue and economic downturn last fiscal year. State tax revenues fell 20 percent. The number of unemployed workers in California increased by 200,000. A key question for this budget is: to what extent and for how long will this downturn persist? The Governor’s budget assumes a quick return to growth, projecting an 8 percent increase in tax revenues in the current fiscal year. While possible, we think this assumption is optimistic. Halfway through the current year, we are yet to see clear signs of such a rebound. Income tax withholding is up only 2 percent. Sales tax collections are down slightly. In the relatively important collections month of December, corporation tax collections posted double digit declines. Unemployment continues to tick up consistently each month. One potential reason for optimism is the rebound in stock prices that occurred over the last year, especially in the spring of 2023. Stock market rallies, however, can reverse as quickly as they start. Further, the relationship between stock price gains and state revenues is complex. Any two similar stock market rallies can have significantly different impacts on state revenues.
Reserve Withdrawals Generally Reasonable. The Governor proposes withdrawing roughly half of the BSA and the entire Safety Net Reserve to help solve the budget problem. While the administration likely could withdraw the entire balance of the BSA under the rules of Proposition 2 (for example, if the Governor declared a budget emergency for multiple years in the budget window), maintaining a sizeable balance in the BSA is prudent given the continued budget problems likely for future years.
Budget Lacks Plan for Implementing Proposed Reductions in School and Community College Spending. The largest source of savings within the Governor’s school and community college spending package is a proposed reduction of $8 billion in 2022‑23 funding. The administration, however, has not explained how its proposal could achieve $8 billion in savings, given the administration also indicates the proposal would not impact school and community college budgets. The Legislature will need significantly more information before it can assess the proposal—including its potential effects on the state budget after 2024‑25. The Legislature also may want to consider alternative solutions, such as making additional withdrawals from the Proposition 98 Reserve, funding fewer augmentations, or making targeted reductions to existing programs.
Governor’s Spending‑Related Solutions Warranted, but Some Solutions Could Pose Challenges. The administration proposes spending‑related solutions (excluding school and community college spending) of $26 billion. This is a good start to solving the budget problem as these reductions largely do not impact the state’s ongoing core service level. There are some solutions, however, that may not yield the savings required to balance the budget. For example, across‑the‑board reductions—like the proposal to allocate general funding cuts to departments based on their vacancy rates—historically have not generated the initially assumed savings. In addition, as discussed earlier, some proposed solutions increase future budget pressure and shift fiscal risk to other entities. In addition to the transportation example provided earlier, the administration suggests the University of California and California State University could use delayed payments as collateral against borrowing. Not only would this proposal increase the pressure on the state to provide these payments next year—despite continued deficits—but it also would shift fiscal risk to these entities in the event the state does not ultimately make these payments.
Despite Spending‑Related Solutions, Governor’s Budget Likely Unsustainable in Future Years. The state faces significant operating deficits in the coming years, which are the result of lower revenue estimates, as well as increased cost pressures. These deficits are somewhat compounded by the Governor’s budget proposals to delay spending to future years and add billions in new discretionary proposals. State revenues in the out‑years would need to exceed the administration’s forecast by roughly $50 billion per year in order to sustain the spending proposed by the Governor’s budget. While our multiyear revenue forecast is somewhat above the administration, it is well below amount needed to close the deficits. Thus, while it may be reasonable to expect some upside to the administration’s multiyear revenues, it is unlikely this upside will resolve the out year deficits.
Overall, the Governor’s budget runs the risk of understating the degree of fiscal pressure facing the state in the future. The Legislature likely will face more difficult choices next year. To mitigate these challenges, we recommend the Legislature develop this year’s budget with a focus on future years. In particular, most of the recommendations we make here would mitigate some of the need for even more difficult decisions in the future, such as reductions to core services and/or revenue increases.
Plan for Lower Revenues. By May, we will be much closer to resolving the question of how much (if at all) revenues will rebound in the current fiscal year. While many outcomes are possible, our assessment of the current evidence suggests the resolution of this question likely will result in the administration revising down their revenue estimates in May. Should this occur, it would necessitate additional budget solutions. We advise the Legislature to begin to consider now what those solutions could be.
Maintain Similar Reserve Withdrawal. We advise the Legislature to use no more in reserves than proposed by the Governor—currently about half of general‑purpose reserves. Given the state is likely to continue to face significant budget problems in the coming years, depleting reserves now would make reductions to ongoing programs and/or ongoing revenue increases more likely.
Develop Plan for School and Community College Funding. Given the lack of clarity in the Governor’s proposal, the Legislature may want to develop its own plan for addressing school and community college funding. As we describe in our Fiscal Outlook , the Legislature could use the existing balance in the Proposition 98 Reserve to help cover spending above the constitutional minimum in 2022‑23. This approach would allow the state to reduce spending in 2022‑23 with no immediate effect on schools and community colleges.
Maximize One‑Time Spending Reductions. The Governor’s budget includes $26 billion in spending‑related solutions (excluding school and community college solutions). While the Governor’s budget likely reflects pulling back most recently approved one‑time and temporary spending, we are still assessing whether any additional such appropriations remain. To the extent they do, we recommend the Legislature assess whether additional pull backs could be achieved, including in the current year. Maximizing one‑time spending reductions allows the Legislature to minimize the use of other budget tools—like reserves—that likely will be needed in future years. To ensure these one‑time savings can be realized, the Legislature may wish to consider early action on current‑year appropriations.
Apply High Bar for Any Discretionary Proposals and Contain Ongoing Service Level. The Governor’s budget includes roughly $2 billion in discretionary proposals for 2024‑25. To balance the budget, these discretionary proposals require additional reductions to already approved expenditures. Consequently, we recommend the Legislature set a very high threshold for approving these new proposals. Specifically, the Legislature would need to view these new proposals as preferable to already approved spending. We also recommend the Legislature avoid growing the ongoing service level by assessing whether to continue approved, but not yet implemented, programs.
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Journalists, researchers and the public often look at society through the lens of generation, using terms like Millennial or Gen Z to describe groups of similarly aged people. This approach can help readers see themselves in the data and assess where we are and where we’re headed as a country.
Pew Research Center has been at the forefront of generational research over the years, telling the story of Millennials as they came of age politically and as they moved more firmly into adult life . In recent years, we’ve also been eager to learn about Gen Z as the leading edge of this generation moves into adulthood.
But generational research has become a crowded arena. The field has been flooded with content that’s often sold as research but is more like clickbait or marketing mythology. There’s also been a growing chorus of criticism about generational research and generational labels in particular.
Recently, as we were preparing to embark on a major research project related to Gen Z, we decided to take a step back and consider how we can study generations in a way that aligns with our values of accuracy, rigor and providing a foundation of facts that enriches the public dialogue.
A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations.
We set out on a yearlong process of assessing the landscape of generational research. We spoke with experts from outside Pew Research Center, including those who have been publicly critical of our generational analysis, to get their take on the pros and cons of this type of work. We invested in methodological testing to determine whether we could compare findings from our earlier telephone surveys to the online ones we’re conducting now. And we experimented with higher-level statistical analyses that would allow us to isolate the effect of generation.
What emerged from this process was a set of clear guidelines that will help frame our approach going forward. Many of these are principles we’ve always adhered to , but others will require us to change the way we’ve been doing things in recent years.
Here’s a short overview of how we’ll approach generational research in the future:
We’ll only do generational analysis when we have historical data that allows us to compare generations at similar stages of life. When comparing generations, it’s crucial to control for age. In other words, researchers need to look at each generation or age cohort at a similar point in the life cycle. (“Age cohort” is a fancy way of referring to a group of people who were born around the same time.)
When doing this kind of research, the question isn’t whether young adults today are different from middle-aged or older adults today. The question is whether young adults today are different from young adults at some specific point in the past.
To answer this question, it’s necessary to have data that’s been collected over a considerable amount of time – think decades. Standard surveys don’t allow for this type of analysis. We can look at differences across age groups, but we can’t compare age groups over time.
Another complication is that the surveys we conducted 20 or 30 years ago aren’t usually comparable enough to the surveys we’re doing today. Our earlier surveys were done over the phone, and we’ve since transitioned to our nationally representative online survey panel , the American Trends Panel . Our internal testing showed that on many topics, respondents answer questions differently depending on the way they’re being interviewed. So we can’t use most of our surveys from the late 1980s and early 2000s to compare Gen Z with Millennials and Gen Xers at a similar stage of life.
This means that most generational analysis we do will use datasets that have employed similar methodologies over a long period of time, such as surveys from the U.S. Census Bureau. A good example is our 2020 report on Millennial families , which used census data going back to the late 1960s. The report showed that Millennials are marrying and forming families at a much different pace than the generations that came before them.
Even when we have historical data, we will attempt to control for other factors beyond age in making generational comparisons. If we accept that there are real differences across generations, we’re basically saying that people who were born around the same time share certain attitudes or beliefs – and that their views have been influenced by external forces that uniquely shaped them during their formative years. Those forces may have been social changes, economic circumstances, technological advances or political movements.
When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.
The tricky part is isolating those forces from events or circumstances that have affected all age groups, not just one generation. These are often called “period effects.” An example of a period effect is the Watergate scandal, which drove down trust in government among all age groups. Differences in trust across age groups in the wake of Watergate shouldn’t be attributed to the outsize impact that event had on one age group or another, because the change occurred across the board.
Changing demographics also may play a role in patterns that might at first seem like generational differences. We know that the United States has become more racially and ethnically diverse in recent decades, and that race and ethnicity are linked with certain key social and political views. When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.
Controlling for these factors can involve complicated statistical analysis that helps determine whether the differences we see across age groups are indeed due to generation or not. This additional step adds rigor to the process. Unfortunately, it’s often absent from current discussions about Gen Z, Millennials and other generations.
When we can’t do generational analysis, we still see value in looking at differences by age and will do so where it makes sense. Age is one of the most common predictors of differences in attitudes and behaviors. And even if age gaps aren’t rooted in generational differences, they can still be illuminating. They help us understand how people across the age spectrum are responding to key trends, technological breakthroughs and historical events.
Each stage of life comes with a unique set of experiences. Young adults are often at the leading edge of changing attitudes on emerging social trends. Take views on same-sex marriage , for example, or attitudes about gender identity .
Many middle-aged adults, in turn, face the challenge of raising children while also providing care and support to their aging parents. And older adults have their own obstacles and opportunities. All of these stories – rooted in the life cycle, not in generations – are important and compelling, and we can tell them by analyzing our surveys at any given point in time.
When we do have the data to study groups of similarly aged people over time, we won’t always default to using the standard generational definitions and labels. While generational labels are simple and catchy, there are other ways to analyze age cohorts. For example, some observers have suggested grouping people by the decade in which they were born. This would create narrower cohorts in which the members may share more in common. People could also be grouped relative to their age during key historical events (such as the Great Recession or the COVID-19 pandemic) or technological innovations (like the invention of the iPhone).
By choosing not to use the standard generational labels when they’re not appropriate, we can avoid reinforcing harmful stereotypes or oversimplifying people’s complex lived experiences.
Existing generational definitions also may be too broad and arbitrary to capture differences that exist among narrower cohorts. A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations. The key is to pick a lens that’s most appropriate for the research question that’s being studied. If we’re looking at political views and how they’ve shifted over time, for example, we might group people together according to the first presidential election in which they were eligible to vote.
With these considerations in mind, our audiences should not expect to see a lot of new research coming out of Pew Research Center that uses the generational lens. We’ll only talk about generations when it adds value, advances important national debates and highlights meaningful societal trends.
Kim Parker is director of social trends research at Pew Research Center .
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ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .
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In the months and years since ChatGPT burst on the scene in November 2022, generative AI (gen AI) has come a long way. Every month sees the launch of new tools, rules, or iterative technological advancements. While many have reacted to ChatGPT (and AI and machine learning more broadly) with fear, machine learning clearly has the potential for good. In the years since its wide deployment, machine learning has demonstrated impact in a number of industries, accomplishing things like medical imaging analysis and high-resolution weather forecasts. A 2022 McKinsey survey shows that AI adoption has more than doubled over the past five years, and investment in AI is increasing apace. It’s clear that generative AI tools like ChatGPT (the GPT stands for generative pretrained transformer) and image generator DALL-E (its name a mashup of the surrealist artist Salvador Dalí and the lovable Pixar robot WALL-E) have the potential to change how a range of jobs are performed. The full scope of that impact, though, is still unknown—as are the risks.
Aamer Baig is a senior partner in McKinsey’s Chicago office; Lareina Yee is a senior partner in the Bay Area office; and senior partners Alex Singla and Alexander Sukharevsky , global leaders of QuantumBlack, AI by McKinsey, are based in the Chicago and London offices, respectively.
Still, organizations of all stripes have raced to incorporate gen AI tools into their business models, looking to capture a piece of a sizable prize. McKinsey research indicates that gen AI applications stand to add up to $4.4 trillion to the global economy—annually. Indeed, it seems possible that within the next three years, anything in the technology, media, and telecommunications space not connected to AI will be considered obsolete or ineffective .
But before all that value can be raked in, we need to get a few things straight: What is gen AI, how was it developed, and what does it mean for people and organizations? Read on to get the download.
To stay up to date on this critical topic, sign up for email alerts on “artificial intelligence” here .
Learn more about QuantumBlack , AI by McKinsey.
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QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.
Artificial intelligence is pretty much just what it sounds like—the practice of getting machines to mimic human intelligence to perform tasks. You’ve probably interacted with AI even if you don’t realize it—voice assistants like Siri and Alexa are founded on AI technology, as are customer service chatbots that pop up to help you navigate websites.
Machine learning is a type of artificial intelligence. Through machine learning, practitioners develop artificial intelligence through models that can “learn” from data patterns without human direction. The unmanageably huge volume and complexity of data (unmanageable by humans, anyway) that is now being generated has increased machine learning’s potential , as well as the need for it.
Machine learning is founded on a number of building blocks, starting with classical statistical techniques developed between the 18th and 20th centuries for small data sets. In the 1930s and 1940s, the pioneers of computing—including theoretical mathematician Alan Turing—began working on the basic techniques for machine learning. But these techniques were limited to laboratories until the late 1970s, when scientists first developed computers powerful enough to mount them.
Until recently, machine learning was largely limited to predictive models, used to observe and classify patterns in content. For example, a classic machine learning problem is to start with an image or several images of, say, adorable cats. The program would then identify patterns among the images, and then scrutinize random images for ones that would match the adorable cat pattern. Generative AI was a breakthrough. Rather than simply perceive and classify a photo of a cat, machine learning is now able to create an image or text description of a cat on demand.
How do text-based machine learning models work how are they trained.
ChatGPT may be getting all the headlines now, but it’s not the first text-based machine learning model to make a splash. OpenAI’s GPT-3 and Google’s BERT both launched in recent years to some fanfare. But before ChatGPT, which by most accounts works pretty well most of the time (though it’s still being evaluated), AI chatbots didn’t always get the best reviews. GPT-3 is “by turns super impressive and super disappointing,” said New York Times tech reporter Cade Metz in a video where he and food writer Priya Krishna asked GPT-3 to write recipes for a (rather disastrous) Thanksgiving dinner .
The first machine learning models to work with text were trained by humans to classify various inputs according to labels set by researchers. One example would be a model trained to label social media posts as either positive or negative. This type of training is known as supervised learning because a human is in charge of “teaching” the model what to do.
The next generation of text-based machine learning models rely on what’s known as self-supervised learning. This type of training involves feeding a model a massive amount of text so it becomes able to generate predictions. For example, some models can predict, based on a few words, how a sentence will end. With the right amount of sample text—say, a broad swath of the internet—these text models become quite accurate. We’re seeing just how accurate with the success of tools like ChatGPT.
Building a generative AI model has for the most part been a major undertaking, to the extent that only a few well-resourced tech heavyweights have made an attempt . OpenAI, the company behind ChatGPT, former GPT models, and DALL-E, has billions in funding from bold-face-name donors. DeepMind is a subsidiary of Alphabet, the parent company of Google, and even Meta has dipped a toe into the generative AI model pool with its Make-A-Video product. These companies employ some of the world’s best computer scientists and engineers.
But it’s not just talent. When you’re asking a model to train using nearly the entire internet, it’s going to cost you. OpenAI hasn’t released exact costs, but estimates indicate that GPT-3 was trained on around 45 terabytes of text data—that’s about one million feet of bookshelf space, or a quarter of the entire Library of Congress—at an estimated cost of several million dollars. These aren’t resources your garden-variety start-up can access.
As you may have noticed above, outputs from generative AI models can be indistinguishable from human-generated content, or they can seem a little uncanny. The results depend on the quality of the model—as we’ve seen, ChatGPT’s outputs so far appear superior to those of its predecessors—and the match between the model and the use case, or input.
ChatGPT can produce what one commentator called a “ solid A- ” essay comparing theories of nationalism from Benedict Anderson and Ernest Gellner—in ten seconds. It also produced an already famous passage describing how to remove a peanut butter sandwich from a VCR in the style of the King James Bible. Image-generating AI models like DALL-E 2 can create strange, beautiful images on demand, like a Raphael painting of a Madonna and child, eating pizza . Other generative AI models can produce code, video, audio, or business simulations .
But the outputs aren’t always accurate—or appropriate. When Priya Krishna asked DALL-E 2 to come up with an image for Thanksgiving dinner, it produced a scene where the turkey was garnished with whole limes, set next to a bowl of what appeared to be guacamole. For its part, ChatGPT seems to have trouble counting, or solving basic algebra problems—or, indeed, overcoming the sexist and racist bias that lurks in the undercurrents of the internet and society more broadly.
Generative AI outputs are carefully calibrated combinations of the data used to train the algorithms. Because the amount of data used to train these algorithms is so incredibly massive—as noted, GPT-3 was trained on 45 terabytes of text data—the models can appear to be “creative” when producing outputs. What’s more, the models usually have random elements, which means they can produce a variety of outputs from one input request—making them seem even more lifelike.
The opportunity for businesses is clear. Generative AI tools can produce a wide variety of credible writing in seconds, then respond to criticism to make the writing more fit for purpose. This has implications for a wide variety of industries, from IT and software organizations that can benefit from the instantaneous, largely correct code generated by AI models to organizations in need of marketing copy. In short, any organization that needs to produce clear written materials potentially stands to benefit. Organizations can also use generative AI to create more technical materials, such as higher-resolution versions of medical images. And with the time and resources saved here, organizations can pursue new business opportunities and the chance to create more value.
We’ve seen that developing a generative AI model is so resource intensive that it is out of the question for all but the biggest and best-resourced companies. Companies looking to put generative AI to work have the option to either use generative AI out of the box or fine-tune them to perform a specific task. If you need to prepare slides according to a specific style, for example, you could ask the model to “learn” how headlines are normally written based on the data in the slides, then feed it slide data and ask it to write appropriate headlines.
Because they are so new, we have yet to see the long tail effect of generative AI models. This means there are some inherent risks involved in using them—some known and some unknown.
The outputs generative AI models produce may often sound extremely convincing. This is by design. But sometimes the information they generate is just plain wrong. Worse, sometimes it’s biased (because it’s built on the gender, racial, and myriad other biases of the internet and society more generally) and can be manipulated to enable unethical or criminal activity. For example, ChatGPT won’t give you instructions on how to hotwire a car, but if you say you need to hotwire a car to save a baby, the algorithm is happy to comply. Organizations that rely on generative AI models should reckon with reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content.
These risks can be mitigated, however, in a few ways. For one, it’s crucial to carefully select the initial data used to train these models to avoid including toxic or biased content. Next, rather than employing an off-the-shelf generative AI model, organizations could consider using smaller, specialized models. Organizations with more resources could also customize a general model based on their own data to fit their needs and minimize biases. Organizations should also keep a human in the loop (that is, to make sure a real human checks the output of a generative AI model before it is published or used) and avoid using generative AI models for critical decisions, such as those involving significant resources or human welfare.
It can’t be emphasized enough that this is a new field. The landscape of risks and opportunities is likely to change rapidly in coming weeks, months, and years. New use cases are being tested monthly, and new models are likely to be developed in the coming years. As generative AI becomes increasingly, and seamlessly, incorporated into business, society, and our personal lives, we can also expect a new regulatory climate to take shape. As organizations begin experimenting—and creating value—with these tools, leaders will do well to keep a finger on the pulse of regulation and risk.
Articles referenced include:
This article was updated in April 2024; it was originally published in January 2023.
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Differences between reports and essays. A report is a piece of informative writing that describes a set of actions and analyses any results in response to a specific brief. A quick definition might be: "This is what I did and this is what it means." You may also have assignments which are not called reports but which are still pieces of informative writing; for instance, some dissertations ...
Key Differences Between Essays and Reports. The following are key differences between essays and reports: Purpose: Essays aim to persuade, inform, or entertain, while reports primarily aim to inform, analyze, or propose solutions. Structure: Essays consist of an introduction, body paragraphs, and a conclusion, whereas reports consist of an ...
WHAT'S THE DIFFERENE ETWEEN REPORTS AND ESSAYS? A report is a piece of informative writing that describes a set of actions and analyses any results in response to a specific brief. A quick definition might be: ^This is what I did and this is what it means. You may also have assignments which are not called reports but which are still pieces of informative writing; for
An essay tends to focus on concepts, issues and/or theory. The focus of a report is more concrete: the report looks at an issue in a real-world context. Essays, especially business essays, often use real-world examples to illustrate a concept or theory but a conceptual, or abstract, idea is the focus. Business reports often focus on a case ...
Here is a comparison table that summarizes the main differences between essays and reports: A piece of writing that gives the author's own argument. A piece of writing that gives information about a particular subject. Essays are typically shorter than reports and are more focused on the author's perspective and argument.
Table of distinctions between reports and essays. Reports. Essays. Reports have a table of contents. Essays do not. Reports are divided into headed and numbered sections and, sometimes, sub-sections. The format is IMRaD (see below). Essays are not divided. However, you may have separate headed appendices.
6. In terms of tone and style, essays are often more personal, allowing for the writer's voice and opinion to shine through. They require critical thinking, evaluation, and a clear line of argument. Reports are more factual and objective. They focus on presenting data, evidence, and facts without the inclusion of personal opinions or emotions. 11.
Essays are used to develop a discussion of a topic or build an argument. Reports present information in a different way from an essay. Whilst essays are generally quite fluid in terms of structure, enabling the author to explore a topic through a series of paragraphs, a report will be highly structured with section headings and subheadings that ...
The writer summarizes the essay in the concluding statement and then adds references. The format of a report is different and starts with an executive summary, where a writer gives a summary of the report. An index page follows, which contains the table of contents and then the introduction comes next. It discusses the origin and the components ...
Answer. Essays differ from reports in a number of ways: Essays require the writer to argue, defend or justify a point of view with respect to a particular topic or question. An essay includes an introduction, body paragraphs and a conclusion. In essays, headings are not normally used, so each new idea needs to be introduced within the paragraph ...
The dictionaries aren't particularly informative here, but there's a bunch of information online if you search for "essay vs. report". The specifics may vary, but usually an essay is a continuous piece of prose presenting an argument, while a report presents information and can include things like bullet points, tables and charts.
Key Differences Between an Essay and a Report. Purpose and Content: Essay: The major purpose of an essay is to discuss, explore, and sometimes to persuade. The content of an essay is mainly argumentative and reflective. Report: A report aims to inform and sometimes to make recommendations. It is based on factual information, research findings ...
Both articles and essays are forms of writing used for communication in many fields. Basically, that makes them somewhat similar and confusing. However, these two genres also have very distinct differences. "The term 'article' is used for the academic field, where it's a scientific sub-genre, and for the journalistic field, where it's ...
Spot the Differences. Spot the Differences is a puzzle game that challenges your observation skills as you hunt for disparities between two images! With countless cute paintings featuring scenes from fairy tales, delicious dishes, and charming street views, each level offers a delightful visual treat. Be careful—you have only three lives per ...
The difference between these estimates is narrower than these topline numbers might suggest. A budget problem is inherently a point‑in‑time estimate that reflects information available at the time of development, forecasts of future revenues and spending, and assumptions about the extent to which changes in costs are due to current policy ...
There may be significant differences between the true number of deaths due to COVID-19 and the official reported counts of those deaths. There may also be variation across the states in the quality and types of data reported. For example, most states report deaths based on the residency of the deceased person rather than the location where they ...
When we can't do generational analysis, we still see value in looking at differences by age and will do so where it makes sense. Age is one of the most common predictors of differences in attitudes and behaviors. And even if age gaps aren't rooted in generational differences, they can still be illuminating.
A 2022 McKinsey survey shows that AI adoption has more than doubled over the past five years, and investment in AI is increasing apace. It's clear that generative AI tools like ChatGPT (the GPT stands for generative pretrained transformer) and image generator DALL-E (its name a mashup of the surrealist artist Salvador Dalí and the lovable ...