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Artificial Intelligence

The U.S. National Science Foundation has invested in foundational artificial intelligence research since the early 1960s, setting the stage for today’s understanding and use of AI technologies.

AI-driven discoveries and technologies are transforming Americans' daily lives — promising practical solutions to global challenges, from food production and climate change to healthcare and education.

The growing adoption of AI also calls for a deeper understanding of its potential risks, like the amplification of bias, displacement of workers, or misuse by malicious actors to cause harm.

As a major federal funder of AI research, NSF advances AI breakthroughs that push the frontiers of knowledge, benefit people, and are aligned to the needs of society.

On this page

What is artificial intelligence?

How does AI affect our daily lives? How does it work in simple terms? Can we trust AI chatbots? In this 10-minute video, Michael Littman, NSF division director for Information and Intelligent Systems, looks at where the field of artificial intelligence has been and where it's going.

Brought to you by NSF

NSF's decades of sustained investments have ensured the continual advancement of AI research. Pioneering work supported by NSF includes:

Reinforcement learning

Which refines chatbots and trains self-driving cars, among other uses.

Neural networks

Which underlie breakthroughs in pattern recognition, image processing and natural language processing.

Large language models

Which power generative AI systems like ChatGPT.

Collaborative filtering

Which fuels content recommendation on the world's largest marketplaces and content platforms, from Amazon to Netflix.

AI-driven learning

Including virtual teachers (both digital and robotic) that incorporate speech, gesture, gaze and facial expression.

What we support

With investments of over $700 million each year, NSF supports:

Innovation in AI methods

We invest in foundational research to understand and develop systems that can sense, learn, reason, communicate and act in the world.

Application of AI techniques and tools

We invest in the application of AI across science and engineering to push the frontiers of knowledge and address pressing societal challenges.

Democratizing AI research resources

We enable access to resources — like computational infrastructure, data, software, testbeds and training — to engage the full breadth of the nation's talent in AI innovation.

Trustworthy and ethical AI

We invest in the development of AI that is safe, secure, fair, transparent and accountable, while ensuring privacy, civil rights and civil liberties.

Education and workforce development

We invest in the creation of educational tools, materials, fellowships and curricula to enhance learning and foster an AI-ready workforce.

Partnerships to accelerate progress

We partner with other federal agencies, industry and nonprofits to leverage expertise; identify use cases; and improve access to data, tools and other resources.

National AI Research Institutes

Launched in 2020, the NSF-led  National Artificial Intelligence Research Institutes  program consists of 25 AI institutes that connect over 500 funded and collaborative institutions across the U.S. and around the world.

The AI institutes focus on different aspects of AI research, including but not limited to:

• Trustworthy and ethical AI.
• Foundations of machine learning.
• Agriculture and food systems.
• AI and advanced cybersecurity.
• Human-AI interaction and collaboration.
• AI-augmented learning.

Learn more by reading the  2020 ,  2021  and  2023  AI Institutes announcements or visiting the AI Institutes Virtual Organization .

National AI Research Institutes: Interactive Map (PDF, 7.96 MB)

AI Institutes Booklet (PDF, 12.58 MB)

Hear from the newest ai research institutes.

• At the Edge of Artificial Intelligence This episode of NSF's Discovery Files podcast features three 2023 AI Research Institutes awardees discussing their work.
• The Frontier of Artificial Intelligence This Discovery Files episode features 2023 AI Research Institutes awardees applying AI to education, agriculture and weather forecasting.

National AI Research Resource Pilot

As part of the "National AI Initiative Act of 2020," the National AI Research Resource (NAIRR) Task Force was charged with creating a roadmap for a shared research infrastructure that would provide U.S.-based researchers, educators and students with significantly expanded access to computational resources, high-quality data, educational tools and user support.

The NSF-led interagency NAIRR Pilot will bring together government-supported, industry and other contributed resources to demonstrate the NAIRR concept and deliver early capabilities to the U.S. research and education community, including the full range of institutions of higher education and federally funded startups and small businesses.

The NAIRR Pilot is aimed to accelerate AI-dependent research such as:

• Societally relevant research on AI safety, reliability, security and privacy.
• Advances in cancer treatment and individual health outcomes.
• Supporting resilience and optimization of agricultural, water and grid infrastructure.
• Improving design, control and quality of advanced manufacturing systems.
• Addressing Earth and environmental challenges via the integration of diverse data and models.

Implementation Plan for a National Artificial Intelligence Research Resource (PDF, 3.02 MB)

Featured funding.

Computer and Information Science and Engineering: Core Programs

Supports foundational and use-inspired research in AI, data science and human-computer interaction — including human language technologies, computer vision, human-AI interaction, and theory of machine learning.

America's Seed Fund (SBIR/STTR)

Supports startups and small businesses to translate research into products and services, including  AI systems and AI-based hardware , for the public good.

Cyber-Physical Systems

Supports research on engineered systems with a seamless integration of cyber and physical components, such as computation, control, networking, learning, autonomy, security, privacy and verification, for a range of application domains.

Engineering Design and Systems Engineering

Supports fundamental research on the design of engineered artifacts — devices, products, processes, platforms, materials, organizations, systems and systems of systems.

Ethical and Responsible Research

Supports research on what promotes responsible and ethical conduct of research in AI and other areas as well as how to encourage researchers, practitioners and educators at all career stages to conduct research with integrity.

Expanding AI Innovation through Capacity Building and Partnerships

Supports capacity-development projects and partnerships within the National AI Research Institutes ecosystem that help broaden participation in artificial intelligence research, education and workforce development.

Experiential Learning for Emerging and Novel Technologies

Supports experiential learning opportunities that provide cohorts of diverse learners with the skills needed to succeed in artificial intelligence and other emerging technology fields.

Responsible Design, Development and Deployment of Technologies  

Supports research, implementation and education projects involving multi-sector teams that focus on the responsible design, development or deployment of technologies.

Research on Innovative Technologies for Enhanced Learning

Supports early-stage research in emerging technologies such as AI, robotics and immersive or augmenting technologies for teaching and learning that respond to pressing needs in real-world educational environments.

Secure and Trustworthy Cyberspace

Supports research addressing cybersecurity and privacy, drawing on expertise in one or more of these areas: computing, communication and information sciences; engineering; economics; education; mathematics; statistics; and social and behavioral sciences.

Smart and Connected Communities

Supports use-inspired research that addresses communities' social, economic and environmental challenges by integrating intelligent technologies with the natural and built environments.

Smart Health and Biomedical Research in the Era of Artificial Intelligence

Supports the development of new methods that intuitively and intelligently collect, sense, connect, analyze and interpret data from individuals, devices and systems.

NSF directorates supporting AI research

Computer and information science and engineering (cise), engineering (eng), technology, innovation and partnerships (tip), mathematical and physical sciences (mps), social, behavioral and economic sciences (sbe), stem education (edu), geosciences (geo), biological sciences (bio), international science and engineering (oise), integrative activities (oia), featured news.

NSF-led National AI Research Resource Pilot awards first round access to 35 projects in partnership with DOE

New NSF grant targets large language models and generative AI, exploring how they work and implications for societal impacts

Saving an endangered species: New AI method counts manatee clusters in real time

Additional resources.

• NAIRR Pilot Explore opportunities for researchers, educators and students, including AI-ready datasets, pre-trained models and other NAIRR pilot resources.
• National Artificial Intelligence Initiative A coordinated federal approach to accelerate AI research and the integration of AI systems across all sectors of the economy and society.
• CloudBank Allows the research and education community to access cloud computing platforms.
• One Hundred Year Study on Artificial Intelligence A study focused on understanding and anticipating how AI will ripple through every aspect of how people work, live and play.
• Expanding the Frontiers of AI: Fact Sheet Learn how NSF is driving cutting-edge research on AI.
• "CHIPS and Science Act of 2022" The act authorizes historic investments in use-inspired, solutions-oriented research and innovation in key technology focus areas.

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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Stanford AI Lab

The Stanford Artificial Intelligence Laboratory (SAIL) has been a center of excellence for Artificial Intelligence research, teaching, theory, and practice since its founding in 1963.

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Carlos Guestrin has been elected to the National Academic of Engineering “for scalable systems and algorithms enabling the broad application of machine learning in science and industry.”

Congratulations to Chris Manning on being awarded 2024 IEEE John von Neumann Medal!

Chris Manning has been awarded the 2024 IEEE John von Neumann Medal “for advances in computational representation and analysis of natural language.” This is one of IEEE’s top awards in computing, given with very broad scope “for outstanding achievements in computer-related science and technology.”

SAIL Faculty and Students Win NeurIPS Outstanding Paper Awards

Congratulations to Sanmi Koyejo and his students for winning the NeurIPS Outstanding Paper Award, and congradulations to Chris Manning, Stefano Ermon, Chelsea Finn, and their students for winning Outstanding Paper Runner Up at NeurIPS!

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Congratulations to Fei-Fei Li for Winning the Intel Innovation Lifetime Achievement Award!

Congratulations Fei-Fei Li on this acknowledgement of her significant contributions in the field!  The award was presented to her by Intel CEO Pat Gelsinger and recognizes her many advances in AI. 

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AI is revolutionizing how business gets done, but popular models can be costly and are often proprietary. At IBM Research, we’re designing powerful new foundation models and generative AI systems with trust and transparency at their core. We’re working to drastically lower the barrier to entry for AI development, and to do that, we’re committed to an open-source approach to enterprise AI.

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Research projects link neuroscience and AI to advance human health

By Julia Diaz

At the intersection of artificial intelligence (AI) and neuroscience is a mutually beneficial relationship with the potential to transform brain health, counter disease, and develop scientifically grounded AI technologies inspired by the versatility and depth of human intelligence.

For the first time, the Wu Tsai Neurosciences Institute and Institute for Human-Centered Artificial Intelligence (HAI) at Stanford have partnered to award a combined $500,000 to four cross-disciplinary research teams who are reimagining how neuroscience and AI can work together to unlock new insights about the human brain in health and disease.

For example, one of the grantees under this program is working on a new approach to at-home stroke rehabilitation therapy, using robotics, brain-computer interface, virtual reality, and wireless technology. By introducing real-time feedback, the researchers believe the system will be able to engage more neural circuits in the patient’s brain and enhance physical therapy.

Other grantees are exploring applications of AI in restoring speech to people with paralysis and tracking the progression of Parkinson's Disease, while the fourth aims to understand the remarkable energy-efficient computational capacity of the human brain to inform next generation computer chips.

“Neuroscience and artificial intelligence have both seen rapid growth in recent years. Many areas of neuroscience will benefit from the infusion of AI,” said Kang Shen , Vincent V.C. Wu Director of the Wu Tsai Neurosciences Institute, and Frank Lee and Carol Hall Professor of biology and of pathology. “We look forward to seeing these research teams pave the way for ethical advancements in human-inspired AI and its impact on understanding the development and function of the brain in health and disease.”

Proposals were selected based on their probability to make strong advances in both fields. “HAI and Wu Tsai Neuro share a commitment to funding proposals that make a persuasive case for how initial results will catalyze further support from internal and external stakeholders,” said James Landay , Stanford HAI Vice Director and Faculty Director of Research.

Sadly, one awardee, electrical engineering professor Krishna Shenoy , passed away in January. The science will go on, however, said co-PIs Zhenan Bao and Shaul Druckmann . "Krishna's longterm vision was to build brain computer interfaces to restore movement and communication to people with paralysis," said Druckmann. "We hope that by shedding light on how the brain controls the complex musculature underlying speech, our devices can contribute to making his vision a reality."

Learn more about the Wu Tsai Neuro & HAI Partnership Grant recipients:

Funded Projects

At-home stroke rehabilitation system based on augmented reality and brain computer interface paradigm.

This team aims to revolutionize future stroke treatment both in clinics and at home by combining a brain-computer interface and augmented reality (AR) into a single rehabilitation platform.

• Ada Poon , Main PI, School of Engineering, Dept of Electrical Engineering
• Monroe Kennedy III , Co-PI, School of Engineering, Dept of Mechanical Engineering
• Maarten Lansberg , Co-PI, School of Medicine, Dept of Neurology

Silent Speech Decoding Using Flexible Electronics and Artificial Intelligence

This team aims to advance augmentative and alternative communication technology for people with communication disorders and enable new forms of human-computer interaction by combining novel materials science with modern machine learning.

Dr. Krishna Shenoy passed away January 21, 2023. Read his obituary  here . Gifts in Krishna’s honor may be made to the  Pancreatic Cancer Action Network .

• Zhenan Bao , Main PI, School of Engineering, Dept of Chemical Engineering
• Shaul Druckmann , Co-PI, School of Medicine, Dept of Neurobiology
• Krishna Shenoy †, Co-PI, School of Engineering, Dept of Electrical Engineering
• Jaimie Henderson , Co-PI, School of Medicine, Dept of Neurosurgery

The Synaptic Organization of Dendrites

This team aims to mine a microscale reconstruction of a millimeter-cube of brain tissue to uncover how dendrites decode patterns of incoming signals. The project will test hypotheses that could confer the energy efficiency of neural circuits on next generation computer chips.

• Kwabena Boahen , Main PI, School of Engineering, Dept of Bioengineering
• Andreas Tolias *, Co-PI, School of Medicine, Dept of Ophthalmology (Joined Stanford January 2023)

Tracking Parkinson’s Disease with Transformer Models of Everyday Looking Behaviors

This project aims to track cognitive decline in Parkinson’s patients by measuring and modeling how patients explore the world with their eyes. The long-term goal of this project is to set a foundation for minimally-invasive and sensitive measures for diagnosing and tracking neurodegenerative diseases.

• Justin Gardner , Main PI, School of Humanities and Sciences, Dept of Psychology
• Leila Montaser Kouhsari , Co-PI, School of Medicine, Dept of Neurology

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Recent progress in the areas of Artificial Intelligence (AI) and Machine Learning (ML) are tremendous. Almost monthly, we see reports announcing breakthroughs in different technological aspects of AI.

As an organization focussing on research and development, we can look back on an increasing number of research projects .

Publicly funded Research Projects

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Goal: Development and validation of a radiological AI assistance system to support dementia diagnosis

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Goal: Real-time artificial intelligence annotation of multimodality endoscopy images in pancreatic cancer, allowing tumor cells to be detected during the examination and treated or removed directly

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Partner: PolyDiagnost GmbH, University Medical Center Göttingen, Institute for Diagnostic and Interventional Radiology, Faculty Engineering & Health of the University of Allied Science and Arts

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Partner: EvoLogics GmbH, IAV GmbH, Fraunhofer IGD, University of Rostock, IOW

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Partner: University of Rostock, University of La Rochelle, Austrian National Library, University of Helsinki, University of Innsbruck, National Library of France, University of Montpellier, University of Vienna

READ (2016)

Goal: Recognition of historical handwritten texts (European cultural heritage 1500 – 1800)

Partner: University of Rostock, University of Greifswald, National Archives Finland, University of Erlangen-Nuremberg, University of Innsbruck, University of Valencia, University of Edinburgh, National Archives Norway, Swedish National Archives, University of Vienna

Automatic Full-Text Recognition (2014)

Goal: Algorithms for automatic full text recognition in handwritten historical documents

ORGANIC (2009)

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A family of ai translation models that enable more natural and authentic communication across languages, a concept for future immersive experiences that turns how-to videos into custom augmented reality and mixed reality tutorials..

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Continue exploring other previous research projects

The next generation of our open source large language model

The first AI to play Diplomacy at a human level

Make-A-Video

An AI system that generates videos from text

No Language Left Behind

Driving inclusion through the power of translation

Protein Fold

ESM Metagenomic Atlas: The first view of the ‘dark matter’ of the protein universe

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Building breakthrough speech recognition with AI

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Finding more efficient and scalable ways to store and use renewable energy

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Learn about our latest AI breakthroughs and innovations from our library of published papers.

No Language Left Behind: Scaling Human-Centered Machine Translation

NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco (Paco) Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Jeff Wang

LLaMA: Open and Efficient Foundation Language Models

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Guillaume Lample

The Casual Conversations v2 Dataset

Bilal Porgali, Vítor Albiero, Jordan Ryda, Cristian Canton Ferrer, Caner Hazirbas

ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks

Hua Qi, Zhijie Wang, Qing Guo, Jianlang Chen, Felix Juefei-Xu, Fuyuan Zhang, Lei Ma, Jianjun Zhao

Emergence of Maps in the Memories of Blind Navigation Agents

Erik Wijmans, Manolis Savva, Irfan Essa, Stefan Lee, Ari Morcos, Dhruv Batra

Online Backfilling with No Regret for Large-Scale Image Retrieval

Seonguk Seo, Mustafa Gokhan Uzunbas, Bohyung Han, Sara Cao, Joena Zhang, Taipeng Tian, Ser Nam Lim

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The best AI tools for research papers and academic research (Literature review, grants, PDFs and more)

As our collective understanding and application of artificial intelligence (AI) continues to evolve, so too does the realm of academic research. Some people are scared by it while others are openly embracing the change. 

Make no mistake, AI is here to stay!

Instead of tirelessly scrolling through hundreds of PDFs, a powerful AI tool comes to your rescue, summarizing key information in your research papers. Instead of manually combing through citations and conducting literature reviews, an AI research assistant proficiently handles these tasks.

These aren’t futuristic dreams, but today’s reality. Welcome to the transformative world of AI-powered research tools!

This blog post will dive deeper into these tools, providing a detailed review of how AI is revolutionizing academic research. We’ll look at the tools that can make your literature review process less tedious, your search for relevant papers more precise, and your overall research process more efficient and fruitful.

I know that I wish these were around during my time in academia. It can be quite confronting when trying to work out what ones you should and shouldn’t use. A new one seems to be coming out every day!

Here is everything you need to know about AI for academic research and the ones I have personally trialed on my YouTube channel.

My Top AI Tools for Researchers and Academics – Tested and Reviewed!

There are many different tools now available on the market but there are only a handful that are specifically designed with researchers and academics as their primary user.

These are my recommendations that’ll cover almost everything that you’ll want to do:

Want to find out all of the tools that you could use?

Here they are, below:

AI literature search and mapping – best AI tools for a literature review – elicit and more

Harnessing AI tools for literature reviews and mapping brings a new level of efficiency and precision to academic research. No longer do you have to spend hours looking in obscure research databases to find what you need!

AI-powered tools like Semantic Scholar and elicit.org use sophisticated search engines to quickly identify relevant papers.

They can mine key information from countless PDFs, drastically reducing research time. You can even search with semantic questions, rather than having to deal with key words etc.

With AI as your research assistant, you can navigate the vast sea of scientific research with ease, uncovering citations and focusing on academic writing. It’s a revolutionary way to take on literature reviews.

• Elicit –  https://elicit.org
• Litmaps –  https://www.litmaps.com
• Research rabbit – https://www.researchrabbit.ai/
• Connected Papers –  https://www.connectedpapers.com/
• Supersymmetry.ai: https://www.supersymmetry.ai
• Semantic Scholar: https://www.semanticscholar.org
• Laser AI –  https://laser.ai/
• Inciteful –  https://inciteful.xyz/
• Scite –  https://scite.ai/
• System –  https://www.system.com

If you like AI tools you may want to check out this article:

• How to get ChatGPT to write an essay [The prompts you need]

AI-powered research tools and AI for academic research

AI research tools, like Concensus, offer immense benefits in scientific research. Here are the general AI-powered tools for academic research. 

These AI-powered tools can efficiently summarize PDFs, extract key information, and perform AI-powered searches, and much more. Some are even working towards adding your own data base of files to ask questions from. 

Tools like scite even analyze citations in depth, while AI models like ChatGPT elicit new perspectives.

The result? The research process, previously a grueling endeavor, becomes significantly streamlined, offering you time for deeper exploration and understanding. Say goodbye to traditional struggles, and hello to your new AI research assistant!

• Consensus –  https://consensus.app/
• Iris AI –  https://iris.ai/
• Research Buddy –  https://researchbuddy.app/
• Mirror Think – https://mirrorthink.ai

AI for reading peer-reviewed papers easily

Using AI tools like Explain paper and Humata can significantly enhance your engagement with peer-reviewed papers. I always used to skip over the details of the papers because I had reached saturation point with the information coming in. 

These AI-powered research tools provide succinct summaries, saving you from sifting through extensive PDFs – no more boring nights trying to figure out which papers are the most important ones for you to read!

They not only facilitate efficient literature reviews by presenting key information, but also find overlooked insights.

With AI, deciphering complex citations and accelerating research has never been easier.

• Aetherbrain – https://aetherbrain.ai
• Explain Paper – https://www.explainpaper.com
• Chat PDF – https://www.chatpdf.com
• Humata – https://www.humata.ai/
• Lateral AI –  https://www.lateral.io/
• Paper Brain –  https://www.paperbrain.study/
• Scholarcy – https://www.scholarcy.com/
• SciSpace Copilot –  https://typeset.io/
• Unriddle – https://www.unriddle.ai/
• Sharly.ai – https://www.sharly.ai/
• Open Read –  https://www.openread.academy

AI for scientific writing and research papers

In the ever-evolving realm of academic research, AI tools are increasingly taking center stage.

Enter Paper Wizard, Jenny.AI, and Wisio – these groundbreaking platforms are set to revolutionize the way we approach scientific writing.

Together, these AI tools are pioneering a new era of efficient, streamlined scientific writing.

• Jenny.AI – https://jenni.ai/ (20% off with code ANDY20)
• Yomu – https://www.yomu.ai
• Wisio – https://www.wisio.app

AI academic editing tools

In the realm of scientific writing and editing, artificial intelligence (AI) tools are making a world of difference, offering precision and efficiency like never before. Consider tools such as Paper Pal, Writefull, and Trinka.

Together, these tools usher in a new era of scientific writing, where AI is your dedicated partner in the quest for impeccable composition.

• PaperPal –  https://paperpal.com/
• Writefull –  https://www.writefull.com/
• Trinka –  https://www.trinka.ai/

AI tools for grant writing

In the challenging realm of science grant writing, two innovative AI tools are making waves: Granted AI and Grantable.

These platforms are game-changers, leveraging the power of artificial intelligence to streamline and enhance the grant application process.

Granted AI, an intelligent tool, uses AI algorithms to simplify the process of finding, applying, and managing grants. Meanwhile, Grantable offers a platform that automates and organizes grant application processes, making it easier than ever to secure funding.

Together, these tools are transforming the way we approach grant writing, using the power of AI to turn a complex, often arduous task into a more manageable, efficient, and successful endeavor.

• Granted AI – https://grantedai.com/
• Grantable – https://grantable.co/

Best free AI research tools

There are many different tools online that are emerging for researchers to be able to streamline their research processes. There’s no need for convience to come at a massive cost and break the bank.

The best free ones at time of writing are:

• Elicit – https://elicit.org
• Connected Papers – https://www.connectedpapers.com/
• Litmaps – https://www.litmaps.com ( 10% off Pro subscription using the code “STAPLETON” )
• Consensus – https://consensus.app/

Wrapping up

The integration of artificial intelligence in the world of academic research is nothing short of revolutionary.

With the array of AI tools we’ve explored today – from research and mapping, literature review, peer-reviewed papers reading, scientific writing, to academic editing and grant writing – the landscape of research is significantly transformed.

The advantages that AI-powered research tools bring to the table – efficiency, precision, time saving, and a more streamlined process – cannot be overstated.

These AI research tools aren’t just about convenience; they are transforming the way we conduct and comprehend research.

They liberate researchers from the clutches of tedium and overwhelm, allowing for more space for deep exploration, innovative thinking, and in-depth comprehension.

Whether you’re an experienced academic researcher or a student just starting out, these tools provide indispensable aid in your research journey.

And with a suite of free AI tools also available, there is no reason to not explore and embrace this AI revolution in academic research.

We are on the precipice of a new era of academic research, one where AI and human ingenuity work in tandem for richer, more profound scientific exploration. The future of research is here, and it is smart, efficient, and AI-powered.

Before we get too excited however, let us remember that AI tools are meant to be our assistants, not our masters. As we engage with these advanced technologies, let’s not lose sight of the human intellect, intuition, and imagination that form the heart of all meaningful research. Happy researching!

Thank you to Ivan Aguilar – Ph.D. Student at SFU (Simon Fraser University), for starting this list for me!

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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Top Five AI Research Projects from Indian Academia from 2021

We have listed down the top five AI research projects by Indian academicians that can help solve some of the most pressing issues we’re facing as a society today. 

“No research without action, no action without research” - Kurt Lewin 

The above-mentioned words rightly portray the value research entails. Working on AI problems define the technology of tomorrow, impacting everyone, hence, calls for multiple research work across many domains. Indian academicians are no exception, they challenge conventions and work towards reimagining technology for the larger good.

India has multiple problems at hand, and smart solutions hold the key. To that end, researchers across India publish hundreds of research papers, conduct fundamental research, and make datasets and tools available to the broader research community for a collaborative approach. To recognise their work, we have listed down the top five AI research projects by Indian academicians that can help solve some of the most pressing issues we’re facing today.  

1. Synchronized Multi Scale and Multi Sensor Traffic Data from Indian Urban Roads 

Researchers: Bhargava Rama Chilukuri, an Assistant Professor in the Department of Civil Engineering, Dr Gitakrishnan Ramadurai, an Associate Professor and part of the transportation systems group, both from IIT-Madras. 

About the project: Congestion and pollution, the two biggest dilemmas for the Indian urban world require an alternative transportation model. The availability of a high-quality traffic dataset is critical for the model to work. However, the Indian datasets suffer from three shortcomings that make them unfit to train AI models: limited Spatio-temporal extents of the collected data a limited number of parameters observed and sources used, and  limited to a single resolution of data  

To overcome these challenges, the research is oriented towards creating a precise traffic dataset for a 25-kilometre urban road system with data from multiple sources and measuring several parameters at both the macroscopic and microscopic levels for the first time in Indian settings. The data collected via sensors including GPS sensors, LIDARs, video cameras, Wi-Fi sensors and even drones will be accessed using RBC-DSAI’s in-house ML and deep-learning techniques. 

2. Indian Urban Data Exchange (IUDX) Project 

Researchers: The project is carried out by the Robert Bosch Centre for Cyber-Physical Systems (RBCCPS), an academic and research centre at the Indian Institute of Science, Bangalore. It is an initiative of the Smart Cities Mission under the Ministry of Housing and Urban Affairs (MoHUA) and the Ministry of Electronics and Information Technology (MeitY). 

About the project: With a need to enable data exchange between government agencies, city departments, citizens and the private sector – IUDX was created to utilise massively available data intelligently towards solving complex urban challenges. The open-source software platform clears the way for authenticated and secure data exchange. IUDX research project was initiated in 2019, with an initial reference code. From managing a green corridor for emergency vehicles to solid waste pickup and health management information systems – IUDX presents a greater opportunity for data-driven innovations. 

3. Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer 

Researchers: Vidhi Malik from Brigham and Women’s Hospital, and other two researchers namely Yogesh Kalakoti and Durai Sundar from IIT-Delhi have contributed towards this project in healthcare. 

About the project: Around 1.8 lakh breast cancer cases were reported in India in 2020, as per the International Agency for Research on Cancer, thereby raising an alarm. In cancer research, the ability to predict the likely development of the disease – survival and drug response are two vital parameters. Here, the team proposed a late multi-omics integrated framework for robustly quantifying breast cancer patient survival and drug response, with an emphasis on the relative prognostic potential of available omics data types. With an accuracy of 94%, the proposed strategy provides an effective way of extracting crucial information from diverse omics data types enabling estimation of prognostic indicators. Such integrative models with high predictive power have a significant utility in precision oncology. 

4. Object Detection Project by Team Autonomous Ground Vehicle (AGV)  

Researchers: A multi-disciplinary research group from IIT Kharagpur with an aim to build a fully operational self-driving car is working on varied projects involving deep learning, computer vision and reinforcement learning. 

About the project: Object detection is one of the primary requisites for autonomous vehicles. Hence, the team developed vision algorithms for tasks such as dynamic obstacle tracking, pedestrian detection, lane detection, etc. The project will help cope with the various requirements of an AV in a diverse environment. Moreover, the reliability of vision algorithms is absolutely critical for real-time decision making, hence ensuring safety. The team worked to solve two important problems including, detection of objects in an image, and identification of an object using convolutional neural networks (CNN) to a large extent. 

5. Aurora – Intelligent UAV Design 

Researchers: A team of BTech, MTech and PhD students at IIIT Delhi with a background in CSE, and experience in fields of aeromodelling, robotics, autonomous systems, computer vision, etc. The team is being provided guidance by faculty. 

About the project: The goal is to create an intelligent UAV with capabilities such as autonomous flying, navigation, and landing, as well as the ability to perform complex tasks such as automatic airdrop, detecting and localising targets of interest, reaching a specified position or target, and so on, with manual remote controls. UAVs having these qualities can be used for a variety of tasks. The project assembles off-the-shelf airframes with the required components to create a basic functional UAV. This is then combined with an autopilot system, which adds sensing and autonomous navigation capabilities. 

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May 22, 2024 | Christine Buckley - College of Liberal Arts and Sciences

Geoscientist Among First Projects Approved by National Artificial Intelligence Research Resource (NAIRR) Pilot 

Lijing Wang, who joins UConn in August, will develop AI models for mountain water flow that aid in climate change predictions

The East River Watershed during an October 2023 research trip (Photo courtesy of Lijing Wang)

Lijing Wang, assistant professor of Earth Sciences in the College of Liberal Arts and Sciences, is among the first scientists in the U.S. to earn support from the National Artificial Intelligence Research Resource (NAIRR) Pilot, a nationwide infrastructure that connects U.S. researchers to the computational data, software, models, and training they need to conduct paradigm-shfting AI research.  

The U.S. National Science Foundation (NSF) and the Department of Energy (DOE) announced last week the first 35 projects awarded computational time through the project, marking what it calls a significant milestone in fostering responsible AI research and democratizing access to AI tools across the country.  

The NAIRR Pilot will support fundamental, translational and use-inspired AI-related research with emphasis on societal challenges. Initial priority topics include safe, secure and trustworthy AI; human health; and environment and infrastructure.  

Wang, who will join UConn as assistant professor of Earth Sciences in the fall, received 10,500 node hours at the DOE Argonne National Laboratory AI Testbed. A node hour is the cumulative amount of time that computing resources equivalent to one individual node, or a single computer within a larger network or cluster, have been active or utilized for computation.  

Her project studies water flow in mountainous areas where the lack of data about snow melt and water movement makes predictions about the area’s future water flow difficult to compute or inaccurate.   

“Mountainous watersheds provide significant water resources,” says Wang. “Conducting intensive monitoring is key to understanding water availability, but it’s not feasible in every catchment. Together with monitoring, an AI tool could help us evaluate these water variations more efficiently in the face of climate change.”  

The work will simulate water movement across multiple mountain slopes under different conditions, and the results will form a dataset for an AI model to predict snow melt, water flow, and groundwater levels. Her results will lead to more rapid water forecasting, which will improve water management and climate change studies.  

Of the 35 projects, 27 will be supported through the NSF-funded advanced computing systems, and eight projects will utilize DOE-supported systems.  

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May 24, 2024

Purdue, Google announce sustainability and AI research partnership

Purdue University and Google announced a partnership that seeks to develop innovative solutions for eco-friendly industrial buildings. Left to right: Joe Sinfield, civil engineering professor and director of Purdue’s Institute for Innovation Science; Travis Horton, civil engineering professor; Ben Townsend, Google global head of infrastructure and sustainability; Alyssa Wilcox, Purdue senior vice president of partnerships and chief of staff. (Purdue University photo provided)

Collaboration seeks to develop innovative solutions for eco-friendly industrial buildings  

WEST LAFAYETTE, Ind. — Purdue University and Google announced Thursday (May 23) a new collaborative research project aimed at exploring the use of AI to develop innovative solutions for low-carbon industrial building design. The project seeks to leverage AI’s power to explore new materials, technologies and design strategies that can significantly reduce the carbon footprint of industrial buildings, such as data centers, not only across the U.S. but globally.

“Google is committed to using AI to address some of the world’s most pressing challenges, including climate change,” said Ben Townsend, global head of infrastructure and sustainability at Google. “We are excited to partner with Purdue University on this important research project, which has the potential to accelerate the adoption of low-carbon building practices in the industrial sector.”

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Conventional construction processes and physical plant operations of industrial structures pose persistent challenges when builders seek to reduce greenhouse gas emissions. That’s one reason why Purdue and Google are partnering on research efforts to develop new, sustainable building design approaches.

One focus area will be to prioritize the use of low-carbon building materials. The partners said research findings from this collaboration will be shared with the wider research community and industry stakeholders, aiming to boost the adoption of sustainable building practices.

“By combining Purdue’s expertise in engineering and building science with Google’s cutting-edge AI capabilities, we aim to develop innovative solutions that can significantly reduce the carbon footprint of industrial buildings,” said Travis Horton, a professor in Purdue’s Lyles School of Civil Engineering who also holds a courtesy appointment in mechanical engineering. “This collaboration is a testament to our commitment to sustainability and our belief in the transformative potential of AI.” 

AI is a foundational component of the Institute for Physical Artificial Intelligence , a Purdue Computes initiative.

The partners said AI has the potential to have a profound impact on industrial construction, as it could provide innovative applications of low-carbon building materials, which would support reducing operational costs and making progress on global sustainability goals. 

“At Google we are committed to sustainability, and we believe that technology can play a critical role in reducing carbon emissions,” said Townsend, who earned bachelor’s and master’s degrees in civil engineering from Purdue.

Google, which owns and operates data centers all over the world, is continually examining systems, including innovative construction design processes, to reduce the carbon footprint of its facilities to ensure efficiency and eco-friendly construction.

“The potential to combine Purdue’s understanding of innovation and design processes with Google’s AI capabilities to enable highly efficient and scalable sustainable design foreshadows the far-reaching promise of AI to help society address its most complex challenges,” said Joe Sinfield, a professor in Purdue’s Lyles School of Civil Engineering and the director of Purdue’s Institute for Innovation Science. 

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Top 14 Artificial Intelligence Projects in 2024 + [Source Code]

Home Blog Data Science Top 14 Artificial Intelligence Projects in 2024 + [Source Code]

AI finds its use in a wide range of applications like marketing, automation, transport, supply chain, and communication, to name a few. From cutting-edge research to real-world applications, here we will investigate the most executed artificial intelligence projects. This article will assist you in discovering plenty of fascinating ideas and insights to inspire you, whether you are a tech fanatic or want to know about the future of AI. Artificial intelligence has had a profound impact on our daily lives, and we employ AI whenever you look through social media, open Spotify, or conduct a fast Google search. Currently, most students and working professionals prefer a Data Science Course to make a smooth transition in the data science field. In this article, we will talk about AI project topics. Let us get started!

What are Artificial Intelligence Projects?

Artificial intelligence (AI) projects are software-based initiatives that utilize machine learning, deep learning, natural language processing, computer vision, and other AI technologies to develop intelligent programs capable of performing various tasks with minimal human intervention.

These AI projects typically involve a collaborative team of software developers, data scientists, machine learning engineers, and subject matter experts. The development process may include tasks such as building and training machine learning models, data collection and cleaning, and testing and optimizing the final product.

AI projects have gained significant traction across multiple sectors, including healthcare, finance, transportation, and retail, due to their potential to revolutionize business operations, improve productivity, reduce costs, and enhance customer service. If you're interested in diving into the world of AI, consider exploring an Artificial intelligence course to gain valuable insights and practical knowledge in this exciting field.

List of Top AI Projects with Source Code in 2024

Artificial Intelligence projects with source code are available on various platforms and can be used by beginners to understand the project flow and build their projects. Let us check the top AI project ideas with their technicalities along with their source code.

AI Project Ideas for Beginner & Intermediate

Here are some examples of AI project topics for beginners, ranging from simple to complex. When choosing a project, it's important to consider your interests, skills, available resources, and tools. These can be considered ideal AI projects for students in their final year and budding AI engineers.

1. Stock Prediction

• Language: Python
• Data set: CSV file
• Source code : Build Your First stock prediction model

The use of artificial intelligence, such as machine learning and deep learning, to forecast future price movements of stocks and other financial instruments is known as stock prediction. Stock prediction aims to use AI to build models that can analyze historical stock data, spot patterns and trends, and forecast future prices.

Several variables can impact stock prices, including news events, market mood, and economic data. As a result, it's crucial to consider these things while developing an AI based stock prediction model. This can be one of the artificial intelligence topics for the project.

2. Lane line detection while driving

• Data set: mp4 file
• Source code: Lane-lines-detection-using-Python-and-OpenCV

Lane line detection is the simple and AI beginners project. The method of detecting and tracking the lanes on a road while driving using a computer vision system is known as lane line detection while employing machine learning. This is an important use of machine learning in autonomous driving systems since it helps the car stay in its lane and prevent accidents.

Lane line identification faces several difficulties, including shifting lighting, shifting road markers, and collisions with other cars. Therefore, it's critical to create reliable machine-learning models to address these issues and deliver precise lane detection in practical settings.

Overall, machine learning-based lane line identification is a crucial computer vision application in autonomous driving systems that can potentially increase the safety and dependability of self-driving cars.

3. AI Health Engine

• Source code : Patient-Selection-for-Diabetes-Drug-Testing

Artificial intelligence (AI) in healthcare is called the "AI Health Engine." It involves analyzing vast amounts of health-related data, including health records, medical images, and genetic information, using machine learning algorithms, natural language processing, computer vision, and other AI technologies to enhance the health of patients, lower costs, and boost the effectiveness of the delivery of healthcare.

By offering better patient outcomes, personalized treatment options, and more accurate diagnoses, AI Health Engines have the potential to transform the healthcare industry completely. The privacy and security of patient data and ensuring that AI algorithms are accurate, dependable, and impartial must be overcome. Therefore, creating ethical and reliable AI Health Engines that can be applied to healthcare safely and efficiently is crucial.

4. AI-powered Search engine

• Data set: text file
• Source code : ai-powered-search

Source: Towards Data Science

An AI-powered search engine is a search engine that incorporates artificial intelligence (AI) technology, such as machine learning and NLP, to deliver more precise and customized search results. These search engines can process data and employ cutting-edge algorithms to decipher the purpose of a user's query and provide relevant results.

AI-driven search engines may deliver more precise and pertinent search results while providing every user with a more individualized search experience. By removing the need for users to modify their searches or sort through unnecessary outcomes manually, they can also help to increase search efficiency.

5. House Security

• Data set: image file
• Source code: Machine-Learning-Face-Recognition-using-openCV

Using artificial intelligence to monitor and secure a home is known as "house security with AI." AI-powered security systems can detect and analyze various events and activities, including motion, sound, and facial recognition, using a variety of sensors and cameras.

By offering more precise and reliable detection of intrusions and other security breaches, AI-powered security systems have the potential to improve home security. By interacting with other intelligent home systems and gadgets, they can also offer a user experience that is more practical and smoother.

6. Loan Eligibility Prediction

• Source code : Loan_Status_Prediction

Source: GeeksforGeeks

The goal of loan eligibility prediction using AI is to forecast the likelihood of loan approval for new applicants by analyzing historical data on borrowers and their loan applications. This can assist banks and other lenders in setting appropriate terms and conditions for accepted loans, as well as helping them make better decisions about whether to approve or reject loan applications.

The security and privacy of borrower data and preventing unintended outcomes like unintentionally barring specific borrower categories are obstacles to be addressed. Creating moral and open loan eligibility prediction systems that work for both lenders and borrowers is therefore crucial. This is one of the best AI projects.

Artificial Intelligence Project Ideas For Advanced Level

These are a few of the many cutting-edge AI initiatives you might consider. It's crucial to consider your hobbies and areas of skill while selecting advanced AI projects and the initiative's potential influence and worth to the larger community.

1. Resume Parser

• Source cod e: keras-english-resume-parser-and-analyzer

Source: DaXtra Technologies

An AI-powered tool called a resume parser pulls pertinent data from resumes or CVs and turns it into structured data. The structured data can be utilized for various tasks, including applicant tracking, hiring, and talent management. Developing a resume parser might be a challenging but rewarding endeavor that can assist businesses and organizations in automating their hiring and talent management procedures.

2. Animal Species Prediction

• Data set: PNG file
• Source code:  animal_detection

In machine learning and computer vision, predicting animal species includes creating an AI system to recognize an animal's species from an image. To reliably categorize animal species using visual characteristics, including shape, color, and texture, animal species prediction attempts to build a model that can do so.

Because it involves dealing with a vast and diverse range of animals with varying physical characteristics, predicting animal species is difficult. However, recent deep learning and computer vision developments have made significant advancements possible in this field.

3. Hidden Interfaces for Ambient Computing

• Source code:  Hidden Interfaces for Ambient Computing

User interfaces that are smoothly incorporated into the environment allow users to engage with ambient computing devices without requiring explicit actions or inputs. These interfaces are referred to as hidden interfaces for ambient computing. The goal of ambient computing devices is to give consumers a smooth and natural experience without forcing them to engage with the device directly. These devices are embedded into the surroundings.

Voice assistants, smart speakers, and intelligent displays are a few examples of hidden interfaces for ambient computing.

4. Improved Detection of Elusive Polyps

• Source code: Polyp-Segmentation-using-UNET-in-TensorFlow-2.0

Source: Science Direct

Artificial intelligence (AI) and computer vision are two methods for enhancing the detection of evasive polyps. Large datasets of colonoscopy images can be used to train AI systems to identify patterns and traits common to various polyp kinds. Computer vision techniques can also improve photographs' quality and highlight important details that human viewers might overlook.

The development of new imaging methods, such as high-definition colonoscopes, and the use of specialized dyes or markers that can aid in identifying polyps are two more strategies for enhancing the detection of elusive polyps.

5. Document Extraction using FormNet

• Data set: PDF file
• Source code: Representation-Learning-for-Information-Extraction

The information must be extracted from unstructured data, such as text documents, PDFs, or photos, to create structured data that may be used for analysis or processing. A deep learning model called FormNet was explicitly designed for extracting documents from scanned forms.

FormNet extracts fields from structured forms using a convolutional neural network (CNN) architecture. The model can learn the common patterns and features associated with various shapes and areas because it is trained on vast datasets of labeled forms.

Applications for document extraction using FormNet include data entry, processing invoices, and form recognition in sectors like healthcare, banking, and law. FormNet may significantly reduce the time and effort needed for human data entry, improve accuracy, and increase the effectiveness of corporate processes by automating the document extraction process.

6. Handwritten Notes recognition

• Source code:  SimpleHTR

Source: AmyGB.ai

Turning handwritten text or notes into computer-readable digital text is called handwritten note recognition. Optical character recognition (OCR) technology, which recognizes and converts handwritten text into a digital format using computer vision techniques, is often used for this operation.

Various machine learning and deep learning algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and recurrent neural networks (RNNs), can be used to achieve OCR technology for handwritten note recognition. These algorithms can learn the patterns and features of various handwriting styles since they have been trained on enormous datasets of labelled handwritten notes.

7. Consumer Sentiment Analysis

• Source code: Consumer Sentiment Analysis

Consumer sentiment analysis examines consumers' attitudes, feelings, and views toward a specific good, service, or brand. Natural language processing (NLP) and machine learning techniques are usually used in this analysis, giving businesses insightful knowledge on how their customers see them.

The analysis entails extracting and categorizing pertinent data, such as keywords, sentiment, emotions, and themes, to detect patterns and trends in consumer feedback. Businesses can utilize consumer sentiment analysis to raise customer happiness, enhance the quality of their goods and services, and gain a competitive advantage.

8. Real-time Translation Tool

• Source code:  Real-time-voice-recognition-based-language-translation-bot

A software program known as a real-time translation tool enables users to translate speech, writing, or other forms of communication from one language to another in real time. Real-time translation tools rely on machine learning and natural language processing (NLP) approaches to translate languages rapidly and reliably.

Various contexts, including international business meetings, travel, and communication with non-native speakers, can benefit from real-time translation tools. They allow users to connect efficiently with persons who speak different languages since they can translate text or speech in real time. These tools simplify connecting and collaborating worldwide by enhancing communication and lowering language barriers.

Open Source Artificial Intelligence Project Ideas: Additional Topics

Here are a few open source AI project suggestions that are popular right now on Google.ai and other sites of such nature:

Why Should You Work on AI Based Projects?

Working on Artificial intelligence based projects can be gratifying for several reasons, including:

• High demand: AI is a fast-expanding subject, and skilled individuals are in tall order. Gaining knowledge of AI can lead to various employment choices and job prospects.
• Innovation: AI initiatives frequently involve going beyond what is currently achievable, which results in fresh discoveries and advances in the area.
• Impact: AI can positively impact society, from healthcare and education to finance and transportation. You can make a meaningful contribution by working on AI-based projects.
• Personal growth: Working on AI-based projects can help you acquire new techniques and concepts in programming, data science, and machine learning, improving your personal and professional development.

Best Platforms to Work on AI Projects

To create machine learning models, these platforms offer a vast array of tools and resources, including pre-built algorithms, data visualization tools, and support for distributed computing. They also feature vibrant developer and research communities that exchange knowledge and support ongoing development. Future AI projects are all dependent on this platform.

Here are some of the top platforms to work on AI project Links:

• Scikit-learn
• Microsoft Cognitive Toolkit
• Apache MXNet

Elevate your expertise and stand out with a CBAP certificate . Unlock new career opportunities and succeed in the field of business analysis.

Learn AI the Smart Way!

Learning AI can be a challenging but worthwhile endeavor. Here are some pointers for clever AI learning:

• Begin with the fundamentals: Start by being familiar with the foundational ideas of AI, such as machine learning, deep learning, and neural networks.
• Take online classes: Work with real-world datasets to put your knowledge into practice. Using real-world datasets is an excellent method to put your knowledge into practice. KnowledgeHut Data Science Course provides online courses with thorough AI instruction.
• Create your projects: Creating your own Artificial Intelligence projects is an excellent opportunity to practice what you've learned and put it to the test.
• Emphasise problem-solving: You can develop the skills to manage challenging AI projects by emphasizing problem-solving and critical thinking.

Studying AI generally involves commitment, perseverance, and a readiness to pick things up quickly and adapt. Using these pointers, you can learn AI intelligently and successfully and accomplish your objectives in this fascinating and promptly expanding topic. 

Frequently Asked Questions (FAQs)

• Stock Prediction 
• Lane line detection while driving
• AI Health Engine
• AI-powered Search engine
• Loan Eligibility Prediction

Because they are relatively straightforward but still challenging enough to offer a worthwhile learning experience, these AI projects are great for beginners. They provide a solid foundation for anyone interested in learning AI because they cover many AI ideas and applications. The above can also be used as artificial intelligence research paper topics.

AI project failures can stem from various issues like poor planning, limited funding, subpar data quality, lack of domain knowledge, ineffective communication, unrealistic objectives, unvalidated assumptions, algorithm bias, ethical/legal issues, and changing business needs. Inadequate planning leads to unclear goals and insufficient resources, while poor data affects AI model accuracy. Insufficient expertise can lead to flawed algorithm selection, and poor communication causes misunderstandings and delays.

AI can be categorized into four types:

• Reactive machines: AI systems that respond to specific situations without using past experiences.
• Limited memory: AI that uses past information for decision-making but lacks critical thinking or long-term planning.
• Theory of mind: AI that understands others' emotions, thoughts, and intentions for informed decision-making.
• Self-aware: AI that is conscious of its own feelings and mental states, utilizing this for improved decisions and behavior adjustments.

You can take the following actions to launch your artificial intelligence career:

• Learn the fundamentals of computer science, statistics, and mathematics.
• Acquire knowledge of programming languages like Python, R.
• Learn how to use AI tools.
• Attend machine learning and AI boot camps or online courses from the  KnowledgeHut data science course .
• Take part in Kaggle tournaments to gain experience creating AI models.
• AI projects with source code can be used for learning

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Northeastern researcher creates AI tools that help gig workers solve problems

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Northeastern professor Saiph Savage works with gig workers to create AI-enhanced collective bargaining tools to change the power dynamic on the gig labor market.

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Researchers at Northeastern University have created artificial intelligence tools to help gig workers organize, collect their own job-related data, analyze their work problems and come up with a strategy to implement solutions.

“Building solid AI-enhanced solutions to enable gig workers’ collective action will pave the way for a fair and ethical gig economy — one with fair wages, humane working conditions and increased job security,” says Saiph Savage , assistant professor and director of the Civic A.I. Lab at Northeastern’s Khoury College of Computer Sciences.

Gig work is typically performed by a freelancer or independent contractor. It is used by rideshare apps such as Uber and Lyft, grocery-delivery services like Instacart, and Upwork, a marketplace that connects companies with temporary on-demand workers.

Gig work provides flexibility to workers and employers, Savage says, and offers economic opportunities to disadvantaged groups. But it also presents challenges for some gig workers such as irregular schedules and unsteady income, lack of job security, isolation and surveillance associated with online work.

The Massachusetts attorney general recently took Uber and Lyft to court over the employment status of its gig workers. The rideshare apps have threatened to leave markets if its drivers are converted from independent contractors to employees.

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For Savage, it’s all about giving gig workers the tools — in this case AI tools — to make the most-informed decisions with the greatest amount of data.

“At Northeastern, we have been developing a lot of AI tools that can support gig workers in their collective action to fight for better opportunities,” Savage says.

Tools recently released by the Civic A.I. Lab include GigSousveillance, GigSense and GigAction.

The tools use large language models and social theories to create “intelligent assistants” that help gig workers understand their collective problems, propose solutions and take collective action. The tools strengthen gig workers’ collective opinion and negotiation power, Savage says.

GigSousveillance allows workers to collect their own job-related data and use that data to measure how big a workplace problem has become.

GigSense equips workers with an online AI assistant that helps make sense of their workplace problems and strategically come up with solutions.

GigAction is an AI assistant that guides workers to implement the solutions.

The AI tools help reinforce gig workers’ collective identity, potentially inspiring them to undertake actions that benefit them as a whole, Savage says.

As part of her research, Savage conducted interviews and collaborative design sessions with gig workers from Upwork, Amazon Mechanical Turk, an Amazon platform that allows businesses to connect with a global workforce, and Toloka, a crowdsourcing platform. Later, she analyzed their answers about using the new AI tools. 

“We focus a lot on helping them to identify what are the key points that they should be aiming to negotiate,” Savage says. “What are the main problems that they should be aiming to address collectively, as well as what things should they aim to bargain for with the companies.”

Savage sees potential in attracting more gig workers through social media groups. 

“From there, we move them into our platforms,” she says.

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Women in AI: Arati Prabhakar thinks it's crucial to get AI 'right'

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To give AI-focused women academics and others their well-deserved — and overdue — time in the spotlight, TechCrunch has been publishing a series of interviews focused on remarkable women who’ve contributed to the AI revolution. We’re publishing these pieces throughout the year as the AI boom continues, highlighting key work that often goes unrecognized. Read more profiles here .

Arati Prabhakar is director of the White House Office of Science and Technology Policy and the science adviser to President Joe Biden . Previously, she served as director of the National Institute of Standards and Technology (NIST) — the first woman to hold the position — and director of DARPA, the U.S. Defense Advanced Research Projects Agency.

Prabhakar has a bachelor's degree in electrical engineering from Texas Tech University and earned her master's in electrical engineering from the California Institute of Technology. In 1984, she because the first woman to earn a doctorate in applied physics from Caltech.

Briefly, how did you get your start in AI?

I came in to lead DARPA in 2012, and that was a moment where machine learning-based AI was burgeoning. We did amazing work with AI, and it was everywhere, so that was the first clue that something big was afoot. I came into this role at the White House in October 2022, and a month later, ChatGPT came out and captured everyone’s imagination with generative AI. That created a moment that President Biden and Vice President Kamala Harris seized upon to get AI on the right track, and that’s been the work that we’ve done over the last year.

What attracted you to the field?

I love big, powerful technologies. They always bring a bright side and a dark side, and that’s certainly the case here. The most interesting work I get to do as a technical person is creating, wrangling and driving these technologies, because ultimately — if we get it right — that’s where progress comes from.

What advice would you give to women seeking to enter the AI field?

It’s the same advice that I would give anyone who wants to participate in AI. There are so many ways to make a contribution, from getting steeped in the technology and building it, to using it for so many different applications, to doing the work to make sure we manage AI’s risks and harms. Whatever you do, understand that this is a technology that brings bright and dark sides. Most of all, go do something big and useful, because this is the time!

What are some of the most pressing issues facing AI as it evolves?

What I am really interested in is: What are the most pressing issues for us as a nation as we drive this technology forward? So much good work has been done to get AI on the right track and manage risks. We have a lot more to do, but the president’s executive order and White House Office of Management and Budget's guidance to agencies about how to use AI responsibly are extremely important steps that put us on the right course.

And now I think the job is twofold. One is to make sure that AI does unfold in a responsible way so that it is safe, effective and trustworthy. The second is to use it to go big and to solve some of our great challenges. It has that potential for everything from health, to education, to decarbonizing our economy, to predicting the weather and so much more. That’s not going to happen automatically, but I think it’s going to be well worth the journey.

What are some issues AI users should be aware of?

AI is already in our lives. AI is serving up the ads that we see online and deciding what’s next in our feed. It’s behind the price you pay for an airline ticket. It might be behind the "yes" or "no" to your mortgage application. So the first thing is, just be aware of how much it is already in our environment. That can be good because of the creativity and the scale that’s possible. But that also comes with significant risks, and we all need to be smart users in a world that’s empowered — or driven, now — by AI.

What is the best way to responsibly build AI?

Like any potent technology, if your ambition is to use it to do something, you have to be responsible. That starts by recognizing that the power of these AI systems comes with enormous risks, and different kinds of risks depending on the application. We know you can use generative AI, for example, to boost creativity. But we also know it can warp our information environment. We know it can create safety and security problems.

There are many applications where AI allows us to be much more efficient and have scope, scale and reach that we’ve never had before. But you better make sure that it’s not embedding bias or destroying privacy along the way before you hit scale. And it has huge implications for work and for workers. If we get this right, it can empower workers by enabling them to do more and earn more, but that won’t happen unless we pay attention. And that’s what President Biden has been clear we must achieve: making sure that these technologies enable, not displace, workers.

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Speaking on stage near Alphabet’s headquarters in Mountain View, Calif., Pichai, Hassabis, and a parade of executives sought to show the company’s progress in the high-stakes AI competition against BigTech rivals such as Microsoft , Meta, and Amazon , as well as richly-funded startups like OpenAI, Anthropic, and Perplexity.

Google said that its Gemini technology is now incorporated, in some way or another, in all of Google’s key products — those that boast more than 2 billion users, including YouTube and Search. And Google unveiled a new, standalone Gemini app for users to play with the latest AI features.

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• 15 May 2024

‘Quantum internet’ demonstration in cities is most advanced yet

• Davide Castelvecchi

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A quantum network node at Delft University of Technology in the Netherlands. Credit: Marieke de Lorijn for QuTech

Three separate research groups have demonstrated quantum entanglement — in which two or more objects are linked so that they contain the same information even if they are far apart — over several kilometres of existing optical fibres in real urban areas. The feat is a key step towards a future quantum internet , a network that could allow information to be exchanged while encoded in quantum states.

Together, the experiments are “the most advanced demonstrations so far” of the technology needed for a quantum internet, says physicist Tracy Northup at the University of Innsbruck in Austria. Each of the three research teams — based in the United States, China and the Netherlands — was able to connect parts of a network using photons in the optical-fibre-friendly infrared part of the spectrum, which is a “major milestone”, says fellow Innsbruck physicist Simon Baier.

How to build a quantum internet

A quantum internet could enable any two users to establish almost unbreakable cryptographic keys to protect sensitive information . But full use of entanglement could do much more, such as connecting separate quantum computers into one larger, more powerful machine. The technology could also enable certain types of scientific experiment, for example by creating networks of optical telescopes that have the resolution of a single dish hundreds of kilometres wide.

Two of the studies 1 , 2 were published in Nature on 15 May. The third was described last month in a preprint posted on arXiv 3 , which has not yet been peer reviewed.

Impractical environment

Many of the technical steps for building a quantum internet have been demonstrated in the laboratory over the past decade or so. And researchers have shown that they can produce entangled photons using lasers in direct line of sight of each other, either in separate ground locations or on the ground and in space.

But going from the lab to a city environment is “a different beast”, says Ronald Hanson, a physicist who led the Dutch experiment 3 at the Delft University of Technology. To build a large-scale network, researchers agree that it will probably be necessary to use existing optical-fibre technology. The trouble is, quantum information is fragile and cannot be copied; it is often carried by individual photons, rather than by laser pulses that can be detected and then amplified and emitted again. This limits the entangled photons to travelling a few tens of kilometres before losses make the whole thing impractical. “They also are affected by temperature changes throughout the day — and even by wind, if they’re above ground,” says Northup. “That’s why generating entanglement across an actual city is a big deal.”

The three demonstrations each used different kinds of ‘quantum memory’ device to store a qubit, a physical system such as a photon or atom that can be in one of two states — akin to the ‘1’ or ‘0’ of ordinary computer bits — or in a combination, or ‘quantum superposition’, of the two possibilities.

The quantum internet has arrived (and it hasn’t)

In one of the Nature studies, led by Pan Jian-Wei at the University of Science and Technology of China (USTC) in Hefei, qubits were encoded in the collective states of clouds of rubidium atoms 1 . The qubits’ quantum states can be set using a single photon, or can be read out by ‘tickling’ the atomic cloud to emit a photon. Pan’s team had such quantum memories set up in three separate labs in the Hefei area. Each lab was connected by optical fibres to a central ‘photonic server’ around 10 kilometres away. Any two of these nodes could be put in an entangled state if the photons from the two atom clouds arrived at the server at exactly the same time.

By contrast, Hanson and his team established a link between individual nitrogen atoms embedded in small diamond crystals with qubits encoded in the electron states of the nitrogen and in the nuclear states of nearby carbon atoms 3 . Their optical fibre went from the university in Delft through a tortuous 25-kilometre path across the suburbs of The Hague to reach a second laboratory in the city.

In the US experiment, Mikhail Lukin, a physicist at Harvard University in Cambridge, Massachusetts, and his collaborators also used diamond-based devices, but with silicon atoms instead of nitrogen, making use of the quantum states of both an electron and a silicon nucleus 2 . Single atoms are less efficient than atomic ensembles at emitting photons on demand, but they are more versatile, because they can perform rudimentary quantum computations. “Basically, we entangled two small quantum computers,” says Lukin. The two diamond-based devices were in the same building at Harvard, but to mimic the conditions of a metropolitan network, the researchers used an optical fibre that snaked around the local Boston area. “It crosses the Charles River six times,” Lukin says.

Challenges ahead

The entanglement procedure used by the Chinese and the Dutch teams required photons to arrive at a central server with exquisite timing precision, which was one of the main challenges in the experiments. Lukin’s team used a protocol that does not require such fine-tuning: instead of entangling the qubits by getting them to emit photons, the researchers sent one photon to entangle itself with the silicon atom at the first node. The same photon then went around the fibre-optic loop and came back to graze the second silicon atom, thereby entangling it with the first.

Pan has calculated that at the current pace of advance, by the end of the decade his team should be able to establish entanglement over 1,000 kilometres of optical fibres using ten or so intermediate nodes, with a procedure called entanglement swapping . (At first, such a link would be very slow, creating perhaps one entanglement per second, he adds.) Pan is the leading researcher for a project using the satellite Micius , which demonstrated the first quantum-enabled communications in space, and he says there are plans for a follow-up mission.

“The step has now really been made out of the lab and into the field,” says Hanson. “It doesn’t mean it’s commercially useful yet, but it’s a big step.”

Nature 629 , 734-735 (2024)

doi: https://doi.org/10.1038/d41586-024-01445-2

Knaut, C. M. et al. Nature 629 , 573–578 (2024).

Article   PubMed   Google Scholar  

Liu, J. L. et al. Nature 629 , 579–585 (2024).

Stolk, A. J. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2404.03723 (2024).

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