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Key Takeaways

•   Artificial intelligence (AI) is a foundational technology that is supercharging other scientific fields and, like electricity and the internet, has the potential to transform societies, economies, and politics worldwide.

•   Despite rapid progress in the past several years, even the most advanced AI still has many failure modes that are unpredictable, not widely appreciated, not easily fixed, not explainable, and capable of leading to unintended consequences.

•   Mandatory governance regimes for AI, even those to stave off catastrophic risks, will face stiff opposition from AI researchers and companies, but voluntary regimes calling for self-governance are more likely to gain support.

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Overview

Artificial intelligence (AI) is the ability of computers to perform functions associated with the human brain, including perceiving, reasoning, learning, interacting, problem solving, and exercising creativity. AI promises to be a fundamental enabler of technological advancement and progress in many fields, arguably as important as electricity or the internet. In 2024, the Nobel Prizes for Physics and Chemistry were awarded for work intimately related to AI.

Three of the most important subfields of AI are computer vision, machine learning, and natural language processing. The boundaries between them are often fluid.

  • Computer vision (CV) enables machines to recognize and understand visual information, convert pictures and videos into data, and make decisions based on the results. 

  • Machine learning (ML) enables computers to perform tasks without explicit instructions, often by generalizing from patterns in data. ML includes deep learning that relies on multilayered artificial neural networks to model and understand complex relationships within data. 

  • Natural language processing (NLP) equips machines with capabilities to understand, interpret, and produce spoken words and written texts.

Although AI draws on other subfi elds, it is mostly based on machine learning (ML), which requires data and computing power, often on an enormous scale. Data can take various forms, including text, images, videos, sensor readings, and more. The quality and quantity of data play a crucial role in determining the performance and capabilities of AI models. Models may generate inaccurate or biased outcomes, especially in the absence of suffi cient high-quality data. Furthermore, the hardware costs of training leading AI models are substantial. Currently, only a select number of large US companies have the resources to build cutting-edge models from scratch.

 

Key Developments

Dominating the AI conversation in 2024 were foundation models, which are large-scale systems trained on very large volumes of diverse data. Such training endows them with broad capabilities, and they can apply knowledge learned in one context to a different context, making them more flexible and efficient than traditional task-specific models.

Large language models (LLMs) are the most familiar type of foundation model and are trained on very large amounts of text. LLMs are an example of generative AI, which can produce new material based on its training and the inputs it is given using statistical prediction about what other words are likely to be found immediately after the occurrence of certain words. These models generate linguistic output surprisingly similar to that of humans across a wide range of subjects, including computer code, poetry, legal case summaries, and medical advice. Specialized foundation models have also been developed in other modalities such as audio, video, and images.

 

Over the Horizon

AI OPPORTUNITIES    

AI users will not be limited to those with specialized training; instead, the average person will interact directly with sophisticated AI applications for a multitude of everyday activities. While AI can automate a wide range of tasks, it promises to enable people to do what they are best at doing. AI systems can work alongside humans, complementing and assisting them. Key sectors poised to take advantage of the technology include healthcare, agriculture, law, and logistics and transportation. 

AI Risks 

One challenge of implementing AI is managing the risks associated with the technology. Some of the known issues with leading AI models include: 

  • Explainability Today’s AI is for the most part incapable of explaining how it arrives at a specific conclusion. Explanations are not always necessary, but in cases such as medical decision-making, they may be critical.

  • Bias and fairness Machine learning models are trained on existing datasets, which means that any bias in the data can skew results.

  • Vulnerability to spoofing For many AI models, data inputs can be tweaked to fool them into drawing false conclusions. 

  • Deepfakes AI provides the capability for generating highly realistic but entirely inauthentic audio and video, with concerning implications for courtroom evidence and political deception.

  • Overtrust As trust in AI grows, the risk of overlooking errors, mishaps, and unforeseen incidents also increases. 

  • Hallucinations AI models can generate results or answers that seem plausible but are completely made up, incorrect, or both.

A second challenge is the future of work in an AI-enabled context. AI models have already demonstrated how they can be used in a wide variety of fields, including law, customer support, coding, and journalism, leading to concerns that the impact of AI on employment will be substantial, especially on jobs that involve knowledge work. In some cases, the technology will help workers to increase their productivity and job satisfaction. In others, AI will lead to job losses—and it is not yet clear what new jobs will arise to take their place.

 

POLICY, LEGAL & REGULATORY ISSUES

Research on foundational AI technologies is difficult to regulate, even among likeminded nations. It is even more difficult, and may well be impossible, to reach agreement between nations that regard each other as strategic competitors and adversaries. The same logic applies to voluntary restrictions on research by companies that compete with each other. Regulation of specific applications of AI may be more easily implemented, in part because of existing regulatory frameworks in domains such as healthcare, finance, and law.

Over the past couple of years, nations have explored possible governance regimes. In the United States, the president’s Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence was issued on October 30, 2023. In November 2023 and May 2024, the European Union and twenty-eight nations collectively endorsed international cooperation to manage risks associated with highly capable general-purpose AI models. The European Union’s AI Act entered into force in August 2024.

Report Preview: Artificial Intelligence

Faculty Council Advisor

fei-fei-li_profilephoto.jpg
Fei-Fei Li
Author
Fei-Fei Li

Fei-Fei Li is the Sequoia Professor of Computer Science and professor, by courtesy, of psychology at Stanford University. She serves as codirector of Stanford’s Human-Centered AI Institute and as an affiliated faculty at Stanford Bio-X. Her current research includes cognitively inspired AI, machine learning, computer vision, and ambient intelligent systems for health-care delivery. She received her PhD in electrical engineering from the California Institute of Technology.

View Bio
fei-fei-li_profilephoto.jpg
Fei-Fei Li

Fei-Fei Li is the Sequoia Professor of Computer Science and professor, by courtesy, of psychology at Stanford University. She serves as codirector of Stanford’s Human-Centered AI Institute and as an affiliated faculty at Stanford Bio-X. Her current research includes cognitively inspired AI, machine learning, computer vision, and ambient intelligent systems for health-care delivery. She received her PhD in electrical engineering from the California Institute of Technology.

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A human story. Stanford professor Fei-Fei Li is an AI technologist known for her work to make the fast-moving technology more human, a crusade she launched via a widely-read 2018 New York Times op-ed. When she started to write a book, she focused on that work—…

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Fei-Fei Li Started an AI Revolution by Seeing Like an Algorithm

Researcher Fei-Fei Li’s ImageNet project provided the feedstock for the deep learning boom that brought the world ChatGPT and other world-changing AI systems.

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This chapter explores applications from each of the 10 technology fields described in the report as they may relate to five important policy themes: economic growth, national security, environmental and energy sustainability, health and medicine, and civil…

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One of the most important and unusual hallmarks of this moment is convergence: emerging technologies are intersecting and interacting in a host of ways, with important implications for policy. This chapter identifies themes and commonalities that cut across…

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This report offers an easy-to-use reference tool that harnesses the expertise of Stanford University’s leading science and engineering faculty in ten major technological areas: artificial intelligence, biotechnology and synthetic biology, cryptography,…

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