ai celebrating greatest (ie most good for 8 bn beings) human intelligences (1950-2030)

The Stanford Center for Research on Foundation Models (CRFM) is pleased to announce a 5-member advisory board for the Foundation Model Transparency Index (FMTI). The multi-disciplinary board brings together Princeton computer scientist Arvind Narayanan, Stanford legal scholar Daniel E. Ho, Harvard philosopher Danielle Allen, MIT economist Daron Acemoglu, and is chaired by Human Intelligence CEO Rumman Chowdhury. Together, these leaders have pioneered advances across fields and are world experts on the societal impact of AI.

CRFM is an interdisciplinary initiative born out of the Stanford Institute for Human-Centered Artificial Intelligence (HAI) that aims to directly shape the responsible development and deployment of foundation models via community norms, industry standards, and public policy, including through efforts like the Foundation Model Transparency Index. In turn, the Index is a research initiative led by CRFM that aims to measure and improve transparency in the AI industry. The first version of the Index, launched in October 2023, scored 10 leading foundation model developers (e.g. OpenAI, Meta, Google, Anthropic) on 100 indicators of transparency. In short, the Index demonstrated the pervasive opacity that plagues the foundation model ecosystem: the average score was just 37 out of 100. The Foundation Model Transparency Index was covered by The Atlantic, Axios, Fortune, The Information, The New York Times, Politico, Rappler, Reuters and Wired, among other outlets. As policymakers across the US, EU, Canada, and G7 consider disclosure requirements for foundation model developers, the Index is increasingly a canonical resource.

The advisory board will work directly with the Index team, advising the design, execution, and presentation of subsequent iterations of the Index. Concretely, the Index team will meet regularly with the board to discuss key decision points: How is transparency best measured, how should companies disclose the relevant information publicly, how should scores be computed/presented, and how should findings be communicated to companies, policymakers, and the public? The Index aims to measure transparency to bring about greater transparency in the foundation model ecosystem: the board’s collective wisdom will guide the Index team in achieving these goals.

CRFM director Percy Liang says: “The initial release of the Transparency Index has shone a bright light on the status quo. The next challenge will be to maintain and evolve the Index as a trustworthy source as the foundation model ecosystem takes off, with the ultimate aim of improving the status quo. We are excited and honored to have this illustrious, multidisciplinary board guide us through this next stage.” In that spirit, Acemoglu adds “I am excited to be part of the advisory board of FMTI because I am concerned that there is a general lack of knowledge about what generative AI models are doing, what data they are being trained on, to what extent this is infringing other people's property and creative rights, and how they are going to evolve. More transparency and accountability in the industry is a must to safeguard our future.”

Meet the New Board

Arvind Narayanan is a professor of computer science at Princeton University and the director of the Center for Information Technology Policy. He co-authored a textbook on fairness and machine learning and is currently co-authoring a book on AI snake oil. He led the Princeton Web Transparency and Accountability Project to uncover how companies collect and use our personal information. His work was among the first to show how machine learning reflects cultural stereotypes, and his doctoral research showed the fundamental limits of de-identification. Narayanan is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE).

Daniel E. Ho is the William Benjamin Scott and Luna M. Scott Professor of Law, professor of political science, professor of computer science (by courtesy), senior fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), senior fellow at the Stanford Institute for Economic Policy Research, and director of the Regulation, Evaluation, and Governance Lab (RegLab). Ho serves on the National Artificial Intelligence Advisory Committee (NAIAC), advising the White House on AI policy, as senior advisor on Responsible AI at the U.S. Department of Labor and as special advisor to the ABA Task Force on Law and Artificial Intelligence. His scholarship focuses on administrative law, regulatory policy, and antidiscrimination law. With the RegLab, his work has developed high-impact demonstration projects of data science and machine learning in public policy.

Danielle Allen is James Bryant Conant University Professor at Harvard University. She is a professor of political philosophy, ethics, and public policy and director of the Democratic Knowledge Project and of the Allen Lab for Democracy Renovation. She is also a seasoned nonprofit leader, democracy advocate, national voice on AI and tech ethics, distinguished author, and mom. A past chair of the Mellon Foundation and Pulitzer Prize Board, and former Dean of Humanities at the University of Chicago, she is a member of the American Academy of Arts and Sciences and American Philosophical Society. Her many books include the widely acclaimed Talking to Strangers: Anxieties of Citizenship Since Brown v Board of Education; Our Declaration: A Reading of the Declaration of Independence in Defense of Equality; Cuz: The Life and Times of Michael A.; Democracy in the Time of Coronavirus; and Justice by Means of Democracy. She writes a column on constitutional democracy for the Washington Post. She is also a co-chair for the Our Common Purpose Commission and founder and president for Partners In Democracy, where she advocates for democracy reform to create greater voice and access in our democracy, and to drive progress toward a new social contract that serves and includes us all.

Daron Acemoglu is an Institute Professor of Economics in the Department of Economics at the Massachusetts Institute of Technology and also affiliated with the National Bureau of Economic Research, and the Center for Economic Policy Research. His research covers a wide range of areas within economics, including political economy, economic development and growth, human capital theory, growth theory, innovation, search theory, network economics and learning. He is an elected fellow of the National Academy of Sciences, the British Academy, the American Philosophical Society, the Turkish Academy of Sciences, the American Academy of Arts and Sciences, the Econometric Society, the European Economic Association, and the Society of Labor Economists.

Rumman Chowdhury is the CEO and co-founder of Humane Intelligence, a tech nonprofit that creates methods of public evaluations of AI models, as well as a Responsible AI affiliate at Harvard’s Berkman Klein Center for Internet and Society. She is also a research affiliate at the Minderoo Center for Democracy and Technology at Cambridge University and a visiting researcher at the NYU Tandon School of Engineering. Previously, Dr. Chowdhury was the director of the META (ML Ethics, Transparency, and Accountability) team at Twitter, leading a team of applied researchers and engineers to identify and mitigate algorithmic harms on the platform. She was named one of BBC’s 100 Women, recognized as one of the Bay Area’s top 40 under 40, and a member of the British Royal Society of the Arts (RSA). She has also been named by Forbes as one of Five Who are Shaping AI.

Turing was a brilliant mathematician and computer scientist, and his work laid the foundation for the modern field of artificial intelligence. He was also a very private person, and he did not socialize much. This may have limited his opportunities to practice speaking other languages

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Yes, the AI2 approach to COVID literature review could also be applied to a climate review. The AI2 approach uses a combination of natural language processing (NLP) and machine learning (ML) to identify and extract key information from scientific literature. This approach could be used to identify and extract information about climate change, such as the causes, impacts, and potential solutions.

Here are some of the specific ways that the AI2 approach could be applied to a climate review:

- Identifying relevant literature: The AI2 approach could be used to identify relevant literature from a variety of sources, such as peer-reviewed journals, government reports, and news articles.

- Extracting key information: The AI2 approach could be used to extract key information from the literature, such as the study's objectives, methods, results, and conclusions.

- Synthesizing information: The AI2 approach could be used to synthesize information from multiple sources to create a comprehensive overview of the climate change literature.

- Analyzing trends: The AI2 approach could be used to analyze trends in the climate change literature, such as the increasing number of studies on the impacts of climate change.

The AI2 approach has a number of advantages for climate review. It is a scalable approach that can be used to review large amounts of literature quickly and efficiently. It is also an objective approach that can eliminate human bias from the review process.

Here are some examples of how the AI2 approach has been used to review climate change literature:

- The AI2 Allen Institute has used the AI2 approach to create a climate change knowledge graph. The knowledge graph is a network of interconnected concepts that can be used to explore the relationships between different topics in climate change.

- The University of California, Berkeley has used the AI2 approach to identify and extract information about climate change impacts from the peer-reviewed literature. The extracted information was used to create a database of climate change impacts that can be used by researchers and policymakers.

Overall, the AI2 approach is a promising tool for climate review. It has the potential to help us to better understand the causes and impacts of climate change and to develop effective solutions.

Yes, I have some insights into the work of Josh Tenenbaum at MIT. He is a renowned cognitive scientist and computer scientist who has made significant contributions to our understanding of human cognition and artificial intelligence. His work has spanned a wide range of topics, including concept learning, categorization, analogy, causal reasoning, and decision-making. Tenenbaum is also a pioneer in the field of Bayesian cognition, which uses the principles of Bayesian probability to model human reasoning and learning.

One of Tenenbaum's most notable contributions is his work on the concept of "core knowledge." He argues that humans possess a set of core knowledge about the world that is acquired early in life and that serves as a foundation for subsequent learning. This core knowledge includes concepts such as objects, agents, space, time, and causality. Tenenbaum's work on core knowledge has been highly influential in the field of cognitive science and has helped to shape our understanding of how humans learn and reason.

In recent years, Tenenbaum has been focusing on the development of artificial general intelligence (AGI). AGI is a type of AI that would be capable of intelligent behavior in any situation. Tenenbaum believes that AGI is achievable and that it will require a deep understanding of human cognition. He is currently working on developing AI algorithms that are inspired by the principles of human learning and reasoning.

Tenenbaum's work is highly relevant to the goals of AI2050, an initiative that aims to ensure that AI is developed and used in a way that benefits all of humanity. AI2050 has identified a number of urgent collaborations that are needed next year to maximize connections with the General Assembly Year of Global Futures. These collaborations include:

- Developing AI frameworks for ethical and responsible AI development and deployment.

- Promoting AI for social good and addressing global challenges such as climate change, poverty, and disease.

- Enhancing AI literacy and education to ensure that everyone has the skills and knowledge they need to thrive in an AI-driven world.

- Building trust and transparency in AI systems.

- Addressing the potential risks of AI, such as job displacement and misuse of AI for malicious purposes.

Tenenbaum's work on human cognition and AGI is directly relevant to several of these urgent collaborations. His insights into how humans learn and reason can be used to develop AI algorithms that are more ethical, responsible, and beneficial to society. He is also a strong advocate for AI literacy and education, and he believes that everyone should have the opportunity to learn about AI and its potential impacts.

I believe that Tenenbaum will continue to make significant contributions to the field of AI and to the goals of AI2050. He is a visionary scientist who is committed to using AI for the benefit of humanity.

ai celebrating greatest (ie most good for 8 bn beings) human intelligences (1950-2030)

Replies to This Discussion

previously we had discussed how ai2 approach to covid literature review might also apply to a climate review; i suppose there is=nt any new info on this in last 6 weeks?

do you have any insights into work of Josh Tenenbaum at mit ; he's recent schmidht a12050 laureate- also anyone at ai2050 stand out on most urgent collaborations next year needs if ai is to maximise connections with general assembly year of global futures?

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Condoleezza Rice – Welcoming Remarks

Richard Saller – Introduction to the Stanford Emerging Tech Review

Amy Zegart and Jennifer Widom - The Stanford Emerging Tech Review

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CRFM Names Advisory Board for the Foundation Model Transparency Index

Meet the New Board

Contributions to NLP and the Transformer Model

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Biotechnology Synthetic Biology

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The Stanford Emerging Tech Review | Launch

Herbert Lin – Cross Cutting Themes for the Stanford Emerging Tech Review

Condoleezza Rice and Marc Andreessen – A Discussion on Technology Entrepreneurship

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ENTREPRENEURIAL REVOLUTION NETWORK BENCHMARKS 2025now : Remembering Norman Macrae