>3/18 24: Similarities between Electronic Computers and the Human Brain: Thank you Jensen Huang for best week of Learning since John Von Neumann shared with The Economist 1956 notes Computer & The Brain
HAPPY 2024: in this 74th year since The Economist started mediating futures of brainworking machines clued by the 3 maths greats NET (Neumann, Einstein, Turing) people seem to be chatting about 5 wholly different sorts of AI. 1BAD: The worst tech system designers don't deserve inclusion in human intel at all, and as Hoover's Condoleezza Rice . 2 reports their work is result of 10 compound techs of which Ai is but one. Those worst for world system designs may use media to lie or multiply hate or hack, and to perpetuate tribal wars and increase trade in arms. Sadly bad versions of tv media began in USA early 1960s when it turned out what had been the nation's first major export crop, tobacco, was a killer. Please note for a long time farmers did not know bac was bad: western HIStory is full of ignorances which lawyer-dominated societies then cover up once inconvenient system truths are seen. A second AI ecommerce type (now 25 years exponential development strong) ; this involves ever more powerful algorithms applied to a company's data platform that can be app'd to hollow out community making relatively few people richer and richer, or the reverse. You can test a nation's use of this ai by seeing if efinance has invested in the poorest or historically most disconnected - see eg bangladesh's bklash, one of the most populous digital cash systems . Digital money is far cheaper to distribute let alone to manually account for so power AI offers lots of lessons but whether its good or not depends in part on whether there are enough engineers in gov & public service to see ahead of what needs regulating. There are 2 very good ai's which have only scaled in recent years that certainly dont need regulating by non engineers and one curious ai which was presented to congress in 2018 but which was left to multiply at least 100 variants today the so-called chats or LLMs. Lets look at the 2 very good ai's first because frankly if your community is concerned about any extinction risks these AI may most likely save you, One I call science AI and frankly in the west one team is so far ahead that we should count ourselves lucky that its originator Hassabis has mixed wealth and societal growth. His deep mind merged with google to make wealth but open sourced the 200 million protein databank equivalent to a billion hours of doctorate time- so now's the time for biotech to save humanity if it ever does. Alongside this the second very good AI graviates around Fei-Fei Li) in developing 20 million imagenet database so that annual competitions training computers to see 20000 of the most everyday sights we humans view around the world including things and life-forms such as nature's plants and animals. Today, students no longer need to go back to 0.1 programming to ask computer about any of these objects; nor do robots or and autonomous vehicles - see fei-fei li's book worlds i see which is published in melinda gates Entrepreneurial Revolution of girl empowerment
EW::ED , VN Hypothesis: in 21st C brainworking worlds how people's times & data are spent is foundational to place's community health, energy and so natural capacity to grow/destroy wealth -thus species will depend on whether 1000 mother tongue language model mediates intelligence/maths so all communities cooperatively celebrate lifetimes and diversity's deep data ) . Check out "Moore exponential patterns" at year 73 of celebrating Game : Architect Intelligence (Ai) - players welcome .. some jargon

Tuesday, May 16, 2023

AI Serial Giant Leapers

Stanford President Tessier + 2 Directors appointed to lead Stanford HAI Fei-Fei Li and John Etchemendy

Reid Hoffman panel moderator of Demis Hassibis Jeff Dean Chris Manning Alison Gopnik

Michael Frank Percy Liang   Serya Ganguli

Bill Gates with Amy Jin and Stephani Tena-Meza - full speakers  -full planning committee


 Lead Peers of Von Neumann had all gone by his death in  1957 but they had left behind at least 3 innovations streams in one: hardware, coiding or software, how would human brains behaviors change the more time spent in digital world-one reminder of slow slow quick quick slow is schools- i was in last gen to use slide rulers; when i git to high school my pride & joy a pocket calculator; whilst i saw people pumnching cards to program mainframes at university in europe at least online terminals linking mini computer arrived circa 1971; and for much longer than that those who did ai were programing in heuristics from experts; this isn't what breakthrough AI has done since 2006 when eg fei-fei li started training a computer's vision analogously to a baby's brain- it took one heck of a lot of computing power to do this; and although the mobile web2 era eclipsted invenet in deep ai fir 7 or more yeras its HAI at Stanford 2019 that I'd suggest as dual benchmark for magic leaps beyond human brainpower alone and desire to maintain AI as tool to augment what humans do best

upd  NAIRR OCT 2021

This month, researchers affiliated with the Stanford Institute for Human-Centered Artificial Intelligence released a blueprint for how to build a National AI Research Resource (NAIRR), a system that would allow the broader AI academic research community to access the expensive computing and data resources to conduct fundamental and non-commercial AI research. The report, a culmination of a multidisciplinary two-quarter practicum offered at Stanford Law School and based on dozens of interviews with leading computer scientists, government officials, and policy experts, outlines the necessary steps to create this resource


  1. Automation and Robotics, Economy and Markets, Human Reasoning: Opening the Gate part 1 https://hai.stanford.edu/news/opening-gate

    Stanford’s new Institute for Human-Centered Artificial Intelligence aims to fundamentally change the field of AI by integrating a wide range of disciplines and prioritizing true diversity of thought.

    Mar 17, 2019 | Fei-Fei Li and John Etchemendy
    It all started in Fei-Fei’s driveway.

    It was the summer of 2016.

    “John,” she said, “As Stanford’s provost, you’ve led an effort to draw an arrow from technology to the humanities, to help humanists innovate their methodology.”

    “It’s time to build another arrow coming back the other direction. It should become a complete feedback loop. We need to bring the humanities and social thinking into tech.”

    She went on to explain an epiphany she had recently had — a problem she could no longer ignore. The people building the future all seemed to come from similar backgrounds: math, computer science and engineering. There were not enough philosophers, historians or behavioral scientists influencing new technology. There were very few women or people from underrepresented groups. “The way we educate and promote technology is not inspiring to enough people. So much of the discussion about AI is focused narrowly around engineering and algorithms,” she said. “We need a broader discussion: something deeper, something linked to our collective future. And even more importantly, that broader discussion and mindset will bring us a much more human-centered technology to make life better for everyone.”

    Standing in Fei-Fei’s driveway, John saw the vision clearly. As a mathematical logician, he had been actively following the progress of AI for decades; as a philosopher, he understood the importance of the humanities as a guide to what we create. It was obvious that not only would AI be foundational to the future — its development was suddenly, drastically accelerating.

    If guided properly, AI could have a profound, positive impact on people’s lives: It could help mitigate the effects of climate change; aid in the prevention and early detection of disease; make it possible to deliver quality medical care to more people; help us find ways to provide better access to clean water and healthy food; contribute to the development of personalized education; help billions of people out of poverty and help solve many other challenges we face as a society.


  2. We believe AI can and should be collaborative, augmentative, and enhancing to human productivity and the quality of our work and life.
    But AI could also exacerbate existing problems, such as income inequality and systemic bias. In the past couple of years, the tech industry has struggled through a dark time. Multiple companies violated the trust and privacy of their customers, communities and employees. Others released products into the world that were not properly safety tested. Some applications of AI turned out to be biased against women and people of color. Still more led to other harmful unintended consequences. Some hoped the technology would replace human workers, not seeing the opportunity to augment them.

    That day began a conversation that continued over many months. We discovered that we both had been on a similar quest throughout our careers: to discover how the mind works — Fei-Fei from the perspective of cognitive science and AI, and John from the perspective of philosophy.

    Meanwhile, Fei-Fei took off for a sabbatical to Google, where she became Chief Scientist of AI at Google Cloud. During her time there, she saw the massive investments the technology industry was making in AI, and worked with many customers from every industry that are in great need of a digital and AI transformation. She became even more committed to the idea of creating a human-centered AI institute at Stanford.

    Our Mission is to advance AI research, education, policy and practice to improve the human condition.
    In 2017, Fei-Fei began discussing the future of AI with Marc Tessier-Lavigne, the university’s new president and a neuroscientist. She brought in Stanford Computer Science Professors James Landay, who specializes in human/computer interaction, and Chris Manning, who specializes in machine learning and linguistics, to further develop the idea. When John stepped down as Provost in 2017, Fei-Fei asked him to co-direct the undertaking. Together they brought in Russ Altman, a Stanford Professor of Bioengineering and Data Science; Susan Athey, an Economics of Technology Professor at Stanford Graduate School of Business; Surya Ganguli, a Stanford Professor of Applied Physics and Neurobiology; and Rob Reich, a Stanford Professor of Political Science and Philosophy. Encouraged by Stanford’s school deans, especially Jon Levin, Jennifer Widom and Debra Satz (Business, Engineering and Humanities and Sciences), the new team evangelized the idea with colleagues and friends. Soon dozens of accomplished faculty members were contributing their perspectives.

    Nearly three years and many deep conversations later, we are humbled and proud to announce the official launch of The Stanford Institute for Human-Centered Artificial Intelligence (HAI).

    At HAI our work is guided by three principles: that we must study and forecast AI’s Human impact, and guide its development in light of that impact; that AI applications should Augment human capabilities, not replace humans; and that we must develop Intelligence as subtle and nuanced as human intelligence.

    Our aim is for Stanford HAI to become an interdisciplinary, global hub for AI learners, researchers, developers, builders and users from academia, government and industry, as well as leaders and policymakers who want to understand and influence AI’s impact and potential.
    These principles extend the discipline of AI far beyond the confines of engineering. Understanding its impact requires expertise from the humanities and social sciences; mitigating that impact demands insights from economics and education; and guiding it requires scholars of law, policy and ethics. Just so, designing applications to augment human capacities calls for collaborations that reach from engineering to medicine to the arts and design. And creating intelligence with the flexibility, nuance and depth of our own will require inspiration from neuroscience, psychology and cognitive science.


  3. Stanford HAI has sponsored multiple symposia bringing together experts on topics including the Future of Work, and AI, Humanities and the Arts. This summer we will be launching our first Executive Education program in partnership with the Graduate School of Business and our first Congressional Bootcamp in partnership with the Freeman-Spogli Institute for International Studies. We are also sponsoring a summer AI research internship program for “graduates” of the AI4All diversity education program, to enable these young people to maintain their interests and hone their skills.
    We are now selecting finalists for three fellowship programs that we have launched in partnership with other Stanford units: HAI Ethics Fellows (with the McCoy Family Center for Ethics in Society), HAI Journalism Fellows (with the John S. Knight Journalism Fellows Program), and HAI International Security Fellows (with the Center for International Security and Cooperation). The winners of these fellowships will join the HAI community in the Fall. We will also soon be announcing the appointments of our first HAI Journalist-in-Residence and our first HAI Artist-in-Residence, both of whom will join us this Fall, as well as our first class of HAI Engineering Fellows.


    It has been quite a journey since that first meeting in Fei-Fei’s driveway three years ago. That meeting happened because John was buying a house adjoining Fei-Fei’s back yard. We became neighbors, and soon after John moved in, he replaced the old fence between our houses. In one spot, connecting Fei-Fei’s back yard to John’s side yard, he built a gate. Little did we know how well-trodden the path between our houses would become thanks to HAI! We hope Stanford HAI will provide the same opportunity for partnership, trust and collaboration to many, many others. Please join us in our quest to improve the human condition through Human-centered AI.

  4. according to Bard May 2023 these are examples of partners of Stanford HAI Some of Stanford HAI's key partners include:

    The University of Oxford
    The University of Cambridge
    The Massachusetts Institute of Technology
    The University of California, Berkeley
    The University of Toronto
    The National University of Singapore
    The Tsinghua University
    The Peking University
    The Indian Institute of Technology Bombay
    The Indian Institute of Science
    These partnerships are helping to create a global network of experts who are committed to using AI for good. By working together, we can ensure that AI is used to solve the world's most pressing problems, and that it does not become a threat to humanity.

    In addition to these formal partnerships, Stanford HAI also works with a number of other organizations to promote the responsible development of AI. These organizations include:

    The World Economic Forum
    The Aspen Institute
    The Brookings Institution
    The Carnegie Endowment for International Peace
    The Center for American Progress
    The Center for Strategic and International Studies
    The Council on Foreign Relations
    The New America Foundation
    The OpenAI Foundation
    The Partnership on AI
    These organizations are playing a critical role in shaping the future of AI. By working together, we can ensure that AI is used to create a better world for all.