Fei-Fei seeing worlds of humanity's greatest intelligence ecosystem linker since neumann-einstein-turing

[chris macrae]

AI probably would not be what King Charles has called the most exciting opportunity humanity has ever seen without DR Fei-Fei Li. To clarify:

big tech has been accelerating its consequences on all of us as digital computation and webs have surrounded us but it was Li who designed an open competition on training smart devices to recognised the 20000 identiies we humans work and play with most - from nature's animals vegetables and minerals to tools and man made constructs of every kind though to parts of the body and body language - eg facial expressions which can indicate how individuals or crowds are being energised. She was one of two 20-something graduate students who recoonected neuro-cyber science with the expectations of 1950s NET (Neumann Einstein Ruring) who gave humanity brainworking machines. The POther was demis hassabis who has trained computers to help humans see the 250000 proteins that nature uses as building blocks of every living form. There were primarily 3 teachers Lecun Hinton Bengio whos studnets developed maths algoritjms needed to bring Li's 15 years of work from an idea to reality. Having seen machines could be trained the way Li and Abed have advanced,    in the last few years huge investments have been put into the superchats or large language models which identofy with potentially every publication or expert briefiing ever reocdred. While LLMs are exciting to chat with already I wouldnt let my life depend on one fpor perhaps 10 years as nobody quite knows when   there will be a missing link in their learning sometimes of quite a basic sort

Meanwhile most of Li's (and perhaps thousands of people who have built on her identity) work can be certified as reliable (although there are others who deliberately abuse good ai just as since teh start of the web age we have had to content with spam, bad hackers and indeed automation of dangerous things such as arms. However please note that big tech has arguably been doing more of the bad stuff than anything that has emerged because of Li. Also her work can be looked at in specific ways as well as the general opportunity to get machines to help with anything we humans surround ourseleves with

Frtom 2015 sponsored firts by melinda gates and jensen hiang builder of the biggest chips, Li has distributed at least 10 education curricul- some like dnace and art can be enjoyed by 1st grade students and teachers up - in fact any age arts can start to be enjoyed. Others introduce ethoics and emotionally intelligent chalenges- before LI came alomng some teachers had decoded these kinds of skills are important from around 3rd grade up. Basically anything whoch involves learning or serving in teams benefits from desigbing ways to prevent chets or bullies spoiling life for the rest of us. And yes some involve the maths (neural netwiorking) algorithms most needed to be a 21st C engineer. Some of the applications Fei-fei li has spent most time on include health service  and national development opportunities NAIRR but most of all she has enbcouraged every university discipline to renew how i can advance humanity - she co-leads stanfird HAI - human centric AI. 

She also supports deep diversity of engineering - femle engineers, bridging digital divides across all skin cvolors or communities that tech has previously missed out. Melinda Gates started publishing the world's most extroridnary library on women empowering the wporld 3 years- this school years's additions are Fei-Fei Li's worlds i see and womens basketbvall supoer AJa Wilsdon - dear black girls. Can every child now twin traditional celebrities with those who will help them develop the most from life - the opportunity many have called the rtevolution in education soon to be realsies- everyone having their own personalised ;erning agent.

https://www.theguardian.com/technology/2023/nov/05/ai-pioneer-fei-f... discussion docs with fei-fei li  https://www.cbsnews.com/news/godmother-of-a-i-on-technology-develop... https://www.ft.com/content/de3f4813-4f36-40c7-9d50-980144674d87

fireside 2021

lectures 4-2015

14-2018

fei-fei li appears to have open sourced over 10 lectures each with over 100 slides co-presented by her team -eg this is lecture7

exercise -fei-fei li is 28 years younger than hinton- her 9 year championship imagenet 9stanfird 2009-2017) nombriefly inate hinton as year 3/4 winner - out of which he got nobel prize for deep learning (toronto became an epicentre of ai until trudua smashed that in 2017 after being told by world's number 1 luddite Donald that canada would be deplugged from defence data unless trudeau stopped good ai as part of stop 5g-

at a time 2018 https://www.c-span.org/video/?447599-1/artificial-intelligence that congress told a trillion times more computation capacity is coming - you aint seen what ai can doi yet) but she kept connecting stanfird as last human gravity of ai- it was actually fei-fei and hassabis phds that reconnecetd neurla networks beloved by neumann and turing after 50 years in the dark apart from some relatively unknown academics (now famous hinton, lecun - unless you know beter- in any event hinton has said he's no lomger able to program and wil spend his last years as philosopher out of london; fei-fei cheerleads everyone - geofrrey lets do one last world tour 

perhaps proof can come from tracking what cooperations she has lifted - apparently there are 175 + partners of human ai since stanford HAI rebranded how to connect FFL and inspirationnal worlds she frees - but lets see what her linked in and twitter as well as diary at stanfodr hai show- from nov 7 her biography of what to see is marketed by womens greatest philanthropy coordinator melinda gates as part of the mosr timely new books any school year has seen

Fei-Fei LiFei-Fei Li• Following• FollowingAI Researcher & Professor, Stanford University; Co-Director, Stanford Human-Centered AI Institute; Scientific Partner, Radical Ventures; Author ("The Worlds I See")AI Researcher & Professor, Stanford University; Co-Director, Stanford Human-Centered AI Institute; Scientific Partner, Radical Ventures; Author ("The Worlds I See"

some dates approx

10/16 debate on most immediate, and potentially catastrophic, risks posed by AI? According to pioneering AI researcher, Dr. Fei-Fei Li, they include disinformation, polarization, biases, a loss of privacy and job losses that could lead to unrest

• karaswisher co-host of the Pivot podcast for New York Media and also a new one coming in the fall, as well as executive producer of the annual Code Conference.

- my view I believe strongly all of us, especially those creating, deploying, and making decisions using AI, should work to ensure a human-centered approach. I know it's hard — I feel the challenges as an AI technologist, a woman of color, and an immigrant every day. But I wrote my book worlds i see for the younger generations, and we need to continue to inspire hope for all of them.

\\

Very much looking forward to being part of The Wall Street Journal's #wsjtechlive event! more at diary of ai superheroines

\\ 10'13 I’m suddenly feeling underdressed! Today HAI added new member LVMH to our Corporate Affiliate Program.

\\10'11 My dear friend Reid Hoffman invited me on his podcast, Masters of Scale, to chat about what he calls traditional AI (I guess that’s now anything other than generative AI? . We talked about one of my passion projects - AI and healthcare.

\\10'10 This is such an honor! As a young immigrant, Princeton University seemed like a place that only existed in my dreams. And as a faculty member, I actually began the project that became ImageNet. Now to be receiving its Woodrow Wilson Award Dei-Fei Li 99 and ornthologist john fitzpatrick 78

\\10/8  

)

1w 

Love this piece in The Wall Street Journal with Stanford University School of Medicine Dean Lloyd Minor discussing our exciting collaboration on the RAISE-Health Initiative. As he points out, AI will revolutionize how physicians are trained, how biomedical research is conducted, and how healthcare is delivered. We MUST build responsible AI that will ensure safe and equitable healthcare for all.

\\10/7 

My conversation with Geoffrey Hinton yesterday at University of Toronto covered a wide range of topics, moderated by our wonderful friend Jordan Jacobs. We discussed the early history of ImageNet and the AlexNet moment, our intertwined scientific journeys toward the dream of building an intelligent machine and unleashing the deep learning revolution in 2012, and the years between 2012 and 2022 where we saw the unfolding of the powerful AI technologies as insiders. We also talked about the profound human and societal impact this powerful technology brings to our world, both the positive possibilities but also the sobering catastrophic risks, including economic changes, dis/misinformation, bias and privacy issues, weaponization, and beyond. Geoff and I enjoyed the conversation so much that we said we should start a tour and travel around and give talks as a duo! And we even managed to have some fun aside from talking AI. Thanks to Radical Ventures for hosting!

[chris macrae]

LI on NPR

https://www.npr.org/2023/11/10/1198908536/fei-fei-li-the-worlds-i-s... Li was fascinated by vision.

"Rather than bury us in the innumerable details of light, color and form, vision turns our world into the kind of discrete concepts we can describe with words," she writes in her book.

Li later learned about a field of AI called computer vision, or the way scientists train computers to recognize and respond to objects. It's used for things like self-driving cars and x-rays. Li says the process is inspired by the human visual system – but instead of eyes and retinas, computers use cameras and sensors to capture images and data. Then, they need to make sense of that data.

To achieve this goal, computer scientists use something called a neural network, which Li says is also inspired by the human brain. While the brain's fundamental unit is a neuron, neural networks are made of millions of "nodes" stacked together in layers. Like neurons in the brain, these layers of nodes take in and process that data.

The mystery of machine intelligence

Despite advances in the field, Li says there are still mysteries about how AI learns.

"Now everybody uses powerful AI products like Chat GPT," she says. "But even there, how come it can talk to you in human-like language, but it does stupid errors in math?"

Li says this generation of AI models is trained on data from across the internet, but how all of that data is processed and how models make decisions is still unknown.

To illustrate this point, she rhetorically asks how computers see, "Because what you get in a photo are just lights and colors and shades — yet you read out a cat."

These questions will only continue to grow as the use of AI becomes more widespread and more researchers enter the field.

Keeping AI ethical

Mystery aside, Li says AI can be used for bad or good. In order to ensure it's used for good, she says scientists must commit to exploring potential problems with AI, like bias.

One solution, she thinks, is for society to start coming up with ways to regulate the technology.

"The biggest issue of today's AI is that the technology is developing really fast, but the governance model is still incomplete. And in a way, it's inevitable," she says. "I don't think we ever create governance models before a technology is ready to be governed. That's just not how our society works."

One solution, she says, is to use AI to enhance human work rather than replace it. This is one reason why she founded the Stanford Institute for Human-Centered Artificial Intelligence and why she thinks the future of AI should include both scientists and non-scientists from all disciplines.

"We should put humans in the center of the development, as well as the deployment applications and governance of AI," Li says.

[chris macrae]

Its charming to see fei-fei li's development both in terms of personal knowledge and the contexts she is questioned in-most notably from 2009 landing stanford with her princeton doctorate and some nsf funds to run an annual neuroscience computer vision competition imagent, the research open process without which AI would still likely be locked away as only a big corporate tool 

In some ways li has seen more contexts than anyone having been the coordinator of how computers visioned 20000 identities spanning any discipline humans work or play with- she ran imagenet from 2009 annually out of stanfird but as a worldwide competition

it was italy's 2012 competition whihc provided the breakthrough as team hinton (alex and ilya) alogoritm based on neural networks made a giant leap in recognition - people like nvidioa's ceo jensen huang say this motovated him spennding next 5 y years building the woeld's biggest chip (in 2023 with 80 nbillion trnsistors). These graphical proicessing units are essential to real time chats making nvidia the biggest corporate growth story as well as purrinf santa clara back in forfront of world chip design started my intel's moore law from 1965

please send us in your5 fei-fei li fav moment

https://www.brainfacts.org/search?q=fei-fei

this link takes to her 2019 speech at 50th neurscience conference - chartmilngly also the first conference she had attended as a 20 something graduate

Beainfacts is a london network connected with gatsby/sainsbury/welcome - gatsby with its lab at university colege london was also one o hassabis first graduate studies event sponsor- in 2009 hassabis attended an event in stanford region where he met fei-fei li - the rest is histiry as far as the ai revolution that could save humanity goes

a dew dates

2012 imagent becomes ai superstar stage computer vision and neyral network alogorithms live up to newumann-einstein-turing expectations foir forst time in 55 years (more strictly 3 french-english speaking professors had kept nn algoirth reseaech going for 25 years hinton lecun bengio) but one of the few svaled apps hab been lecun nn work adopted by us post office to read handwritten zip codes

2015 asked by melinda gates and jensen huang nvidia to lead 10 ai-4-allmicrocurriculum of ai including some for 1st grade up ed ai dance ai drawing aligned with li's vision of wanting girls of all ages tro become as proliudic ai ebgineers as boys

2017 part of aiforggod itu summit and melinda gates helping gutteres vision un2.0 roadmapping round ai for good and radical digital change to educatio on all ages

in 2017 a lot more happened ; imagenet funding by nsf ended and was taken up by seattles paul allen foundation and ai2 under leadership of etzoni; fei-fe started nearly 2 years at google clud - at mountain view about 10 miles down th road

in 2018 dei-fei li representing ai-4-all and a cofounder of openaoi testify to0 congress - nvidia'gpu is coming out multiplying computational capacity venture capitalists inspired by li and hassabis deep mind are turining to nlp - chat ai will soon be the 3rd and perhaps most popular neurosciece-human brain ai revolution- this will return us to visions of einstein persnalsed learning agency. neumaan 1000 mother tongues llm to maths as science purest-simplest language

turing's deep data is far more than any ine market database however bug the coroprate leader of that matket

also thre more coherent data the more machine learns - when it comes to ai algoithms small is ugly

2018's testimony to congress starts every place and un needing their own hi level ai adviser boards and hopefully commitments to public investmemts in nairr natioal a research resourcres

[chris macrae]

The Hoover Institution and the School of Engineering at Stanford University invite you to the DC launch of the Stanford Emerging Technology Review at the Hoover DC office on Thursday, January 25th, from 4:00 PM - 5:30 PM ET. 

The discussion will highlight the findings of the Stanford Emerging Technology Review (SETR) Report on Ten Key Technologies and Their Policy Implications. 

This panel discussion will be in person at the Hoover DC office and will be followed by a cocktail reception. This event will also be available via livestream. 

CLICK HERE TO REGISTER

 

FEATURING

Condoleezza Rice 
Tad and Dianne Taube Director | Thomas and Barbara Stephenson Senior Fellow, Hoover Institution

Jennifer Widom 
Frederick Emmons Terman Dean of the School of Engineering, Stanford University

Amy Zegart  
Morris Arnold and Nona Jean Cox Senior Fellow, Hoover Institution

Herb Lin
Hank J. Holland Fellow in Cyber Policy and Security, Hoover Institution

Fei-Fei Li 
Denning Co-Director, Stanford Institute for Human-Centered Artificial Intelligence 
Sequoia Capital Professor, Stanford University

Drew Endy 
Associate Professor of Bioengineering, Stanford University 
Senior Fellow, Hoover Institution

[chris macrae]

Some of fei fei li's recent twitter entries: https://twitter.com/silviocinguetta 

Fei-Fei Li reposted

Silvio Savarese

@silviocinguetta

·

Jan 17

The discussion on how small foundation models are reshaping the landscape of enterprise continues! Read more in a recent Financial Times’ piece (where I am quoted) https://on.ft.com/4aYOmy8 and my own blog: http://tinyurl.com/mr3nzzsz #SalesforceAI #AI #ML

Fei-Fei Li reposted

Stanford HAI

@StanfordHAI

·

Jan 16

AI trends to watch in 2024:

@StanfordHAI

co-director

@drfeifei

expects AI policy to heat up this year, as Congress debates passing legislation like the CREATE AI Act. https://stanford.io/3O7S270

2

Fei-Fei Li, Sequoia Professor in the Computer Science Department and Co-Director of Stanford HAI.

Asking Big Questions, Applying New Policies

One of my hopes for 2024 is that we can have the wherewithal to continue to ask the hard questions, the critical questions about what we want from artificial intelligence in our lives, in our communities, in education, in our society. I don't think we've ever seen a year quite like this. More and more kinds of this generative AI technology are going to embed themselves and entrench into our work, play and communication. How does this year make us feel about ourselves? 

I think we need to give ourselves the time and space to articulate what we think is permissible and where we should put the limits. One of the first realizations regarding this current generation of AI was back in February 2023 when (academic journal publisher) Springer Publishing issued a statement in which they said large language models can be used in drafting articles, but will not be permitted as a coauthor on any publication. And the rationale they cited, and I think this is so important, is accountability. That doesn't mean Springer is locked into this forevermore. But that’s so critical: putting something out there in earnest, understanding what your rationales are, and saying this is where we are right now with the way we understand it and in the future we may add more nuances into these policies. And I think that institutions and organizations must have that perspective and try to put guidelines down on a page in 2024.

22

Fei-Fei Li reposted

Vivi

@vivilinsv

·

Jan 14

Diving into "The Worlds I See" by

@drfeifei

Professor Fei-Fei Li, I'm captivated by her storytelling that weaves personal struggles with the evolution of AI. As an immigrant myself, I find her journey from China to America, post-1989, deeply relatable. Her exploration of…

Show more

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[chris macrae]

Correspondents with Fei-Fei Li and Condoleezza Rice Stanford Emerging TEch Review

CONTRIBUTORS
Co-chairs
Condoleezza Rice is the Tad and Dianne Taube Director
of the Hoover Institution and a senior fellow on public
policy. She is the Denning Professor in Global Business
and the Economy at the Stanford Graduate School of
Business. She served as the sixty-sixth secretary of state
(2005–9) and the nineteenth national security advisor
(2001–5). She is author and coauthor of nine books.
Her research interests include education, security and
defense, US foreign policy, law and policy, politics and
public opinion, and the US presidency. She received her
PhD in political science from the University of Denver.
John B. Taylor is the George P. Shultz Senior Fellow
in Economics at the Hoover Institution and the Mary
and Robert Raymond Professor of Economics at
Stanford University. He is also the director of Stanford’s
Introductory Economics Center. He specializes in monetary policy, fiscal policy, and international economics.
He served as undersecretary for the Treasury for international affairs (2001–5) and on the President’s Council
of Economic Advisers. He has authored and coauthored twenty books, including First Principles: Five
Keys to Restoring America’s Prosperity, winner of the
2012 Hayek Prize. He received his PhD in economics
from Stanford University.
Jennifer Widom is the Frederick Emmons Terman
Dean of the School of Engineering and the Fletcher
Jones Professor in Computer Science and Electrical
Engineering at Stanford University. Her research interests span many aspects of nontraditional data management. She is a fellow of the Association for Computing
Machinery and a member of the National Academy of
Engineering and the American Academy of Arts and
Sciences. She received her PhD in computer science
from Cornell University.
Amy Zegart is the Morris Arnold and Nona Jean Cox
Senior Fellow at the Hoover Institution and professor of
political science (by courtesy) at Stanford University. She
is also a senior fellow at Stanford’s Institute for HumanCentered Artificial Intelligence and at its Freeman
Spogli Institute for International Studies. The author of
five books, she specializes in US intelligence, emerging
technologies and national security, grand strategy, and
global political risk management.
Director and Editor in Chief
Herbert S. Lin is senior research scholar at the Center
for International Security and Cooperation and Hank
J. Holland Fellow in Cyber Policy and Security at the
Hoover Institution, both at Stanford University. His
research interests relate broadly to emerging technologies and national security. He is an elected fellow
of the American Association for the Advancement of
Science. He received his doctorate in physics from the
Massachusetts Institute of Technology.
Faculty Council
Zhenan Bao is the K. K. Lee Professor in Chemical
Engineering and professor, by courtesy, of chemistry
and materials science and engineering at Stanford
University. She has close to seven hundred refereed
publications and more than eighty US patents. Her current research focuses on organic electronics, including
skin-inspired materials, dynamic energy storage, and
recyclable, reprocessable materials. She received her
PhD in chemistry from the University of Chicago.
Dan Boneh is professor of cryptography and electrical
engineering at Stanford University, codirector of the
Stanford Computer Security Lab, and a senior fellow at
the Freeman Spogli Institute for International Studies.
His research focuses on applied cryptography and
computer security. He has authored over one hundred
publications. He received his PhD in computer science
from Princeton University.
152 STANFORD EMERGING TECHNOLOGY REVIEW
Yi Cui is the director of the Precourt Institute for Energy,
the Fortinet Founders Professor of materials science
and engineering, and professor of energy science and
engineering and, by courtesy, of chemistry at Stanford
University. He is also a senior fellow at the Stanford
Woods Institute for the Environment and codirector of
Stanford StorageX Initiative. His research focuses on
nanomaterials, nanobiotechnology, and nanoenvironmental technologies. He received his PhD in chemistry
from Harvard University.
Simone D’Amico is associate professor of aeronautics
and astronautics and professor, by courtesy, of geophysics at Stanford University, where he serves as the
W. M. Keck Faculty Scholar of Engineering. His research
explores the intersection of advanced astrodynamics,
spacecraft navigation and control, autonomous decision
making, and space system engineering. He currently
leads satellite swarm and formation-flying projects
for NASA and the National Science Foundation. He
received his PhD in aerospace engineering from Delft
University of Technology.
Drew Endy is the Martin Family University Fellow in
Undergraduate Education (bioengineering), codirector of degree programs for the Hasso Plattner Institute
of Design (the d.school), core faculty at the Center
for International Security and Cooperation (CISAC),
and senior fellow (courtesy) of the Hoover Institution
at Stanford University. He serves as president and
director of the Biobricks Foundation and director of
the iGEM Foundation and the Biobuilder Educational
Foundation. His research focuses on the foundations of
synthetic biology along with broader societal aspects.
He earned a PhD in biotechnology and biochemical
engineering from Dartmouth College.
Siegfried Glenzer is professor of photon science and,
by courtesy, of mechanical engineering at Stanford
University, where he serves as director of the High Energy
Density Science division at SLAC National Accelerator
Laboratory. His research explores matter in extreme
conditions and the development of fusion energy sciences and technologies. He has authored or coauthored
more than 550 journal publications. He received his PhD
in physics from Ruhr University Bochum.
Mark A. Horowitz is the Yahoo! Founders Professor
in the School of Engineering and professor of electrical engineering and computer science at Stanford
University. His research has contributed to early RISC
(reduced instruction set computer) microprocessors,
multiprocessor designs, and high-speed interfaces, and
he currently works to create new agile design methodologies for analog and digital VLSI (very-large-scale
integration) circuits. He received his PhD in electrical
engineering from Stanford University.
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
Institute for Human-Centered AI 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.
Allison Okamura is the Richard M. Weiland Professor
of mechanical engineering in the School of Engineering
and professor, by courtesy, of computer science at
Stanford University. She is a deputy director of the Wu
Tsai Neurosciences Institute, affiliated faculty at Stanford
Bio-X and Stanford’s Institute for Human-Centered AI,
and a fellow of the Institute of Electrical and Electronics
Engineers. Her research interests include haptics, teleoperation, mixed reality, and medical and soft robotics.
She received her PhD in mechanical engineering from
Stanford University.
Kang Shen is the Frank Lee and Carol Hall Professor
of biology and professor of pathology at Stanford
University, where he serves as the Vincent V. C. Woo
Director of the Wu Tsai Neurosciences Institute and
affiliated faculty at Stanford Bio-X. His research focuses
on neuronal cell biology and developmental neuroscience. He has authored or coauthored more than one
hundred journal articles. He received his PhD in cell
biology from Duke University.
Contributors 153
Advisory Board
Steven Chu is the William R. Kenan Jr. Professor of
Physics and professor of molecular and cellular physiology and of energy science and engineering at Stanford
University. He previously served as US secretary of
energy (2009–13) and as director of the Lawrence
Berkeley National Laboratory. He received his PhD in
physics from the University of California–Berkeley.
Robert Gates served as the twenty-second secretary of
defense (2006–11) and as the director of central intelligence (1991–93) following nearly twenty-seven years
in the CIA and on the National Security Council. He
is the current chancellor of William & Mary and previously served as president of Texas A&M University.
He received his PhD in Russian and Soviet history from
Georgetown University.
Susan M. Gordon is currently the director at CACI
International and served as the fifth principal deputy
director of national intelligence at the Office of the
Director of National Intelligence (2017–19). She was
previously deputy director of the National Geospatial
Intelligence Agency (2015–17) and served twentyseven years in the CIA, including as director of the CIA’s
Information Operations Center.
John L. Hennessy is professor of electrical engineering and computer science at Stanford University and
the chairman of Alphabet Inc. He previously served as
the tenth president of Stanford University (2000–16)
and is the founder and director of the Knight-Hennessy
Scholars Program, the world’s largest fully endowed
graduate-level scholarship program. He received his
PhD in computer science from Stony Brook University.
Jerry McNerny is the former US representative for
California’s ninth congressional district (2007–23),
where he served on the Committee on Energy and
Commerce, the Veterans’ Affairs Committee, and
the Space, Science, and Technology Committee. He
received his PhD in mathematics from the University of
New Mexico–Albuquerque.
Mary Meeker is a cofounder and general partner of
BOND and serves on the boards of Block/Square,
Genies, Nextdoor, and Plaid. Her investments at BOND
(and, previously, Kleiner Perkins) include Spotify, Waze,
DocuSign, Ring, Checkr, Ironclad, and On Running. She
was previously a managing director at Morgan Stanley,
focused on emerging technology companies, coauthor
of USA Inc.: A Basic Summary of America’s Financial
Statements, and publisher of the widely distributed
Internet Trends Report.
Lloyd B. Minor is the Carl and Elizabeth Naumann
Dean of the School of Medicine and vice president for
medical affairs at Stanford University. He is a professor
of otolaryngology–head and neck surgery and professor, by courtesy, of bioengineering and neurobiology
at Stanford University. Prior to joining Stanford, he was
provost and senior vice president for academic affairs
at Johns Hopkins University. He is an elected member
of the National Academy of Medicine.
Peter L. Scher is the vice chairman of JPMorgan Chase
& Co. and a member of the firm’s operating committee
and cofounder and chairman of the board of directors
of the Greater Washington Partnership. Prior to joining
JPMorgan Chase in 2008, he was the managing partner
of the Washington, DC, office of law firm Mayer Brown
LLP and spent nearly a decade in public service.
Eric Schmidt is cofounder of Schmidt Futures, a philanthropic initiative that bets early on exceptional people
making the world better, and chair of the board of
trustees of the Broad Institute. Previously, he was CEO
and chairman of Google (2001–11) and executive chairman and technical adviser to Alphabet until 2020. He
received his PhD in electrical engineering and computer
science from the University of California–Berkeley.
Thomas M. Siebel is the chairman and CEO of C3 AI
and was the founder and CEO of Siebel Systems. He
is a member of the colleges of engineering boards
at the University of Illinois at Urbana-Champaign
and the University of California–Berkeley and is an
elected member of the American Academy of Arts and
Sciences.
154 STANFORD EMERGING TECHNOLOGY REVIEW
Stanford Emerging Technology
Review Fellows
Alaa Eldin Abdelaal, robotics
Jesus Miguens Blanco, biotechnology and
synthetic biology
Stefano Cestellos-Blanco, materials science
Alec Friedrich Condon, neuroscience
Sneha Goenka, semiconductors
Tommaso Guffanti, space
Melanie Hannebelle, biotechnology and
synthetic biology
Nicholas Hartley, nuclear technologies
Sang Cheol Kim, energy
John Klich, biotechnology and synthetic biology
Nicolas Lee, space
Hope Tianfeng Leng, biotechnology and
synthetic biology
Daniil Lukin, semiconductors
Walter Joshua Manuel, space
Lukas Felix Michalek, materials science
Neil Perry, cryptography
Tathagata Srimani, semiconductors
Artem Trotsyuk, biotechnology and synthetic
biology
Senior Research Staff
Ashil Jhaveri
Taylor McLamb
Alexandra Sander
Corie Wieland

[chris macrae]

lab https://svl.stanford.edu/research/

Current Highlighted Projects

BEHAVIOR

BEHAVIOR is a benchmark in simulation where embodied AI agents need to plan and execute navigation and manipulation strategies based on sensor information to fulfill up to 1,000 household activities. BEHAVIOR tests the ability of agents to perceive the environment, plan, and execute complex long-horizon activities that involve multiple objects, rooms, and state changes, all with the reproducibility, safety, and observability offered by a realistic physics simulation.

Check out our Link.

ObjectFolder

ObjectFolder models the multisensory behaviors of real objects with 1) ObjectFolder 2.0, a dataset of 1,000 neural objects in the form of implicit neural representations with simulated multisensory data, and 2) ObjectFolder Real, a dataset that contains the multisensory measurements for 100 real-world household objects, building upon a newly designed pipeline for collecting the 3D meshes, videos, impact sounds, and tactile readings of real-world objects.

Check out our Link.

Multi-Object Multi-Actor (MOMA)

Multi-Object Multi-Actor (MOMA) is a compositional and hierarchical activity recognition framework for complex activities that involve multiple humans utilizing a variety of objects to accomplish certain tasks. We introduce activity graphs as the overarching and human interpretable representation of human activities in videos and activity parsing as the task of generating activity graphs.

Check out our Link.

People, AI & Robots (PAIR)

We work on challenging open problems at the intersection of computer vision, machine learning, and robotics. We develop algorithms and systems that unify in reinforcement learning, control theoretic modeling, and 2D/3D visual scene understanding to teach robots to perceive and to interact with the physical world.

Check out our Link and Papers.

Partnership in AI-Assisted Care (PAC)

The Partnership in AI-Assisted Care (PAC) is an interdisciplinary collaboration between the School of Medicine and the Computer Science department focusing on cutting edge computer vision and machine learning technologies to solve some of healthcare's most important problems.

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Trustworthy AI

Stanford Trustworthy AI (STAI) aims to supercharge innovations in artificial intelligence with human understanding. We engage in translational research across fairness, explainability, privacy, and robustness, guided by ethics. Our group does both theoretical and applied research across multiple domains of artificial intelligence.

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Past Highlighted Projects

JackRabbot

Our work at the SVL is making practical a new generation of autonomous agents that can operate safely alongside humans in dynamic crowded environments such as terminals, malls, or campuses. The Stanford “Jackrabbot”, which takes it name from the nimble yet shy Jackrabbit, is a self-navigating automated electric delivery cart capable of carrying small payloads.

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ActivityNet

Our benchmark aims at covering a wide range of complex human activities that are of interest to people in their daily living. We illustrate three scenarios in which ActivityNet can be used to compare algorithms for human activity understanding: global video classification, trimmed activity classification and activity detection.

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Visual Genome

Short Description: To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. Visual Genome is a new dataset to connect dense, structured image concepts to language.

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ObjectNet3D

ObjectNet3D is a large scale database for 3D object recognition. With 90,127 images, 201,888 objects in these images and 44,147 3D shapes, ObjectNet3D offers a powerful tool for learning to recognize 3D pose and 3D shape of objects from 2D images.

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ImageNet Challenge

The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) evaluates algorithms for object detection and image classification at large scale. One high level motivation is to allow researchers to compare progress in computer vision across a wider variety of objects -- taking advantage of the quite expensive labeling efforts.

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ImageNet

ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. As the largest labelled image data set at the time of its release in 2009, ImageNet has helped to pave the way for today’s advances in visual recognition.

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Contact Us

Stanford Vision and Learning Lab (SVL)

Admin: Helen Roman

Email: hmroman at stanford dot edu

Computer Science Department

Stanford University

353 Jane Stanford Way, Stanford, CA 94305-9025