HAI Stanford 2019 -2023

Hai Stanford (Human Centric AI) was started in 2019 with support of Stanford President ndleladership of Fei-Fe Li and   John Etchemendy, professor of philosophy and former Stanford University provost

Stanford Video

HAI Founding Distinguished Fellows

The inaugural group of Distinguished Fellows will include: Yoshua Bengio, University of Montreal; Rodney Brooks, MIT; Erik Brynjolfsson, MIT; Jeff Dean, Google; Daniel Dennett, Tufts University; Susan Dumais, Microsoft Research; Edward Feigenbaum, Stanford University; Barbara Grosz, Harvard; Demis Hassabis, DeepMind; Geoff Hinton, University of Toronto; Eric Horvitz, Microsoft Research; James Manyika, McKinsey & Company; John Markoff, Center for Advanced Study in the Behavioral Sciences; Helen Nissenbaum, Cornell Tech; Judea Pearl, UCLA; Stuart Russell, UC Berkeley; Mustafa Suleyman, DeepMind; Terry Winograd, Stanford University; and Hal Varian, Google.

tSanford President Marc Tessier-Lavigne said artificial intelligence has the potential to radically change how we live our lives. “Now is our opportunity to shape that future by putting humanists and social scientists alongside people who are developing artificial intelligence,” he said. “This approach aligns with Stanford’s founding purpose to produce knowledge for the betterment of humanity. I am deeply thankful to our supporters who are providing foundational funding for the institute, which is a critical element for our vision for the future of Stanford University.”

Stanford HAI formally launches at a symposium on Monday, March 18 featuring speakers such as Microsoft founder and philanthropist Bill Gates and California Governor Gavin Newsom, as well as leading experts Kate Crawford of NYU, Jeff Dean of Google, Demis Hassabis of DeepMind, Alison Gopnik of UC Berkeley, Reid Hoffman of Greylock Partners and Eric Horvitz of Microsoft Research. (Watch the livestream here.)

2023- Hai Stanford is celebrating leas forward from covid era with extraordinary talks like this

We are reducing this 90 minute youtube and Transcript to key cases and methods mentioned by fei-fei li and demis hassibis -of course you are referred to the 90 minute version at youtube

I am Fei-Fe Li   HAI stanford ie lab human-centered AI

Welcome Demis hassibis founder DeepMind 2010- now an alphabet/google companu

Demis superpiwers began in games, and doctirate in cognitive neuroscience -brain architecture - at MIT (check him out as alumni of Uni College London, MIT and Harvard so this is how he came to machine intel – DEEPMIND 2010 truly one of first new wave AI companies aquired google 2014 but retaining its sphere og influence out of London. (indeed this may be Demis first sam francisco region visit since covid even though he was on HAI stanfrd founding council 2019

deepmind commercial breakthroughs include center energy and mapping the world’s proteins through the very impressive AlphaFold AI stream work

 a world leader in realms of deep learning and reinforcement learning and this reflects gaming roots -eg AlphaGo world champion

there are hopes that DL will become climate AI leader – invite student Q&A on that at end indeed Demis is in midst of pioneering Artificial General iItelligence as potentially Epoch Changing Tech changing the very fabric of human lives

AI is having a pubcli Awakening moment and it's no longer just a niche field that

nerds like us play around ; it's impacting human life society and our future and

Hai Stanford has been missioned to be one of the forums that will host this kind of

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intellectual discourse about Ai  - I cant think of more timely speaker than Demis

========================as at 5/3 rest undrr construction

 

 

Demis – thanks so much Fei-Fei – yes now covid’s hoefully passing I hope to be in bay area more often and share  real passion of mine which is to use AI to accelerate

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scientific discovery with generative Ai and and large language models and and the work we're doing

deepmind was founded way back in 2010 that was almost like medieval times

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in 2010 it was very difficult to o raise our seed round of you know a few hundred thousand dollars compared with today’s billion dollar rounds  - in 2010 few were  talking about AI Shae Legg DM’s chief scientst annd I felt very isdolated – people seemed to have forgotten AI  as field to build a human-like intelligence so it's been astounding what the last few months have brought as wwe see comvergence of a lot of different

DM has always been big proponent of reinforcement learning and understanding

7 the human brain - my PhD I worked on the hippocampus and memory systems and Imagination made some interesting discoveries in in that domain that I thought would also potentially carry over into ideas for AI systems architects together with QC Advent of lot of compute power and specifically GPUs (Graphic Processing Units) which ironically of course were invented for games so everything for me as you'll see comes back to games one way or another um

We began DM as king of Apollo Program of Games

First we revited earliest game systems Atari space invaders and 50 games from the 1970s  -we uh and maximized the score jaround n the raw pixels on the screen so it was very much um probably the first example of a kind of end-to-end learning system on something that working on something really challenging perceptually

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challenging lan Atari game swas a incredible moment for us and I remember when back in 2011 when we were struggling to even win a single point at a game like pong and we was jwondering well maybe we're just 20 years too early with these ideas of of learning systems and then suddenly it won a point then it won a game and then it didn't lose any points; by 2013 it was playing all the Atari games of course we then took that much further and and

However our big opportunity came with Alphago  - world champion of game like GO

the super complex game that's played in Asia

obviously famously in 2016 we had this massive million dollar challenge match in Seoul 200 million people watched thematch around the world an alphago famously won that match for one but more important than it winning move 37 which blew away all past human players startegies

Today we can design game winners one thing that's holding us back is that we don't really know how to ask aplha to design a great game not in a way that it could understand

 As yet we havent found way to ask alpha to invent a game that only takes five minutes to learn but many lifetimes to master is aesthetically beautiful can be completed in in 10 hours of you know play so it fits into a human day

 

Thesek are inds of things is what I would kind of give as instructions and then I'd hoped it would come up with something like go but there's no real way to do tgis yet

so how does the self-play system work and I'm just going to combine together um actually a range of systems together

Back to what we can do16:10

Face-Off match a hundred game match of old play -version V1 versus new veriosn V2 and threshold in our case we set a 55 win rate threshold where if V2 beats V1 by

above that threshold you assume that it is significantly uh better and then you

replace the Master System that the generator system with that new V2 system now and you go around of course

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iterating this round so now you could play another 100 000 games with V2 so it's slightly stronger so that means the

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generated data is slightly better quality -continue to generate more data another hundred thousand games with V2 so then now you have 200 000 games uh to train a

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general way of thinking about  Ai and the and the um the idea of coming up with a solution to a problem

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se've been very fortunate over the last decade we've been part of um kind of creating many big breakthroughs um inall sorts of different games all of them kind of landmark uh results at the Atari one the alphago one I mentioned Alpha

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zero I just talked about generalizing that to every two-player game and then finally Alpha star which was our program

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to uh beat Grand Masters players at Starcraft 2 which is the most complex

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real-time strategy game computer game and it has extra challenges over board games of being partially observable uh

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it needs things like long-term planning so it's complex in in in in more challenging ways than a board game and

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so this was all of our work in in games now of course although I love games always have done playing them designing

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them using them for training for AI I've sort of used games in every way possible but they've always although it's been

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very fun to do that um it's always been a kind of means to an end not an end in itself right the end was never to just win it go or Win

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It win at Starcraft it was to build it was to use games as a convenient proxy

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to test out our algorithmic ideas so that we could apply them to important real world problems