Dr. Percy Liang, Stanford computer science professor and Director of the Center for Research on Foundation Models, reflects on shifting attitudes toward AGI and existential risk.
“I think that for a while it was, you know, perceived by most of the community as you know, laughable.”
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More from Dr. Percy Liang
AssertionNot checkable as stated
Liang: The AI industry is retreating from its open culture
“And what we're seeing now is sort of a retreat of that open culture where models are now being only accessible via APIs. We don't really know all the secret sauce that's going behind them, and there's sort of limited access.”
Dr. Percy LiangApr 25, 2023▶ 5:15No Priors Ep. 7 | With Stanford Professor Dr. Percy Liang
Insight
Liang: Big tech scale forced AI academia to focus on understanding models
“And now today I think it's the dynamic is, is quite different because it's no longer academia's job isn't just to get things to work because you can do that in other ways. There's a lot of resources going into big tech companies where there's if you have data …”
Dr. Percy LiangApr 25, 2023▶ 7:58No Priors Ep. 7 | With Stanford Professor Dr. Percy Liang
Insight
Liang: AI benchmarks should target superhuman reliability over human mimicry
“I think we're getting to a point where along many axes, it's a superhuman or should be superhuman. And I think we should maybe define more of an objective measure of like what we actually want. We want something that's very reliable, is grounded. You know, I o…”
Dr. Percy LiangApr 25, 2023▶ 12:21No Priors Ep. 7 | With Stanford Professor Dr. Percy Liang
Opinion
Liang: LLMs are not just memorizing because novel concept fusion requires invention
“You know, people say that sometimes all language models just memorize because they're so big and train on clearly a lot of texts, but these examples, I think really indicate that there's no way That these language models are just memorizing because this text j…”
Dr. Percy LiangApr 25, 2023▶ 21:53No Priors Ep. 7 | With Stanford Professor Dr. Percy Liang
Insight
Liang: Next-token prediction forces language models to build world models
“If you think about predicting the next word, It's, it seems very simple, but you have to really internalize a lot of what is going on in this context. What are the previous words? What's the syntax? What's who's saying them? And all of that information and con…”
Dr. Percy LiangApr 25, 2023▶ 24:54No Priors Ep. 7 | With Stanford Professor Dr. Percy Liang
Insight
Liang: Foundation Models Cause the Concept of an AI Task to Dissolve
“And this was a paradigm shift, in my opinion, because it changed the way that we conceptualize Machine learning and NLP systems from these bespoke systems where you're, it's trained to do question answering, to train to do this, to just a general substrate whe…”
Dr. Percy LiangApr 25, 2023▶ 3:00No Priors Ep. 7 | With Stanford Professor Dr. Percy Liang
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