Dr. Percy Liang
Director, Stanford CRFM. On 1 show, 1 appearance. The Shows tab opens the full record on each.
academicscientistfounder@percyliang ↗LinkedIn ↗cs.stanford.edu/~pliang ↗Wikipedia ↗
Dr. Percy Liang is a Professor of Computer Science at Stanford University and the Director of the Center for Research on Foundation Models. A prominent machine learning researcher who coined the term "foundation models," he also co-founded Together AI.
1shows
1appearances
20statements
4resolved
4supported
0contradicted
100%fully supported
Everything Dr. Percy Liang said on any show that made the record, most notable first. Each card names its show and opens the statement there.
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.”
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 …”
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…”
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…”
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…”
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…”
Liang: Future AI models may be too powerful for unrestricted release
“I think these models are extremely powerful and maybe the models right now I think are, well, if they were out and open, it would be maybe okay, but in the future these models could be extremely good and having them, you know, anyone, anything goes. Might we m…”
Liang: Interconnected AI accepting external inputs risks cascading jailbreak exploits
“If these models start interacting with the world and accepting external inputs, now you can not only just sort of jailbreak your own model, but you can jailbreak other people's model and get them to do various things. And then, so that could lead to sort of a …”
Liang: AI model producers should provide transparency analogous to nutrition labels
“The analogy I like to think about is, you know, nutrition labels or any sort of specification sheets on electronic devices. There's some sort of, ah, obligation. I think that you know, producers of some products should have to make sure that their product is u…”
Liang: Foundation models pose more pressing near-term issues than existential risks
“Certainly these are powerful technologies and could have extreme social consequences, but there's a lot of more near-term issues.”
Liang: Language models should use calculators instead of computing internally
“There are cases where you want to just map natural language into say people call it tool use. Like you ask some question that reverse calculation, you should just use a calculator rather than trying to sort of quote unquote do it in the transformers head.”
Liang: Core symbolic AI problems like planning are relevant again
“There's neural versus symbolic. For a while, symbolic AI was dominant. Now, neural AI has come really taken off and become dominant, but some of those central problems of how do you do planning, how do you do reasoning, which was the focus and study of symboli…”
Liang: Controlling AI hallucination is easier once models understand the concept
“So I think there's pre-training, which is predicting the next word and developing a world model, so to speak. And with those capabilities, then you can, you still have to say don't hallucinate, but it will be much easier to control that model if it has a notio…”
Liang: Stanford researchers developed attention-free architectures competitive with transformers
“So one of my colleagues, Chris Ray and his students have developed other architectures, which are actually at smaller scales, competitive with transformers. And actually don't require the central operation of attention.”
Stanford and MosaicML's BiomedLM Achieved State-of-the-Art on the USMLE
“We've trained a model here at CRFM in collaboration with Mosaic and called Biomed LM. It's not a huge model, but it's trained on PubMed articles. And it exhibits you know, pretty good, you know, performance on various benchmarks for a while you know, we were a…”
Liang: AI with human oversight can produce class-project-level research
“I think you're at the level where it could probably generate things and, you know, I think it would still be a lot of, you know, human loop, but you could generate probably let's say I don't know, a class project type of project.”
Liang: The AI research community previously viewed AGI as laughable
“I think that for a while it was, you know, perceived by most of the community as you know, laughable.”
Liang: The term 'Foundation Models' was coined because LLMs missed multimodality
“We coined the term Foundation Models because we thought it, there was something that was happening in the world that was, that somehow large language models didn't really capture the significance. And it was not just about language, it was about images and mul…”
Liang: Google beat Stanford's medical AI with a model 200x larger
“Google did come up with a model that was, I think, 200 times larger and they beat that model.”
One line per show, most statements first. The link opens Dr.'s full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 NO PRIORSLEDGER | Director, Stanford CRFM | 1 | 20 | 100% 4/4 | full record on No Priors → |