Lex Fridman Podcast / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
23 published records
01 / prediction
“Can you build it by predicting words? And the answer is most probably no, because language is very poor in terms of weak or low bandwidth if you want, there’s just not enough information there.”
02 / belief
“Still value systems. But still even with the objectives of how to build a bioweapon, for example, I think something you’ve commented on, or at least there’s a paper where a collection of researchers is trying to understand the social impacts of these LLMs.”
03 / observation
“Otherwise, if it’s a system that is on the more classical services, it can be ad supported or there’s several models. But the point is, if you have a big enough potential customer base and you need to build that system anyway for them, it doesn’t hurt you to actually distribute it to the open source.”
04 / belief
“You made a lot of strong points. And I believe that people are fundamentally good.”
05 / belief
“Moravec’s paradox is a consequence of realizing that the world is not as easy as we think.”
06 / belief
“The next decade I think is going to be really interesting in robots, the emergence of the robotics industry has been in the waiting for 10, 20 years without really emerging other than for pre-program behavior and stuff like that.”
07 / belief
“Of course we can, but as a pass towards human-level intelligence, they’re missing essential components. And then there is another tidbit or fact that I think is very interesting.”
08 / belief
“I think that’s a crazy difficult task because of all the safety required and all this kind of stuff.”
09 / uncertainty
“I would love to sort of linger on your skepticism around auto regressive LLMs. So one way I would like to test that skepticism is everything you say makes a lot of sense, but if I apply everything you said today and in general to I don’t know, 10 years ago, maybe a little bit less, no, let’s say three years ago, I wouldn’t be able to predict the success of LLMs.”
10 / belief
“Whether we should say that you don’t like the term AGI, and we’ll probably argue I think every single time I’ve talked to you, we’ve argued about the G in AGI.”
11 / belief
“Now we have an abstract representation of the thought of the answer, representation of the answer, we feed that to basically an autoregressive decoder, which can be very simple, that turns this into a text that expresses this thought. So that, in my opinion, is the blueprint of future data systems, they will think about their answer, plan their answer by optimization before turning it into text, and that is turning complete.”
12 / belief
“I think in one of your slides, you have this nice plot that is one of the ways you show that LLMs are limited.”
13 / belief
“I believe that people are fundamentally good, and so if AI, especially open source AI can make them smarter, it just empowers the goodness in humans.”
14 / evaluation
“Again, I think the answer to this is open source platforms and then enabling a widely diverse set of people to build AI assistance that represent the diversity of cultures, opinions, languages, and value systems across the world so that you’re not bound to just be brainwashed by a particular way of thinking because of a single AI entity.”
15 / prediction
“Before we get all those things to work together, and then on top of this, have systems that can learn hierarchical planning, hierarchical representations, systems that can be configured for a lot of different situation at hand, the way the human brain can, all of this is going to take at least a decade and probably much more because there are a lot of problems that we’re not seeing right now that we have not encountered, so we don’t know if there is an easy solution within this framework.”
16 / prediction
“Because if I were to take your wisdom and intuition at face value, I would say there’s no way autoregressive LLMs, one token at a time, would be able to do the kind of things they’re doing.”
17 / evaluation
“The idea of doing this has been floating around for a long time and at FAIR, some of our colleagues and I have been trying to do this for about 10 years, and you can’t really do the same trick as with LLMs because LLMs, as I said, you can’t predict exactly which word is going to follow a sequence of words, but you can predict the distribution of words.”
18 / evaluation
“It’s hard to represent all the complexities that we take completely for granted in the real world that we don’t even imagine require intelligence, right? This is the old Moravec paradox, from the pioneer of robotics, hence Moravec, who said, how is it that with computers, it seems to be easy to do high-level complex tasks like playing chess and solving integrals and doing things like that, whereas the thing we take for granted that we do every day, like, I don’t know, learning to drive a car or grabbing an object, we can’t do with computers, and we have LLMs that can pass the bar exam, so they must be smart, but then they can’t learn to drive in 20 hours like any 17-year old, they can’t learn to clear out the dinner table and fill up the dishwasher like any 10-year old can learn in one shot.”
19 / evaluation
“First, that would be incredibly expensive, but it will also be completely impossible because you don’t know all the conditions of what’s going to happen, how long it’s going to take to catch a taxi or to go to the airport with traffic.”
20 / commitment
“There’s a few breakthroughs that we have to basically go through before we can get there, but you’ll be able to monitor our progress because we publish our research.”
21 / prediction
“You can be looking at a menu in a foreign language, and I think we will translate it for you, or we can do real time translation if we speak different languages.”
22 / preference
“There is another set of methods which are non-contrastive, and I prefer those, and those non-contrastive methods basically say, the energy function needs to have low energy on pairs of XYs that are compatible that come from your training set.”
23 / evaluation
“I think people are fundamentally good and in fact, a lot of doomers are doomers because they don’t think that people are fundamentally good.”