Lex Fridman: belief

Transcript context

…This relates a lot to this kind of SF zeitgeist of: what is AGI, Artificial General Intelligence, and what is ASI, Artificial Superintelligence? What are the language models that we have today capable of doing? I think language models can solve a lot of tasks, but a key milestone for the AI community is when AI can replace any remote worker, taking in information and solving digital tasks. The limitation is that a language model will not learn from feedback the same way an employee does. If you hire an editor, they might mess up, but you will tell them, and they don’t do it again. But language models don’t have this ability to modify themselves and learn very quickly. The idea is, if we are going to get to something that is a true, general adaptable intelligence that can go into any remote work scenario, it needs to be able to learn quickly from feedback and on-the-job learning. I’m personally more bullish on language models being able to just provide very good context. You can write extensive documents where you say, “I have all this information. Here are all the blog posts I’ve ever written. I like this type of writing; my voice is based on this.” But a lot of people don’t provide this to models. The agentic models are just starting. So it’s this kind of trade-off: do we need to update the weights of this model with this continual learning thing to make them learn fast? Or, the counterargument is we just need to provide them with more context and information, and they will have the appearance of learning fast by just having a lot of context and being very smart. So we should mention the terminology here. Continual learning refers to changing the weights continuously so that the model adapts and adjusts based on the new incoming information, and does so continually, rapidly, and frequently. And then the thing you mentioned on the other side of it is generally referred to as in-context learning. As you learn stuff, there’s a huge context window. You can just keep loading it with extra information every time you prompt the system, which I think both can legitimately be seen as learning. It’s just a different place where you’re doing the learning. I think, to be honest with you, continual learning—the updating of weights—we already have that in different flavors. I think the distinction here is: do you do that on a personalized custom model for each person, or do you do it on a global model scale? And I think we have that already with going from GPT-5 to 5.1 and 5.2. It’s maybe not immediate, but it is like a quick curated update where there was feedback by the community on things they couldn’t do. They updated the weights, released the next model, and so forth. So it is kind of a flavor of that. Another even finer-grained example is RLVR; you run it, it updates. The problem is you can’t just do that for each person because it would be too expensive to update the weights for each person. Even at OpenAI scale, building the data centers, it would be too expensive. I think that is only feasible once you have something on the device where the cost is on the consumer. Like what Apple tried to do with the Apple Intelligence models, putting them on the phone so they learn from the experience.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.