The Exchanges

Q Can I ask in terms of like the proprietary data element, pre-trained data changes a lot. Can you just help anyone who doesn't know understand what is pre-trained data and how does it change the game for a lot of companies that don't have existing data modes?

A Yeah, so I, I think maybe it's useful to kind of go through the, the steps if you want to build your own chat GPT, like what do you need? And so the first thing you need is a core pre-trained model, and this tends to be just trained on the web. The, the task you're training it on is just next word prediction. Then once you have that core model, then you want to do supervised fine tuning. So essentially you want to fix the user interface to that model because the model doesn't really. Uh, know how to follow instructions, for example. So you want the model to listen to you, but it has only been trained on predicting the next word, so it doesn't really know how to do that. So that supervised fine tuning, that's also proprietary data, uh, if you want, um, you can, you can get a much better model out of that. And then the final step is RLHF, reinforcement learning from human feedback, where you get this feedback loop to make the model even better for your specific use case, even if you don't have signal At the word level, you just have signal at the sequence level. So you can tell it like, okay, that was a good response, or that wasn't a good response, but you can tell it like, what did you do wrong necessarily? So if you, if you go through those three steps, then you get a ChatGPT. It's as easy as that. Um, but obviously there's a, the devil's in the details.

AI assessment note: “the first thing you need is a core pre-trained model, and this tends to be just trained on the web”

Q Can I ask you, which company do you think has the best data acquisition flywheel? When you look at them today, who do you admire and respect most?

A Open AI. Um, so maybe that's not the answer you expected, but so it's just an incredible company and they've really shown the world what's possible. Um, and they haven't even really trained as far as I know on the data that comes out of ChatGPT going viral, right? So they had ChatGPT, it went viral. This led to this giant, giant data mode that they haven't even really used, used yet. Um, so I, I think in terms of data modes and, and maybe you, you've seen this come by actually, there was this Google memo, uh, from an internal Google employee who, who had written that open AI and Google have no mode. I, I think for me, for me as a AI researcher, when I read that memo, I was like, this person has no idea what they're talking about.

AI assessment note: “Open AI. Um, so maybe that's not the answer you expected”

Q Okay. Uh, so they do actually have the notes. Um, What do you think are the biggest challenges that they face? Because I think we all dismissed Google quite significantly, if I'm honest, and then Bard came out and was pretty impressive. Like, how do you evaluate Bard and Google's display, actually?

A Yeah. Language model evaluation is, is a whole separate topic. It's, it's a super interesting question. Actually, I've been fascinated by AI evaluation for a really long time. And, and the answer is we don't really know how to evaluate the quality of these models anymore. Uh, so what we've seen people do in the field now is they're using GPD for, to evaluate the quality of other language models. Uh, and that, that just feels wrong. So I think there's a giant opportunity in the market actually for. A startup or several startups becoming like the Moody's or the SMP, uh, sort of, uh, uh, you know, uh, the folks who, who evaluate the quality of AI for specific use cases, um, because nobody really knows. It's really the Wild West out there. Um, and one of the big problems, for example, is data contamination where a bunch of these language models are trained on the things that they are being evaluated on. So GPT-IV looks like it's an amazing coder, but it might also just be, uh, trained on the data that it's evaluated on, which means that it's not actually that great of a coder.

AI assessment note: “the answer is we don't really know how to evaluate the quality of these models”

Q on the show who we discussed earlier, obviously a very big proponent of kind of open models. Um, it's kind of a very big debate, obviously between open versus closed. Where do you sit in terms of the model that rules for the next five to 10 years? And is it different for the model that rules for the next five years versus that that rules for the next 10?

A So the way I think about the language model space is kind of as a pyramid. So at the top of the pyramid, we have these Frontier models. Uh, so these are GPT four and, and tropic models and things like that, that are just much better than everything else, but also much more expensive and much bigger than everything else. Um, and then at the bottom of the pyramid, you have open source models. Anybody can train on them. Anybody can fine tune them on their data. Uh, there's, you can run them on your phone now and on your laptop and things like that. So that's a, that's a very, uh, fruitful area for research. But I think the most interesting part is kind of the middle piece of that pyramid where you have the, the most bang for your buck. Uh, so that's from a business perspective, the most interesting part where you have Mid-sized models that have capabilities that you don't really see at this bottom of the pyramid, um, that you can monetize it in various ways. So I, I think it's not gonna be the case that there's just one model that wins everything. It's going to be lots of models at different parts, uh, uh, different layers of, of this pyramid being used for different kinds of applications. So if you have very strong AGI requirements, you probably want to have a frontier model. If you, Care about it a bit less. Maybe you want to have artificial specialized intelligence. If you care…

AI assessment note: “I think it's not gonna be the case that there's just one model that wins”

Q love that. Um, no, I totally get you. You mentioned regulation there. I think a big question for me is, like, I don't think the chasm has ever been greater between private, like, company knowledge, especially around AI, specifically around AI, and then also the regulator's knowledge, which is significantly, ah, behind. How can effective regulation be set with such a large chasm between private sector knowledge and regulator knowledge?

A Yeah, we have to, uh, invest a lot in educating regulators. Uh, and I, I think the AI community has been terrible at this. And, um, I think the broader populace just needs to understand much better what AI is and what it can do and what it can't do. Um, and, and I think Uh, it, it's been slightly self-interest driven, I think, uh, in that a lot of folks in, in AI have just wanted to keep the technology for themselves. And that's why they haven't really invested in, in educating, uh, the rest of society. Uh, so I, I think that that's really a, a huge issue. And so there's a bit of a side point there, but I think the people who tend to write the regulation, they generally don't really understand technology all that way, all that much anyway. Um, so if you look at like the Senate hearings with Zuck, where the editors were asking him questions about the social network algorithm or whatever, and they didn't know what an algorithm was. Right. So the, that, that didn't you love it?

AI assessment note: “we have to, uh, invest a lot in educating regulators.”

Q I wonder why I'm such a natural venture capitalist when I have a proclivity for making money. Um, but you then spend time at Hugging Face, ok, and so with that transition, again, at one of the most prominent, kind of, rising stars in the space, how did that impact your mindset today?

A Yeah, Hugging Face is really a fascinating company. And, um, so I was at Meta, I was looking for something new. I was thinking about maybe doing a startup already, um, and then, uh, figured I needed to get some more experience first at a successful AI startup and Hugging Face, um, very clearly is a very successful AI startup. And it also really aligns with some of my values around open source and open science and things like that. And they have Amazing people like Mick Mitchell working there. Um, so I went there and I think what really impressed me actually, um, uh, is how good they are just at marketing and branding and community building. It's like, everybody just loves the company. Um, and, and I still, I don't understand how they do that. Um, so it's, it's really, uh, uh, yeah, some, something to see, even when you're on the inside, it's like, wow, like, Uh, look how, look how popular we are.

AI assessment note: “what really impressed me actually, um, uh, is how good they are just at marketing”

Q Sorry, I'm, I'm really stupid, so forgive me for this. Does that take more time then? If you have smaller models with more data, given you need to feed more data through the model, does it not take more time than if you needed less data going through the model?

A Yeah, it depends. So it's a trade off here, right? Um, but, but these big models also need a lot of data. So, uh, it really is, um, uh, a function of the number of GPUs that you have available. And so if you, let's say you have a thousand GPUs, you can choose to train a huge model on, on relatively, uh, little data and it will be okay, but it will be under trained. So you have some sort of optimal point. Where you can train the model to perfection. Um, and, and I think in the field, we were underestimating where that optimal point is. And it seems that data is much more important than model size when it comes to what's optimal.

AI assessment note: “it really is, um, uh, a function of the number of GPUs”

Q of a black box. I had someone on the show the other day, and they said, open or closed, you still don't really know what's going on in the core foundational model layer. The open or closed is not really the point. You still don't know. Is that true? And how do we think about actual true transparency of knowing what's going on and why it's producing what it is?

A Yes. So we're not going to be able to, um, really know why a neural net, what does what it does at the scale that neural networks operate, uh, at. So this is kind of like your own brain, right? Like, so I, I think your behavior is, is relatively predictable. Um, uh, so that goes for every human, right? We all like can predict each other's actions, but I have no idea what's going on in your brain and I will never know. There's no way I can know. The only, the only way I can kind of find out is by asking you. Um, but if you train the architecture the way we are training it right now, then at least you make sure that the model has learned to rely on the information that it finds. Um, and that gives you much stronger attribution than if it's just predicting the next word based on what it has seen before, uh, because it doesn't have this ability from birth basically to find relevant information and ground its generation, uh, on that, uh, uh, For the thing it found.

AI assessment note: “Yes. So we're not going to be able to, um, really know why”

Q Sorry, I'm, I'm really stupid, so forgive me for this. Does that take more time then? If you have smaller models with more data, given you need to feed more data through the model, does it not take more time than if you needed less data going through the model?

A Yeah, it depends. So it's a trade off here, right? Um, but, but these big models also need a lot of data. So, uh, it really is, um, uh, a function of the number of GPUs that you have available. And so if you, let's say you have a thousand GPUs, you can choose to train a huge model on, on relatively, uh, little data and it will be okay, but it will be under trained. So you have some sort of optimal point. Where you can train the model to perfection. Um, and, and I think in the field, we were underestimating where that optimal point is. And it seems that data is much more important than model size when it comes to what's optimal.

AI assessment note: “Yeah, it depends. So it's a trade off here, right?”

Q You mentioned the philosophy degree earlier. How do AI and philosophy help each other in your day-to-day role?

A Yeah, I'm very happy that I studied philosophy. So philosophy is really about conceptualizing anything and kind of any arbitrary level of abstraction. Um, and, and that ability, uh, you can use anywhere, right? So, um, for AI in particular, I, I think that, uh, philosophy and AI are, are an interesting combination because philosophy is about the stuff that you can't really do science about yet. So at some point, uh, the things that people are philosophizing about now, they become scientific questions that just have answers or hypotheses, and then they're no longer philosophy, right? So natural philosophy, That used to be a thing. We now call that physics and mathematics. And I think with AI, there are lots of questions that we still don't even really know how to ask yet. And philosophy is great for thinking about those kinds of questions. So, uh, yeah, philosophy of AI is a fascinating topic.

AI assessment note: “there are lots of questions that we still don't even really know how to ask”

Q I'm going to apologize in advance for this one. Okay. I'm sorry. I'm asking it anyway. What do you think the timeline is for super intelligence?

A Um, so, so despite like Nick Bostrom's book, I, I still think that super intelligence is, is actually very ill defined. Um, and, um, in, in many ways we have already achieved super intelligence, right? So in the fifties, we achieved mathematical super intelligence. So computers in the fifties were already better at Calculating stuff than humans. Um, so, so I, I don't think that that really is a, uh, A well-formed question. If you're asking about AGI, right? So, uh, and, and I think AGI itself, a lot of people, and this, this is a mistake I made where I thought AGI kind of meant artificial consciousness or something like that, which also doesn't really have a meaning. But if you look at how OpenAI and Anthropic and these places define AGI, it's as, um, um, systems achieving capabilities that allow them to Uh, effectively do the work of humans, uh, for the majority of economically valuable human tasks. Then we're not that far away from it. Um, and so, uh, I think in the next like five to 10 year, that sort of economic displacement is, uh, likely to happen.

AI assessment note: “I don't think that that really is a well-formed question. If you're asking about AGI”

Q I wonder why I'm such a natural venture capitalist when I have a proclivity for making money. Um, but you then spend time at Hugging Face, ok, and so with that transition, again, at one of the most prominent, kind of, rising stars in the space, how did that impact your mindset today?

A Yeah, Hugging Face is really a fascinating company. And, um, so I was at Meta, I was looking for something new. I was thinking about maybe doing a startup already, um, and then, uh, figured I needed to get some more experience first at a successful AI startup and Hugging Face, um, very clearly is a very successful AI startup. And it also really aligns with some of my values around open source and open science and things like that. And they have Amazing people like Mick Mitchell working there. Um, so I went there and I think what really impressed me actually, um, uh, is how good they are just at marketing and branding and community building. It's like, everybody just loves the company. Um, and, and I still, I don't understand how they do that. Um, so it's, it's really, uh, uh, yeah, some, something to see, even when you're on the inside, it's like, wow, like, Uh, look how, look how popular we are.

AI assessment note: “what really impressed me actually, um, uh, is how good they are”