Research teams need both bottom-up and top-down bets

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 31:53

   Yeah, but we can do so much better. Yeah, yeah. I guess let me just talk about the culture of AI research a little bit. Like, you know, Noam, you were obviously part of the, you know, a leading part of the original Transformer paper. Both of you have been a part of so many breakthroughs. You know, people like to write these thought pieces about, you know, the culture that drove these innovations. Curious, like, what you think the main takeaways you have after, you know, having done this research, you know, some periods of great success, some periods maybe of more frustration.

2. 32:17

   like what lessons do you take away from what actually you know works from a cultural and team structure perspective to drive this stuff forward maybe maybe uh sort of ai research is kind of where uh chemistry was in the 15th century same alchemy it's alchemy like we don't know why it works like but it's it's highly experimental uh You know, in terms of, you know, you get some idea, but really the proof is always in try it out.

3. 32:46

   And then, you know, and then you have various observations. You come to hypotheses about why, you know, okay, this thing works. You know, why? What's the key thing? And, you know, so sometimes you're right, sometimes you're wrong. it usually takes takes more more experimentation to find out or you can just like you know just claim that okay it works because of my magic XYZ and you have your assistant swallowing frogs or something and or the or the equivalent you know I find you know naturally researchers just, you know, love to share and get excited about, you know, about what they're doing.

4. 33:34

   And, you know, it is always important to try to, you know, credit people, you know, liberally because, you know, it's, you know, it's often very complicated to, you know, okay, what idea led to what idea, what was the key insight. And I think at some point, we'll just kind of have to give up on credit assignment temporarily and take a super intelligence-based approach of, we'll wait for the super intelligence to sort it all out once we get it.

5. 34:17

   Super intelligence will write some great thought pieces on the culture that drove these transformations. But it's super fun working at Google with this group of brilliant people and collaborating with everybody. One thing I'm struck by the Transformer story, I think, is it involved, I think you heard randomly that some people were working on it. When you tell the story, it sounds like, wow, that could have easily not happened.

6. 34:38

   I don't know. If you weren't walking down that hall one day, does it feel like inevitably someone six months later would have figured that out? How much just random happenstance is there that kind of leads us toward a different path from just people in this building colliding in different ways? Oh, interesting. Yeah, we would have all been using LSTMs or something. I mean, I guess maybe it's like, okay, you ask the same question about like, okay, if somebody hadn't invented like an internal combustion engine, would we still be using steam engines at this point?

7. 35:10

   I mean, I think someone would have come up with something like Transformer. From my vantage point, like, over in London, it did feel like you were circling around the, there was, like, neural GPU, there was, like, which was also, like, okay, the key thing is we're not going to have an RNN anymore, we're going to paralyze, but it's not just going to be a conf net, we're going to have, like, a notion of, like, the, I don't know, like, depth is going to be some kind of function of your sequence length, that was an idea in neural GPU, it didn't quite work out.

8. 35:41

   But it was, like, the key idea of, like, get rid of RNNs felt like that vibe was there. Yeah, right, because there were all this work on confnets running around. Everyone wanted to kill the LSTM. And, yeah, tension had been floating around from the translation models. So it kind of needed to come together. I love that. also kind of on the culture side, I think one of the interesting things of how I understand Google works is basically there's like this bottoms-up compute allocation, right, of folks, you know, getting to do different projects and then convincing other people to kind of come and allocate compute for that.

9. 36:23

   And obviously, that's one model. There's other places that are like, we're going to go all in on like one thing and they're much more top-down on the compute side. How do you think about like the trade-offs between those two models? Yeah, I mean, we've been through both or I've been through both, I guess. Google brain When I was at Google previously was, you know, mostly bottom-up, you know, as you describe, and then DeepMind has been mostly top-down. Yeah, it's a bit more top-down. A bit more top-down and, you know, different philosophies. And, you know, there are pluses and minuses to both. I think, you know, top-down can be good for getting people to collaborate and getting, like, larger training runs working, but bottom-up I think is also great for collaboration because then you bring someone new on your project

10. 37:15

    it doesn't mean you have fewer resources per person, you have more total resources so that's great and that there are so many abstraction-breaking ideas that there's no great way to categorize them so if you're saying, okay, this is the compute for pre-training and this is the compute for post-training, well, okay, you've got something completely different that doesn't fall into those things nicely and then it falls between the cracks. So we're bringing back a good measure of bottoms up because I think that's super important.

11. 37:56

    Yeah, it feels like an interesting balance to strike. Yeah, I guess I worked at OpenAI briefly, and I did like the way that concentrated bets were made and that obviously paid off well for them in certain areas, and that was nice. I also liked the way concentrated bets were made at DeepMind, especially kind of like, yeah, I do think there was a bit of a vision sometimes for like, say AlphaGo, I was asked to join the AlphaGo team right at the beginning because they needed an engineer and I was a research engineer and I just didn't get it.

12. 38:29

    I was like, why would anyone care about a board game? I don't get it. So you do really need like good research leads that have vision to drive these things. You can't expect everyone from a like, that doesn't have always the best like vantage point to like know what's, where the impact is going to be. Right now within like, for example, thinking as like the area of research that we kind of work within, it's like incredibly important.

13. 38:52

    We have a reasonable non-trivial investment in like just bottom up research where we're really not dictating anything. And then it's just a fun process of, it's kind of a fun meta research process of like how can we make these people maximally efficient? How do you make your baselines lightweight? How do you make these signal bearing? How can people move as fast as possible? And that's very important.

14. 39:11

    And then it's just very important that it can't all be that. It needs to be, and here is a mandate of top-down bets that we have to deliver on. But it's always humbling. I think we have a very good scope of what everything is happening and we get a sense of like, here are the areas that are going to be really important. And usually you get humbled by the bottom-up research of a thing that you weren't even thinking about ends up being way more impactful than you thought.

15. 39:34

    So just always keeping that running is super impactful.