Latent Space / 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
36 published records
01 / belief
“I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on.”
02 / belief
“I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.”
03 / belief
“Uh, cool. Uh, I think. There, there’s more general, like broad questions, but like, I guess what, what do you, uh, wish you were asked more in, in, in general, like, you know, like you, you have such a broad scope.”
04 / belief
“I think, uh, one thing that, uh, anti-gravity from, from Google also did was like, just come out the gate to very, very strong multimodal, including videos, and that’s the highest bandwidth communication prompt that you can give to the model, which is fantastic.”
05 / uncertainty
“I don’t know if you have any predictions that you, that you like to keep, you know, like, uh, one, one way to do this is you have your tests whenever a new model comes out that you run, uh, what’s something that you’re, you’re not quite happy with yet.”
06 / belief
“You could generate way more code, uh, and check that the code is cracked with a chain of thought reasoning. So I think, you know, being able to do that at 10,000 tokens per second would be awesome.”
07 / belief
“Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns.”
08 / belief
“I mean, I think I actually wrote a one-page memo saying we were being stupid by fragmenting our resources.”
09 / belief
“Can I do it with a hundred, if I need a trillion healthcare tokens, it’s like, they’re probably not out there that you don’t have, you know, I think that’s really like the.”
10 / belief
“Yeah, my, my, uh, you know, it’s just on this, like, like big prompting and, and, uh, iteration, you know, I think that coming back to your latency point, um, I always, I always try to one, one AB test or experiment or benchmark or research I would like is what is the performance difference between, let’s say three dumb fast model calls with human alignment because the human will correct human alignment, being human looks at the first one and produces a new prompt.”
11 / belief
“Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.”
12 / belief
“Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it’s an important set of capabilities to have and also inference time scaling.”
13 / belief
“Um, so for example, um, I think, uh, telescope and, uh, binoculars were both in the training, uh, categories for the image model, but, um, microscope was not.”
14 / uncertainty
“Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don’t know how they work.”
15 / belief
“I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving.”
16 / belief
“Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big.”
17 / belief
“I mean, it’s actually, I think people kind of are not necessarily aware of what the Gemini models can actually do.”
18 / belief
“Yeah, I’m, I’m a big believer in pushing on latency because I think being able to have really low latency interactions with a system you’re using is just much more delightful than something that is, you know, 10 times as slow or 20 times as slow.”
19 / belief
“I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page.”
20 / belief
“Uh, w w while we’re on this topic, you know, I think there’s a lot of, um, uh, this, the concept of precision at all is weird when we’re sampling, you know, uh, we just, at the end of this, we’re going to have all these like chips that I’ll do like very good math.”
21 / belief
“Um, but, uh, you know, I think combining a lot of those techniques and really just trying to push on scaling things up over the last, you know, 15 years has been, you know, really important.”
22 / belief
“Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that’s where you see what capabilities now exist that didn’t exist at the sort of slightly less capable last year’s version or last six months ago version.”
23 / belief
“I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected.”
24 / belief
“Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there’s probably hundreds of modalities of data where you’d like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world.”
25 / belief
“Um, I still think there’s a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.”
26 / uncertainty
“You can interact with five, five of those teams and they’re off doing things on your behalf, but I don’t know exactly what the, how this is going to unfold.”
27 / evaluation
“I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it’s sort of, it’s either the case that you’ve now achieved that capability, or there’s also the issue of leakage in public data or very related kind of data being, being in your training data.”
28 / evaluation
“Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we’d like in those areas.”
29 / prediction
“We’re trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where.”
30 / evaluation
“I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that’s out there.”
31 / commitment
“I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you’re trying to design a chip starting today and that design might take two years before it even lands in a data center.”
32 / preference
“We, you could train it on because we want it to have a balanced set of capabilities.”
33 / preference
“Because people are saying like ternary is like, uh, yeah, I mean, I’m a big fan of very low precision because I think that gets, that saves you a tremendous amount of time.”
34 / recommendation
“I mean, it was important, but it wasn’t sort of the thing. That drove the actual creative process quite as much as if you specify what software you want the agent to write for you, you’d better be pretty darn careful of and how you specify that because that’s going to dictate the quality of the output, right?”
35 / evaluation
“Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you’re going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X.”
36 / evaluation
“I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it’s also a lower latency. And I think latency is actually a pretty important characteristic for these models because we’re going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so.”