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
22 published records
01 / evaluation
“I don't trust benchmarks, especially when the numbers are close. I'm like, okay, this is useless now because it is completely gamified, right?”
02 / commitment
“I think we're still approximating what we have available. It's a super interesting topic, but It really depends on like how you define it, and we will have to have a discussion on the definition and then how you measure it.”
03 / belief
“I think the problem here comes from like, I think we understand how to do this in a normal ML context, but when you're trying to build AGI, the real world is everything.”
04 / commitment
“Just physics sounded so cool from their perspective, reading their books that I was like, okay, I'm going to try this, but sadly I will not be able to replicate some of them.”
05 / belief
“Distill the knowledge of the benchmark into a model, and then obviously it's going to perform better on the benchmark. But I think what's less understood now is, um, you know, the sort of un gamable leaderboards, like the LMSys leaderboard, like some, it's also possible to game those things, and you can distill smaller models to do well on those.”
06 / belief
“I think with gaming, I believe there is a new even way of interaction that's coming up with this AI experiences.”
07 / belief
“I think that's maybe the, not mistake, but like misunderstanding that people have when they think of NPCs.”
08 / uncertainty
“Is there any well known brand that People can link to, uh, you know, I know about like AI influencers, like on Instagram or AI wrappers, but I don't know about brand, uh, identities.”
09 / belief
“Especially for newer folks that have like a lot more training data out there, so to speak. I think of like, you know, Sean Carroll.”
10 / belief
“I'm sure it's very uncomfortable for him, but I think, I think he kind of embraces it.”
11 / belief
“I would say to bring people up to speed as well in like very recent developments.”
12 / uncertainty
“You see it in the fan fiction world, you know, people just come out with new things about the same franchise, like Harry Potter, just to have more things to read. So, yeah, I'm curious what that does, especially to, uh, allowing new IP kind of to come up when you have like such as iteration of successful ones, but I don't know.”
13 / evaluation
“The character would look at you and be like, why are you looking at me with that face? And that changes the whole flow, because right now, if you just talk to talk, it's not the same as if it sees you, it sees your reaction, and then it begins a conversation and it changes and you make a state based on that and all of that.”
14 / prediction
“So when you, when you quantize things, obviously you're going to lose precision because you just have less bits to store information in.”
15 / evaluation
“Like we, we've seen papers like textbooks is all you need, right? And that is because the textbooks are starting informationally dense and it's years of a human carefully crafting like word after word after word of what they are saying.”
16 / recommendation
“I think that makes a lot of sense and we're still maybe in the, everybody wants something else that is not transformers, you know, uh, but maybe the, the lesson is to not, to not move away too much.”
17 / prediction
“The v100 is about 130 teraflops of kind of like compute the gb200 at fp4 is like 20, 000 teraflops so the hardware alone today got much more powerful and I would love to maybe hear from you how at the time you were thinking about optimizing for the hardware today versus how much of an insight you had into the hardware that was coming especially working at NVIDIA and maybe people have the same discussion today it's like you know Should we optimize for the hardware of today or like for the hardware of tomorrow, because we need the results today, you know, as a business, but sometimes maybe we waste some time.”
18 / evaluation
“They use samples. And obviously this is not necessarily apples to apples comparison because you need to check at the fine print as to how they are running this.”
19 / evaluation
“But there were product requirements, right? And this is where inference becomes very interesting because it's not about making it the fastest, it's about meeting the human perceived latency.”
20 / prediction
“The way that people were doing it was by putting tags, like literally QR codes, onto the item such that they had some ground truth and then they would label it. But that's impossible, like this is the case where synthetic data really becomes important because there is no way you're going to get the pose of the item in every single position.”
21 / evaluation
“I don't know what to make about it because I don't think it's adopted seriously by the large labs.”
22 / prediction
“As we discover what are the use cases that are truly valuable, we are going to figure out what is the data that was actually valuable through this training process, I think, and we are going to be able to.”