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
21 published records
01 / belief
“How do you think about the pro market, so to speak? Because I think lowering the barrier to some of these things is great.”
02 / belief
“My sense is that, like, what did what did Bark lean off of like, because obviously, I think there was a lot of preceding TTS work that was in open source.”
03 / commitment
“You know, what I will say is that people are always surprised to learn that we don't only train on music.”
04 / belief
“I think to a large extent, what we do is we benefit from all of the beautiful things people do with transformers and text.”
05 / belief
“I think, you know, honestly, everybody is so focused on LLMs for good reason, and information processing and intelligence there.”
06 / belief
“You think so? I think so. Okay. Maybe part of your roadmap. You teased a little bit your V3 model.”
07 / belief
“Gosh, there's there's a lot, you know, I think from the model side, it's still really early innings.”
08 / belief
“We've gone almost 30 minutes without making any music on this podcast. So, I think maybe we can fix that and jump into a demo.”
09 / belief
“Yeah, no, that's a I think that's a good point overall about AI generated anything, you know, because I think recently T-Pain, he did like a an album of covers.”
10 / belief
“We are only scratching the surface of figuring out how to do stuff like that. And for example, the thumbs up and the thumbs down for other things like sharing telemetry on plays, all of these things are stuff that in the future, I think we would be able to leverage to make things amazing.”
11 / belief
“I think the, the temptation here is to always try to bake in some specialized knowledge about music or audio.”
12 / evaluation
“I think before we wrap, you have written a blog post that can show about good hearts law impact in ML, which is, you know, when you measure something, then the thing that you measure is not a good metric anymore because people optimize for it.”
13 / evaluation
“I think you could think about this, just in general, you have these like giant, really powerful models that are so prone to overfitting, that are so poorly understood, that are so easy to steer in one direction or another, not only from human feedback.”
14 / evaluation
“I think there are certainly some parallels there because, especially what I just said about being an active participant, mid-journey turns the joyful experience in mid-journey is the act of creating the image and not necessarily the act of consuming the image.”
15 / preference
“Without taking sides, this is kind of figuring out how to divvy up the current pie in the most fair way. And I think what we are super focused on is making that pie much bigger and increasing how much people are actually interested in music and participating in music.”
16 / preference
“That is the corner that we like to focus on. And I would say the last thing is much more geared toward professional musicians, which is basically AI tools for music production.”
17 / prediction
“I know people will do some diffusion for text, but I think nobody's like really doing that for real. And so we, we prefer transformers for a variety of reasons.”
18 / preference
“I think we intend to make many more modes of interaction with our stuff, but we are very much not focused on, quote unquote, professionals right now.”
19 / prediction
“Maybe I'm wrong there, but I think that would be technologically difficult. And then the other thing is that so much progress happens in shrinking models down for the same performance in text that I'm hopeful, at least, that a lot of our issues will get solved and we will figure out how to do better things with smaller models or relatively smaller models.”
20 / preference
“We try not to impose anything about music or audio in general into the model, and we kind of let the models learn things by themselves. And I think things are beginning to pay off, but it's, you know, it's not necessarily obvious from the beginning that that was the right thing to do.”
21 / evaluation
“It's so far behind images and text that there's like so much more to do. And honestly, I think a lot of people when we started a company told us to focus on speech.”