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Speaker unverified: belief

13 Feb 2025 Latent Space Bee AI: The Wearable Ambient Agent

“Although like, I think there might be an intermediary step with things to become expensive, which is like, we're really interested because like the pipeline is very tedious and like a lot of tuning.”

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Speaker
Speaker unverified
Attribution
Not verified from this transcript
Claim type
belief
Recorded
13 Feb 2025
Publisher
Latent Space

Transcript context

…You do several layers. You do live. Yeah. Yeah. So the speech to text is like the most challenging part actually, because you know, it requires like real time processing and then like later processing with a larger model. And one thing that is fairly obvious is that like, you don't need to transcribe things that don't have any voice in it. Right? So good voice activity is key, right? Because like the majority of most people's day is not spent with voice activity. Right? So that is the first step to cutting down the amount of compute you have to do. And voice activity is a fairly cheap thing to do. Very, very cheap thing to do. The models that need to summarize, you don't need a Sonnet level kind of model to summarize. You do need a Sonnet level model to like execute things like the agent. And we will be having a subscription for like features like that because it's, you know, although now with the R1, like we'll see, we haven't evaluated it. A deep seek? Yeah. I mean, not that one in particular, but like, you know, they're already there that can kind of perform at that level. I was like, it's going to stay in six months, but like, yeah. So self-hosted models help in the things where you can. So you are self-hosting models. Yes. You are fine tuning your own ASR. Yes. I will say that I see in the future that everything's trending down. Although like, I think there might be an intermediary step with things to become expensive, which is like, we're really interested because like the pipeline is very tedious and like a lot of tuning. Right. Which is brutal because it's just a lot of trial and error. Whereas like, well, wouldn't it be nice if an end to end model could just do all of this and learn it? If we could do transcription with like an LLM, there's so many advantages to that, but it's going to be a larger model and hence like more compute, you know, we're optimistic. Maybe we could distill something down and like, we kind of more than focus on reducing the cost of the existing pipeline or trying to the next generation. Cause it's very clear that like all ASR, all speech to the text is going to be pretty obsolete pretty soon. So like investing into that is probably kind of a dead end. Cause it's just going to be. It's going to be obsolete. It's interesting. Like I think when I initially invested in tab this is, this shows you how wrong I was. I was like, oh, this is a sort of razor blades, blade razors and blades model where you sell a cheap hardware and you make up a subscription, like a monthly subscription. And now I just checked friend is a one-time sale, $99 limitless one-time sale, $99. These guys one-time sale, $49 and inference is free. What? Wow. It's crazy.…

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