Ankit Kumar

“When you build the thing, right, when you're building the product and using it every day, you know, there are some things that you work on that don't get into the demo because they're going to take longer and you want to ship the demo. You kind of know how big…”

“But really, I think with some of these more product experience questions, there's something qualitative about it that is very hard to quantify. That is one of the big challenges internally, actually, is how do you hill climb effectively on what is really an ML…”

“Misleading at times because you tried so much and you don't have, at least when we're trying it internally, you don't have such a diversity of users that you get kind of the first reaction over and over, right? You only get so many first reactions. And then wh…”

“I do think that there is kind of an under-investment or an under-focus in the sort of Strong AI team world on product experience and sort of creative taste and kind of humanities maybe, in a sense, to kind of bring AI to experiences that kind of everyday peopl…”

“And I think that we will see a lot of not just Sesame, but other kinds of media, let's say, that are sort of AI native in a way that Bring some creativity, bring some like storytelling into AI, or maybe bring AI into those categories. And I think they'll make …”

“Like we had to build the models that we're going to open source from scratch in order to get them to a point where they can achieve this experience.”

“I think from my perspective, good taste in ML today, because it's such a fast moving field with so many people working across, you know, open source and APIs and big labs and so forth. Really, you're trying to identify What part of the ecosystem or what part o…”

“At least to our knowledge, there's not another model out there that, that is open source that kind of is a sort of contextual thing where you kind of can put two participants in a conversation, even more, three, and generate kind of a conversation between them…”

“No one's going to replace phones anytime soon, or laptops for that matter.”

“Sometimes it feels like an API or something like that is like relatively easy to do. And, you know, it's not like, it's not maybe as hard as some of the other things that we're doing, but everything is a drag on engineering, right?”

“We want You know, sub-five hundred millisecond response times, and a lot of things that feel like not a big deal, 50 milliseconds here, 50 milliseconds there, can really add up.”

“Especially when you're smaller, you know, you don't really want to harden, like, you know, you have this team that is super, super niche and doing only this thing, because you don't know exactly what the stack is going to look like tomorrow. Things change on t…”

“Earlier on in the speech generation world in the community, very often you'd look at like word error rate where you look at transcription, like you kind of have a sentence and you generate and you transcribe it and you see if it's the same. And those metrics a…”

“We also have some data sets that are kind of like Just two people in a conversation or sometimes they're actors, but it's trying to be a real conversation. And so we'll kind of take the conditioning of some snippet of the conversation and then show a human rat…”

“I think you, I think it's much easier. It would be much easier to make a system that produces text chats with you that feels like you're texting a human because there's such a compression of like what the entity on the other side is into just like text. Wherea…”

“You need to make decisions at the hundred millisecond, let's say, Time segment so that if you're talking and the other person, you know, starts sort of making some noises that make it seem like they're trying to interrupt you or they want to say something, you…”

“And the models that we have today, like CSM, for example, And probably some of the models that we'll have in the short term that will make the experience better will still not be modeling the conversational dynamics because they're making decisions that kind o…”

“They're making assistance. They're making utilities. I love those products. I use them all the time. They're great products. We want to make a companion. And so our prioritization of features and of, let's say, post training kind of personality, et cetera, wil…”

“The other companies, the other sort of chat products and so forth, they will get better voices. They're all, like, it's not gonna, we don't have some magical secret sauce on the technical side that is gonna be, like, impossible to replicate. They're gonna get …”

“We think that that interface layer, it's really not kind of a core bigger, bigger models, better, better reasoning question. It's really a product experience question. It's really a question of, can you make a system that people actually want to interact with,…”

“I think that over time, I think we will see more of these, you know, bigger companies trying to operate this layer. Like I said, I think that there is not enough effort on that right now, making these systems delightful to interact with, you know, and I think …”

“I did this chat to be plugin system before, and I think it's still there probably. And it kind of didn't fully take off really.”

“I think that the models still need to get better basically to utilize plugins essentially in a way that's kind of reliable enough that someone will go out and look for a plugin for, you know, their kind of downstream service of choice because they just want, y…”