Mira: Beyond Co-Pilots - Making AI Production-Ready | Crypto x AI Events
Episode publishedPodcast: The Delphi PodcastThe Delphi Podcast
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Alright, everyone. Welcome back to Telp5's crypto AI month. We've had a lot of great talks, but I'm really excited to, you know, step into the ring and host this one. You know, and then that really nice to have you here. Really excited about this conversation. You know, before we dive in, I wanted to give some context to, you know, what we'll be discussing today and kinda like the framing I'll be asking my questions from. Right? So for anyone who's, you know, watching this outside of where, I'm one of the founders and CEO of Delphi Digital. As you've probably seen from all our crypto AI conten
Yeah. Thanks for taking the time, Anir, chatting about this. I know we've got some interesting research coming out in the next couple of weeks here, and happy to talk about that as well, if you'd like. My background is, you know, traditional big tech, for a long time, over a decade at Amazon and Uber, most recently led marketplace for Uber delivery worldwide. And prior to that, spent time with Amazon and Smart Home, Amazon AI, or in Cambridge. And this new round of AI that sort of, like, sprung up, you know, couple of years ago got me really interested. And as I was thinking about, you know, w
Definitely. Yeah. I think we'll definitely, you know, cover Mira, and I'm I'm excited to dive into it and kinda like the intersection you guys are building at. But, you know, AI's always, like, obviously been this huge buzzword lately. Mhmm. And, you know, I know, you know, I I know it's gonna take some time and and patience, but, you know, I think we're still not seeing AI live up to its, full potential as of today even though we've had some, you know, really powerful LOM models released in the last, you know, 18, 24 months. So, you know, what do you think is going on with AI independent of c
See, I think independent of crypto, AI in many ways is sort of like an extension of neural networks and ML from the last decade. Right? And the key innovation there was it was always matrix multiplication to some extent, but the key innovation in the couple last couple of years was the transformer paper that basically said, why don't we actually try and find every permutation and combination between every token and every other token? So, traditionally, you sort of, like, do a single pass on the training run, and then and you'd be done. And one of the reasons you think about why Facebook actual
Got it. Yeah. So, you know, when you bring up, like, AI reliability, like, what what's the core issue with that? And, you know, why isn't anyone yeah. I'm sure people are working on it, but, like, why don't you hear more about this? And, yeah, like, I'm curious how you think about that. You
know, my sense is that as an industry, we we saw some really exciting outcomes from foundation models. GPD, you know, 3.5 was more powerful than 3. 4 you know, 4 was more powerful than 3 and than 35. And so a lot of the focus was on that exciting end of things. Now that you've gotten to a sort of like asympt
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