Tri Dao: preference

Transcript context

…Yeah, I think the Dolly-15K data set is a good example of a company saying, let's do this smaller thing, just make sure we make it open. We had Mike Conover from Databricks on the podcast, and he was like, people just bought into it and leadership was bought into it. You have companies out there with 200,000, 300,000 employees. It's like, just put some of them to label some data. It's going to be helpful. So I'm curious to see how that evolves. What made you decide to join Together? For Together, the focus has been focusing a lot on open source model. And I think that aligns quite well with what I care about, of course. I also know a bunch of people there that I know and trust, and I'm excited to work with them. Philosophically, the way they've been really open with data set and model release, I like that a lot. Personally, for the stuff, for example, the research that I've developed, like we also try to make code available, free to use and modify and so on, contributing to the community. That has given us really valuable feedback from the community and improving our work. So philosophically, I like the way Together has been focusing on open source model. And the nice thing is we're also going to be at the forefront of research and the kind of research areas that I'm really excited about, things like efficient training and inference, aligns quite well with what the company is doing. We'll try our best to make things open and available to everyone. Yeah, but it's going to be fun being at the company, leading a team, doing research on the topic that I really care about, and hopefully we'll make things open to benefit the community. Awesome. Let's jump into the lightning round. Usually, I have two questions. So one is on acceleration, one on exploration, and then a takeaway. So the first one is, what's something that already happened in AI machine learning that you thought would take much longer than it has?…

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