Eradicating Machine Learning Pain Points with Weights & Biases CEO Lukas Biewald

**Sarah Guo** (0:05)
We've talked to many practitioners who are pushing the state of the art. This week on the podcast, we're exploring the dominant ML developer tool, Weights & Biases.
Elad and I are sitting down with CEO and co-founder, Lukas Biewald. He has an act for creating companies that support pain points in ML development. His first company, Figure Eight, addressed the problem of data collection for model training. And his second company, Weights & Biases, has created an experimentation platform that supports AI practitioners at companies including NVIDIA, OpenAI, Microsoft, and many more. Lucas, thanks for doing this. Welcome to No Priors.

**Lukas Biewald** (0:36)
Thank you. Great to be here.

**Sarah Guo** (0:38)
Lucas, you studied at Stanford, where I assume you discovered your interest in machine learning. And under one of our previous No Priors guests, Daphne Koller.
Can you talk about when you started working in AI and learning from Daphne?

**Lukas Biewald** (0:51)
Yeah, totally.
As a kid, I was obsessed with playing games, and I got really into Go, and I was super into the idea of thinking about how would computers win at these games. And so I actually sent Daphne an email, maybe as a freshman, being like, hey, can I work with you? I'm really interested in games. I want to learn how to beat Go. And Daphne wrote me actually a pretty polite email being like, that's not what I do.
A few years later, I took her course, and I studied math at Stanford. And I have to say, Daphne cared about a thousand times more about teaching than even the best professor in the math department. And so it was really just eye-opening. I just loved how much she actually cared about teaching, and it got me really excited about the AI that was working there. And I went on to be a research assistant for her.
And the funny thing at that time was like nothing really worked. Like it was just before kind of, you know, Google was thought to be really like page rank at the time was the thing that was making them work. And I think later, you know, it became clear that machine learning was a big part of that. But really when I was doing ML, it was like searching for applications that were working. And Daphne was actually really obsessed at the time with a thing called Bayes Nets, which you don't hear about too much anymore because I don't think they ever really, you know, worked for many applications. I hope I'm not offending anyone, but that's my understanding. I actually think, you know, the thing that I really took away from Daphne that really lasted with me was, I mean, she is one of the smartest people I've ever encountered. And she had this incredible clarity of thought and an intolerance for sloppy thinking that just really served me well. I think that's sort of separate from Machine Learning. You'd see other professors would come and give guest talks.
And they would say something kind of lazy, and we'd all just be sitting there just waiting for Daphne to eviscerate them. And I think her personality has mellowed a little bit over time, but I kind of miss, I just miss that sort of aggressive, clear thinking. And I really admire it.

**Sarah Guo** (2:51)
I don't think we got a taste of that, but we did talk about whether or not probabilistic graphs are coming back a little bit. How did you go from Stanford to founding Figure Eight?

**Lukas Biewald** (3:03)
Yeah, you know, it's funny. I actually really struggled doing research with Daphne.
Basically the things that I tried just barely, barely worked. I published a couple of papers that I feel kind of ashamed of, where it was sort of like go from 68% accuracy to 70% accuracy in a task nobody cares about by throwing like a thousand X to compute.
And by the way, like kind of guessing the most likely answer is probably like 64% accuracy. So, you know, it just it felt honestly kind of pointless and sad. Like I love the idea of like computers learning to do things, but it's hard to sort of sustain the enthusiasm for that when everything you try just completely, you know, doesn't work. And even the things that do work, you kind of wonder if you're like p-value hacking, like, OK, I tried a thousand things, you know. So I guess something's going to be like a little bit more accurate than a baseline.

**Sarah Guo** (3:54)
What tasks were you working on? Did you end up working on Go or games or anything?

**Lukas Biewald** (3:59)
No, Daphne is not interested in games, let me tell you. And it's actually another I kind of admire that that perspective too, as much as I love games.