Mike Conover: evaluation

Transcript context

…Our last episode was one of the fan favorites, and I think this will be just as good. So for those that have not listened to the first episode, which might be many because the podcast has grown a lot since then, thanks to people like Mike who have interesting conversations on it. You spent a bunch of years doing ML at some of the best companies on the internet, things like Workday, you know, Skipflag, LinkedIn, most recently at Databricks where you were leading the open source large language models team working on Dolly. And now you're doing Brightwave, which is in the financial services space. But this is not something new, I think when you and I first talked about Brightwave, I was like, why is this guy doing a financial services company? And then you look at your background and you were doing papers on The Nature Magazine about LinkedIn data predicting S&P 500 stock movement, like many, many years ago. So what are some of the tying elements in your background that maybe people are overlooking that brought you to do this? Yeah, sure. Yeah. So my PhD research was funded by DARPA and we had access to the Twitter data set early in the natural history of the availability of that data set, and it was focused on the large scale structure of propaganda and misinformation campaigns. And LinkedIn, we had planet scale descriptions of the structure of the global economy. And so primarily my work was homepage news feed relevant. So when you go to LinkedIn.com, you'd see updates from one of our machine learning models. But additionally, I was a research liaison as part of the economic graph challenge and had this Nature Communications paper where we demonstrated that 500 million jobs transitions can be hierarchically clustered as a network of labor flows and could predict next quarter S&P 500 market gap changes. And at Workday, I was director of financials machine learning. You start to see how organizations are organisms. And I think of the way that like an accountant or the market encodes information in databases similar to how social insects, for example, organize their work and make collective decisions about where to allocate resources or time and attention. And that especially with the work on Twitter, we would see network structures relating to polarization emerge organically out of the interactions of many individual components. And so like much of my professional work has been focused on this idea that our lives are governed by systems that we're unable to see from our locally constrained perspective. And when humans interact with technology, they create digital trace data that allows us to observe the structure of those systems as though through a microscope or a telescope. And particularly as regards finance, I think the markets are the ultimate manifestation and record of that collective decision making process that humans engage in. Just to start going off script right away, how do you think about some of these interactions creating the polarization and how that reflects in the language models today because they're trained on this data? Like do you think the models pick up on these things on their own as well?…

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