When I had entered into my graduate program, I was told that it was a waste of time to do computer science and programming, that I would never be respected if all I did was like build tools or build libraries or write code to solve science problems.

How did the best machine learning practitioners get involved in the field? What challenges have they faced? What has helped them flourish? Let's ask them. Welcome to Learning for Machine Learning. I'm your host, Seth Levine. Welcome to Learning for Machine Learning. This episode, we have a very special guest, Paige Bailey, the lead product manager for generative models at Google DeepMind. And at such an exciting time, this week was Google I.O, where they got to announce new advancements for Google Bard, talk about Gemini, and how they're incorporating many aspects of generative models into all of their products, really. Paige, welcome to the show. Awesome.

Thank you for having

me. It's such a pleasure to have you. You have such an interesting background. You want to just give a little bit of background, introduce yourself, how you got interested in computers and machine learning?

Sure, that sounds good. So my background, I did geophysics and applied math when I was in school. That was kind of the focus of my career. I think when I was younger, I wanted to be like some sort of lady, Carl Sagan, like focused on planetary science and sort of being able to explain these complicated technical topics to the world. I got into computers very early. I grew up in quite a small town. And my family kind of rescued an apple to from being thrown away. And that was how I first learned how to program. And then I really, I think this time in particular is kind of what I've been waiting my entire career for. I've been doing machine learning for a bit over a decade. And previously, you would have to go through all of this pain to get the data, to build a model, to try to choose an algorithm, and then to do the really hard work of trying to get those models into production. And even then, those models were just kind of single task models. And today, we have these highly capable, very general purpose models that are doing an overwhelming number of things. And really, we keep discovering new ways that they're useful based on the way that people are sort of testing them out and stress testing their boundaries. So it's been really exhilarating, honestly. I would never have imagined that this would happen back when I started doing machine learning in 2009, 2010.