 with John Furrier, we are at the Women in Data Science conference, the first ever John data science conference here at Stanford at the Ariaga Center, about 500 women going all day. They've got panels, keynotes, breakout sessions. It's a lot of horsepower, John. I was a little confused by some of that early algorithm stuff, heavy science. Yeah, this is not one of those women in tech promotional things. This is like down and dirty hardcore computer science at Stanford, PhDs, big name, industry folks in the air, but also really a lot of tech athletes, as we say, the people who are actually doing amazing work. I mean, when I say amazing work, we're talking about machine learning, we're talking about companies like Netflix, Walmart, Labs, Intel, and just a lot of academic PhD and master students. So this is absolutely a computer science oriented program. But with data science, you bring in physics, you bring all kinds of interdisciplinary. So that's what I was most impressed on is the quality of the people in the room and the interdisciplinary around computer science and science in general. And just overall, just the content was phenomenal. Looking at the background of some of the guests, John, most of these people came straight out of their PhD, straight into Google, straight into Netflix. And it's amazing. Caitlin Smallwood from Netflix talking about the level of granularity of detail. They have 50,000 descriptive lines for one of my panels when I go to Netflix. The amount of attention to detail experimentation is really phenomenal. Yeah, and they go in the great detail. And the problems that they're solving are significant. So this is like really amazing and inspiring to see all these women in tech and these ladies just kicking butt here with the science. But it's interesting that dynamics are different than the men's shows, or the standard shows, which is mostly dominated by men. The hallway conversation is highly engaging. And they're also camera shy. I just tried to get them, come on, camera. Oh no. So there's a little bit of a fear that their employer will get wind of it, that they're afraid of some PR, because with data science there's a huge PR problem. For instance, one company I won't say was talking to us at the table about the ramifications of just really good science and how it could be misconstrued in the PR function because it would look too weird. Oh my God, you're playing with the data. Are you playing, are you manipulating? So there's an element of awesome science going on that's just not well understood by mainstream right now. So that's why I love Stanford here, this event. They're working on some really cutting edge science around algorithms. And it's all tight now in computer science. It's not messy, like it was in the 80s when I was growing up. It was like data structures with algorithms and compilers and operating systems. Now you can be an algorithms person and you don't have to take the data structures and all this stuff kind of the requirements. But really, really clean computer science programs now and they're bringing in other disciplines. So really exciting to see. Yeah, it was great. You know, it was not that long ago. You were talking with Michael Jordan at the GE event and he talked about the differences between computer science and math. And you know, computer science, everybody wants the right answer. Math, everything's got a confidence factor, right? There is no right answer. And really it feels here at this data science summit that they're trying to bring in as many disciplines as they can. And also what keeps coming up over and over even from Google that has infinite resources, you would think, computing and money. You still have to make trade-off decisions. You still have to have a hypothesis. You can still start the process and save a lot of computational power because again, there always has to be a value component to it. So these are the people that are at the big companies that are at the cutting edge of the science. And it was another thing that was interesting, John, during the keynotes, kept referencing research, research, research, research. Most of the research articles reference were 04, 07, 09. There was one 1999. It's all relatively new. It's all relatively fresh and people are in a position to really cutting edge, move this thing forward. Yeah, you know, Jeff, that's a great point. And we noticed at MIT with Andrew McAfee and Dr. Lowe. And now here at Stanford is it's a really great time for this type of approach with data science, with interdisciplinary and whatnot, these stuff that's coming together. Because it's so clean now. A lot of the foundational work has been done in the science and the computer science side. It's not as messy as I was saying before. So what you're seeing now is a real explosion of multi-talented individuals, men, women, across multiple disciplines. So a lot cleaner is not as messy. And again, a lot of that foundational work on the computer science stuff is done so you're starting to see explosion. And that is super exciting. And that brings up these conversations. You're starting to see more people involved. You're seeing a PhD person in physics who's really engaged with the discipline of physics, programming because it's part of the tooling of their curriculum. And that spawns more creativity, more collaboration. So the computer science flywheel is rolling. The foundational work is done. And you're starting to people really sink their teeth into solving big problems. Yeah, I'm reminded of my conversation with Hilary Mason at Grace Hopper a couple of weeks back where she called me out on my computer science done, or data science done well is magic data science done poorly is creepy. She's like, no, no, no, it's not the data science. It's how you use it. It's what you do with it. And really there's conversations about ethics and how you do experimentation and really being sensitive and doing the right thing. You know, you just don't willy-nilly start playing with people's experience. They're your customers. They're paying you money. We were having a conversation just at lunch there about Facebook's controversy around are they manipulating the emotions of the users? And this was a really, I mean, I'm sitting all these PhDs excited like, yeah, I'm a little brain dead right now, but no, but here's what the bottom line was. It actually is iterating on the data aspect of a user experience. On one hand, you can look at that saying, whoa, we're being manipulated, but really Facebook's trying to just do great work to create the best user experience possible. So in the effort to be iterative and to be agile, there is some misconstrued situations of, wait a minute, it's not evil. Don't do no evil, as Google says. So in computer science, you're going to have this element of manipulations, element of experimentation. And that's where people get confused. Experimentation and iteration for a great user experience where data's at the heart of the value proposition should not be misconstrued as evil. And that really is going to be where people in the science and the field and journalism have to do their work to identify the signal versus the noise. Yeah, and the other piece I think that's really great is people have this perception that, grab a bunch of data, throw it in a Hadoop cluster, the answers come out. That's really not the way it works, right? You've got to have a hypothesis. You've got to have some relative knowledge about what you're trying to discover to move the process along. Still a lot of great opportunities for kind of O-line merchants, O-line people that have been in the business for a long time to really partner with data scientists, partner, give them the context, leverage their expertise and really get to a one plus one makes three opportunity. Yeah, this is going to be multiple decades of innovation because like I said, the foundational work's done. You're going to see an explosion of great things because it's just so early, even in the room there, we were having another conversation we were having at the table was, you know, can you imagine in middle America, this conversation, we were like, no, we can't, this is Silicon Valley. It's a very special place here at Stanford. And the top minds of women in computing are here talking about some of these crazy, cool things. To the middle of America, we'll be like, oh my God, and the press will try to make, you know, create fear. So, you know, there's a whole transformation happening and I personally think it is one of the most exciting times to be in computer science. It is one of the most exciting times to be young and be a developer, to be an entrepreneur because so many new things are changed. It's really going to transform the work streams, the value chains and everything else in business and in science. John, it's great and we're very fortunate. We live close by. We can come over here because a lot of innovation happening here at Stanford. A lot of great companies coming out of here. A lot of brilliant PhDs partnering with the business school people to come out and really create transformative companies. And so we're excited to cover it. Excited to be in the neighborhood. I'm Jeff Frick with John Furrier. We are at Stanford University at the Women in Data Science Summit. They're our first one. Look forward to being back next year. Thanks for watching.