 And hello, this is Dazza Greenwood from the MIT Media Lab, or actually from my home this fine Saturday, just with an informal quick prep talk with our featured speaker, Jason Barron, who, Jason R. Barron, who is actually going to, he doesn't know this yet, but he's going to be the kickoff speaker for a new computational law lecture series. This will be the first MIT computational law lecture series as a way to get out some of the best thinking and from thought leaders and technical innovators that we work with at law.mit.edu this Tuesday, February 7th, at 4 PM Eastern. And so you can go to law.mit.edu to learn more about that. And Jason, you're a good support for taking time on your Saturday to do this prep talk, mostly to help me get some of the team at MIT into the topic. And also to take that iterative legal hacker approach that we love so much here. I appreciate you're doing it, Jason. And if you don't mind just perhaps introducing yourself briefly, and let's get right into the content of your slides. Well, thanks, Dazza. I'm delighted to be part of your lecture series. And given that it's Saturday, you and I don't know whether Tom Brady and the Patriots won the Super Bowl this weekend. So we are in suspense. And that's all I'll say. I am a lawyer at the law firm of Drinker Biddle doing something called information governance. As my day job, I also teach at a law school in DC at American University, Washington College of Law. And for a long time, I was in government at the Justice Department as a trial lawyer and as the first director of litigation at the National Archives. And that's where I, in both of those places, I became exposed to the subject of electronic records and became fascinated with the discovery and hence this lecture. OK, so with that, I will make sure I have this screen that is on. And we'll see if this works here. OK, so my talk today is on the path to predictive coding in e-discovery search in an era of big data. And it is clear to some of us, I grew up in Lexington, Massachusetts. I am a graduate of BU Law School in 1980. And when I started practice, I was like the lawyer at the top. In the course of my career, I've gone from a paper-based world where a large case would involve many boxes of documents as opposed to just a few. They could involve a warehouse of documents with several lawyers going through that warehouse or even teams of lawyers. But from there in the 1980s, we now live in a new reality. Sometime around the turn of the century, the world got very, very different. We all know about, of course, the introduction of the internet in the 1990s, the introduction of email. And the world we are now in is a world where lawyers have to confront the prospect of tremendously vast collections of evidence in digital form that they need to sort through. They need to search through. They need to find relevant evidence. This talk is all about how lawyers got smarter over the last 15 years or so by asking questions as to how well they were doing in searching for relevant documents in a digital world. And that question is one where no profession has gotten smarter, but we are still on a path where we need to be much smarter in the future. The world that we are in is exponential. Data is doubling every two to three years. The internet of things in an acceleration of that pace with smart devices from every corner of the earth, feeding data, having streams of data that need to be analyzed. But we're also in a world of increasing text and increasing videos, increasing audios, and all types of media. In fact, the live streaming of this talk is an example of the tremendous volume of data that is being captured and all of which lawyers might find to be useful to look through in particular settings. So sub-universe of the larger universe of near-infinite data may be useful, in particular cases, to collect and sift through and find relevant documents for litigation. We have entered the era where big data is essentially infinite. None of us as lawyers, and I dare say even MIT community types, do not have a visceral feel of the difference between a terabyte, a petabyte, an exabyte, a yottabyte, and beyond. Or a jickerbite. These are all just a term. Just so you know, your screen is not being shared yet, and it would be helpful if you could go to screen share. OK. Thanks. Let me see. You're on a roll. And well, can you see that, or is it from current slide? Does that work, or can you not see it? So you would go up to the Hangout window, move your cursor around on the video to invoke the vertical menu on the left. Yes. And then click the green button section from the top that says screen share to your slides. And? And something, yep, so now we're in the video window, and now we're off of screen share. Right. Let me just look at this here from the current slide. Oh, I see. The problem is, is that now? So what had happened a moment ago is you started screen share. You screen, and then you need to just, once you click the screen share button, you then need to note what's on the screen, and you'll see you'll have a selector at that point. Make the selection after you click the green screen share button of your slides. So first you go to the video window and kind of move your cursor around to invoke the vertical menu. That's where you'll find the screen share options. Right, yes. So let me know when you've done that. That's the screen share. OK, and then select, you should have full screen or application. Maybe select application, and then OK, something's happening. Some things. OK, we're in. Thank you. One of the free services we provide is Google Hangout for experts, and for everyone else to like, nobody can figure this out. We can see your slides now, and back to you. OK, well, maybe, I don't know whether you saw these slides, but this was me in the 1980s through the 2000s. This is the exponential world I was talking about. This is a big data world with one of my favorite people, Freeman Dyson, talking about infant data. Hopefully, you can see this. This is what I consider to be a dinosaur, a Jurassic Park slide. I should have a large dinosaur rearing up, because in the era of information of inflation that we have been in since the 1990s and 2000s, we no longer can do what this.