 Hi, Jefferyk here with theCUBE. We're at Santa Clara at Intel, at the MIT Chief Data Officer West Coast, the second edition. We've covered the East Coast one, I think for three or four years at MIT, wanted to come out and see what's going on at the West Coast. They had a terrific movie presentation, a panel discussion, they have a full day planned for tomorrow and we're excited to actually be just joined by the moderator of the panel tonight, Ben Sharma, Director of Strategy and Product at Intel for Big Data. Welcome. Thank you. Glad to be here. So it's been a long day. Thanks for hosting us. And not a fire drill, but like fire trucks and everything. It's been exciting. Yes, it was a fire alarm and it was a good demonstration of Intel's responsiveness, I guess. And we went to our designated assembly areas very, very well. So terrific movie. I've never seen that movie before. A lot of really neat examples. Really, it did put a face on Big Data much more than just getting my latte coupon when I walked by my local Starbucks. And there was one story in particular I wanted to follow up with you where one of the scientists had a baby set up cameras in every room of his house and let them run nonstop for two years. What did you think of that scene? It brought back a lot of really good memories for me. So I grew up in a household where my mom was actually linguist. She studied language formation in kids and she would experiment on her kids. So we had, as myself and my sister, a record, a daily journal that we kept of all the words that we uttered at what time. And it was very painstaking. I can tell you it was a lot of data, but it was nowhere close to the amount of data that the researcher at MIT collected from his child. And to me, that scope, that scale, was awesome in not only what they were able to collect, but the change in how we understand the origin of words and language in children. And to me, that was a pretty compelling story for turning something that you take for granted, the acquisition of language by a child into something that could now be predicted based on the context in which the words are uttered and what the parents say when and what else happens in the household. It was a classic story. Now, I'm interested though why you guys were so diligent about recording your own kind of learning of language. What was the motivation behind that? Because that's a pretty significant effort as well. Was it just for record keeping? Was it for giving gold stars when you learn new words? What kind of was the motivation? That's an interesting story. Well, it was actually we were, so I mean, if it's not already obvious, I grew up in India and India has over a thousand different languages. And when you are a family that moves around a lot, as we did, you start to pick up words from various different languages and start to use them in interesting ways. And so, in fact, what my mother was studying was bilingualism, the ability to use two languages simultaneously and see how kids did that. And today, if we were to do that process, we would, in fact, instrument a child's every utterance. And it doesn't have to be as gargantuan as scope as having video cameras in every room in the house. But you can imagine things that we've taken for granted in a variety of sciences, such as linguistics, in history, in archeology. Or industries from agriculture, as we saw, to precision medicine, to anything, being transformed by our ability to collect data from sensors, to be able to collect and aggregate it in a central location, whether it's a data center or a cloud, and then analyze it in such a way that you're turning insights. Isn't just insights that are aha or interesting. It's actually insights that overturn what is currently understood in that science or that industry. That, to me, is the real value for big data analytics. And Intel, again, is right in the middle of everything. X86 is the gift that keeps on giving. It's running all the servers and all these data centers that really power the cloud. As somebody said, the cloud's just really somebody else's computer. Chances are it's probably powered by an Intel microprocessor. The other interesting story I thought in that movie was about preemie babies and monitoring preemie babies and the fact that even though they had sophisticated equipment that's monitoring their heartbeats and their blood pressure, their temperature, et cetera, up until recently, most of that data just literally went onto the floor. They would only capture one data point, the whole set of data points every hour, even though that thing was kicking out data all along. So it's an interesting phenomenon and how now we have the ability with cheap storage, cheaper computing, things like Hadoop and these massive databases. So now we can really collect that stream of data, that huge stream of data in real time and then we can actually go back and do the analysis to find out when and where important things did happen. We just couldn't do that before. We couldn't and we couldn't, in some ways, what I find really interesting is that we're now bold enough to ask those questions that require data to be collected at that scale. It used to be that we wouldn't think to or question what we took for granted in the way things were done. So whether it's hospitals in their treatment of patients and you assume, for example, that pharmaceuticals when they develop drugs that they have a certain efficacy that they're looking for, that they're running tests based on the data that they're collecting statistically. But imagine now being able to use the sensor-driven data to be able to predict the efficacy of a drug. And now you have a significantly substantial, greater accuracy in the way that you analyze the data. So case in point, let's say you took something that's a chronic disease like Parkinson's. One of the projects Intel was involved in a couple of months. Again, in fact, it's almost a year now was to work with the Michael J. Fox Foundation where we had the researchers working with the foundation were looking to understand the progression of the disease. And today you go into a doctor's office, you sit down and they ask you to stand up, walk 10 paces, turn around and sit back down and ask you how you felt compared to when you last visited. But imagine being able to wear a watch with just an accelerometer in it that measures the tremors and the progression of the tremors over time. And now you have a much more nuanced, finely grained model for how the disease is progressing. But in addition to that, you have a smartphone app that says I took the medication at this time. And now you can figure out whether the medication had an effect in changing the tremor. And you start to get a much clearer view of whether the drug is working or not. I think that's a really cool illustration of what we're now able to do and the questions we're able to ask compared to what was available before. And then you combine that with, again, the power of Moore's law that keeps giving. And again, they referenced in the movie that they'll be able to eventually sequence all of our genomes, eventually $1,000 a person, at some point $100 a person, $10 a person. So now the ability to economically get that piece of data, map that back to the reactions to particular drugs, treatments. It's really a whole new world of medical science. It is. And I think that although we take for granted again some of the technologies that go into it. Moore's law is and does seem like an unstoppable force and it has not only sort of engendered industries and destroyed them, but it keeps, as you said, giving. What we expect is that some of those technologies that we need to develop to be able to bring the cost of a genome sequence down to $10 is also some of the same technologies that go into relating that data with your medical records, with your wearable data. And so I think we have yet to start on a very fruitful journey of not only collecting data but combining them, blending them into models and using advanced algorithms and advanced analytics to decide for the patterns inside them. So there's a lot more to come. Exciting times and enough to keep you busy, I'm sure. Sure is. All right. Well, thanks for hosting the panel. It was a great job. Well done, great panel. And thanks for hosting us as well. And thanks Intel for providing the facilities for this. Absolutely. So I'm Jeff Frick, you are watching theCUBE. We're at the MIT Chief Data Officer West. Thanks for watching. We'll catch you next time.