WEBVTT - Big Applications for Big Data 0:00:00.160 --> 0:00:07.400 Brought to you by Toyota. Let's go places. Welcome to 0:00:07.560 --> 0:00:15.120 Forward Thinking. Hey there everyone, and welcome to Forward Thinking, 0:00:15.200 --> 0:00:17.400 the podcast that looks at the future and says, here's 0:00:17.400 --> 0:00:19.880 a little story. I've got to tell about three bad podcasters. 0:00:19.880 --> 0:00:24.120 You know, so well, I'm Jonathan Strickland and I'm Joe McCormick, 0:00:24.320 --> 0:00:27.320 and you just you don't dig the BC boys they're 0:00:27.360 --> 0:00:29.800 Lauren No, no, I do that. That was That was lovely, 0:00:29.960 --> 0:00:33.199 Thank you, terrific, thank you. Uh, and we want to 0:00:33.240 --> 0:00:36.279 talk a little bit more about big data. You know, 0:00:36.720 --> 0:00:39.960 we talked about big data. It's okay, I'm going to 0:00:40.080 --> 0:00:45.040 use both pronunciations. Okay, fair enough, so big info, uh no, 0:00:45.200 --> 0:00:47.720 big data because we're all you are talking about computer 0:00:47.880 --> 0:00:50.800 information here and not the android from Star not the 0:00:50.840 --> 0:00:53.920 android from Star Trek. Yeah, just get that all the way. 0:00:54.320 --> 0:00:58.320 So we're we wanted to talk more about applications of 0:00:58.360 --> 0:01:02.200 what what different organizations, companies, governments are using big data 0:01:02.240 --> 0:01:04.600 for what they're mining out of this huge amount of 0:01:04.640 --> 0:01:07.679 information that we are generating every day. Now. You may 0:01:07.680 --> 0:01:11.560 remember in our last podcast we said that we're generating 0:01:11.600 --> 0:01:16.080 about two point five quintillion bytes of information per day 0:01:16.160 --> 0:01:18.440 and not just humans, but you know, sensors, things that 0:01:18.480 --> 0:01:22.320 are uh indirectly yeah, internet things, stuff that's connected to 0:01:22.360 --> 0:01:25.559 the Internet that we're not directly in putting data into. 0:01:25.880 --> 0:01:28.240 And for those of us who missed the last podcast, 0:01:28.319 --> 0:01:31.640 what's the difference between this paradigm of big data and 0:01:31.680 --> 0:01:35.919 just say a lot of data. Big data? We're talking huge, huge, 0:01:36.160 --> 0:01:39.600 enormous amounts of information. When we're talking two point five 0:01:39.720 --> 0:01:45.200 quintillion bytes, that's half of all the spoken words that 0:01:45.360 --> 0:01:49.720 humans have uttered since the dawn of language. So we're 0:01:49.760 --> 0:01:53.680 talking about in two days, you generate as much information 0:01:53.880 --> 0:01:57.040 as all the words we've ever spoken ever of all 0:01:57.080 --> 0:02:00.120 the people. So there's this issue of volume, but also 0:02:00.400 --> 0:02:04.520 these characteristics we talked about, like velocity and variety. So 0:02:04.600 --> 0:02:07.960 it's not just the amount of data, but it's interacting 0:02:08.560 --> 0:02:13.440 exactly extremely fast. We're gathering it at an incredible pace. 0:02:13.639 --> 0:02:17.000 I mean, you're you're gathering data at an unprecedented pace. 0:02:17.160 --> 0:02:20.320 You are. It's rich and intense and everywhere. Yeah, and 0:02:20.360 --> 0:02:23.400 it's all different types of information. And also there's a 0:02:23.400 --> 0:02:27.560 fourth v that we can mention, which is voracity, which 0:02:27.600 --> 0:02:31.160 is the quality of the information truthiness. Yeah, yeah, there 0:02:31.160 --> 0:02:35.280 you go. Uh we're gonna tell you some strategicies that 0:02:35.360 --> 0:02:38.440 people use with big data. Uh it does mean truthiness. 0:02:38.480 --> 0:02:42.639 I mean it means that the data is essentially high quality. Right. Yeah. 0:02:42.760 --> 0:02:45.880 Voracity is really kind of their way of saying, how 0:02:45.960 --> 0:02:48.919 good is your information? And sometimes you don't know how 0:02:48.919 --> 0:02:51.239 good your information is, because again, you've got a lot 0:02:51.280 --> 0:02:53.560 of it. Until you analyze it, you don't really know 0:02:54.040 --> 0:02:58.280 if there's anything useful there. Uh. IBM is in the 0:02:58.400 --> 0:03:02.760 business of of leveraging big data and helping other companies 0:03:02.840 --> 0:03:07.600 leverage it. And uh and and they say very clearly 0:03:07.639 --> 0:03:10.200 on their website and in their white papers that the 0:03:10.240 --> 0:03:12.760 important part here is that you have to figure out 0:03:12.800 --> 0:03:16.280 what your goal is before you start just looking at 0:03:16.280 --> 0:03:18.080 big data and saying we need to be part of this. 0:03:18.639 --> 0:03:21.600 Because whatever your goal is, that's going to end up 0:03:21.720 --> 0:03:25.440 informing your approach to using that information in a way 0:03:25.480 --> 0:03:28.560 that makes sense. Otherwise, you're just talking about an enormous 0:03:28.600 --> 0:03:32.200 resource that may not be directly useful to you. You're 0:03:32.240 --> 0:03:35.400 just kind of looking at it and thinking, I want 0:03:36.000 --> 0:03:38.240 to make use of that information. I just it's too 0:03:38.280 --> 0:03:41.000 big a problem for me to even get a grasp 0:03:41.040 --> 0:03:44.440 on how I want to use it. So you can't 0:03:44.480 --> 0:03:47.080 just run at it and say, give me some ones 0:03:47.120 --> 0:03:49.960 and zeros, I'm gonna make magic happen. You have to 0:03:49.960 --> 0:03:52.000 have a plan in place first. But I've got a 0:03:52.000 --> 0:03:54.720 lot of really clever people have figured out really cool 0:03:54.760 --> 0:03:58.720 stuff to do with this information. Sure. In the last podcast, 0:03:58.840 --> 0:04:01.960 we talked a little bit about traffic analysis, which was 0:04:02.080 --> 0:04:04.800 a very kind of you know, you know, it's an 0:04:04.800 --> 0:04:08.520 easy to understand application of big data, right, So let 0:04:08.520 --> 0:04:13.320 me give you an example using Google's approach. So the 0:04:13.320 --> 0:04:17.120 way Google would generate traffic on Google Maps. If you 0:04:17.160 --> 0:04:20.000 were using Google Maps on a mobile device and you 0:04:20.080 --> 0:04:22.080 wanted to try and get from point A to point B, 0:04:22.200 --> 0:04:25.120 and you wanted traffic to be part of that that equation, 0:04:25.480 --> 0:04:28.880 that route, then what it would do is it would 0:04:28.880 --> 0:04:32.560 start looking at information of other Google users. Uh. There 0:04:32.560 --> 0:04:36.479 are other systems that use this, the Dash system, which 0:04:36.960 --> 0:04:39.800 doesn't really exist anymore, but uh, it used a very 0:04:39.839 --> 0:04:44.920 similar approach where it would send anonymous data about vehicles 0:04:44.960 --> 0:04:48.160 that were moving through a particular region and the speed 0:04:48.240 --> 0:04:52.800 at which they were moving. It was just sampling the vehicle, yeah, 0:04:52.800 --> 0:04:55.680 sampling the vehicle's location. It would get the GPS coordinates 0:04:56.000 --> 0:04:57.880 and it would just sample it over a certain amount 0:04:57.880 --> 0:05:01.440 of time and derive how fat that vehicle was moving 0:05:01.480 --> 0:05:04.600 down the street based upon the information, right saying, okay, 0:05:04.600 --> 0:05:06.880 well it was at this point at this time, and 0:05:06.920 --> 0:05:09.000 it was at this other point a little bit later, 0:05:09.080 --> 0:05:10.760 and this other point a little bit later. Therefore, that 0:05:10.760 --> 0:05:13.839 means traffic is moving at this speed down this particular street, 0:05:14.400 --> 0:05:18.080 and then it's extrapolates that sends it out to everyone 0:05:18.120 --> 0:05:21.120 so that you know which routes have the heaviest traffic. 0:05:22.000 --> 0:05:26.000 Now the Yeah, it's it's a very simple approach to 0:05:26.160 --> 0:05:29.039 big data in the sense that it's just taking real 0:05:29.120 --> 0:05:33.600 time information, analyzing it, and sending the results back very quickly. 0:05:33.600 --> 0:05:36.880 It's not storing information, it's not trying to be transformative 0:05:36.920 --> 0:05:39.240 with the information. It's just trying to make sense of 0:05:39.320 --> 0:05:41.680 all these different pieces of information that are coming in 0:05:42.040 --> 0:05:44.720 and then making it meaningful to the people who are 0:05:44.800 --> 0:05:49.240 using the service. So that's one example, but that's just one. 0:05:49.560 --> 0:05:53.760 You can actually see some pretty interesting patterns when you 0:05:53.800 --> 0:05:56.640 get huge amounts of information. You can see patterns where 0:05:56.680 --> 0:05:59.640 you might have thought there was just chaos before. So 0:05:59.760 --> 0:06:03.640 you you can look at a system that you might say, well, 0:06:04.279 --> 0:06:07.560 from the outset, it just looks like stuff is happening. 0:06:07.880 --> 0:06:10.279 But now when I see all this information, it's broken 0:06:10.279 --> 0:06:12.679 down like this, I can actually see trends where before 0:06:12.720 --> 0:06:16.800 I just saw stuff. So education is a good example 0:06:16.800 --> 0:06:19.480 of this. So let's say you're a teacher and you're 0:06:19.480 --> 0:06:25.520 teaching a class, and you have your classes submitting schoolwork 0:06:25.560 --> 0:06:28.800 in a into a system that can then analyze the 0:06:28.839 --> 0:06:32.040 school work. So you're grading the kids. You you might 0:06:32.080 --> 0:06:34.880 actually just be inserting the grades into the system. In fact, 0:06:34.880 --> 0:06:37.960 it may not have any connection with the kids directly 0:06:38.000 --> 0:06:41.200 at all. You might just be the teacher in the system. 0:06:41.240 --> 0:06:44.240 The system would be able to, if you're using a 0:06:44.360 --> 0:06:47.520 very sophisticated approach, be able to start detecting trends in 0:06:47.600 --> 0:06:52.880 each individual student's progress. So you might be able to say, oh, 0:06:53.120 --> 0:06:59.320 while while student A isn't failing, the trend indicates that 0:06:59.720 --> 0:07:02.120 the student is beginning to struggle. So I need to 0:07:02.200 --> 0:07:05.960 adjust my way of reaching this student so that I 0:07:06.360 --> 0:07:10.080 am not leaving the student without any support. Oh, I wonder, 0:07:10.200 --> 0:07:15.280 so would that involve comparing uh little signals with millions 0:07:15.280 --> 0:07:18.080 of other students, Like if we we've seen that when 0:07:18.120 --> 0:07:22.480 these things start to happen, statistically, that means like we're 0:07:22.480 --> 0:07:25.679 heading towards failure. It would mean that It would also 0:07:25.760 --> 0:07:28.040 mean that on a larger level, you might see that 0:07:28.120 --> 0:07:31.120 an entire classroom is having some issues, which would tell 0:07:31.160 --> 0:07:33.800 the teacher I need to change my approach. I I 0:07:34.240 --> 0:07:37.040 you know this, this concept that I've tried to teach. 0:07:37.080 --> 0:07:39.880 Obviously this has not worked out. So I need to 0:07:39.920 --> 0:07:42.720 find a new way of getting this across in a 0:07:42.760 --> 0:07:44.880 way that makes sense to my students, or perhaps help 0:07:44.920 --> 0:07:47.720 an entire school system figure out how to how to 0:07:48.520 --> 0:07:51.960 grade and test better. Exactly. Yeah, you know how if 0:07:52.000 --> 0:07:55.000 you take a survey um and your survey has just 0:07:55.040 --> 0:07:57.440 a hundred people in it, well, it's probably not very 0:07:57.440 --> 0:08:00.240 representative of the entire population, but the big but you 0:08:00.280 --> 0:08:03.080 can play family feud, right if you have a thousand people, 0:08:03.120 --> 0:08:06.400 it's better. If you keep increasing your sample size, your 0:08:06.440 --> 0:08:11.240 statistics become better and better at representing stronger trends that 0:08:11.280 --> 0:08:13.800 you want to look for. And this is the same 0:08:13.840 --> 0:08:16.880 thing you'd see. It's why big data is great. Right, 0:08:16.560 --> 0:08:20.720 you're increasing your sample size, right, Yes, As you increase 0:08:20.760 --> 0:08:23.440 that sample size, then you can actually start to recognize 0:08:23.480 --> 0:08:25.760 things that are truly trends and not just a one off. 0:08:26.400 --> 0:08:29.520 They're less likely to be anomalies. Yes, exactly, And this 0:08:29.600 --> 0:08:32.920 has tracked a lot in especially in consumer segments. I mean, 0:08:32.920 --> 0:08:34.880 you know, like every time you buy something on Amazon, 0:08:34.960 --> 0:08:37.360 it it collects a little group of other things that 0:08:37.440 --> 0:08:40.000 people who have bought that thing have also bought in 0:08:40.080 --> 0:08:42.600 case you want to buy that thing, um and and 0:08:42.720 --> 0:08:45.840 can be very useful not just for not just for 0:08:45.880 --> 0:08:49.160 that basic you know, like we want to sell more stuff, 0:08:49.440 --> 0:08:53.560 but uh yeah, I mean with Amazon sometimes it's creepy 0:08:53.679 --> 0:08:57.400 how how on spot it is? Or it's creepy how 0:08:57.440 --> 0:09:00.000 not on spot it is, and you wonder if they're 0:09:00.120 --> 0:09:02.680 something you don't know about yourself. My my favorite, my 0:09:02.760 --> 0:09:05.440 favorite is that if you start to find the products 0:09:05.440 --> 0:09:08.840 that are on Amazon that have the ridiculous reviews, like 0:09:08.920 --> 0:09:11.480 the ones that are just you know, it's the product 0:09:11.559 --> 0:09:19.000 itself is absurd for or the gallon of milk there 0:09:19.120 --> 0:09:21.840 they're there are examples of products that are on Amazon 0:09:22.000 --> 0:09:26.720 that people have written like novellas in review and the 0:09:26.800 --> 0:09:30.360 novellas are hilarious. Like you get to a point where 0:09:30.360 --> 0:09:32.760 it's like it's like a soap opera that opens up 0:09:32.800 --> 0:09:34.320 and it is this whole thing, and then the last 0:09:34.360 --> 0:09:37.720 line will be some throwaway review of the product or whatever, 0:09:38.120 --> 0:09:41.040 and it's it's I mean, I love it for the absurdity. 0:09:41.240 --> 0:09:44.800 What's interesting is that the related items always tend to 0:09:44.800 --> 0:09:48.120 be the other ones that have similar ridiculous reviews, which 0:09:48.160 --> 0:09:51.439 means that even then always tend to include horse mask. 0:09:51.800 --> 0:09:56.680 Horsehead mask. You know, yeah, people like you bought this thing, right, 0:09:56.720 --> 0:10:01.560 but at any rate, the I wonder if you click 0:10:01.600 --> 0:10:04.040 on that, if you also get like the Godfather as 0:10:04.040 --> 0:10:08.320 a suggestion, you've seen horsehead mask? Right, I've seen horsehead mask. Yes, 0:10:08.440 --> 0:10:11.480 I've used the internet, so yes, I have seen it. 0:10:12.080 --> 0:10:15.520 But yeah, that is another example. Right. Amazon uses this 0:10:15.640 --> 0:10:20.480 in order to make more sales, because the thought is, 0:10:20.520 --> 0:10:23.000 if you are interested in this one particular kind of product, 0:10:23.200 --> 0:10:25.719 then you're probably interested in these other products, especially if 0:10:25.720 --> 0:10:29.000 there's a history of other people having bought those together, 0:10:29.240 --> 0:10:31.720 or at least you know, I bought them at some 0:10:31.800 --> 0:10:35.120 other point in their and their history. And then you 0:10:35.120 --> 0:10:37.760 can you can start pouring that information not just into 0:10:38.160 --> 0:10:41.640 what are people going to buy next, but into why 0:10:41.640 --> 0:10:44.720 are people buying this thing, and start tracking things like 0:10:44.800 --> 0:10:48.800 flu outbreaks. Right, Okay, so this is this is This 0:10:48.880 --> 0:10:50.880 is one of those things that I thought was really, 0:10:51.720 --> 0:10:54.440 uh an interesting example of using big data in a 0:10:54.480 --> 0:10:57.960 way that you wouldn't necessarily first think about. This was 0:10:58.000 --> 0:11:01.080 something that Google wrote up a white paper on there's 0:11:01.120 --> 0:11:06.040 actually a full paper about Google using information to detect 0:11:06.160 --> 0:11:10.000 influenza outbreaks. And the way that they did it was 0:11:10.040 --> 0:11:14.280 they essentially found search queries that people were putting in 0:11:14.840 --> 0:11:18.800 that indicated that someone was feeling sick, especially things like 0:11:18.960 --> 0:11:22.920 various symptoms and stuff. And then by relating that information 0:11:23.000 --> 0:11:27.440 to specific regions in the world and seeing multiple people 0:11:27.640 --> 0:11:32.400 requesting this information from say a particular city, they could say, 0:11:32.440 --> 0:11:35.400 this looks like this is an outbreak of the flu. 0:11:35.679 --> 0:11:37.960 They said that there was a reporting lag of about 0:11:38.120 --> 0:11:42.640 one day, so a day after a certain you know, 0:11:43.320 --> 0:11:46.120 a large enough sample size of people are looking for this, 0:11:46.400 --> 0:11:50.400 Google could say there's a potential flu outbreak in this 0:11:50.880 --> 0:11:54.320 very specific area of the world. Maybe we need to 0:11:54.520 --> 0:11:57.680 you know, by a learning something like the Center for 0:11:57.760 --> 0:12:00.560 Disease Control the CDC, They could say, we need to 0:12:00.600 --> 0:12:03.679 head this off before it becomes some sort of pandemic, right, 0:12:03.800 --> 0:12:06.280 which basically says to me that that that because of 0:12:06.320 --> 0:12:08.640 Google and big data we're going to be able to 0:12:08.800 --> 0:12:13.880 uh prevent the inevitable zombie. But exactly the scary thing 0:12:13.920 --> 0:12:17.320 is will probably never even find out about it, right 0:12:18.280 --> 0:12:21.680 because of stuff like this. They'll get to the government 0:12:21.800 --> 0:12:25.320 before the public knows. No, there there has to be 0:12:25.520 --> 0:12:28.960 enough queries of dead uncle trying to eat my face 0:12:30.040 --> 0:12:32.560 for at least a few people to take notice. If 0:12:32.640 --> 0:12:37.400 you happen to be checking Google trends on dead uncle 0:12:37.400 --> 0:12:40.720 turned on Twitter long before. Yeah, you know, just the 0:12:40.760 --> 0:12:44.000 tumbler alone would be But this is funny. Actually, Google 0:12:44.040 --> 0:12:48.640 trends is a great, really simple, really straightforward example of 0:12:48.800 --> 0:12:53.079 how big data is really interesting, like looking at um 0:12:53.120 --> 0:12:56.360 the popularity of a search term over a period of time. 0:12:56.640 --> 0:12:59.040 I mean, it's so cool. You can get lost in 0:12:59.080 --> 0:13:01.920 these bore to see is watching the spikes when you 0:13:01.960 --> 0:13:05.000 know different movies or books or cultural events happened. Yeah, 0:13:05.360 --> 0:13:08.640 when Kanye West does something embarrassing and immediately shoots to 0:13:08.679 --> 0:13:11.760 the top. One I love is when you can look 0:13:11.800 --> 0:13:15.559 at the historical ones because they have every book ever written, right, 0:13:15.920 --> 0:13:18.600 They've scanned that in and then they can you say, 0:13:18.840 --> 0:13:22.079 historical ones. I just said, they're thinking like bad play. 0:13:22.240 --> 0:13:27.319 My American cousin just think like Google trends throughout the centurial. 0:13:27.480 --> 0:13:29.360 You can look at you can look at like back 0:13:29.400 --> 0:13:32.400 to eighteen hundred in the books they've scanned, right, um, 0:13:32.440 --> 0:13:35.679 and you can you can chart changes in spelling. Right. 0:13:35.880 --> 0:13:39.840 You can compare spelling a word one way versus spelling 0:13:39.840 --> 0:13:42.200 it another way, and watch one go down with while 0:13:42.240 --> 0:13:44.600 the other goes up one one data artists that I'm 0:13:44.600 --> 0:13:46.760 going to talk about in a little bit created a 0:13:46.840 --> 0:13:51.960 graph of the use of hope versus despair and in 0:13:52.040 --> 0:13:55.240 recent years, just just watching the times when when despair 0:13:55.400 --> 0:13:59.079 overlapped hope. Interesting, beautiful. I can't wait to talk about that. Well, 0:13:59.120 --> 0:14:02.959 and and you know, let's start with other applications. Well, 0:14:03.000 --> 0:14:04.360 I was going to mention that, you know, when you 0:14:04.360 --> 0:14:08.760 think about Google, Google's mission statement is all about big data, 0:14:08.800 --> 0:14:14.440 because they're about organizing the universe's information, which when you 0:14:14.480 --> 0:14:16.560 think about that, you know they want to index and 0:14:16.760 --> 0:14:21.200 organize all the information everywhere that we ever encounter. That 0:14:21.400 --> 0:14:26.720 is big data. That's that's as clear as you can get. Yeah, 0:14:26.760 --> 0:14:29.360 And so the fact that they're able to demonstrate the 0:14:29.480 --> 0:14:34.200 usefulness of this proves the the the the utility of 0:14:34.240 --> 0:14:36.960 their company, right because otherwise if if all they did 0:14:37.040 --> 0:14:41.360 was index this and there was no uh, actually useful, Yeah, 0:14:41.400 --> 0:14:43.240 then you'd be like, well, this company is just not 0:14:43.240 --> 0:14:47.280 going to stick around. So besides education or predicting a 0:14:47.320 --> 0:14:51.320 flu outbreak, you can actually use it to monitor cybersecurity 0:14:51.360 --> 0:14:54.040 and check a network's health. So if you see a 0:14:54.080 --> 0:14:56.160 spike in network activity, you can check it out and 0:14:56.160 --> 0:14:58.400 make sure that it's not a d d o S attack, 0:14:58.400 --> 0:15:00.760 a distributed denial of service attack, so that a hacker 0:15:01.000 --> 0:15:05.560 hasn't said, hey, this website has raised my ire, I 0:15:05.600 --> 0:15:09.560 shall direct my zombie computers to attack it. UM. You know, 0:15:09.840 --> 0:15:11.800 being able to see that kind of stuff and respond 0:15:11.800 --> 0:15:13.600 to it in real time is really useful. And you 0:15:13.680 --> 0:15:16.960 obviously need to have a robust, robust system to deal 0:15:17.040 --> 0:15:19.320 with a lot of information because it may just be 0:15:19.560 --> 0:15:24.080 that it's a heavy amount of traffic for completely legitimate reasons. 0:15:24.520 --> 0:15:28.480 So that's another implementation of big data. UH. It's also 0:15:28.560 --> 0:15:30.280 part of what they're talking about when they talk about 0:15:30.320 --> 0:15:34.280 the smart grid UM for electrical electrical companies to be 0:15:34.320 --> 0:15:35.880 able to get energy to the right places at the 0:15:35.960 --> 0:15:39.840 right times and prevent brownouts and blackouts. UM system overloads. 0:15:40.160 --> 0:15:43.200 People have talked about using UM tracking the number of 0:15:43.280 --> 0:15:47.440 ups packages sent to track how well the economy is doing. Interesting. Yeah, 0:15:47.440 --> 0:15:49.480 I've definitely heard about the smart grid stuff. I mean, 0:15:49.520 --> 0:15:52.280 there are a lot of utility companies that are running 0:15:52.360 --> 0:15:56.680 at close to full capacity, and being able to to 0:15:56.760 --> 0:15:59.680 see where a demand is going to be at any 0:15:59.720 --> 0:16:04.120 given time means that you are reducing the demand on 0:16:04.240 --> 0:16:07.320 any individual power company because they can all work in 0:16:07.400 --> 0:16:11.520 concert together and that way you don't have these you 0:16:11.640 --> 0:16:16.720 reduce the possibility of a brown out or a blackout. Um. So, 0:16:16.760 --> 0:16:18.800 I mean that's clearly important, but that is a lot 0:16:18.800 --> 0:16:21.960 of information. You're constantly getting feedback from all the different 0:16:22.800 --> 0:16:25.760 meters essentially smart meters, and even if you get down 0:16:25.760 --> 0:16:28.440 to it, you can have smart appliances that are very 0:16:28.480 --> 0:16:32.120 specifically giving both you and the network more information about 0:16:32.160 --> 0:16:36.440 power consumption. So that's all also important. Weather forecasting another 0:16:36.480 --> 0:16:40.920 important part. Talk about gathering all the information from weather 0:16:41.240 --> 0:16:45.480 sensors around the world and looking at the information and 0:16:45.560 --> 0:16:50.000 detecting patterns because our forecasting abilities, don't know if you noticed, 0:16:50.800 --> 0:16:54.600 not so great. Sometimes it's hilarious because when you think 0:16:54.640 --> 0:16:59.480 about it, we have so much power and technology devoted 0:16:59.560 --> 0:17:03.120 to produce acting the weather and sometimes we're still so 0:17:04.480 --> 0:17:07.600 it's such a complex night. The other night, the hourly 0:17:07.640 --> 0:17:11.800 weather on online was telling me zero percent chance of precipitation. 0:17:12.080 --> 0:17:15.120 We had a storm that was knocking limbs out of trees. 0:17:15.280 --> 0:17:18.920 It was like you couldn't see ten feet for the rain. Yeah. Yeah, 0:17:19.000 --> 0:17:21.600 And it's hilarious when you take a look and say, wait, 0:17:21.720 --> 0:17:25.879 they forecast ten days out, How how reliable is that 0:17:25.960 --> 0:17:31.480 tenth day? There wrong about what's happening right now. Well, 0:17:31.680 --> 0:17:35.600 and beyond that you have things like uh fraud detection, 0:17:35.920 --> 0:17:40.800 and also governments can use big data for tax collection purposes, 0:17:41.119 --> 0:17:43.840 looking at trends in taxes and the way that people 0:17:43.880 --> 0:17:47.000 are paying taxes, and maybe comparing the way people are 0:17:47.000 --> 0:17:50.359 paying taxes versus what they supposedly, oh in Texas, and 0:17:50.400 --> 0:17:53.080 finding out if there are big gaps there, because right now, 0:17:53.119 --> 0:17:55.560 the way it tends to work is it's after the fact, 0:17:55.720 --> 0:17:58.399 right people file their taxes, and then a certain number 0:17:58.400 --> 0:18:03.719 of those taxes tax reports are picked to be looked 0:18:03.760 --> 0:18:06.720 over in more careful detail, and it's only if they 0:18:06.760 --> 0:18:12.000 start to detect a pretty uh significant pattern that they'll 0:18:12.040 --> 0:18:16.320 look at any individual's taxes specifically, unless you're part of 0:18:16.359 --> 0:18:20.120 some political controversy which we won't get into, but this 0:18:20.320 --> 0:18:22.560 big data thing would allow you to take a look 0:18:22.600 --> 0:18:27.040 at a much larger scale and focus in on particular problems, 0:18:28.160 --> 0:18:31.960 as opposed to just hoping that the sheet of reports 0:18:31.960 --> 0:18:35.120 that you just pulled from the printer includes people who 0:18:35.200 --> 0:18:39.720 are not paying their fair share. So I've got a question. Yeah, 0:18:39.880 --> 0:18:45.359 now that we're talking about the government using big data 0:18:45.440 --> 0:18:49.720 to predict near duells, and uh, I think I see 0:18:49.720 --> 0:18:53.240 where this is going. Yeah, y'all seen that movie Minority Report, 0:18:53.400 --> 0:18:57.159 documentary Minority Report. Well, okay, so I want to explain 0:18:57.200 --> 0:18:59.359 a little bit. In that movie, they've got a they've 0:18:59.359 --> 0:19:04.119 got a division of law enforcement called pre crime. Where 0:19:04.160 --> 0:19:07.000 they are now in the movie, it's kind of they've 0:19:07.000 --> 0:19:10.920 got these like psychic prelugs. But let's just say replace 0:19:11.040 --> 0:19:15.720 the psychics with really, really really powerful computers, right that 0:19:15.800 --> 0:19:21.000 look at trends pattern com make extremely accurate predictions about 0:19:21.000 --> 0:19:26.159 what's about to happen. I can definitely foresee a future 0:19:26.240 --> 0:19:30.320 where it might not be all that impossible for computers 0:19:30.359 --> 0:19:33.639 to predict when somebody is very likely to commit a crime? 0:19:35.280 --> 0:19:37.080 Could I can? I can tell you. Let me give 0:19:37.080 --> 0:19:40.240 you a little more, a little more insight. From my perspective, 0:19:40.280 --> 0:19:41.760 I don't know that we're going to get to a 0:19:41.760 --> 0:19:43.680 point where we're going to be able to predict when 0:19:44.240 --> 0:19:47.119 a specific individual is likely to commit a crime. We 0:19:47.160 --> 0:19:49.840 can definitely get a little more probabilistic, you know, sit 0:19:49.880 --> 0:19:52.600 there and say, what is the probability of any person 0:19:52.680 --> 0:19:56.480 at any given time to commit a crime? Um, there 0:19:56.480 --> 0:19:58.640 are some things that we can say. For example, there 0:19:58.640 --> 0:20:01.120 are law enforcement agencies there that are now using big 0:20:01.200 --> 0:20:05.560 data in order to predict crime trends. So not a 0:20:05.600 --> 0:20:09.800 specific person not saying, you know, yeah yeah ne'er dowell, 0:20:09.960 --> 0:20:13.919 Johnny today is gonna knock over the liquor store. They 0:20:13.920 --> 0:20:15.800 are not doing that. What they are doing is saying, 0:20:16.720 --> 0:20:19.080 looking at this big data, I'm seeing this trend where 0:20:19.080 --> 0:20:22.920 this particular part of town tends to be a target 0:20:23.000 --> 0:20:26.680 for vandalization. In burglary, let's say those those are two 0:20:26.720 --> 0:20:29.840 crimes that often tons of factors, you know, based on 0:20:29.960 --> 0:20:34.240 weather or right. Apparently things like burglaries, um kind of 0:20:34.240 --> 0:20:37.040 go in rashes. Yeah. That's another thing is that if 0:20:37.040 --> 0:20:41.280 a place is hit by burglars, then there is there 0:20:41.320 --> 0:20:44.760 tends to be a increased risk of the same thing 0:20:44.760 --> 0:20:48.040 happening in and in the sanginal area. Yeah, so if 0:20:48.080 --> 0:20:52.160 there's a successful burglary attempt in one particular home, for example, 0:20:52.200 --> 0:20:55.960 other homes in that neighborhood could be also um prone 0:20:56.040 --> 0:20:59.399 to being hit by burglars. So that's one example that 0:20:59.480 --> 0:21:01.280 law enforced we can use. They can use it as 0:21:01.320 --> 0:21:04.600 a reactionary thing, saying all right, well, because we know this, 0:21:04.880 --> 0:21:07.919 we should end up increasing patrols in this area for 0:21:07.960 --> 0:21:10.520 the time being so that we can discourage any other 0:21:10.600 --> 0:21:13.679 crime or catch the criminals before they're able to hit 0:21:13.720 --> 0:21:17.680 another another house or another business. Another thing is that 0:21:18.359 --> 0:21:21.159 for crimes like burglary and vandalism, those are crimes that 0:21:21.280 --> 0:21:26.240 generally go down when you increase patrols. They they are 0:21:26.760 --> 0:21:31.280 considered low intensity but high frequency crimes. So if you 0:21:31.359 --> 0:21:35.960 were to adjust patrols so that there is a more 0:21:36.119 --> 0:21:40.360 frequent patrol of police through that area, you reduce the 0:21:40.440 --> 0:21:43.920 likelihood of those crimes being committed. And by using big 0:21:44.000 --> 0:21:47.159 data and and and really analyzing where these crimes are 0:21:47.160 --> 0:21:50.520 taking place within a city, you can redraw patrol routes 0:21:50.880 --> 0:21:55.720 so that police are taking the most efficient patrol they can, 0:21:55.800 --> 0:21:58.119 so they're not having to patrol an area that's way 0:21:58.240 --> 0:22:00.480 larger than what they're capable of doing in a in 0:22:00.520 --> 0:22:04.680 a given shift, and you also will hit the areas 0:22:04.720 --> 0:22:08.359 that are most likely to be targeted and help reduce crime. 0:22:08.400 --> 0:22:10.439 That way, you're preventing it from happening. So you're not 0:22:10.480 --> 0:22:12.879 going out and arresting someone for a crime they haven't 0:22:12.920 --> 0:22:17.320 committed yet. That's not the same thing at all. But 0:22:17.520 --> 0:22:21.480 you can help attack those sort of crimes, things like 0:22:21.720 --> 0:22:26.200 murder much less you know, much less prone to any 0:22:26.240 --> 0:22:30.280 sort of pattern that you can predict. It's that's something 0:22:30.280 --> 0:22:34.280 that's a high intensity but low frequency crime as opposed 0:22:34.320 --> 0:22:38.160 to low intensity, high frequency like vandalism and burglary. So 0:22:38.760 --> 0:22:41.160 they don't tend to take that kind of crime into 0:22:41.160 --> 0:22:44.439 consideration when they're looking at this big data in this sense, 0:22:44.840 --> 0:22:48.160 other than to uh perhaps say that, you know, this 0:22:48.280 --> 0:22:51.199 particular area of town needs to have a stronger police 0:22:51.240 --> 0:22:53.399 presence in order to help can back to what we 0:22:53.480 --> 0:22:57.080 were talking about with sample size, Yeah, and uh, Like 0:22:57.119 --> 0:23:00.919 in Santa Cruz, California, police use this approach to and 0:23:01.359 --> 0:23:03.840 identify homes that were more likely to be hit by 0:23:03.880 --> 0:23:06.919 a burglar so they could redraw their patrol roots to 0:23:07.040 --> 0:23:10.719 take that into consideration and prevent that from happening. Now, 0:23:11.520 --> 0:23:14.919 when we do talk about criminals and the likelihood of 0:23:14.960 --> 0:23:19.080 someone to to commit a particular crime, there is some 0:23:19.160 --> 0:23:23.159 statistical evidence to suggest that people who are who have 0:23:23.240 --> 0:23:27.639 committed a crime are more likely to commit another crime 0:23:27.720 --> 0:23:30.480 than someone who has never committed a crime like that, 0:23:31.000 --> 0:23:34.960 there's like a fort recidivism rate. But part of that 0:23:35.200 --> 0:23:38.280