WEBVTT - Big Data's Big Date with the Movies 0:00:00.160 --> 0:00:07.200 Brought to you by Toyota. Let's go places. Welcome to 0:00:07.400 --> 0:00:15.080 Forward Thinking. Hey there, and welcome to Forward Thinking, the 0:00:15.200 --> 0:00:19.360 podcast that looks at the future and says everything is awesome. 0:00:19.880 --> 0:00:23.120 I'm Jonathan Strick like online bum, and I'm Joe McCormick, 0:00:23.600 --> 0:00:26.960 and today you are going to be hearing a very 0:00:27.040 --> 0:00:33.040 special comparing performance treat from us. It will be our 0:00:33.200 --> 0:00:37.920 second attempt at this podcast, because the first one was 0:00:37.920 --> 0:00:45.000 was well recorded and vanished mysteriously under dubious circumstances. We 0:00:45.200 --> 0:00:48.159 think it was stolen by the Russians. I always suspect 0:00:48.200 --> 0:00:51.199 the Russians. I think it's a good bad guy, the Russians. 0:00:52.400 --> 0:00:55.720 Um which is which is actually really appropriate because today 0:00:55.720 --> 0:00:59.440 we are talking about movies. Yeah, we're gonna talk about 0:01:00.040 --> 0:01:05.360 scifically big data and the movies, both with the Oscars, 0:01:05.800 --> 0:01:08.920 which here in the United States they were just recently 0:01:09.040 --> 0:01:12.160 that that award ceremony was just recently held, and also 0:01:12.280 --> 0:01:16.160 just with movies in general, and how how big data 0:01:16.280 --> 0:01:21.000 is shaping the movies or at least or may shape 0:01:21.080 --> 0:01:23.960 or may misshape the movies as the case may be. 0:01:24.800 --> 0:01:27.760 As I said last time when we recorded this OSCAR 0:01:27.840 --> 0:01:31.759 season is a time when you're gonna hear me getting 0:01:31.760 --> 0:01:34.400 on my high horse and and just allow me to 0:01:34.560 --> 0:01:39.440 ug please, man, I can't stand awards ceremonies. Something about 0:01:39.480 --> 0:01:42.280 the whole enterprise of it just seems so wrong and 0:01:42.319 --> 0:01:46.120 stupid to me. Like You're going to put these movies 0:01:46.200 --> 0:01:48.680 next to each other and say which one was better? 0:01:49.360 --> 0:01:53.840 What was better Selma or the Grand Budapest Hotel? And 0:01:53.840 --> 0:01:57.400 I'm like, why are you comparing them? Like what what 0:01:57.480 --> 0:01:59.960 would even cause you to do that? And I think 0:02:00.120 --> 0:02:03.080 some things you can, like in the grand scheme of things, 0:02:03.120 --> 0:02:05.160 if you were to take all movies, Sure, there are 0:02:05.200 --> 0:02:07.080 movies we can point to and say these are really 0:02:07.080 --> 0:02:08.680 good movies, and there are movies that we can point 0:02:08.720 --> 0:02:11.359 to and say these are really bad movies. But when 0:02:11.360 --> 0:02:14.080 you get to the point where you're talking about contenders 0:02:14.120 --> 0:02:18.359 for something like the Oscars, presumably all of these films 0:02:18.400 --> 0:02:22.679 are representative of of excellence in some manner. And once 0:02:22.720 --> 0:02:24.640 you get to that point, it's really a matter of 0:02:24.680 --> 0:02:29.040 subjectivity which one is quote unquote the best picture, right, 0:02:29.240 --> 0:02:34.000 but being nominated it really kind of is in a way, 0:02:34.040 --> 0:02:36.200 because I mean, how do you how do you judge 0:02:36.280 --> 0:02:38.880 how do you judge a killer comedy, like something that 0:02:39.480 --> 0:02:42.560 genuinely makes you laugh and you have a great time 0:02:42.600 --> 0:02:45.720 and you truly enjoy it and everything's done well. Very 0:02:45.760 --> 0:02:50.120 serious historical drama about something tragic or or just or 0:02:50.160 --> 0:02:53.600 just a heartwarming story that tugs at your heart strings. 0:02:53.600 --> 0:02:55.440 I mean, if any of these things, if they're really 0:02:55.440 --> 0:02:57.720 effective at what they're supposed to do, then they are 0:02:57.800 --> 0:03:00.400 great movies. Right, So how can you tell what what 0:03:00.480 --> 0:03:02.160 which one is better than the other. But that's all 0:03:03.080 --> 0:03:05.520 kind of beside the point. And also I need to 0:03:05.560 --> 0:03:08.880 point out that what's really important in art is winning. 0:03:10.320 --> 0:03:13.520 You know, it's funny that we're pointing out the problem 0:03:13.600 --> 0:03:18.079 here is the subjectivity that comes into evaluating things like movies. 0:03:18.160 --> 0:03:21.480 But the fact that their subjectivity doesn't mean you can't 0:03:21.560 --> 0:03:25.440 get nasty and just quantify it by brute force. Well, 0:03:25.480 --> 0:03:27.680 and that brings us back to our to our topic 0:03:28.280 --> 0:03:30.720 big data. In the movie. It's actually kind of crazy 0:03:30.720 --> 0:03:32.840 because you do think, like when you think of something 0:03:32.880 --> 0:03:34.960 like art, you think of that as being a very 0:03:35.080 --> 0:03:40.760 human endeavor, right, something that is not necessarily quantifiable, something 0:03:40.840 --> 0:03:44.280 that has has an element that goes beyond what we 0:03:44.400 --> 0:03:50.600 can achieve with an artificial construct, That an artificial machine 0:03:50.680 --> 0:03:53.280 of some sort could not create or appreciate art the 0:03:53.280 --> 0:03:57.120 way we can. Uh, And therefore you wouldn't expect to 0:03:57.160 --> 0:04:00.560 be able to use these machines to evaluate that art 0:04:01.040 --> 0:04:03.240 and then come up with a good guess as to 0:04:03.320 --> 0:04:06.400 which one deserves an award. However, you'd be wrong. This 0:04:06.520 --> 0:04:08.760 is the weird thing. But then in a way, it's 0:04:08.840 --> 0:04:12.480 it's more about figuring out how people think rather than 0:04:12.600 --> 0:04:16.760 the quality the quality of the art itself. That's I 0:04:16.800 --> 0:04:19.240 think a distinction we have to make. So we're really 0:04:19.279 --> 0:04:22.839 talking here in this specific part about big data and 0:04:23.240 --> 0:04:27.240 the movies and how big data can help a sophisticated 0:04:27.279 --> 0:04:31.839 system predict which movies are most likely to win awards. Okay, 0:04:31.880 --> 0:04:35.200 so what do we mean when we say big data. Well, 0:04:35.800 --> 0:04:39.080 so we we generated a lot of information every single 0:04:39.200 --> 0:04:44.080 day right about everything. We generate it, we we analyze it, 0:04:44.160 --> 0:04:46.760 We come up with new figures based upon the analysis 0:04:46.800 --> 0:04:50.440 of the old figures that joins the data. Because everything 0:04:50.440 --> 0:04:53.839 that we do online is in some way quantifiable. Even 0:04:53.880 --> 0:04:56.800 if no one is tracking it, probably someone's tracking it. 0:04:57.839 --> 0:05:02.080 That's probably being recorded somewhere. It's being anything useful or 0:05:02.160 --> 0:05:06.359 otherwise is being done with it. Is that's another question. Yeah, exactly. 0:05:06.440 --> 0:05:10.120 So we get this these mountains of data, and if 0:05:10.120 --> 0:05:12.599 you figure out a good way to analyze that data, 0:05:12.640 --> 0:05:14.920 you might be able to make something meaningful from it. Now, 0:05:14.960 --> 0:05:17.760 some of that data is already categorized in some way, 0:05:17.800 --> 0:05:20.479 which makes it a little easier to analyze. Some of 0:05:20.520 --> 0:05:24.600 it is quote unquote loose data that doesn't have any 0:05:24.839 --> 0:05:28.039 categorization or any other meta analysis that attached to it. 0:05:28.160 --> 0:05:33.520 So it's still valuable, but it's harder to incorporate into 0:05:33.720 --> 0:05:36.960 a system or an algorithm. But there are a lot 0:05:37.000 --> 0:05:40.680 of companies that are rising up around the challenge of 0:05:40.720 --> 0:05:44.160 analyzing big data for the benefit of other entities. So, 0:05:44.240 --> 0:05:48.320 for example, market analysis huge with big data. Right, So, 0:05:49.080 --> 0:05:51.640 if you're a company and you're looking to launch a product, 0:05:52.440 --> 0:05:55.919 you might you might end up consulting a big data 0:05:55.960 --> 0:05:59.320 analysis company to find out things like what sort of 0:05:59.320 --> 0:06:01.760 aesthetic a roach you need to take with your product, 0:06:01.839 --> 0:06:05.839 what sort of advertising strategy should you incorporate? Lots of 0:06:05.880 --> 0:06:09.760 different bits and pieces that would go into the determination 0:06:10.000 --> 0:06:12.440 of how you would go forward to give yourself the 0:06:12.440 --> 0:06:16.920 best chance of success. Uh yeah, Or if you just 0:06:16.960 --> 0:06:19.320 want to place bets on Oscar winners and make some 0:06:19.360 --> 0:06:23.080 money off of your friends. Uh there's companies for that. Yeah, well, 0:06:23.120 --> 0:06:26.200 I mean I'm not encouraging gambling. Well, I guess what 0:06:26.240 --> 0:06:30.440 I said was just encouraging. But well, well, let's say, 0:06:30.760 --> 0:06:32.920 for example, I've got a friend who throws an oscar 0:06:32.920 --> 0:06:35.160 party every year, and one of the things she does 0:06:35.200 --> 0:06:38.159 is she hands out ballots and you go and you 0:06:38.200 --> 0:06:41.040 pick your choices for who's going to win what, and 0:06:41.080 --> 0:06:44.640 then the person who has the most correct answers wins 0:06:44.720 --> 0:06:46.560 whatever the prize happens to me, And usually it's one 0:06:46.600 --> 0:06:51.919 of those things that says most soulless hack, usually that 0:06:52.040 --> 0:06:55.279 T shirt that sounds like a great T shirts opinion 0:06:55.400 --> 0:06:59.080 brought to you. But so uh no, it's it's usually 0:06:59.120 --> 0:07:01.120 in our case, it's you really like you throw two 0:07:01.120 --> 0:07:04.280 bucks into the bedding pool, and whoever gets a wins 0:07:04.360 --> 0:07:07.480 the kiddie or whatever. Uh So, yeah, it's kind of 0:07:07.520 --> 0:07:11.520 a fun little exercise. Although I didn't participate this year, 0:07:11.560 --> 0:07:13.240 I had threatened that I was going to take the 0:07:13.280 --> 0:07:15.560 information from one of these companies and and put it 0:07:15.640 --> 0:07:18.880 to use. So we wanted to talk a little bit 0:07:19.000 --> 0:07:23.480 about the ways that big data has or that that 0:07:23.600 --> 0:07:25.640 marketing firms have used big data to try and do 0:07:25.720 --> 0:07:29.000 things like predict oscar winners. Now, keep in mind, this 0:07:29.080 --> 0:07:31.800 was not for business purposes, right, It wasn't or a 0:07:31.800 --> 0:07:34.080 lot directly for business purposes. It's not like there are 0:07:34.080 --> 0:07:37.320 companies that are in business of predicting the oscars and 0:07:37.320 --> 0:07:39.960 that's how they make money. This is probably more just 0:07:40.000 --> 0:07:42.400 to show off what they can do exactly, and it's 0:07:42.440 --> 0:07:44.800 a fun way to say, like, hey, look, we're real 0:07:44.840 --> 0:07:47.600 good at crunching numbers, right right. If if we get 0:07:47.640 --> 0:07:50.800 a certain number of our guess is correct, then you 0:07:50.880 --> 0:07:54.280 know that our methodology is really reliable, and therefore you 0:07:54.280 --> 0:07:57.400 should hire us to do whatever else we do. And 0:07:57.760 --> 0:08:00.320 I have a personal caveat about that, but I'll wait 0:08:00.360 --> 0:08:03.600 until the end, all right. So the first one I 0:08:03.640 --> 0:08:05.880 wanted to talk about, the one that I first looked 0:08:05.920 --> 0:08:08.160 at when I was looking into this whole idea, is 0:08:08.200 --> 0:08:11.360 a company called far Site, which started making predictions in 0:08:11.400 --> 0:08:13.360 two thousand thirteen, and I was looking to see if 0:08:13.400 --> 0:08:15.640 they made any for this past year, but didn't find 0:08:15.680 --> 0:08:19.840 any information on that. Fourteen they did, and I think 0:08:19.840 --> 0:08:23.680 I know why they might have stopped. In fourteen, they 0:08:24.120 --> 0:08:29.960 predicted they made six predictions right of the big categories 0:08:30.480 --> 0:08:33.640 in UM in the Oscars, like Best Picture, Best Director, 0:08:34.120 --> 0:08:37.440 Best Actor, Actress, that kind of thing. I think Supporting 0:08:37.440 --> 0:08:40.040 Actor and supporting Actress were the last two, and they 0:08:40.080 --> 0:08:45.439 got all but one correct in two be director, that's right, 0:08:45.480 --> 0:08:48.320 that's right. And then in two fourteen they got all 0:08:48.480 --> 0:08:51.400 six correct, which is why I think they might have stopped, 0:08:52.640 --> 0:08:55.560 because why they were like, well, yah see, let's just 0:08:55.679 --> 0:09:00.760 let's just stop exactly why risk my risk a bad year? 0:09:01.800 --> 0:09:04.640 And and granted this is me guessing they may have 0:09:04.679 --> 0:09:07.880 had other very valid reasons. This is just a wild 0:09:07.880 --> 0:09:10.640 guess on my part. But at any rate, they did 0:09:10.679 --> 0:09:13.720 get six out of six, which sounds pretty impressive. You know, 0:09:13.920 --> 0:09:17.120 I do wonder how would a company make a prediction 0:09:17.200 --> 0:09:21.160 like that, since these evaluations are supposed to be subjective. 0:09:21.240 --> 0:09:25.600 You know, it's hard to watch a an actor's performance 0:09:25.600 --> 0:09:27.880 in one scene, then watch a different actor perform a 0:09:27.920 --> 0:09:30.920 different scene and put a number on which one is 0:09:30.960 --> 0:09:34.160 more likely to win, right, And and if this were 0:09:34.200 --> 0:09:38.880 a pure experience where every single person in the academy 0:09:39.000 --> 0:09:42.800 was completely unaffected by anything on the outside of it, 0:09:43.000 --> 0:09:46.520 and furthermore, the voting process was more straightforward right. If 0:09:46.559 --> 0:09:49.240 all of that were true, I think it'd be very 0:09:49.240 --> 0:09:53.280 difficult for any algorithm to truly predict what would happen. However, 0:09:54.080 --> 0:09:56.760 we live in a world where this voting process is 0:09:56.760 --> 0:10:00.280 a little more complicated and it is very much wound 0:10:00.360 --> 0:10:04.360 up in the Hollywood politics, and Hollywood politics can include 0:10:04.360 --> 0:10:07.679 things like the buzz that a film gets, things that 0:10:07.920 --> 0:10:12.160 like scandals, can affect which movies or actors or directors 0:10:12.160 --> 0:10:15.280 may or may not win that year, Things that are 0:10:15.320 --> 0:10:20.079 outside of just the quality of the film or performance itself. 0:10:20.559 --> 0:10:24.280 So ultimately, that's another reason why oscars feel a little silly, 0:10:24.360 --> 0:10:28.480 because you can never truly be sure that the award 0:10:28.600 --> 0:10:31.640 is going to be the best and maybe going to 0:10:31.720 --> 0:10:36.040 someone for reasons that are on top of the quality 0:10:36.160 --> 0:10:38.400 of their performance. And keep in mind, like we said, 0:10:39.200 --> 0:10:41.679 if if a film is up for nomination, chances are 0:10:42.080 --> 0:10:44.280 it's reached a certain level of quality. So it's not 0:10:44.320 --> 0:10:47.400 like these are necessarily bad movies, but maybe it's not 0:10:47.520 --> 0:10:50.880 the best movie or best performance. I'd like to point 0:10:50.920 --> 0:10:53.520 to Return of the King. I'm a huge Lord of 0:10:53.520 --> 0:10:55.920 the Rings fan. Don't get me wrong, love it, but 0:10:55.960 --> 0:10:57.679 I get a feeling like a lot of the awards 0:10:57.720 --> 0:10:59.880 that went to Return of the King that year were 0:11:00.040 --> 0:11:02.679 because the previous entries of the Lord of the Ring 0:11:02.760 --> 0:11:06.360 series were largely overlooked by the oscars, and this was 0:11:06.400 --> 0:11:12.000 the last chance, I think, so, I think that really 0:11:12.080 --> 0:11:15.120 was part of it. One year, Leonardo DiCaprio may win 0:11:15.160 --> 0:11:18.120 an Oscar for some for not his best role, but 0:11:18.360 --> 0:11:21.959 people just kind of think, right, he like, why doesn't 0:11:21.960 --> 0:11:25.800 even Oscar already? Yeah, this performance he did, uh, you know, 0:11:26.000 --> 0:11:29.600 and and the the reboot of the uh Ernest Goes 0:11:29.640 --> 0:11:33.160 to Camp series, we gotta give it to him for something. 0:11:33.640 --> 0:11:36.760 I would watch that movie. Yeah, that's kind of great. 0:11:37.520 --> 0:11:41.640 I was. I was literally just grasping at straws there. 0:11:41.679 --> 0:11:44.920 But I'm glad you like it. So anyway, farst sight, 0:11:45.000 --> 0:11:48.240 their algorithm actually took a lot of these other elements 0:11:48.280 --> 0:11:52.079 into consideration, these things like industry buzz stuff that again 0:11:52.120 --> 0:11:55.000 that would seem really kind of weird to quantify. So 0:11:55.120 --> 0:11:58.040 they would identify trends and biases that were in the 0:11:58.440 --> 0:12:01.960 media about all of the different nominees. Uh. It took 0:12:02.000 --> 0:12:05.400 into account news events, and so it took all this 0:12:05.559 --> 0:12:09.760 stuff and they quantified it in a way that they 0:12:09.800 --> 0:12:12.559 have not completely revealed because of course they're not going 0:12:12.640 --> 0:12:15.760 to do that. Yeah they want, they want, Yeah, they 0:12:15.760 --> 0:12:18.880 want people to say, oh, well, you have the secret sauce, right, 0:12:19.000 --> 0:12:21.240 that makes this work. So therefore you're not going to 0:12:21.360 --> 0:12:23.800 share the secret sauce because then it's not secret anymore, 0:12:23.840 --> 0:12:26.920 and then anyone could do it. So at any rate, 0:12:26.920 --> 0:12:30.280 it worked really well. Now, some people would say, and 0:12:30.360 --> 0:12:34.800 by some people I mean like some media journalists people 0:12:34.800 --> 0:12:37.640 who cover this stuff for a living, would say that 0:12:37.760 --> 0:12:41.680 a lot of these approaches do very little, that than 0:12:41.840 --> 0:12:45.520 a little more than what pundits do on a regular basis, 0:12:46.080 --> 0:12:48.920 that essentially, this is the same stuff that the people 0:12:48.960 --> 0:12:52.920 who analyze this, like human beings who just watch and 0:12:52.960 --> 0:12:58.360 analyze this, they do the same thing. Yeah. Well, well, 0:12:58.360 --> 0:13:00.320 but the whole point is that, um, you know, you 0:13:00.320 --> 0:13:02.920 don't have to be an industry expert. You can just 0:13:03.080 --> 0:13:05.760 hire a company like this to be your industry sure, 0:13:05.800 --> 0:13:09.200 And and they're able to take in way more data 0:13:09.280 --> 0:13:11.720 than a person could write. They were factoring in things 0:13:11.760 --> 0:13:15.160 like the betting odds in Vegas over which nominee was 0:13:15.200 --> 0:13:18.840 most likely to win, So, you know, taking all of 0:13:18.840 --> 0:13:22.480 that and making it makes sense in a streamlined fashion. 0:13:22.840 --> 0:13:25.240 That's a real challenge even for someone who watches the 0:13:25.280 --> 0:13:28.400 industry closely, right, but for a machine that can crunch 0:13:28.440 --> 0:13:31.920 all that and way the different factors, look at previous 0:13:32.559 --> 0:13:35.800 OSCAR ceremonies and who won, and kind of look at 0:13:35.800 --> 0:13:39.240 the different criteria and say, which of these seems to 0:13:39.360 --> 0:13:43.640 matter the most. Let's build our algorithm to give those 0:13:43.720 --> 0:13:48.240 the most. Uh, wait for the predictions. And that's what's 0:13:48.240 --> 0:13:50.679 going to guide our choices. And it seemed to work. 0:13:51.640 --> 0:13:54.200 Now they're not the only company that's in this. And 0:13:54.240 --> 0:13:56.600 I've got some other ones to mention for this that's 0:13:56.720 --> 0:14:00.600 past year. Uh that when we you know, finally got 0:14:00.640 --> 0:14:03.560 to see the Oscars in two thousand fifteen, uh, and 0:14:03.679 --> 0:14:06.640 kind of didn't watch who won. I was watching w 0:14:06.800 --> 0:14:09.000 w E fast Lane, actually, so I missed out the 0:14:09.000 --> 0:14:12.720 Oscars too. So hul Cogan won the Oscars. Hogan was 0:14:12.760 --> 0:14:16.040 not at ww E fast Like. Man, you're just uninformed 0:14:16.040 --> 0:14:20.040 about professional wrestling and uninformed about the Oscars. Joe, Come on, 0:14:20.120 --> 0:14:22.400 what were you doing that Sunday. I'm pretty sure hul 0:14:22.480 --> 0:14:25.480 Cogan was was a wrestler at any rate. Some of 0:14:25.520 --> 0:14:28.760 the other companies that got into this have acknowledged that 0:14:28.760 --> 0:14:32.560 it's tricky to make predictions about who's going to win 0:14:32.600 --> 0:14:34.760 the Oscars. For one thing, you're working with a much 0:14:34.880 --> 0:14:38.280 smaller data sample than than you would with some of 0:14:38.360 --> 0:14:41.800 the other market analysis these firms would typically do. Right, 0:14:41.840 --> 0:14:45.120 So we are generally talking about data analysis and it's 0:14:45.120 --> 0:14:49.080 not even necessarily all that big of a data question. 0:14:49.200 --> 0:14:51.920 Sometimes it's kind of medium data. Yeah. Yeah, if you're 0:14:51.920 --> 0:14:55.240 talking about trying to predict what six thousand, six hundred 0:14:55.320 --> 0:14:57.960 or so people are going to vote on, that's that's 0:14:58.000 --> 0:15:01.240 really not when we talk about big data. That's not 0:15:01.440 --> 0:15:06.280 that huge the grand scheme of things UM and hypothetically 0:15:06.280 --> 0:15:09.400 the members of the Academy are largely anonymous. That so 0:15:09.480 --> 0:15:13.480 it's you know, you can't you can't pull the individual people. 0:15:13.960 --> 0:15:18.720 So one company UM called Exponential what that their approach 0:15:18.840 --> 0:15:21.400 was They wanted to get a group of web users 0:15:21.440 --> 0:15:26.720 together that they thought would reflect the academy's overall uh 0:15:26.840 --> 0:15:30.160 you know, uh philosophies and biases, that sort of thing. 0:15:30.440 --> 0:15:33.160 So they actually ended up they had something like thirty 0:15:33.160 --> 0:15:36.600 two thousand people that they looked at to create kind 0:15:36.600 --> 0:15:39.680 of a sample academy. And they even went so far 0:15:39.680 --> 0:15:43.120 as to say, oh, well, these people are really in 0:15:43.160 --> 0:15:45.720 this sample are really super liberal, and that doesn't reflect 0:15:45.720 --> 0:15:48.360 the Academy, because the Academy is not super liberal. It's 0:15:48.440 --> 0:15:50.600 it's a little liberal, but not as liberal as these guys. 0:15:50.680 --> 0:15:53.240 So we can't use these guys as our predictor group. 0:15:53.800 --> 0:15:55.920 Like and they would even make say say things like 0:15:55.920 --> 0:15:59.200 people who really liked such and such politician would be 0:15:59.240 --> 0:16:01.280 most likely to vote for such and such movie. But 0:16:01.520 --> 0:16:03.880 because that doesn't reflect the Academy, we're going to ignore that. 0:16:03.920 --> 0:16:06.600 And I'm just like, wow, okay, so you're they were 0:16:06.600 --> 0:16:11.240 finding weird correlations based upon philosophies and movie preferences at 0:16:11.240 --> 0:16:14.880 any rate. Once they managed to get their uh, their 0:16:14.920 --> 0:16:19.160 representative group that they felt reflected the Academy, they discovered 0:16:19.520 --> 0:16:22.200 that it was either going to be Birdman or the 0:16:22.200 --> 0:16:25.640 Imitation Game that would take home the best Picture. Oscar 0:16:25.720 --> 0:16:29.280 and Birdman did um, although pundits were also saying that 0:16:29.360 --> 0:16:32.320 Birdman was going to so take that with a grain 0:16:32.320 --> 0:16:36.000 as salt. Another company called predict Wise analyzes information from 0:16:36.040 --> 0:16:39.520 production markets and award histories. It also predicted Birdman would win. 0:16:40.200 --> 0:16:43.000 But not all of them were so flawless. There was 0:16:43.000 --> 0:16:46.000 a company, there's a company called movie Graph and their 0:16:46.040 --> 0:16:48.920 approach was very different. They looked at the tonal elements 0:16:49.080 --> 0:16:53.040 of the actual films. They looked at recent Oscar winners 0:16:53.480 --> 0:16:55.560 and the movies that won the Oscars, and said, what 0:16:55.640 --> 0:16:57.920 are the elements that that have won Oscars in the 0:16:57.960 --> 0:17:01.520 past few years, because that's indicated trends that people find 0:17:01.840 --> 0:17:06.560 to be really noteworthy and award worthy. And based upon 0:17:06.600 --> 0:17:10.440 that they said, um well, according to to that analysis, 0:17:10.440 --> 0:17:13.720 American Sniper is going to win. Totally did not win. 0:17:14.640 --> 0:17:18.760 Um So not every method works equally. You know, I 0:17:18.800 --> 0:17:21.639 haven't seen American Snipers, so I can't actually judge it. 0:17:21.680 --> 0:17:23.760 But by the title, it does sound like one of 0:17:23.760 --> 0:17:29.680 those directed DVD Tom Bringer movies. Yeah, no comment. Uh 0:17:29.720 --> 0:17:32.919 So again the l A Times, which obviously you know, 0:17:32.960 --> 0:17:36.720 it's right there in the heart of the film industry. 0:17:36.840 --> 0:17:39.720 Uh pointed out that many of these predictions were made 0:17:39.760 --> 0:17:44.040 by media journalists who had been just watching the the 0:17:44.400 --> 0:17:48.919 the awards ceremonies unfold over time and kind of just 0:17:48.960 --> 0:17:51.560 the trends that were going on in Hollywood and have 0:17:51.720 --> 0:17:55.200 suggested that perhaps while some of these stories, like like 0:17:55.359 --> 0:17:58.720 uh the earlier ones might seem really impressive to get 0:17:58.800 --> 0:18:04.160 six out of six predictions, correct, it's not necessarily the 0:18:04.280 --> 0:18:08.960 hard science that some of these companies might be presenting. Okay, 0:18:09.600 --> 0:18:11.239 can I have a Lauren rent Time? Now? You can 0:18:11.480 --> 0:18:13.640 absolutely a Lauren rant about can we have the intry 0:18:13.720 --> 0:18:19.679 music and everything? All right, Lauren rent Time? All right? 0:18:19.720 --> 0:18:22.399 So I do think that it's really cool that we 0:18:22.440 --> 0:18:24.800 are making computers that are as smart as humans and 0:18:24.880 --> 0:18:27.639 making these predictions. I I do genuinely think that that 0:18:27.760 --> 0:18:32.160 is an impressive feat. However, a word about the Academy. 0:18:32.320 --> 0:18:35.520 Al right, So, though yes, the Academy does keep its 0:18:35.600 --> 0:18:39.600 membership list private, several researchers have seted out a majority 0:18:39.680 --> 0:18:43.040 of its membership and found that it's over white, over 0:18:43.080 --> 0:18:46.480 seventy mail, and has a median age of over sixty years. 0:18:47.359 --> 0:18:50.360 With that kind of skew, Odd posit that it's perhaps 0:18:50.680 --> 0:18:54.080 not the hardest thing to determine the winners that these 0:18:54.119 --> 0:18:57.199 people are choosing, right, Yeah, Diversity is not one of 0:18:57.240 --> 0:19:05.080 the Academy's many um uh facets. Lets say, um yeah, 0:19:05.119 --> 0:19:07.960 there there a lot of people have pointed out over 0:19:08.320 --> 0:19:10.520 you know, recently and actually you know, throughout the entire 0:19:10.600 --> 0:19:13.960 history the Academy. People have pointed out the flaws in 0:19:14.040 --> 0:19:17.920 the in the Academy. Uh hasn't stopped the Oscars from 0:19:17.960 --> 0:19:22.679 being portrayed as this incredibly glamorous and prestigious awards ceremony. 0:19:22.720 --> 0:19:25.520 But I keep hoping that with the increased amount of 0:19:25.520 --> 0:19:28.480 attention and focus that we will start to see a 0:19:28.520 --> 0:19:33.320 better representation of a broader spectrum of people, so that 0:19:33.480 --> 0:19:37.760 films that are legitimately awesome but rarely get that kind 0:19:37.760 --> 0:19:41.199 of consideration are grouped in with the ones that you 0:19:41.240 --> 0:19:44.040 know you would as soon as you see the trailer 0:19:45.640 --> 0:19:48.879 is coming out this year exactly and involves a volleyball 0:19:48.960 --> 0:19:52.400 and whatever soon overcoming adversity and injury and that kind 0:19:52.400 --> 0:19:55.240 of thing. A tragedy, personal tragedy. Gotta have that in there. 0:19:55.600 --> 0:19:59.719 I think it's generally a crime. The lack of films 0:19:59.760 --> 0:20:02.600 where presented at the Academy that have the word chainsaw 0:20:02.640 --> 0:20:05.920 in the title. I think they're really missing on the 0:20:06.040 --> 0:20:09.679 important leather face demographic there. There are a lot of 0:20:09.680 --> 0:20:12.680 people who are wearing human skin out there, and they're 0:20:12.680 --> 0:20:17.560 really not being represented. You know, speaking no, no, no, no, no, no. 0:20:17.600 --> 0:20:20.600 We're not segue yet, Joe, Joe, you are obsessed with this, 0:20:20.680 --> 0:20:22.679 all right. So here's a little peek behind the curtain. Folks. 0:20:23.080 --> 0:20:26.200 Joe and I are working on on an upcoming episode 0:20:26.200 --> 0:20:29.520 of tech Stuff, and Joe has been pitching really, really 0:20:29.520 --> 0:20:31.640 hard that I should do a full text of episode 0:20:31.640 --> 0:20:35.760 on Chainsaws. So the fact that it also has spilled 0:20:35.800 --> 0:20:38.080 over into fourth thinking tells me that he's truly passionate 0:20:38.119 --> 0:20:41.200 about this. I need to reconsider my original. Heck, no, 0:20:42.760 --> 0:20:45.960 Chainsaw technology, I'm curious more interesting than you think. No, 0:20:46.119 --> 0:20:48.880 I don't think about Chainsaws night and day, just all 0:20:48.920 --> 0:20:54.560 of two days. That's fair. Okay. So we've talked about 0:20:54.600 --> 0:20:58.760 how you can perhaps use quantitative analysis stated by computers 0:20:59.240 --> 0:21:04.800 to predict how humans would evaluate art and entertainment. But 0:21:04.880 --> 0:21:07.520 what about going at the art and entertainment from the 0:21:07.560 --> 0:21:12.600 other direction. What if we used computer aided quantitative analysis 0:21:12.680 --> 0:21:17.560 and maybe even some aspects resembling big data to determine 0:21:17.600 --> 0:21:21.280 how we make the movies in the first place. So, 0:21:21.280 --> 0:21:25.080 so like talking about a computer writer, like someone's a 0:21:25.119 --> 0:21:28.800 computer writing a screenplay, Well, you know, obviously we can't 0:21:28.800 --> 0:21:31.240 do anything like that yet, but we've had whole episodes 0:21:31.280 --> 0:21:35.879 about that. Yeah, it's kind of unpleasant to realize how 0:21:35.920 --> 0:21:39.399 close we are to that in some ways. Now I 0:21:39.480 --> 0:21:43.200 want to give a little analogy here. If you work 0:21:43.200 --> 0:21:48.720 in creating content, you've heard of content, Yeah, creating content 0:21:48.920 --> 0:21:52.680 for the web, you will know that there are certain 0:21:53.600 --> 0:21:56.320 things you can look at in your data analytics. You 0:21:56.359 --> 0:22:00.200 might look at Google Analytics for your website and come 0:22:00.280 --> 0:22:04.600 up with uh tips for how you create new content. So, 0:22:04.680 --> 0:22:07.840 for example, you might look at the thousand articles you 0:22:07.880 --> 0:22:10.840 have so far and say, Huh, whenever we do a 0:22:11.080 --> 0:22:15.960 list article, ones that start with an even number get 0:22:16.040 --> 0:22:19.520 this percentage fewer clicks than ones that start with an 0:22:19.560 --> 0:22:23.760 odd number. So maybe when we create new content that 0:22:23.920 --> 0:22:26.120 is list based, we should always start it with an 0:22:26.119 --> 0:22:30.600 odd number. Or maybe we use this word in the title, 0:22:30.600 --> 0:22:33.520 and when when we use this word, it gets this 0:22:33.680 --> 0:22:37.159 percentage fewer clicks than when we use a synonym for 0:22:37.200 --> 0:22:40.080 the word that's this other word. You can get into 0:22:40.200 --> 0:22:43.000 really minute analysis. Oh yeah yeah. And and if that 0:22:43.080 --> 0:22:46.080 sounds a little bit too cold and calculating, I mean 0:22:46.240 --> 0:22:48.600 we we do at a certain point here. It has 0:22:48.600 --> 0:22:52.480 to works, uh, use that kind of information to guide 0:22:52.960 --> 0:22:55.840 what kind of information we put out. Like for example, 0:22:55.840 --> 0:22:57.679 on one of our video shows, brain Stuff, we we 0:22:57.760 --> 0:23:00.440 look at we look at the videos that we watched 0:23:00.480 --> 0:23:03.960 the most often and we say once about animals are 0:23:04.000 --> 0:23:07.119 getting less attention than one's about the human body, So 0:23:07.200 --> 0:23:09.040 let's do more about the human body, because that's what 0:23:09.080 --> 0:23:11.439 people seem to like. But the kind of concerns that 0:23:11.520 --> 0:23:15.560 go into how you say title content for the web 0:23:15.680 --> 0:23:19.440 to have better traffic performance, that's a fairly small concern. 0:23:20.119 --> 0:23:23.120 You're not like writing the article based on this. It's 0:23:23.200 --> 0:23:25.879