Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says everything is awesome.
Speaker 1: I'm Jonathan Strick like online bum, and I'm Joe McCormick,
Speaker 1: and today you are going to be hearing a very
Speaker 1: special comparing performance treat from us. It will be our
Speaker 1: second attempt at this podcast, because the first one was
Speaker 1: was well recorded and vanished mysteriously under dubious circumstances. We
Speaker 1: think it was stolen by the Russians. I always suspect
Speaker 1: the Russians. I think it's a good bad guy, the Russians.
Speaker 1: Um which is which is actually really appropriate because today
Speaker 1: we are talking about movies. Yeah, we're gonna talk about
Speaker 1: scifically big data and the movies, both with the Oscars,
Speaker 1: which here in the United States they were just recently
Speaker 1: that that award ceremony was just recently held, and also
Speaker 1: just with movies in general, and how how big data
Speaker 1: is shaping the movies or at least or may shape
Speaker 1: or may misshape the movies as the case may be.
Speaker 1: As I said last time when we recorded this OSCAR
Speaker 1: season is a time when you're gonna hear me getting
Speaker 1: on my high horse and and just allow me to
Speaker 1: ug please, man, I can't stand awards ceremonies. Something about
Speaker 1: the whole enterprise of it just seems so wrong and
Speaker 1: stupid to me. Like You're going to put these movies
Speaker 1: next to each other and say which one was better?
Speaker 1: What was better Selma or the Grand Budapest Hotel? And
Speaker 1: I'm like, why are you comparing them? Like what what
Speaker 1: would even cause you to do that? And I think
Speaker 1: some things you can, like in the grand scheme of things,
Speaker 1: if you were to take all movies, Sure, there are
Speaker 1: movies we can point to and say these are really
Speaker 1: good movies, and there are movies that we can point
Speaker 1: to and say these are really bad movies. But when
Speaker 1: you get to the point where you're talking about contenders
Speaker 1: for something like the Oscars, presumably all of these films
Speaker 1: are representative of of excellence in some manner. And once
Speaker 1: you get to that point, it's really a matter of
Speaker 1: subjectivity which one is quote unquote the best picture, right,
Speaker 1: but being nominated it really kind of is in a way,
Speaker 1: because I mean, how do you how do you judge
Speaker 1: how do you judge a killer comedy, like something that
Speaker 1: genuinely makes you laugh and you have a great time
Speaker 1: and you truly enjoy it and everything's done well. Very
Speaker 1: serious historical drama about something tragic or or just or
Speaker 1: just a heartwarming story that tugs at your heart strings.
Speaker 1: I mean, if any of these things, if they're really
Speaker 1: effective at what they're supposed to do, then they are
Speaker 1: great movies. Right, So how can you tell what what
Speaker 1: which one is better than the other. But that's all
Speaker 1: kind of beside the point. And also I need to
Speaker 1: point out that what's really important in art is winning.
Speaker 1: You know, it's funny that we're pointing out the problem
Speaker 1: here is the subjectivity that comes into evaluating things like movies.
Speaker 1: But the fact that their subjectivity doesn't mean you can't
Speaker 1: get nasty and just quantify it by brute force. Well,
Speaker 1: and that brings us back to our to our topic
Speaker 1: big data. In the movie. It's actually kind of crazy
Speaker 1: because you do think, like when you think of something
Speaker 1: like art, you think of that as being a very
Speaker 1: human endeavor, right, something that is not necessarily quantifiable, something
Speaker 1: that has has an element that goes beyond what we
Speaker 1: can achieve with an artificial construct, That an artificial machine
Speaker 1: of some sort could not create or appreciate art the
Speaker 1: way we can. Uh, And therefore you wouldn't expect to
Speaker 1: be able to use these machines to evaluate that art
Speaker 1: and then come up with a good guess as to
Speaker 1: which one deserves an award. However, you'd be wrong. This
Speaker 1: is the weird thing. But then in a way, it's
Speaker 1: it's more about figuring out how people think rather than
Speaker 1: the quality the quality of the art itself. That's I
Speaker 1: think a distinction we have to make. So we're really
Speaker 1: talking here in this specific part about big data and
Speaker 1: the movies and how big data can help a sophisticated
Speaker 1: system predict which movies are most likely to win awards. Okay,
Speaker 1: so what do we mean when we say big data. Well,
Speaker 1: so we we generated a lot of information every single
Speaker 1: day right about everything. We generate it, we we analyze it,
Speaker 1: We come up with new figures based upon the analysis
Speaker 1: of the old figures that joins the data. Because everything
Speaker 1: that we do online is in some way quantifiable. Even
Speaker 1: if no one is tracking it, probably someone's tracking it.
Speaker 1: That's probably being recorded somewhere. It's being anything useful or
Speaker 1: otherwise is being done with it. Is that's another question. Yeah, exactly.
Speaker 1: So we get this these mountains of data, and if
Speaker 1: you figure out a good way to analyze that data,
Speaker 1: you might be able to make something meaningful from it. Now,
Speaker 1: some of that data is already categorized in some way,
Speaker 1: which makes it a little easier to analyze. Some of
Speaker 1: it is quote unquote loose data that doesn't have any
Speaker 1: categorization or any other meta analysis that attached to it.
Speaker 1: So it's still valuable, but it's harder to incorporate into
Speaker 1: a system or an algorithm. But there are a lot
Speaker 1: of companies that are rising up around the challenge of
Speaker 1: analyzing big data for the benefit of other entities. So,
Speaker 1: for example, market analysis huge with big data. Right, So,
Speaker 1: if you're a company and you're looking to launch a product,
Speaker 1: you might you might end up consulting a big data
Speaker 1: analysis company to find out things like what sort of
Speaker 1: aesthetic a roach you need to take with your product,
Speaker 1: what sort of advertising strategy should you incorporate? Lots of
Speaker 1: different bits and pieces that would go into the determination
Speaker 1: of how you would go forward to give yourself the
Speaker 1: best chance of success. Uh yeah, Or if you just
Speaker 1: want to place bets on Oscar winners and make some
Speaker 1: money off of your friends. Uh there's companies for that. Yeah, well,
Speaker 1: I mean I'm not encouraging gambling. Well, I guess what
Speaker 1: I said was just encouraging. But well, well, let's say,
Speaker 1: for example, I've got a friend who throws an oscar
Speaker 1: party every year, and one of the things she does
Speaker 1: is she hands out ballots and you go and you
Speaker 1: pick your choices for who's going to win what, and
Speaker 1: then the person who has the most correct answers wins
Speaker 1: whatever the prize happens to me, And usually it's one
Speaker 1: of those things that says most soulless hack, usually that
Speaker 1: T shirt that sounds like a great T shirts opinion
Speaker 1: brought to you. But so uh no, it's it's usually
Speaker 1: in our case, it's you really like you throw two
Speaker 1: bucks into the bedding pool, and whoever gets a wins
Speaker 1: the kiddie or whatever. Uh So, yeah, it's kind of
Speaker 1: a fun little exercise. Although I didn't participate this year,
Speaker 1: I had threatened that I was going to take the
Speaker 1: information from one of these companies and and put it
Speaker 1: to use. So we wanted to talk a little bit
Speaker 1: about the ways that big data has or that that
Speaker 1: marketing firms have used big data to try and do
Speaker 1: things like predict oscar winners. Now, keep in mind, this
Speaker 1: was not for business purposes, right, It wasn't or a
Speaker 1: lot directly for business purposes. It's not like there are
Speaker 1: companies that are in business of predicting the oscars and
Speaker 1: that's how they make money. This is probably more just
Speaker 1: to show off what they can do exactly, and it's
Speaker 1: a fun way to say, like, hey, look, we're real
Speaker 1: good at crunching numbers, right right. If if we get
Speaker 1: a certain number of our guess is correct, then you
Speaker 1: know that our methodology is really reliable, and therefore you
Speaker 1: should hire us to do whatever else we do. And
Speaker 1: I have a personal caveat about that, but I'll wait
Speaker 1: until the end, all right. So the first one I
Speaker 1: wanted to talk about, the one that I first looked
Speaker 1: at when I was looking into this whole idea, is
Speaker 1: a company called far Site, which started making predictions in
Speaker 1: two thousand thirteen, and I was looking to see if
Speaker 1: they made any for this past year, but didn't find
Speaker 1: any information on that. Fourteen they did, and I think
Speaker 1: I know why they might have stopped. In fourteen, they
Speaker 1: predicted they made six predictions right of the big categories
Speaker 1: in UM in the Oscars, like Best Picture, Best Director,
Speaker 1: Best Actor, Actress, that kind of thing. I think Supporting
Speaker 1: Actor and supporting Actress were the last two, and they
Speaker 1: got all but one correct in two be director, that's right,
Speaker 1: that's right. And then in two fourteen they got all
Speaker 1: six correct, which is why I think they might have stopped,
Speaker 1: because why they were like, well, yah see, let's just
Speaker 1: let's just stop exactly why risk my risk a bad year?
Speaker 1: And and granted this is me guessing they may have
Speaker 1: had other very valid reasons. This is just a wild
Speaker 1: guess on my part. But at any rate, they did
Speaker 1: get six out of six, which sounds pretty impressive. You know,
Speaker 1: I do wonder how would a company make a prediction
Speaker 1: like that, since these evaluations are supposed to be subjective.
Speaker 1: You know, it's hard to watch a an actor's performance
Speaker 1: in one scene, then watch a different actor perform a
Speaker 1: different scene and put a number on which one is
Speaker 1: more likely to win, right, And and if this were
Speaker 1: a pure experience where every single person in the academy
Speaker 1: was completely unaffected by anything on the outside of it,
Speaker 1: and furthermore, the voting process was more straightforward right. If
Speaker 1: all of that were true, I think it'd be very
Speaker 1: difficult for any algorithm to truly predict what would happen. However,
Speaker 1: we live in a world where this voting process is
Speaker 1: a little more complicated and it is very much wound
Speaker 1: up in the Hollywood politics, and Hollywood politics can include
Speaker 1: things like the buzz that a film gets, things that
Speaker 1: like scandals, can affect which movies or actors or directors
Speaker 1: may or may not win that year, Things that are
Speaker 1: outside of just the quality of the film or performance itself.
Speaker 1: So ultimately, that's another reason why oscars feel a little silly,
Speaker 1: because you can never truly be sure that the award
Speaker 1: is going to be the best and maybe going to
Speaker 1: someone for reasons that are on top of the quality
Speaker 1: of their performance. And keep in mind, like we said,
Speaker 1: if if a film is up for nomination, chances are
Speaker 1: it's reached a certain level of quality. So it's not
Speaker 1: like these are necessarily bad movies, but maybe it's not
Speaker 1: the best movie or best performance. I'd like to point
Speaker 1: to Return of the King. I'm a huge Lord of
Speaker 1: the Rings fan. Don't get me wrong, love it, but
Speaker 1: I get a feeling like a lot of the awards
Speaker 1: that went to Return of the King that year were
Speaker 1: because the previous entries of the Lord of the Ring
Speaker 1: series were largely overlooked by the oscars, and this was
Speaker 1: the last chance, I think, so, I think that really
Speaker 1: was part of it. One year, Leonardo DiCaprio may win
Speaker 1: an Oscar for some for not his best role, but
Speaker 1: people just kind of think, right, he like, why doesn't
Speaker 1: even Oscar already? Yeah, this performance he did, uh, you know,
Speaker 1: and and the the reboot of the uh Ernest Goes
Speaker 1: to Camp series, we gotta give it to him for something.
Speaker 1: I would watch that movie. Yeah, that's kind of great.
Speaker 1: I was. I was literally just grasping at straws there.
Speaker 1: But I'm glad you like it. So anyway, farst sight,
Speaker 1: their algorithm actually took a lot of these other elements
Speaker 1: into consideration, these things like industry buzz stuff that again
Speaker 1: that would seem really kind of weird to quantify. So
Speaker 1: they would identify trends and biases that were in the
Speaker 1: media about all of the different nominees. Uh. It took
Speaker 1: into account news events, and so it took all this
Speaker 1: stuff and they quantified it in a way that they
Speaker 1: have not completely revealed because of course they're not going
Speaker 1: to do that. Yeah they want, they want, Yeah, they
Speaker 1: want people to say, oh, well, you have the secret sauce, right,
Speaker 1: that makes this work. So therefore you're not going to
Speaker 1: share the secret sauce because then it's not secret anymore,
Speaker 1: and then anyone could do it. So at any rate,
Speaker 1: it worked really well. Now, some people would say, and
Speaker 1: by some people I mean like some media journalists people
Speaker 1: who cover this stuff for a living, would say that
Speaker 1: a lot of these approaches do very little, that than
Speaker 1: a little more than what pundits do on a regular basis,
Speaker 1: that essentially, this is the same stuff that the people
Speaker 1: who analyze this, like human beings who just watch and
Speaker 1: analyze this, they do the same thing. Yeah. Well, well,
Speaker 1: but the whole point is that, um, you know, you
Speaker 1: don't have to be an industry expert. You can just
Speaker 1: hire a company like this to be your industry sure,
Speaker 1: And and they're able to take in way more data
Speaker 1: than a person could write. They were factoring in things
Speaker 1: like the betting odds in Vegas over which nominee was
Speaker 1: most likely to win, So, you know, taking all of
Speaker 1: that and making it makes sense in a streamlined fashion.
Speaker 1: That's a real challenge even for someone who watches the
Speaker 1: industry closely, right, but for a machine that can crunch
Speaker 1: all that and way the different factors, look at previous
Speaker 1: OSCAR ceremonies and who won, and kind of look at
Speaker 1: the different criteria and say, which of these seems to
Speaker 1: matter the most. Let's build our algorithm to give those
Speaker 1: the most. Uh, wait for the predictions. And that's what's
Speaker 1: going to guide our choices. And it seemed to work.
Speaker 1: Now they're not the only company that's in this. And
Speaker 1: I've got some other ones to mention for this that's
Speaker 1: past year. Uh that when we you know, finally got
Speaker 1: to see the Oscars in two thousand fifteen, uh, and
Speaker 1: kind of didn't watch who won. I was watching w
Speaker 1: w E fast Lane, actually, so I missed out the
Speaker 1: Oscars too. So hul Cogan won the Oscars. Hogan was
Speaker 1: not at ww E fast Like. Man, you're just uninformed
Speaker 1: about professional wrestling and uninformed about the Oscars. Joe, Come on,
Speaker 1: what were you doing that Sunday. I'm pretty sure hul
Speaker 1: Cogan was was a wrestler at any rate. Some of
Speaker 1: the other companies that got into this have acknowledged that
Speaker 1: it's tricky to make predictions about who's going to win
Speaker 1: the Oscars. For one thing, you're working with a much
Speaker 1: smaller data sample than than you would with some of
Speaker 1: the other market analysis these firms would typically do. Right,
Speaker 1: So we are generally talking about data analysis and it's
Speaker 1: not even necessarily all that big of a data question.
Speaker 1: Sometimes it's kind of medium data. Yeah. Yeah, if you're
Speaker 1: talking about trying to predict what six thousand, six hundred
Speaker 1: or so people are going to vote on, that's that's
Speaker 1: really not when we talk about big data. That's not
Speaker 1: that huge the grand scheme of things UM and hypothetically
Speaker 1: the members of the Academy are largely anonymous. That so
Speaker 1: it's you know, you can't you can't pull the individual people.
Speaker 1: So one company UM called Exponential what that their approach
Speaker 1: was They wanted to get a group of web users
Speaker 1: together that they thought would reflect the academy's overall uh
Speaker 1: you know, uh philosophies and biases, that sort of thing.
Speaker 1: So they actually ended up they had something like thirty
Speaker 1: two thousand people that they looked at to create kind
Speaker 1: of a sample academy. And they even went so far
Speaker 1: as to say, oh, well, these people are really in
Speaker 1: this sample are really super liberal, and that doesn't reflect
Speaker 1: the Academy, because the Academy is not super liberal. It's
Speaker 1: it's a little liberal, but not as liberal as these guys.
Speaker 1: So we can't use these guys as our predictor group.
Speaker 1: Like and they would even make say say things like
Speaker 1: people who really liked such and such politician would be
Speaker 1: most likely to vote for such and such movie. But
Speaker 1: because that doesn't reflect the Academy, we're going to ignore that.
Speaker 1: And I'm just like, wow, okay, so you're they were
Speaker 1: finding weird correlations based upon philosophies and movie preferences at
Speaker 1: any rate. Once they managed to get their uh, their
Speaker 1: representative group that they felt reflected the Academy, they discovered
Speaker 1: that it was either going to be Birdman or the
Speaker 1: Imitation Game that would take home the best Picture. Oscar
Speaker 1: and Birdman did um, although pundits were also saying that
Speaker 1: Birdman was going to so take that with a grain
Speaker 1: as salt. Another company called predict Wise analyzes information from
Speaker 1: production markets and award histories. It also predicted Birdman would win.
Speaker 1: But not all of them were so flawless. There was
Speaker 1: a company, there's a company called movie Graph and their
Speaker 1: approach was very different. They looked at the tonal elements
Speaker 1: of the actual films. They looked at recent Oscar winners
Speaker 1: and the movies that won the Oscars, and said, what
Speaker 1: are the elements that that have won Oscars in the
Speaker 1: past few years, because that's indicated trends that people find
Speaker 1: to be really noteworthy and award worthy. And based upon
Speaker 1: that they said, um well, according to to that analysis,
Speaker 1: American Sniper is going to win. Totally did not win.
Speaker 1: Um So not every method works equally. You know, I
Speaker 1: haven't seen American Snipers, so I can't actually judge it.
Speaker 1: But by the title, it does sound like one of
Speaker 1: those directed DVD Tom Bringer movies. Yeah, no comment. Uh
Speaker 1: So again the l A Times, which obviously you know,
Speaker 1: it's right there in the heart of the film industry.
Speaker 1: Uh pointed out that many of these predictions were made
Speaker 1: by media journalists who had been just watching the the
Speaker 1: the awards ceremonies unfold over time and kind of just
Speaker 1: the trends that were going on in Hollywood and have
Speaker 1: suggested that perhaps while some of these stories, like like
Speaker 1: uh the earlier ones might seem really impressive to get
Speaker 1: six out of six predictions, correct, it's not necessarily the
Speaker 1: hard science that some of these companies might be presenting. Okay,
Speaker 1: can I have a Lauren rent Time? Now? You can
Speaker 1: absolutely a Lauren rant about can we have the intry
Speaker 1: music and everything? All right, Lauren rent Time? All right?
Speaker 1: So I do think that it's really cool that we
Speaker 1: are making computers that are as smart as humans and
Speaker 1: making these predictions. I I do genuinely think that that
Speaker 1: is an impressive feat. However, a word about the Academy.
Speaker 1: Al right, So, though yes, the Academy does keep its
Speaker 1: membership list private, several researchers have seted out a majority
Speaker 1: of its membership and found that it's over white, over
Speaker 1: seventy mail, and has a median age of over sixty years.
Speaker 1: With that kind of skew, Odd posit that it's perhaps
Speaker 1: not the hardest thing to determine the winners that these
Speaker 1: people are choosing, right, Yeah, Diversity is not one of
Speaker 1: the Academy's many um uh facets. Lets say, um yeah,
Speaker 1: there there a lot of people have pointed out over
Speaker 1: you know, recently and actually you know, throughout the entire
Speaker 1: history the Academy. People have pointed out the flaws in
Speaker 1: the in the Academy. Uh hasn't stopped the Oscars from
Speaker 1: being portrayed as this incredibly glamorous and prestigious awards ceremony.
Speaker 1: But I keep hoping that with the increased amount of
Speaker 1: attention and focus that we will start to see a
Speaker 1: better representation of a broader spectrum of people, so that
Speaker 1: films that are legitimately awesome but rarely get that kind
Speaker 1: of consideration are grouped in with the ones that you
Speaker 1: know you would as soon as you see the trailer
Speaker 1: is coming out this year exactly and involves a volleyball
Speaker 1: and whatever soon overcoming adversity and injury and that kind
Speaker 1: of thing. A tragedy, personal tragedy. Gotta have that in there.
Speaker 1: I think it's generally a crime. The lack of films
Speaker 1: where presented at the Academy that have the word chainsaw
Speaker 1: in the title. I think they're really missing on the
Speaker 1: important leather face demographic there. There are a lot of
Speaker 1: people who are wearing human skin out there, and they're
Speaker 1: really not being represented. You know, speaking no, no, no, no, no, no.
Speaker 1: We're not segue yet, Joe, Joe, you are obsessed with this,
Speaker 1: all right. So here's a little peek behind the curtain. Folks.
Speaker 1: Joe and I are working on on an upcoming episode
Speaker 1: of tech Stuff, and Joe has been pitching really, really
Speaker 1: hard that I should do a full text of episode
Speaker 1: on Chainsaws. So the fact that it also has spilled
Speaker 1: over into fourth thinking tells me that he's truly passionate
Speaker 1: about this. I need to reconsider my original. Heck, no,
Speaker 1: Chainsaw technology, I'm curious more interesting than you think. No,
Speaker 1: I don't think about Chainsaws night and day, just all
Speaker 1: of two days. That's fair. Okay. So we've talked about
Speaker 1: how you can perhaps use quantitative analysis stated by computers
Speaker 1: to predict how humans would evaluate art and entertainment. But
Speaker 1: what about going at the art and entertainment from the
Speaker 1: other direction. What if we used computer aided quantitative analysis
Speaker 1: and maybe even some aspects resembling big data to determine
Speaker 1: how we make the movies in the first place. So,
Speaker 1: so like talking about a computer writer, like someone's a
Speaker 1: computer writing a screenplay, Well, you know, obviously we can't
Speaker 1: do anything like that yet, but we've had whole episodes
Speaker 1: about that. Yeah, it's kind of unpleasant to realize how
Speaker 1: close we are to that in some ways. Now I
Speaker 1: want to give a little analogy here. If you work
Speaker 1: in creating content, you've heard of content, Yeah, creating content
Speaker 1: for the web, you will know that there are certain
Speaker 1: things you can look at in your data analytics. You
Speaker 1: might look at Google Analytics for your website and come
Speaker 1: up with uh tips for how you create new content. So,
Speaker 1: for example, you might look at the thousand articles you
Speaker 1: have so far and say, Huh, whenever we do a
Speaker 1: list article, ones that start with an even number get
Speaker 1: this percentage fewer clicks than ones that start with an
Speaker 1: odd number. So maybe when we create new content that
Speaker 1: is list based, we should always start it with an
Speaker 1: odd number. Or maybe we use this word in the title,
Speaker 1: and when when we use this word, it gets this
Speaker 1: percentage fewer clicks than when we use a synonym for
Speaker 1: the word that's this other word. You can get into
Speaker 1: really minute analysis. Oh yeah yeah. And and if that
Speaker 1: sounds a little bit too cold and calculating, I mean
Speaker 1: we we do at a certain point here. It has
Speaker 1: to works, uh, use that kind of information to guide
Speaker 1: what kind of information we put out. Like for example,
Speaker 1: on one of our video shows, brain Stuff, we we
Speaker 1: look at we look at the videos that we watched
Speaker 1: the most often and we say once about animals are
Speaker 1: getting less attention than one's about the human body, So
Speaker 1: let's do more about the human body, because that's what
Speaker 1: people seem to like. But the kind of concerns that
Speaker 1: go into how you say title content for the web
Speaker 1: to have better traffic performance, that's a fairly small concern.
Speaker 1: You're not like writing the article based on this. It's
Speaker 1: just you know, you're you're doing something about how you
Speaker 1: phrase the title or the pagination structure or something like that.
Speaker 1: It's really presentation. Yeah, you can apply the same thing
Speaker 1: to movies, and it's really up to you how far
Speaker 1: you want to go with this, with this you know,
Speaker 1: scrambling of elements. Yeah, it's so. First of all, it
Speaker 1: is not unusual and and the screenwriting world to have
Speaker 1: a writer turn in a screenplay. Uh, it's been attached
Speaker 1: to a production and director has been attached. And then
Speaker 1: for the either the movie studio, the director or producer
Speaker 1: someone involved in the production to decide, you know, the
Speaker 1: screenplay needs to be punched up a bit. It can't.
Speaker 1: It's it's not quite where we need need it to
Speaker 1: be in order to make the movie we want to make.
Speaker 1: You ever get to the credits in a movie that
Speaker 1: really wasn't all that great, and then you see written
Speaker 1: by and they're like six names yelp, and there's probably
Speaker 1: another six or seven that got left off for some reason. Right, yeah,
Speaker 1: this is this is where you get people doctoring a
Speaker 1: screenplay in order to try and uh and make it
Speaker 1: more appealing or more more marketable, whatever it is. You're
Speaker 1: just better, just more punchy. Times, sometimes it means removing
Speaker 1: an entire character from a story. You might look in
Speaker 1: and say, well, this character superfluous. Let's remove them, give
Speaker 1: some of the lines to some of these other characters,
Speaker 1: cut the rest and move forward, or adding another character,
Speaker 1: like in Silent Hill when they realized that they were
Speaker 1: literally no men in the movie other than pyramid Head,
Speaker 1: and then Sean Bean happen to a wild ghost movie.
Speaker 1: He actually doesn't. It's like the one movie series that happens. No,
Speaker 1: he's just left in in this bleak, gray solitude forever. Alright.
Speaker 1: Well that's well at any rates. So normally we assign
Speaker 1: humans the task of doctoring screenplays. However, there have been
Speaker 1: a couple of companies that have come out and suggested
Speaker 1: that they have some algorithms that can help analyze the screenplay.
Speaker 1: See what elements would really uh supposedly at any rate
Speaker 1: um uh do really well with audiences, which ones might
Speaker 1: fall flat? And therefore you could follow the very cold
Speaker 1: algorithm and doctor your screenplay so that it has the
Speaker 1: best potential to be a big hit. Right, So we're
Speaker 1: talking about taking a process that used to be done
Speaker 1: more on the say the perhaps well informed intuitions of
Speaker 1: somebody in char charge. They look at a script and
Speaker 1: say this, and right, we need to make some changes.
Speaker 1: Now we can actually base this on data. Yeah, and
Speaker 1: you can say, well, we've analyzed seven thousand movies over
Speaker 1: the past, however, and and movies that had this element
Speaker 1: didn't do as well as movies that substituted it for
Speaker 1: this other element. Therefore, we can recommend the following script changes. Yeah.
Speaker 1: Worldwide Motion Pictures Group UH introduced an algorithmic that the
Speaker 1: company said that it could evaluate a script based upon
Speaker 1: past films successes and failures. Were largely talking about box
Speaker 1: office success here, so not necessarily awards consideration, but or
Speaker 1: like rotten Tomatoes score. But yeah, right right, you know
Speaker 1: they're literally looking at how many tickets were sold. That's
Speaker 1: the mark of success as far as they're concerned, not
Speaker 1: artistic success, nothing like that. It's specifically how many butts
Speaker 1: did you put in seats. We're not running a charity here.
Speaker 1: Shakespeare got to get paid son. Okay, So it could
Speaker 1: actually analyze script elements and have historically derived audience reaction
Speaker 1: to those elements. So let's say that you're writing your
Speaker 1: screenplay and you want to incorporate a sweet old lady
Speaker 1: who dispenses pithy advice, and then you would check the
Speaker 1: algorithm to see how such characters had resonated with past
Speaker 1: audiences in various other movies. If this sounds horribly mechanical
Speaker 1: and uninspiring, you're pretty much right. I want to give
Speaker 1: a few examples. For there was a New York Times
Speaker 1: piece from covering what this Worldwide Motion Pictures Group did,
Speaker 1: and they explained their services by saying, okay, here's an
Speaker 1: example in movies with demons. You remember a while back,
Speaker 1: demon movies were really big activity all, and they said
Speaker 1: that quote targeting demons unquote do better than quote summoned demons.
Speaker 1: So if you have a scene where you've got characters
Speaker 1: who are using a weigia board or they draw a
Speaker 1: big pentagram on the ground and bring demons up out
Speaker 1: of how Those are not as good as demons that
Speaker 1: show up from out of nowhere and harass you of
Speaker 1: their own agency. Right, So they have they have chosen,
Speaker 1: They have chosen to torment you for reasons that the
Speaker 1: demon is aware of, but the character may or may
Speaker 1: not be. They certainly weren't summoned. So if you write
Speaker 1: a horror screenplay where the characters get out the Wigi
Speaker 1: board to get in contact with the demon and that's
Speaker 1: how it all starts, these guys will tell you no, no, no, no, no,
Speaker 1: no no, you're gonna lose money on this. You've got
Speaker 1: to get rid of the Weiji board and just have
Speaker 1: the demons show up. Yeah, you might start to think, hey,
Speaker 1: this suggests that it doesn't matter how how good the
Speaker 1: writing is. It just matters which elements you put into
Speaker 1: the movie. And there in lies one of the problems.
Speaker 1: I want to cover a few more examples they give.
Speaker 1: Also mentioned in the New York Times piece was that
Speaker 1: quote guardian heroes do better than quote cursed heroes. Because
Speaker 1: that Star Wars movie did so poorly. I'm so glad
Speaker 1: that they've put it this out. I guess it all
Speaker 1: depends on what how they are defining guardian versus curse. Okay,
Speaker 1: all right, that's fair. This the same company was also
Speaker 1: covered in a piece I'm going to talk about in
Speaker 1: the second that was a marketplace piece on this type
Speaker 1: of business, and they said that, for example, in slasher
Speaker 1: films where there are killings, the killings should be random
Speaker 1: rather than motivated by a quote something rational like revenge
Speaker 1: your money. One last one the Worldwide Motion Pictures Group people.
Speaker 1: They said that in a film that's based on a
Speaker 1: true story, you've got to stick to the facts. They
Speaker 1: said that films that deviate from the facts pretty far
Speaker 1: in a quote based on a true story don't do
Speaker 1: as well. Because they give this explanation, the audience will
Speaker 1: google the story when they leave the theater. More than
Speaker 1: half the audience. They said, Yeah, I don't know where
Speaker 1: they get that, which is weird because you would think, like,
Speaker 1: when they're leaving the theater, they've already bought the ticket
Speaker 1: to see the movie. Don't matter one way. Well, but
Speaker 1: they could tell their friends. Yea. So this company likes
Speaker 1: to emphasize, or let's say, liked because we have some
Speaker 1: news about how they end up. Yeah, they liked to
Speaker 1: emphasize that the decisions and the suggestions made for changing
Speaker 1: these screenplays are not made by machines. They don't like
Speaker 1: run the document of the screenplay through a computer program
Speaker 1: and the computer tells you what to do. Instead, it's
Speaker 1: humans aided by data. So it's humans who are using data,
Speaker 1: and that is, you know, like obviously computer aided machine
Speaker 1: generated data, but the humans, humans are making the recommendation.
Speaker 1: So I think of it kind of like imagine the
Speaker 1: earliest days of Pandora, where you would, uh, you know,
Speaker 1: they get a piece of music and they would start
Speaker 1: to analyze it and add all these meta tags to
Speaker 1: that music to identify the various components of it, and
Speaker 1: then uh, they would start to map the those to
Speaker 1: other pieces of music that shared at least a certain
Speaker 1: number of those same qualities. And therefore, when you would
Speaker 1: build a Pandora radio station based off a particular song,
Speaker 1: you would get other songs that had elements at least
Speaker 1: of that first song in common. Uh. And I think
Speaker 1: of it similarly to that, like they're looking at all
Speaker 1: this accumulated information that they have stored in some form
Speaker 1: of database, but that it's a person who's actually going
Speaker 1: through and reading the screenplay to identify them exactly. Um,
Speaker 1: and the company, Uh, this particular company isn't it doesn't
Speaker 1: isn't around anymore. It imploded in two thousand and fourteen.
Speaker 1: The company was in debt and controversially, a whole bunch
Speaker 1: of folks were either fired or left the company, and
Speaker 1: some of the big players immediately formed another company called
Speaker 1: C four and then bought up all the previous companies
Speaker 1: intellectual property assets. Um. So, would you say it was
Speaker 1: a heroic implosion or a guardian employer cursed guarding? Yeah,
Speaker 1: I think it was a I think it was a
Speaker 1: random slasher implosion maybe, but at any rate, Yeah. So,
Speaker 1: the the idea behind this was just to check and
Speaker 1: see if there there are elements within a script that
Speaker 1: could be tweaked. So it wasn't meant to be like
Speaker 1: a here are the twenty different things, uh the perfect
Speaker 1: movie script should have in it, because if you did that,
Speaker 1: you would probably end up with a movie very similar
Speaker 1: to the that music we talked about with the songs
Speaker 1: that were generated by all the elements people identified as
Speaker 1: either being wonderful or awful. Remember those? Um, we all
Speaker 1: agreed that the worst song was way more interesting than
Speaker 1: the best one. I still sing that song to myself
Speaker 1: sometimes do all your shopping at Walmart. Uh yeah, so,
Speaker 1: but yeah, you wanted to talk about this other company
Speaker 1: to write Joe, Well, just that there was an other
Speaker 1: company who did a similar service that were called epagogics,
Speaker 1: and they style themselves with the capitalized decks at the end,
Speaker 1: I believe, But they do a similar kind of thing.
Speaker 1: They have analysts that read a script and they've got
Speaker 1: a computer enhanced algorithm that sort of compares the script
Speaker 1: elements against data that they've collected about what what elements
Speaker 1: perform well, and they make recommendations. It's a similar idea.
Speaker 1: They can actually go through a script and give it
Speaker 1: a score that is aided by this data, and then
Speaker 1: they feed that score through their computer algorithm and it
Speaker 1: makes a prediction about how much money this script would
Speaker 1: make at the box office given some kind of error bar. Obviously,
Speaker 1: they would claim that they they offer a very valuable service.
Speaker 1: It's hard to know the level of quality in the
Speaker 1: services these kinds of companies provide, especially because I'm I'm
Speaker 1: sure lots of customers who use their services are not
Speaker 1: super forthcoming about that fact. Yeah, and also I mean
Speaker 1: without being able to see what the product was before
Speaker 1: versus after, it's impossible for us to make any determination
Speaker 1: at all right now. On one hand, I can see how,
Speaker 1: especially as time goes on, services like this could get
Speaker 1: more and more accurate and effective. Like I I don't
Speaker 1: doubt that you can probably get some pretty good predictions
Speaker 1: online just by analyzing script elements about what kinds of
Speaker 1: things will make money. But personally I hate this idea.
Speaker 1: I'm not a big fan either. Well, there's there's nothing
Speaker 1: saying that plugging in a little puzzle piece like this
Speaker 1: making a change. Like for example, this epico jics company
Speaker 1: UM did a did a piece with with NPRS Marketplace
Speaker 1: a while back and told a story about one movie
Speaker 1: that they that they went in to help doctor and
Speaker 1: they recommended cutting down the roles of one of the
Speaker 1: one of the supporting characters. And the head you know,
Speaker 1: the person that they were talking to, laughed and said like, oh,
Speaker 1: that's great. You just saved me like twelve million bucks
Speaker 1: because I realized that, uh, this big actress that I
Speaker 1: was going to get for this big supporting role, I
Speaker 1: don't need her anymore I can, I can hire someone cheaper,
Speaker 1: right because now now this, if this role is not
Speaker 1: going to be that prominent, then why would I spend
Speaker 1: the money on someone huge a huge name for that.
Speaker 1: But but you know, there's nothing saying that that role
Speaker 1: that would have gone to Angelina Jolie wouldn't be better
Speaker 1: served by having a different actress there. Uh. And just
Speaker 1: to remind again, this is not a process that's new.
Speaker 1: Script doctoring has been around. We're just talking about applying new,
Speaker 1: newly quantitative methods to it. Instead of going on the
Speaker 1: intuitions of somebody in charge, we're going on data that
Speaker 1: we have, what we can compare a script. Right, This
Speaker 1: is this is you know the for you might think
Speaker 1: of script doctoring as something in the screenplay is just
Speaker 1: not working. Something is it's clunky, or the story seems
Speaker 1: to get bogged down in the second act, something along
Speaker 1: those lines. Whereas in this approach, you're thinking you should
Speaker 1: change this, uh, this old lady character into a young
Speaker 1: man character, because young men male characters in this particular
Speaker 1: role end up selling x number more tickets than if
Speaker 1: you were to use an old lady character in this
Speaker 1: particular type of role within your story and that'specially if
Speaker 1: they're played by an actor named Corey. And that's when
Speaker 1: it starts to that's when it really starts to feel
Speaker 1: like this is getting a little too weird and cold
Speaker 1: and calculated and less like art. Right, it's I don't know,
Speaker 1: like like to to too Devil's advocate and I love
Speaker 1: making that noun a verb. Uh. You know, I would
Speaker 1: still argue that it's up to the the artists, even
Speaker 1: if they are working off of cold data, It's up
Speaker 1: to the artists to do what they will with that
Speaker 1: data and go like, oh, no, you're right, like like
Speaker 1: this young man character could could be an amazing character,
Speaker 1: and maybe that voice would be a really interesting voice.
Speaker 1: And and maybe the reason I don't know, I mean,
Speaker 1: you know, it's what you make of it, is all
Speaker 1: I'm saying. I can see that. But I can also
Speaker 1: see as a writer, someone writing something and you create,
Speaker 1: you create a vision and you write it down and
Speaker 1: then having a producer come in and say, yeah, this
Speaker 1: character that you create, the strong young female character needs
Speaker 1: to be a strong male character, because yeah, and that
Speaker 1: would really upset me. So I mean, these are the
Speaker 1: sort of things and also, like the other objections here, Again,
Speaker 1: this is looking at specific story elements, not necessarily the
Speaker 1: execution of those elements, but just whether they're they're or
Speaker 1: not right. So, I mean, obviously you can't tell by
Speaker 1: looking into script what kind of star power or or
Speaker 1: just presence the actors are going to bring, or just
Speaker 1: how well written the part is. Right, Yeah, you can't
Speaker 1: even tell about the I guess maybe the quality or
Speaker 1: the dial of the dialogue. Yeah, if you if all
Speaker 1: you're doing is doing the meta data approach, saying, uh,
Speaker 1: it's you know, the demographic information of whatever element it is,
Speaker 1: or you know, you know this movie is set in Seattle,
Speaker 1: and film set in Seattle, blah blah blah, you should
Speaker 1: really set this movie, uh, you know over in Portland
Speaker 1: instead or something along those lines. And you'll be like, well,
Speaker 1: you know, it's it seems so arbitrary. But also it's
Speaker 1: all based upon past data, and that suggests that you
Speaker 1: can't do anything original because it has to be weighed
Speaker 1: against things that have already happened. And it could be
Speaker 1: that there's a lot of really crappy movies that have
Speaker 1: been made, but you've created a really cool idea that
Speaker 1: could be a huge hit. But because all the other
Speaker 1: movies in that space have been crappy. You don't even
Speaker 1: get the chance. So the example I gave it's a
Speaker 1: bad example. But let's say that you want write a
Speaker 1: dinosaur movie, and you know, let's imagine the Jurassic Park
Speaker 1: never happened, but uh, Tammy and the t Rex and
Speaker 1: Theodore Rex both happened, and they are terrible movies. So
Speaker 1: then you write a movie where there are humans and
Speaker 1: dinosaurs it together. And because you've got these these other
Speaker 1: pieces that are poisoning the well, the data comes back
Speaker 1: and says, there's no way the audiences don't respond well
Speaker 1: to this kind of movie. And then we never would
Speaker 1: have gotten Carnosaur. Yeah, and that would have been a
Speaker 1: shame because that that dinosaur was adorably vicious. But yeah,
Speaker 1: the real the real point here is that it doesn't.
Speaker 1: It discourages people from trying to go in innovative directions
Speaker 1: because there's not data supporting whether or not that would succeed.
Speaker 1: And then producers are saying, well, I'm not gonna put
Speaker 1: money into something where I don't expect to get a
Speaker 1: payback on my investment. Right, Well, this is a thing
Speaker 1: that's common in all kinds of businesses where people want
Speaker 1: a safe bet. Yeah. You know, in a lot of cases,
Speaker 1: the person at the top of a business, they're they're
Speaker 1: looking at a film as an investment. They have to
Speaker 1: spend money to make the film, and they'd rather be
Speaker 1: sure they're get going to get a pretty decent return
Speaker 1: on a film then take a risk. Might you might
Speaker 1: pay off big or it might be a total flop.
Speaker 1: And movies are expensive, Yeah, And so they're looking at
Speaker 1: it as an investment. And and they're saying, well, you know,
Speaker 1: if if I make something that nobody has ever seen
Speaker 1: before and I can't compare it to things that have
Speaker 1: been successful in the past, I'm taking more of a risk.
Speaker 1: I'm putting more of my money on the line, and
Speaker 1: I can't be sure about what I'm going to get back.
Speaker 1: And that's I can understand that from a business perspective.
Speaker 1: I can sympathize with that in terms of making an investment,
Speaker 1: but it is kind of sad that that results in
Speaker 1: fewer movies that take those risks that turn out to
Speaker 1: be the movies we love. I mean, almost all of
Speaker 1: the movies we really love are the kinds of movies
Speaker 1: that do something new. They take a risk they do
Speaker 1: something you've never seen before. I don't know how well
Speaker 1: movies like that would do when compared against the data
Speaker 1: of past hits and Blockbuster Now. Fortunately, I think that
Speaker 1: while we we will continue to see the companies like
Speaker 1: this provide a service to movie studios, I also don't
Speaker 1: think that it's ultimately going to be the thing that
Speaker 1: defines the industry as a whole. So, you know, we
Speaker 1: were painting a lot of doom and gloom, but really, honestly,
Speaker 1: we're not thinking that this is going to happen all
Speaker 1: the time. Always sure, Sure, And also I think there's
Speaker 1: a huge space to uh to come to some kind
Speaker 1: of happy medium with looking at this kind of data
Speaker 1: and and also allowing new ideas to blossom, like like
Speaker 1: like the way that Netflix models its original series. This
Speaker 1: is really cool. So Netflix, you know, they pay people
Speaker 1: to watch Netflix, and then those people what they're doing
Speaker 1: is they are they're essentially creating the tags, very much
Speaker 1: like the Pandora example I gave earlier, critically acclaimed movies
Speaker 1: starring strong female characters that like coffee. Right, Yeah, but
Speaker 1: if you've ever really dug through Netflix where it has
Speaker 1: the very aus categories, sometimes those categories get really hysterical,
Speaker 1: like they can sometimes just the categories all the entertainment
Speaker 1: you need. You don't even need to watch a movie.
Speaker 1: You just look at what the category name is. But
Speaker 1: it's yeah. This comes out of the fact that there
Speaker 1: are people who are tagging these movies and they say, oh,
Speaker 1: you know, this is related to these other films in
Speaker 1: these different ways, so you get you know, you can
Speaker 1: do an analysis. You can compare to films or two
Speaker 1: TV series with Netflix whatever against one another and see
Speaker 1: how how well they match up as far as those
Speaker 1: meta tags go. And this is kind of how Netflix
Speaker 1: creates that recommendation engine, so that if you watch, let's say,
Speaker 1: for instance, I just watched an independent, uh foreign horror
Speaker 1: film last night, and so based upon that, I will
Speaker 1: probably get other recommendations of other independent foreign horror films,
Speaker 1: which is that's exactly what I want. So that's fantastic.
Speaker 1: But they're also using it to guide their decisions. Like
Speaker 1: you said, Lauren, with their original series. Net produces its
Speaker 1: own films and TV shows, and by looking to see
Speaker 1: what people are watching, they get an idea of which
Speaker 1: elements people are most interested in, and then they can
Speaker 1: say all right, Let's develop a show that caters to
Speaker 1: this audience. Yeah, let's let's green light a show. And
Speaker 1: the terrific thing about Netflix's green lighting process is that
Speaker 1: they don't uh, let someone do a pilot and then
Speaker 1: have that pilot sinker swim. That they have someone create
Speaker 1: an entire first season yea, when they release it all
Speaker 1: at once. Yeah, which is so giving to the to
Speaker 1: the creators. Sure, I totally agree. I'm just wondering that
Speaker 1: how come No matter what I watch on Netflix, it
Speaker 1: tells me that the thing I should watch next is
Speaker 1: teenage Mutant Ninja Turtles three Turtles in Time, because it
Speaker 1: knows you, because that is what you should watch next
Speaker 1: to Joe's Surprise. It's not just all the films that
Speaker 1: have the word chainsaw in them. No, Actually, I think
Speaker 1: what it thinks I like is uh, violent mind bending
Speaker 1: thrillers with a strong female lead. That's actually a category
Speaker 1: that I get. Yeah, yeah, I occasionally get that as well.
Speaker 1: So another way we're seeing big data affect the film
Speaker 1: industries through marketing. When a film gets released in one
Speaker 1: part of the world, they might see what types of
Speaker 1: approaches are working or not working, and therefore they can
Speaker 1: actually tweak how they market a film and other regions
Speaker 1: to try and maximize that marketing effect. You know, keep
Speaker 1: in mind, a film's budget is almost nearly matched by
Speaker 1: the marketing budget to kind of push that film out there.
Speaker 1: So if you hear that a film cost like two
Speaker 1: million dollars to make, it probably was another hundred hundred
Speaker 1: fifty million to market. It's crazy how much money is
Speaker 1: going into this, and they of course wanted to be
Speaker 1: as effective as possible. For instance, I've seen uh previews
Speaker 1: for a film that made it look like it was
Speaker 1: kind of a comedy, and later on seeing previews for
Speaker 1: the same film that make it look like it's a
Speaker 1: you know, a nail biting thriller. I think, like, what
Speaker 1: which movie am I going to actually see? What? It
Speaker 1: couldn't be a third third trailer will play up the romance,
Speaker 1: And there was one recently. I wish I could remember
Speaker 1: what film it was, but I remember when I saw
Speaker 1: the preview, I thought, this seems like a totally different
Speaker 1: movie than the previews I saw like four months ago,
Speaker 1: But I can't remember off the top of my head
Speaker 1: where it was so good story Jonathan Um. It's also
Speaker 1: telling us more about how people consume content. So, uh,
Speaker 1: Netflix releasing all those episodes, like releasing a full season
Speaker 1: on one day, as opposed to having each episode come
Speaker 1: out week by week. It's there. You know. That's because
Speaker 1: Netflix has seen people will sit down and binge watch
Speaker 1: a show. That's how they like to consume stuff. They
Speaker 1: don't want to necessarily have to wait for a full
Speaker 1: week for the next segment of that story. And so
Speaker 1: that's why Netflix, when they greenlit their series, they say,
Speaker 1: all right, we're gonna fund the whole thing, and then
Speaker 1: we're releasing it all at once and people can choose
Speaker 1: to watch it all at that same time more, they
Speaker 1: can parse it out however long, however much they want,
Speaker 1: or they can leave their houses sometimes. Yeah. Sorry, I
Speaker 1: say that, you know, having completely done the like seven
Speaker 1: or eight episode benders. Yeah, when the Arrested Development season
Speaker 1: came out, I pretty much consumed it in two days.
Speaker 1: I haven't Orange is the New Black problem. Yeah, that's understandable. Um,
Speaker 1: So the question now is where's this going in the future.
Speaker 1: I mean, we have said that we're probably going to
Speaker 1: continue to see this sort of doctoring approach to at
Speaker 1: least get an idea of you know, is this script
Speaker 1: actually marketable or is it not marketable? But beyond that,
Speaker 1: are we going to start seeing things like a computer
Speaker 1: actually write a story taking all these elements, And are
Speaker 1: we going to get like the most disjointed, weird story
Speaker 1: with all the fantastic elements crammed into it. Man, I
Speaker 1: kind of hope, so I would. I would go that
Speaker 1: sounds like a goofy wonderful thing. Sounds like being turtles
Speaker 1: in time. You just see like superhero superheroes, robotic figures
Speaker 1: writing dinosaurs which may or may not also be super robotic.
Speaker 1: You know, I'm wondering how long it's going to take
Speaker 1: us to get to that thing we were worried about
Speaker 1: at the beginning, where we've actually pretty much got computers
Speaker 1: writing the scripts for us, and other computers playing the
Speaker 1: characters for us, and other computers watching it. People have
Speaker 1: just completely been taken out of the equation. We're simply
Speaker 1: thrown on top of the furnace to fuel all the computers.
Speaker 1: Um went to that dark place, that's yeah, yeah, I
Speaker 1: mean that's pretty likely. But you know, before we get
Speaker 1: to that point when humans are still watching the movies
Speaker 1: and still acting in the movies, are we going to
Speaker 1: get to a point where, you know, it's easier if
Speaker 1: we just cut out the middleman, because we're getting to
Speaker 1: the point where there's too much data for these human
Speaker 1: analysts to mess with. We just need to have a
Speaker 1: program that can generate a script that they know has
Speaker 1: all the right, you know, money making elements. It doesn't
Speaker 1: have any of these low scoring elements like the old
Speaker 1: lady who dispenses pithy advice. You know, can that that
Speaker 1: lady right? Don't even bother? Why waste your time with
Speaker 1: a draft that has that when you can just have
Speaker 1: the program create a storyline for you. I could certainly
Speaker 1: see this being done as an experiment, like, yeah, the
Speaker 1: way that algorithm based poetry is sometimes beautiful, Yeah it's
Speaker 1: sometimes it's sometimes beautiful in a kind of just sort
Speaker 1: of odd alien way. But and I imagine a film
Speaker 1: that would probably at least the first few incarnations of
Speaker 1: the sort of thing would be similar like one of
Speaker 1: those things where like this feels like someone who almost
Speaker 1: understands all humans thing but doesn't quite go the whole way.
Speaker 1: Would make a movie, I don't know. I would want
Speaker 1: a double feature of like that, like you know, starring
Speaker 1: some like like good people who have good senses of
Speaker 1: humor about it, and you know, it could really play
Speaker 1: it up and I would totally see a double feature
Speaker 1: of like that, and like whatever Michael Bay is doing this.
Speaker 1: I've got so many comments and they all pretty much
Speaker 1: say that I'm guessing the Bay movie would be the
Speaker 1: one I'd hate. But uh, yeah, well, I'd certainly see
Speaker 1: this as an experiment I'm talking about. Oh no, the
Speaker 1: whole industry. I don't see it becoming the norm, partially
Speaker 1: because if you are taking this approach where you're trying
Speaker 1: to make all of your decisions based upon past outcome popularity,
Speaker 1: then ultimately all your stories are going to end up
Speaker 1: being incredibly formulaic and similar to one another. And it
Speaker 1: turns out you don't need computers for that. Our friend
Speaker 1: Michael Bay has really mastered this particular Yeah, I'm like,
Speaker 1: the terrible dystopia you guys are talking about is the
Speaker 1: one we are living in right now. Well, there's there
Speaker 1: was an episode, you know, And I know Joe has
Speaker 1: seen some of the re letter media videos um that
Speaker 1: they do a lot of film criticism, and they do
Speaker 1: it in in a very snarky way, but they do they
Speaker 1: make legitimate, you know, film criticism. Fun Yeah, they have
Speaker 1: some smart things to say. The warning if you go
Speaker 1: check them out, they're not exactly family friendly, that's true.
Speaker 1: It is adult content, but yeah, but give it a listen.
Speaker 1: They are they are very insightful and very funny and
Speaker 1: very inappropriate at times. But one of the things they did,
Speaker 1: and it was kind of as a gag as the
Speaker 1: fourth Transformer film was coming out, was they put all
Speaker 1: of the first three Transformer films on concurrently and sat
Speaker 1: down on a couch to watch three televisions as they
Speaker 1: all played out, and they saw that these three movies
Speaker 1: had the same beats, like within within thirty seconds of
Speaker 1: each other. They were identical as far as big action sequence,
Speaker 1: stupid character moment, terrible comedy relief and yeah, and like
Speaker 1: not the same way that people say that Pink Floyd
Speaker 1: matches up with uh with a Wizard of Oppositive Yeah,
Speaker 1: but like actually yeah, like the way that if you
Speaker 1: play Nickelback songs at the same time, they have all
Speaker 1: the same changes. Yeah, very similar to that, very similar
Speaker 1: to that. And it was actually pretty and and you
Speaker 1: hear them like they start laughing because they see, like, look, now,
Speaker 1: these two are in the same moment, because it wasn't
Speaker 1: always all three. I think at one point one of
Speaker 1: the film's got like they lagged behind because they ended
Speaker 1: up either pausing it or something excellently. But at then
Speaker 1: you rate they just they talked about how frequently they
Speaker 1: matched up, and it just showed that there was a
Speaker 1: very formulaic approach. So this is something that's happening already.
Speaker 1: I just suggest that if we were to move to
Speaker 1: an area where computers were making these decisions and they
Speaker 1: were basically purely on past experience, we would see way
Speaker 1: more formulaic films. They would that would be the majority
Speaker 1: if that was like how they were being generated. And uh,
Speaker 1: because we haven't reached a point where we can create
Speaker 1: a computer that not only can draw from past experience,
Speaker 1: but innovate from that create something more brand new. That
Speaker 1: is is paradigm shifting. I think we're a lot closer
Speaker 1: to a computer that can write a basic story than
Speaker 1: a computer that could actually generate a script with dialogue
Speaker 1: and stuff. And I think that those kind of like
Speaker 1: human textual elements would still have to be filled in
Speaker 1: by So this would be like the actual beats like this,
Speaker 1: this is what happens in this scene. This is what
Speaker 1: happens in the scene. And then I can you imagine
Speaker 1: dialogue very easily a computer telling you, maybe even today,
Speaker 1: if somebody spent time working on creating this program, that
Speaker 1: you need to have these types of characters, you need
Speaker 1: to have these types of events in the movie, and
Speaker 1: they need to come in this order. And then then
Speaker 1: Diesel needs to race buy in a car super high speed. Um.
Speaker 1: There's there's a film that I wanted to bring up
Speaker 1: that came out in ten called The Congress. I don't
Speaker 1: believe it's scene wide release yet, um, but I managed
Speaker 1: to catch it at the Atlanta Film Festival. Uh. It
Speaker 1: was starring Robin Wright and it was directed and written
Speaker 1: for screened by Ari Fullman, based on a novel by
Speaker 1: Stanis law Lem which I would like to nominate as
Speaker 1: the best name, um Stanis law anyway. Uh yeah, yeah.
Speaker 1: The Congress is about this near future wherein movie studios
Speaker 1: just upload actors basically and uh and then create films
Speaker 1: by computer algorithm. And it's a really really beautiful piece,
Speaker 1: um that explores these kind of tropes of what happens
Speaker 1: to the human element when you've taken the human element
Speaker 1: out of art. I highly recommend getting ahold of it
Speaker 1: if you can, um, because it's it's it's just so
Speaker 1: beautiful and creative and impossible to imagine having come from
Speaker 1: a computer. So uh So, yeah, if you're if you're
Speaker 1: interested in this and interested in seeing a creative portrayal
Speaker 1: of it, then yeah, I can definitely see. I mean,
Speaker 1: you guys, remember when the commercials were coming out where
Speaker 1: they were digitally uh inserting you know, dance whoever they want. Yeah,
Speaker 1: it's usually for vacuums. I remember those in particular or
Speaker 1: um like Fred Astaire or Gene Kelly or something like that. Uh,
Speaker 1: And that raised a bunch of like people were starting
Speaker 1: to say, oh, are we moments away from being able
Speaker 1: to cast like the Dream film where you can get
Speaker 1: young Marlon Brando in a film, Uh, you know, with
Speaker 1: actors and actresses that would not have been alive or
Speaker 1: not have been participate right or at the peak of
Speaker 1: their performing potential or any of that at the same
Speaker 1: time you could put them all together. Of course, we're
Speaker 1: still far away from that too. Uh. It will be
Speaker 1: interesting to see how this develops. I'm sure we will
Speaker 1: see a lot of experiments come out about this. I mean,
Speaker 1: this seems like the sort of thing that artificial intelligence
Speaker 1: labs would do as a means of kind of just
Speaker 1: a you know, just an experiment. See what happens if
Speaker 1: you can do this. Uh, I don't expect it to
Speaker 1: be necessarily, at least at the beginning, anymore successful than that,
Speaker 1: you know, the greatest song ever written in the worst
Speaker 1: song ever written. Uh. Projects were which were again in interesting,
Speaker 1: but um not not something that you would necessarily want
Speaker 1: on your on your NB three player. Although I do
Speaker 1: have the worst song online. Yeah, yeah, no, it's good. Um,
Speaker 1: I know I totally encourage someone to go make this
Speaker 1: if if any of you guys listening are our filmmakers,
Speaker 1: please like, like, I'll if you need a bad actor,
Speaker 1: I'll come acting in this experiment. That sounds beautiful. Yeah.
Speaker 1: I only agree to do it if I can be
Speaker 1: the best boy. I don't want to be the second
Speaker 1: best boy. I don't want to be the worst boy.
Speaker 1: I got to be the best boy. Jonathan, you're my
Speaker 1: key grip like, oh you're so close, You're so close. Uh.
Speaker 1: Before we make tons of other jokes about gaffers and
Speaker 1: uh and and other people who are absolutely key to
Speaker 1: getting a video or film or television shoot done. Uh,
Speaker 1: let's wrap some of them are so key it's in
Speaker 1: their name. It is. Let's wrap this up. So you know,
Speaker 1: this was a fun time, big to talk about. And
Speaker 1: you know, we've been doing a lot of episodes that
Speaker 1: have been um inspired by listeners who have written in,
Speaker 1: and we're looking for more of that because we love
Speaker 1: hearing from you. Guys. We've got We've received so many
Speaker 1: cool suggestions. We can't wait to tackle them all. But
Speaker 1: don't let that stop you send in more. And you
Speaker 1: can do that by sending us an email the addresses
Speaker 1: f W Thinking at how Stuff Works dot com, or
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