Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there everyone, and welcome to Forward Thinking,
Speaker 1: the podcast that looks at the future and says, here's
Speaker 1: a little story. I've got to tell about three bad podcasters.
Speaker 1: You know, so well, I'm Jonathan Strickland and I'm Joe McCormick,
Speaker 1: and you just you don't dig the BC boys they're
Speaker 1: Lauren No, no, I do that. That was That was lovely,
Speaker 1: Thank you, terrific, thank you. Uh, and we want to
Speaker 1: talk a little bit more about big data. You know,
Speaker 1: we talked about big data. It's okay, I'm going to
Speaker 1: use both pronunciations. Okay, fair enough, so big info, uh no,
Speaker 1: big data because we're all you are talking about computer
Speaker 1: information here and not the android from Star not the
Speaker 1: android from Star Trek. Yeah, just get that all the way.
Speaker 1: So we're we wanted to talk more about applications of
Speaker 1: what what different organizations, companies, governments are using big data
Speaker 1: for what they're mining out of this huge amount of
Speaker 1: information that we are generating every day. Now. You may
Speaker 1: remember in our last podcast we said that we're generating
Speaker 1: about two point five quintillion bytes of information per day
Speaker 1: and not just humans, but you know, sensors, things that
Speaker 1: are uh indirectly yeah, internet things, stuff that's connected to
Speaker 1: the Internet that we're not directly in putting data into.
Speaker 1: And for those of us who missed the last podcast,
Speaker 1: what's the difference between this paradigm of big data and
Speaker 1: just say a lot of data. Big data? We're talking huge, huge,
Speaker 1: enormous amounts of information. When we're talking two point five
Speaker 1: quintillion bytes, that's half of all the spoken words that
Speaker 1: humans have uttered since the dawn of language. So we're
Speaker 1: talking about in two days, you generate as much information
Speaker 1: as all the words we've ever spoken ever of all
Speaker 1: the people. So there's this issue of volume, but also
Speaker 1: these characteristics we talked about, like velocity and variety. So
Speaker 1: it's not just the amount of data, but it's interacting
Speaker 1: exactly extremely fast. We're gathering it at an incredible pace.
Speaker 1: I mean, you're you're gathering data at an unprecedented pace.
Speaker 1: You are. It's rich and intense and everywhere. Yeah, and
Speaker 1: it's all different types of information. And also there's a
Speaker 1: fourth v that we can mention, which is voracity, which
Speaker 1: is the quality of the information truthiness. Yeah, yeah, there
Speaker 1: you go. Uh we're gonna tell you some strategicies that
Speaker 1: people use with big data. Uh it does mean truthiness.
Speaker 1: I mean it means that the data is essentially high quality. Right. Yeah.
Speaker 1: Voracity is really kind of their way of saying, how
Speaker 1: good is your information? And sometimes you don't know how
Speaker 1: good your information is, because again, you've got a lot
Speaker 1: of it. Until you analyze it, you don't really know
Speaker 1: if there's anything useful there. Uh. IBM is in the
Speaker 1: business of of leveraging big data and helping other companies
Speaker 1: leverage it. And uh and and they say very clearly
Speaker 1: on their website and in their white papers that the
Speaker 1: important part here is that you have to figure out
Speaker 1: what your goal is before you start just looking at
Speaker 1: big data and saying we need to be part of this.
Speaker 1: Because whatever your goal is, that's going to end up
Speaker 1: informing your approach to using that information in a way
Speaker 1: that makes sense. Otherwise, you're just talking about an enormous
Speaker 1: resource that may not be directly useful to you. You're
Speaker 1: just kind of looking at it and thinking, I want
Speaker 1: to make use of that information. I just it's too
Speaker 1: big a problem for me to even get a grasp
Speaker 1: on how I want to use it. So you can't
Speaker 1: just run at it and say, give me some ones
Speaker 1: and zeros, I'm gonna make magic happen. You have to
Speaker 1: have a plan in place first. But I've got a
Speaker 1: lot of really clever people have figured out really cool
Speaker 1: stuff to do with this information. Sure. In the last podcast,
Speaker 1: we talked a little bit about traffic analysis, which was
Speaker 1: a very kind of you know, you know, it's an
Speaker 1: easy to understand application of big data, right, So let
Speaker 1: me give you an example using Google's approach. So the
Speaker 1: way Google would generate traffic on Google Maps. If you
Speaker 1: were using Google Maps on a mobile device and you
Speaker 1: wanted to try and get from point A to point B,
Speaker 1: and you wanted traffic to be part of that that equation,
Speaker 1: that route, then what it would do is it would
Speaker 1: start looking at information of other Google users. Uh. There
Speaker 1: are other systems that use this, the Dash system, which
Speaker 1: doesn't really exist anymore, but uh, it used a very
Speaker 1: similar approach where it would send anonymous data about vehicles
Speaker 1: that were moving through a particular region and the speed
Speaker 1: at which they were moving. It was just sampling the vehicle, yeah,
Speaker 1: sampling the vehicle's location. It would get the GPS coordinates
Speaker 1: and it would just sample it over a certain amount
Speaker 1: of time and derive how fat that vehicle was moving
Speaker 1: down the street based upon the information, right saying, okay,
Speaker 1: well it was at this point at this time, and
Speaker 1: it was at this other point a little bit later,
Speaker 1: and this other point a little bit later. Therefore, that
Speaker 1: means traffic is moving at this speed down this particular street,
Speaker 1: and then it's extrapolates that sends it out to everyone
Speaker 1: so that you know which routes have the heaviest traffic.
Speaker 1: Now the Yeah, it's it's a very simple approach to
Speaker 1: big data in the sense that it's just taking real
Speaker 1: time information, analyzing it, and sending the results back very quickly.
Speaker 1: It's not storing information, it's not trying to be transformative
Speaker 1: with the information. It's just trying to make sense of
Speaker 1: all these different pieces of information that are coming in
Speaker 1: and then making it meaningful to the people who are
Speaker 1: using the service. So that's one example, but that's just one.
Speaker 1: You can actually see some pretty interesting patterns when you
Speaker 1: get huge amounts of information. You can see patterns where
Speaker 1: you might have thought there was just chaos before. So
Speaker 1: you you can look at a system that you might say, well,
Speaker 1: from the outset, it just looks like stuff is happening.
Speaker 1: But now when I see all this information, it's broken
Speaker 1: down like this, I can actually see trends where before
Speaker 1: I just saw stuff. So education is a good example
Speaker 1: of this. So let's say you're a teacher and you're
Speaker 1: teaching a class, and you have your classes submitting schoolwork
Speaker 1: in a into a system that can then analyze the
Speaker 1: school work. So you're grading the kids. You you might
Speaker 1: actually just be inserting the grades into the system. In fact,
Speaker 1: it may not have any connection with the kids directly
Speaker 1: at all. You might just be the teacher in the system.
Speaker 1: The system would be able to, if you're using a
Speaker 1: very sophisticated approach, be able to start detecting trends in
Speaker 1: each individual student's progress. So you might be able to say, oh,
Speaker 1: while while student A isn't failing, the trend indicates that
Speaker 1: the student is beginning to struggle. So I need to
Speaker 1: adjust my way of reaching this student so that I
Speaker 1: am not leaving the student without any support. Oh, I wonder,
Speaker 1: so would that involve comparing uh little signals with millions
Speaker 1: of other students, Like if we we've seen that when
Speaker 1: these things start to happen, statistically, that means like we're
Speaker 1: heading towards failure. It would mean that It would also
Speaker 1: mean that on a larger level, you might see that
Speaker 1: an entire classroom is having some issues, which would tell
Speaker 1: the teacher I need to change my approach. I I
Speaker 1: you know this, this concept that I've tried to teach.
Speaker 1: Obviously this has not worked out. So I need to
Speaker 1: find a new way of getting this across in a
Speaker 1: way that makes sense to my students, or perhaps help
Speaker 1: an entire school system figure out how to how to
Speaker 1: grade and test better. Exactly. Yeah, you know how if
Speaker 1: you take a survey um and your survey has just
Speaker 1: a hundred people in it, well, it's probably not very
Speaker 1: representative of the entire population, but the big but you
Speaker 1: can play family feud, right if you have a thousand people,
Speaker 1: it's better. If you keep increasing your sample size, your
Speaker 1: statistics become better and better at representing stronger trends that
Speaker 1: you want to look for. And this is the same
Speaker 1: thing you'd see. It's why big data is great. Right,
Speaker 1: you're increasing your sample size, right, Yes, As you increase
Speaker 1: that sample size, then you can actually start to recognize
Speaker 1: things that are truly trends and not just a one off.
Speaker 1: They're less likely to be anomalies. Yes, exactly, And this
Speaker 1: has tracked a lot in especially in consumer segments. I mean,
Speaker 1: you know, like every time you buy something on Amazon,
Speaker 1: it it collects a little group of other things that
Speaker 1: people who have bought that thing have also bought in
Speaker 1: case you want to buy that thing, um and and
Speaker 1: can be very useful not just for not just for
Speaker 1: that basic you know, like we want to sell more stuff,
Speaker 1: but uh yeah, I mean with Amazon sometimes it's creepy
Speaker 1: how how on spot it is? Or it's creepy how
Speaker 1: not on spot it is, and you wonder if they're
Speaker 1: something you don't know about yourself. My my favorite, my
Speaker 1: favorite is that if you start to find the products
Speaker 1: that are on Amazon that have the ridiculous reviews, like
Speaker 1: the ones that are just you know, it's the product
Speaker 1: itself is absurd for or the gallon of milk there
Speaker 1: they're there are examples of products that are on Amazon
Speaker 1: that people have written like novellas in review and the
Speaker 1: novellas are hilarious. Like you get to a point where
Speaker 1: it's like it's like a soap opera that opens up
Speaker 1: and it is this whole thing, and then the last
Speaker 1: line will be some throwaway review of the product or whatever,
Speaker 1: and it's it's I mean, I love it for the absurdity.
Speaker 1: What's interesting is that the related items always tend to
Speaker 1: be the other ones that have similar ridiculous reviews, which
Speaker 1: means that even then always tend to include horse mask.
Speaker 1: Horsehead mask. You know, yeah, people like you bought this thing, right,
Speaker 1: but at any rate, the I wonder if you click
Speaker 1: on that, if you also get like the Godfather as
Speaker 1: a suggestion, you've seen horsehead mask? Right, I've seen horsehead mask. Yes,
Speaker 1: I've used the internet, so yes, I have seen it.
Speaker 1: But yeah, that is another example. Right. Amazon uses this
Speaker 1: in order to make more sales, because the thought is,
Speaker 1: if you are interested in this one particular kind of product,
Speaker 1: then you're probably interested in these other products, especially if
Speaker 1: there's a history of other people having bought those together,
Speaker 1: or at least you know, I bought them at some
Speaker 1: other point in their and their history. And then you
Speaker 1: can you can start pouring that information not just into
Speaker 1: what are people going to buy next, but into why
Speaker 1: are people buying this thing, and start tracking things like
Speaker 1: flu outbreaks. Right, Okay, so this is this is This
Speaker 1: is one of those things that I thought was really,
Speaker 1: uh an interesting example of using big data in a
Speaker 1: way that you wouldn't necessarily first think about. This was
Speaker 1: something that Google wrote up a white paper on there's
Speaker 1: actually a full paper about Google using information to detect
Speaker 1: influenza outbreaks. And the way that they did it was
Speaker 1: they essentially found search queries that people were putting in
Speaker 1: that indicated that someone was feeling sick, especially things like
Speaker 1: various symptoms and stuff. And then by relating that information
Speaker 1: to specific regions in the world and seeing multiple people
Speaker 1: requesting this information from say a particular city, they could say,
Speaker 1: this looks like this is an outbreak of the flu.
Speaker 1: They said that there was a reporting lag of about
Speaker 1: one day, so a day after a certain you know,
Speaker 1: a large enough sample size of people are looking for this,
Speaker 1: Google could say there's a potential flu outbreak in this
Speaker 1: very specific area of the world. Maybe we need to
Speaker 1: you know, by a learning something like the Center for
Speaker 1: Disease Control the CDC, They could say, we need to
Speaker 1: head this off before it becomes some sort of pandemic, right,
Speaker 1: which basically says to me that that that because of
Speaker 1: Google and big data we're going to be able to
Speaker 1: uh prevent the inevitable zombie. But exactly the scary thing
Speaker 1: is will probably never even find out about it, right
Speaker 1: because of stuff like this. They'll get to the government
Speaker 1: before the public knows. No, there there has to be
Speaker 1: enough queries of dead uncle trying to eat my face
Speaker 1: for at least a few people to take notice. If
Speaker 1: you happen to be checking Google trends on dead uncle
Speaker 1: turned on Twitter long before. Yeah, you know, just the
Speaker 1: tumbler alone would be But this is funny. Actually, Google
Speaker 1: trends is a great, really simple, really straightforward example of
Speaker 1: how big data is really interesting, like looking at um
Speaker 1: the popularity of a search term over a period of time.
Speaker 1: I mean, it's so cool. You can get lost in
Speaker 1: these bore to see is watching the spikes when you
Speaker 1: know different movies or books or cultural events happened. Yeah,
Speaker 1: when Kanye West does something embarrassing and immediately shoots to
Speaker 1: the top. One I love is when you can look
Speaker 1: at the historical ones because they have every book ever written, right,
Speaker 1: They've scanned that in and then they can you say,
Speaker 1: historical ones. I just said, they're thinking like bad play.
Speaker 1: My American cousin just think like Google trends throughout the centurial.
Speaker 1: You can look at you can look at like back
Speaker 1: to eighteen hundred in the books they've scanned, right, um,
Speaker 1: and you can you can chart changes in spelling. Right.
Speaker 1: You can compare spelling a word one way versus spelling
Speaker 1: it another way, and watch one go down with while
Speaker 1: the other goes up one one data artists that I'm
Speaker 1: going to talk about in a little bit created a
Speaker 1: graph of the use of hope versus despair and in
Speaker 1: recent years, just just watching the times when when despair
Speaker 1: overlapped hope. Interesting, beautiful. I can't wait to talk about that. Well,
Speaker 1: and and you know, let's start with other applications. Well,
Speaker 1: I was going to mention that, you know, when you
Speaker 1: think about Google, Google's mission statement is all about big data,
Speaker 1: because they're about organizing the universe's information, which when you
Speaker 1: think about that, you know they want to index and
Speaker 1: organize all the information everywhere that we ever encounter. That
Speaker 1: is big data. That's that's as clear as you can get. Yeah,
Speaker 1: And so the fact that they're able to demonstrate the
Speaker 1: usefulness of this proves the the the the utility of
Speaker 1: their company, right because otherwise if if all they did
Speaker 1: was index this and there was no uh, actually useful, Yeah,
Speaker 1: then you'd be like, well, this company is just not
Speaker 1: going to stick around. So besides education or predicting a
Speaker 1: flu outbreak, you can actually use it to monitor cybersecurity
Speaker 1: and check a network's health. So if you see a
Speaker 1: spike in network activity, you can check it out and
Speaker 1: make sure that it's not a d d o S attack,
Speaker 1: a distributed denial of service attack, so that a hacker
Speaker 1: hasn't said, hey, this website has raised my ire, I
Speaker 1: shall direct my zombie computers to attack it. UM. You know,
Speaker 1: being able to see that kind of stuff and respond
Speaker 1: to it in real time is really useful. And you
Speaker 1: obviously need to have a robust, robust system to deal
Speaker 1: with a lot of information because it may just be
Speaker 1: that it's a heavy amount of traffic for completely legitimate reasons.
Speaker 1: So that's another implementation of big data. UH. It's also
Speaker 1: part of what they're talking about when they talk about
Speaker 1: the smart grid UM for electrical electrical companies to be
Speaker 1: able to get energy to the right places at the
Speaker 1: right times and prevent brownouts and blackouts. UM system overloads.
Speaker 1: People have talked about using UM tracking the number of
Speaker 1: ups packages sent to track how well the economy is doing. Interesting. Yeah,
Speaker 1: I've definitely heard about the smart grid stuff. I mean,
Speaker 1: there are a lot of utility companies that are running
Speaker 1: at close to full capacity, and being able to to
Speaker 1: see where a demand is going to be at any
Speaker 1: given time means that you are reducing the demand on
Speaker 1: any individual power company because they can all work in
Speaker 1: concert together and that way you don't have these you
Speaker 1: reduce the possibility of a brown out or a blackout. Um. So,
Speaker 1: I mean that's clearly important, but that is a lot
Speaker 1: of information. You're constantly getting feedback from all the different
Speaker 1: meters essentially smart meters, and even if you get down
Speaker 1: to it, you can have smart appliances that are very
Speaker 1: specifically giving both you and the network more information about
Speaker 1: power consumption. So that's all also important. Weather forecasting another
Speaker 1: important part. Talk about gathering all the information from weather
Speaker 1: sensors around the world and looking at the information and
Speaker 1: detecting patterns because our forecasting abilities, don't know if you noticed,
Speaker 1: not so great. Sometimes it's hilarious because when you think
Speaker 1: about it, we have so much power and technology devoted
Speaker 1: to produce acting the weather and sometimes we're still so
Speaker 1: it's such a complex night. The other night, the hourly
Speaker 1: weather on online was telling me zero percent chance of precipitation.
Speaker 1: We had a storm that was knocking limbs out of trees.
Speaker 1: It was like you couldn't see ten feet for the rain. Yeah. Yeah,
Speaker 1: And it's hilarious when you take a look and say, wait,
Speaker 1: they forecast ten days out, How how reliable is that
Speaker 1: tenth day? There wrong about what's happening right now. Well,
Speaker 1: and beyond that you have things like uh fraud detection,
Speaker 1: and also governments can use big data for tax collection purposes,
Speaker 1: looking at trends in taxes and the way that people
Speaker 1: are paying taxes, and maybe comparing the way people are
Speaker 1: paying taxes versus what they supposedly, oh in Texas, and
Speaker 1: finding out if there are big gaps there, because right now,
Speaker 1: the way it tends to work is it's after the fact,
Speaker 1: right people file their taxes, and then a certain number
Speaker 1: of those taxes tax reports are picked to be looked
Speaker 1: over in more careful detail, and it's only if they
Speaker 1: start to detect a pretty uh significant pattern that they'll
Speaker 1: look at any individual's taxes specifically, unless you're part of
Speaker 1: some political controversy which we won't get into, but this
Speaker 1: big data thing would allow you to take a look
Speaker 1: at a much larger scale and focus in on particular problems,
Speaker 1: as opposed to just hoping that the sheet of reports
Speaker 1: that you just pulled from the printer includes people who
Speaker 1: are not paying their fair share. So I've got a question. Yeah,
Speaker 1: now that we're talking about the government using big data
Speaker 1: to predict near duells, and uh, I think I see
Speaker 1: where this is going. Yeah, y'all seen that movie Minority Report,
Speaker 1: documentary Minority Report. Well, okay, so I want to explain
Speaker 1: a little bit. In that movie, they've got a they've
Speaker 1: got a division of law enforcement called pre crime. Where
Speaker 1: they are now in the movie, it's kind of they've
Speaker 1: got these like psychic prelugs. But let's just say replace
Speaker 1: the psychics with really, really really powerful computers, right that
Speaker 1: look at trends pattern com make extremely accurate predictions about
Speaker 1: what's about to happen. I can definitely foresee a future
Speaker 1: where it might not be all that impossible for computers
Speaker 1: to predict when somebody is very likely to commit a crime?
Speaker 1: Could I can? I can tell you. Let me give
Speaker 1: you a little more, a little more insight. From my perspective,
Speaker 1: I don't know that we're going to get to a
Speaker 1: point where we're going to be able to predict when
Speaker 1: a specific individual is likely to commit a crime. We
Speaker 1: can definitely get a little more probabilistic, you know, sit
Speaker 1: there and say, what is the probability of any person
Speaker 1: at any given time to commit a crime? Um, there
Speaker 1: are some things that we can say. For example, there
Speaker 1: are law enforcement agencies there that are now using big
Speaker 1: data in order to predict crime trends. So not a
Speaker 1: specific person not saying, you know, yeah yeah ne'er dowell,
Speaker 1: Johnny today is gonna knock over the liquor store. They
Speaker 1: are not doing that. What they are doing is saying,
Speaker 1: looking at this big data, I'm seeing this trend where
Speaker 1: this particular part of town tends to be a target
Speaker 1: for vandalization. In burglary, let's say those those are two
Speaker 1: crimes that often tons of factors, you know, based on
Speaker 1: weather or right. Apparently things like burglaries, um kind of
Speaker 1: go in rashes. Yeah. That's another thing is that if
Speaker 1: a place is hit by burglars, then there is there
Speaker 1: tends to be a increased risk of the same thing
Speaker 1: happening in and in the sanginal area. Yeah, so if
Speaker 1: there's a successful burglary attempt in one particular home, for example,
Speaker 1: other homes in that neighborhood could be also um prone
Speaker 1: to being hit by burglars. So that's one example that
Speaker 1: law enforced we can use. They can use it as
Speaker 1: a reactionary thing, saying all right, well, because we know this,
Speaker 1: we should end up increasing patrols in this area for
Speaker 1: the time being so that we can discourage any other
Speaker 1: crime or catch the criminals before they're able to hit
Speaker 1: another another house or another business. Another thing is that
Speaker 1: for crimes like burglary and vandalism, those are crimes that
Speaker 1: generally go down when you increase patrols. They they are
Speaker 1: considered low intensity but high frequency crimes. So if you
Speaker 1: were to adjust patrols so that there is a more
Speaker 1: frequent patrol of police through that area, you reduce the
Speaker 1: likelihood of those crimes being committed. And by using big
Speaker 1: data and and and really analyzing where these crimes are
Speaker 1: taking place within a city, you can redraw patrol routes
Speaker 1: so that police are taking the most efficient patrol they can,
Speaker 1: so they're not having to patrol an area that's way
Speaker 1: larger than what they're capable of doing in a in
Speaker 1: a given shift, and you also will hit the areas
Speaker 1: that are most likely to be targeted and help reduce crime.
Speaker 1: That way, you're preventing it from happening. So you're not
Speaker 1: going out and arresting someone for a crime they haven't
Speaker 1: committed yet. That's not the same thing at all. But
Speaker 1: you can help attack those sort of crimes, things like
Speaker 1: murder much less you know, much less prone to any
Speaker 1: sort of pattern that you can predict. It's that's something
Speaker 1: that's a high intensity but low frequency crime as opposed
Speaker 1: to low intensity, high frequency like vandalism and burglary. So
Speaker 1: they don't tend to take that kind of crime into
Speaker 1: consideration when they're looking at this big data in this sense,
Speaker 1: other than to uh perhaps say that, you know, this
Speaker 1: particular area of town needs to have a stronger police
Speaker 1: presence in order to help can back to what we
Speaker 1: were talking about with sample size, Yeah, and uh, Like
Speaker 1: in Santa Cruz, California, police use this approach to and
Speaker 1: identify homes that were more likely to be hit by
Speaker 1: a burglar so they could redraw their patrol roots to
Speaker 1: take that into consideration and prevent that from happening. Now,
Speaker 1: when we do talk about criminals and the likelihood of
Speaker 1: someone to to commit a particular crime, there is some
Speaker 1: statistical evidence to suggest that people who are who have
Speaker 1: committed a crime are more likely to commit another crime
Speaker 1: than someone who has never committed a crime like that,
Speaker 1: there's like a fort recidivism rate. But part of that
Speaker 1: is due to the way that we handle criminals and
Speaker 1: how we try to reintroduce criminals to society. So it
Speaker 1: may not be that people just have this statistical likelihood
Speaker 1: of committing a crime again once they've already done. So
Speaker 1: some of its institutionalize, right, It's a social construct, not
Speaker 1: a personal propensity. Right, So therefore that wouldn't it wouldn't
Speaker 1: be the issue what the cause was. It would just
Speaker 1: be like that, you see it. Well, no, there's an
Speaker 1: issue about what the cause was, because if you can
Speaker 1: treat the cause, then you remove the I'm saying that
Speaker 1: criminals matter, and Joe, you're just throwing them away. I
Speaker 1: think you know, you know, I'm talking about what the
Speaker 1: cause was. Wouldn't affect how how how well? Just like
Speaker 1: I was, I was teasing Joe, but we were very
Speaker 1: clearly talking about two different things of the same problem. Yeah,
Speaker 1: And and what is scary about all of this is
Speaker 1: the thought that someone could say, well, all of all
Speaker 1: of this is is reactive in looking for this kind
Speaker 1: of crime, and why can't we be proactive and well
Speaker 1: and getting into that scary minority. And that's and and
Speaker 1: I think, I mean, I'm not saying that we'll never
Speaker 1: get to a point where we where where statistical models
Speaker 1: won't give, at least again, a probabilistic approach of how
Speaker 1: likely is person A to commit a crime versus person B.
Speaker 1: And you take everything into account, and you compare that
Speaker 1: against all the information you've ever gathered and come up
Speaker 1: with a probability that's probably gonna happen at some point.
Speaker 1: But I don't think that we're ever going to act
Speaker 1: on that. I'm just saying that, I think if a
Speaker 1: computer can predict that Jonathan Strickland is likely to buy
Speaker 1: a horsehead mask, it can also probably predict that Jonathan
Speaker 1: Strickland is more likely than the average person to Robert
Speaker 1: Jimmy Johns. But what was what horrifying how accurate you are?
Speaker 1: But there's a Jimmy Johns with then walking distance of
Speaker 1: this office. What we should always keep in mind is
Speaker 1: even if computers are that good, we shouldn't ever let
Speaker 1: that prejudice our approach to Jonathan Strickland, because he may
Speaker 1: very well not buy a horsehead mask, and he may
Speaker 1: very well not Robert Jimmy Johns. That's true. That was
Speaker 1: my point. Okay. So yeah, So while while Minority Report
Speaker 1: definitely had this sort of scary science fictionary approach to
Speaker 1: you know, uh, stopping people arresting people for crimes they
Speaker 1: had not yet committed, but we're going to come it. Uh.
Speaker 1: And and they, you know, they had the benefit of
Speaker 1: having psychics who are apparently infallible, except they're not. When
Speaker 1: you watch the movie spoiler. Yeah for a movie that's
Speaker 1: that old. I'm sorry anyone who's listened to this. Yeah.
Speaker 1: So anyway, Uh, I don't think we're ever gonna I
Speaker 1: don't think we're ever going to get to a point
Speaker 1: where big day is when pre crime comes up. Vote
Speaker 1: no right to your representative, say no to pre crime things.
Speaker 1: You got to vote no one. Vote no one pre
Speaker 1: crime and vote no on giving artificial intelligence the right
Speaker 1: to vote. Those are the two things you have to
Speaker 1: make sure deals. Yeah, those are two big strikes, Lauren,
Speaker 1: can you tell us something happy? I can? Well, okay, So,
Speaker 1: so part of what part of what is scary about
Speaker 1: all this data is that it's really hard for us
Speaker 1: to understand what's out there, what we're generating, what it's
Speaker 1: being used for, and what all that looks like. I mean,
Speaker 1: because you know, like we were talking about like at
Speaker 1: a certain point, we're like, oh, sure, a quadrille, what's
Speaker 1: what's the number. It's a lot, Yeah, it's it's a
Speaker 1: it's a one with fifteen zeros. And that's a lot
Speaker 1: of zeros. And and there are a group of data
Speaker 1: artists out there. Are you giggling at more than a
Speaker 1: dozen zeros? That is more than a dozen zero? Let's
Speaker 1: thank you. Let's not let Joe talk anymore. Take the
Speaker 1: X away from Joe. I haven't been touching it. Please
Speaker 1: please go on. Um, they're there are a group of
Speaker 1: data artists out there who are working to to put
Speaker 1: all of this into some kind of meaningful and and
Speaker 1: also culturally meaningful unit that that we can process and
Speaker 1: u And there there's there's one particular fellow by the
Speaker 1: name of Jared Thorpe who used to be the data
Speaker 1: artist in residence at the New York Times and has
Speaker 1: as of I think December or January of UM gone
Speaker 1: off and found did the Office for Creative Research as
Speaker 1: as it's being called a company of his UM and
Speaker 1: and he he posits that that this data art is
Speaker 1: going to help people understand what all of this data
Speaker 1: means and what it's being used for. UM and he's
Speaker 1: got some really interesting just personal projects that he did
Speaker 1: a TED talk that's that's pretty pretty terrific UM or
Speaker 1: you can you can see, you know, he's he's taken
Speaker 1: Twitter data from from people saying good morning and putting
Speaker 1: it into this kind of gorgeous bouncy map of of
Speaker 1: just of just tracking when people are waking up and
Speaker 1: saying good morning to Twitter. He also shows what time
Speaker 1: they say good morning based upon the color of the
Speaker 1: block that appears. So if they say good morning earlier,
Speaker 1: it's a green block, and the later they say good morning,
Speaker 1: it goes into goes into the reds. And so he
Speaker 1: also could show trends that way, like around the world,
Speaker 1: showing trends of when people would say good morning and
Speaker 1: uh in general, Let's say the West Coast wakes up
Speaker 1: at around eleven am and the East coast we're early risers,
Speaker 1: not only because the sun gets to us first, but
Speaker 1: because the west coast is sleeping in. It's all those
Speaker 1: actors who say good morning at two pm. You're an actor,
Speaker 1: that's true, I am, but I get up at you know,
Speaker 1: five in the morning. The trick is you don't say
Speaker 1: good morning, just a grump coffee everyone. That's pretty much
Speaker 1: meet hate everyone? That what what when am I not
Speaker 1: tweeting that I hate everyone? So so that's an interesting
Speaker 1: example of data visualization right right, and and and that's
Speaker 1: that's what they're working for, is that visualization of getting
Speaker 1: something down to a graphic scale where we can go like, oh,
Speaker 1: that's still a way too huge for me to comprehend,
Speaker 1: but at least it looks kind of pretty and I
Speaker 1: get it now. I can totally see that how that
Speaker 1: would help people understand what data means. H um he
Speaker 1: was talking about in one UM one talk that he
Speaker 1: gave it pop tech. I believe about about how people
Speaker 1: have been saying that data is the new oil, and
Speaker 1: and how kind of grandiose and lovely that sounds. For
Speaker 1: a second, because people are thinking like oil, oil is money, money,
Speaker 1: is good. But but but it's how how terrifying that
Speaker 1: is in a certain way because because oil for for
Speaker 1: you know, a very specific example has been a resource
Speaker 1: that has been so misused and is so poisonous and
Speaker 1: terrible global instability and war and and all of this.
Speaker 1: But and that that you know, similarly, this data could
Speaker 1: be used or misused rather for um, you know, not
Speaker 1: very good purposes like like we were talking about. But
Speaker 1: if we you know, if if we use these kind
Speaker 1: of resources, and by resources, I mean people who are
Speaker 1: processing this, um too, get away from all of the
Speaker 1: capitalism that the capitalism is terrible. But but but using
Speaker 1: this data for the greater common good, for some things
Speaker 1: like predicting influenza outbreaks and being able to respond quickly
Speaker 1: before it becomes a pandemic. I mean, clearly you're talking
Speaker 1: about benefiting potentially millions of people. We've seen flu outbreaks
Speaker 1: affect millions of people, and if you're able to respond
Speaker 1: fast enough so that you could contain that, then that
Speaker 1: would be an obvious, you know, benefit to everybody. So yeah,
Speaker 1: that's and that's just one example. There's some that are
Speaker 1: more like, well, this makes my life easier. The traffic
Speaker 1: stuff for example. But even even in the bigger scheme,
Speaker 1: if you talk about traffic, that seems kind of trivial.
Speaker 1: You know, all it means that I don't have to
Speaker 1: spend you know, extra time sitting in traffic. That also
Speaker 1: means you're spending less time running a gasoline power at
Speaker 1: engine I mean, unless you have an electric vehicle or whatever.
Speaker 1: But and the stress levels which relate to your to
Speaker 1: your heart rate and health, um, the your productivity at work.
Speaker 1: If you could get everyone in Atlanta to work in
Speaker 1: half an hour less than they currently spend on the road,
Speaker 1: I mean, we would probably just be looking at pictures
Speaker 1: of cats on the internet anyway. But um, but either way, yeah, yeah, no,
Speaker 1: I agree entirely. So there are and you know, I
Speaker 1: like the I like the artistic vision of showing this
Speaker 1: as a way to demonstrate this is just one way
Speaker 1: of looking at the information. And uh, and you know,
Speaker 1: the ways that you've heard of are just the tip
Speaker 1: of the iceberg. We haven't even really explored the full
Speaker 1: extent of what we can use this data for. And
Speaker 1: in some cases it may be truly transformative. We won't
Speaker 1: have to necessarily reinvent or or invent brand new technology
Speaker 1: to make the world a better place. We may have
Speaker 1: all the tools already, it's just in that information we
Speaker 1: have to feagure. Yeah, and then part of that is
Speaker 1: getting people interested in this field and creating a culture
Speaker 1: around it. Yeah. Yeah, agreed. Well that's awesome. I mean
Speaker 1: it's I've only seen one of those, uh those examples.
Speaker 1: I saw the good morning example for Twitter. It was
Speaker 1: a spinning globe and all the little uh pop ups
Speaker 1: of showing where people had said good morning. Um. It
Speaker 1: also made me feel better about my tweets because I don't.
Speaker 1: I don't tend to say good morning. No, I don't know.
Speaker 1: I also don't LaVar Burton does. Is LaVar Burton not
Speaker 1: good enough for you? No, LaVar Burton is good enough
Speaker 1: for me. Um. You know, I I like to take
Speaker 1: He was data. That was terrible. His data's best friend
Speaker 1: was about to say, uh, what was this just the
Speaker 1: worst next generation? Well, yeah, okay, So LaVar Burton can
Speaker 1: say good morning, that's fantastic. I don't. I don't have
Speaker 1: enough followers to say good morning. I do occasionally quote
Speaker 1: half of a song lyric here's a there's a shock
Speaker 1: just to see how many people who follow me know
Speaker 1: what I'm quoting and see. Yeah, so if you know
Speaker 1: the end of the sky is blue and all the
Speaker 1: grass is green, my heart's as full as a baked potato.
Speaker 1: You let me know, all right. Well, I think that
Speaker 1: wraps up our discussion about applications of big data and
Speaker 1: and what we're using it for. And again, it's just
Speaker 1: kind of a hint at what big data will be
Speaker 1: used for. And while yes, there are certainly examples of
Speaker 1: how companies, governments could abuse big data in a way
Speaker 1: that are legitimately scary, there are also some truly amazing
Speaker 1: uses that could be very beneficial. So I don't think
Speaker 1: we should shy away from it because of the uh,
Speaker 1: the possibility of things being used in a scary way.
Speaker 1: We just need to be aware of it and be
Speaker 1: and make sure that we don't go down that pathway
Speaker 1: because the benefits are too great for us just to ignore. Well,
Speaker 1: of course, it can go either way. I mean, it's elemental,
Speaker 1: it's knowledge and know. Yeah, it's a tool, and you
Speaker 1: know a tool is it's going to be used the
Speaker 1: way the person who's using the tool wants to use it,
Speaker 1: So like a drill, so a third so so not
Speaker 1: nice you really carried it home there, We're just going
Speaker 1: to I'm just gonna end here, guys. If you have
Speaker 1: suggestions for future episode topics or you want to tell
Speaker 1: me the end of the song lyric I quoted rite
Speaker 1: us let's know. Are you know? Just as FW Thinking
Speaker 1: at discovery dot com or go to f W thinking
Speaker 1: dot com. Check out our blogs, check out the podcasts,
Speaker 1: check out the videos. We've got some really fun ones
Speaker 1: up there. I think you guys will really like it,
Speaker 1: and we'll talk to you again really soon. For more
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