July 5, 2013 • 35 mins
How is big data being used today? How can big data predict flu outbreaks? Could law enforcement use big data to predict crimes?
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Episode Transcript
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Speaker 1 (00:00):
Brought to you by Toyota. Let's go places. Welcome to
Forward Thinking. Hey there everyone, and welcome to Forward Thinking,
the podcast that looks at the future and says, here's
a little story. I've got to tell about three bad podcasters.
You know, so well, I'm Jonathan Strickland and I'm Joe McCormick,
(00:24):
and you just you don't dig the BC boys they're
Lauren No, no, I do that. That was That was lovely,
Thank you, terrific, thank you. Uh, and we want to
talk a little bit more about big data. You know,
we talked about big data. It's okay, I'm going to
use both pronunciations. Okay, fair enough, so big info, uh no,
(00:45):
big data because we're all you are talking about computer
information here and not the android from Star not the
android from Star Trek. Yeah, just get that all the way.
So we're we wanted to talk more about applications of
what what different organizations, companies, governments are using big data
for what they're mining out of this huge amount of
information that we are generating every day. Now. You may
(01:07):
remember in our last podcast we said that we're generating
about two point five quintillion bytes of information per day
and not just humans, but you know, sensors, things that
are uh indirectly yeah, internet things, stuff that's connected to
the Internet that we're not directly in putting data into.
And for those of us who missed the last podcast,
(01:28):
what's the difference between this paradigm of big data and
just say a lot of data. Big data? We're talking huge, huge,
enormous amounts of information. When we're talking two point five
quintillion bytes, that's half of all the spoken words that
humans have uttered since the dawn of language. So we're
(01:49):
talking about in two days, you generate as much information
as all the words we've ever spoken ever of all
the people. So there's this issue of volume, but also
these characteristics we talked about, like velocity and variety. So
it's not just the amount of data, but it's interacting
exactly extremely fast. We're gathering it at an incredible pace.
(02:13):
I mean, you're you're gathering data at an unprecedented pace.
You are. It's rich and intense and everywhere. Yeah, and
it's all different types of information. And also there's a
fourth v that we can mention, which is voracity, which
is the quality of the information truthiness. Yeah, yeah, there
you go. Uh we're gonna tell you some strategicies that
(02:35):
people use with big data. Uh it does mean truthiness.
I mean it means that the data is essentially high quality. Right. Yeah.
Voracity is really kind of their way of saying, how
good is your information? And sometimes you don't know how
good your information is, because again, you've got a lot
of it. Until you analyze it, you don't really know
if there's anything useful there. Uh. IBM is in the
(02:58):
business of of leveraging big data and helping other companies
leverage it. And uh and and they say very clearly
on their website and in their white papers that the
important part here is that you have to figure out
what your goal is before you start just looking at
big data and saying we need to be part of this.
(03:18):
Because whatever your goal is, that's going to end up
informing your approach to using that information in a way
that makes sense. Otherwise, you're just talking about an enormous
resource that may not be directly useful to you. You're
just kind of looking at it and thinking, I want
to make use of that information. I just it's too
big a problem for me to even get a grasp
(03:41):
on how I want to use it. So you can't
just run at it and say, give me some ones
and zeros, I'm gonna make magic happen. You have to
have a plan in place first. But I've got a
lot of really clever people have figured out really cool
stuff to do with this information. Sure. In the last podcast,
we talked a little bit about traffic analysis, which was
(04:02):
a very kind of you know, you know, it's an
easy to understand application of big data, right, So let
me give you an example using Google's approach. So the
way Google would generate traffic on Google Maps. If you
were using Google Maps on a mobile device and you
wanted to try and get from point A to point B,
(04:22):
and you wanted traffic to be part of that that equation,
that route, then what it would do is it would
start looking at information of other Google users. Uh. There
are other systems that use this, the Dash system, which
doesn't really exist anymore, but uh, it used a very
similar approach where it would send anonymous data about vehicles
(04:44):
that were moving through a particular region and the speed
at which they were moving. It was just sampling the vehicle, yeah,
sampling the vehicle's location. It would get the GPS coordinates
and it would just sample it over a certain amount
of time and derive how fat that vehicle was moving
down the street based upon the information, right saying, okay,
well it was at this point at this time, and
(05:06):
it was at this other point a little bit later,
and this other point a little bit later. Therefore, that
means traffic is moving at this speed down this particular street,
and then it's extrapolates that sends it out to everyone
so that you know which routes have the heaviest traffic.
Now the Yeah, it's it's a very simple approach to
big data in the sense that it's just taking real
(05:29):
time information, analyzing it, and sending the results back very quickly.
It's not storing information, it's not trying to be transformative
with the information. It's just trying to make sense of
all these different pieces of information that are coming in
and then making it meaningful to the people who are
using the service. So that's one example, but that's just one.
(05:49):
You can actually see some pretty interesting patterns when you
get huge amounts of information. You can see patterns where
you might have thought there was just chaos before. So
you you can look at a system that you might say, well,
from the outset, it just looks like stuff is happening.
But now when I see all this information, it's broken
(06:10):
down like this, I can actually see trends where before
I just saw stuff. So education is a good example
of this. So let's say you're a teacher and you're
teaching a class, and you have your classes submitting schoolwork
in a into a system that can then analyze the
school work. So you're grading the kids. You you might
(06:32):
actually just be inserting the grades into the system. In fact,
it may not have any connection with the kids directly
at all. You might just be the teacher in the system.
The system would be able to, if you're using a
very sophisticated approach, be able to start detecting trends in
each individual student's progress. So you might be able to say, oh,
(06:53):
while while student A isn't failing, the trend indicates that
the student is beginning to struggle. So I need to
adjust my way of reaching this student so that I
am not leaving the student without any support. Oh, I wonder,
so would that involve comparing uh little signals with millions
(07:15):
of other students, Like if we we've seen that when
these things start to happen, statistically, that means like we're
heading towards failure. It would mean that It would also
mean that on a larger level, you might see that
an entire classroom is having some issues, which would tell
the teacher I need to change my approach. I I
you know this, this concept that I've tried to teach.
(07:37):
Obviously this has not worked out. So I need to
find a new way of getting this across in a
way that makes sense to my students, or perhaps help
an entire school system figure out how to how to
grade and test better. Exactly. Yeah, you know how if
you take a survey um and your survey has just
a hundred people in it, well, it's probably not very
(07:57):
representative of the entire population, but the big but you
can play family feud, right if you have a thousand people,
it's better. If you keep increasing your sample size, your
statistics become better and better at representing stronger trends that
you want to look for. And this is the same
thing you'd see. It's why big data is great. Right,
you're increasing your sample size, right, Yes, As you increase
(08:20):
that sample size, then you can actually start to recognize
things that are truly trends and not just a one off.
They're less likely to be anomalies. Yes, exactly, And this
has tracked a lot in especially in consumer segments. I mean,
you know, like every time you buy something on Amazon,
it it collects a little group of other things that
people who have bought that thing have also bought in
case you want to buy that thing, um and and
(08:42):
can be very useful not just for not just for
that basic you know, like we want to sell more stuff,
but uh yeah, I mean with Amazon sometimes it's creepy
how how on spot it is? Or it's creepy how
not on spot it is, and you wonder if they're
something you don't know about yourself. My my favorite, my
(09:02):
favorite is that if you start to find the products
that are on Amazon that have the ridiculous reviews, like
the ones that are just you know, it's the product
itself is absurd for or the gallon of milk there
they're there are examples of products that are on Amazon
that people have written like novellas in review and the
(09:26):
novellas are hilarious. Like you get to a point where
it's like it's like a soap opera that opens up
and it is this whole thing, and then the last
line will be some throwaway review of the product or whatever,
and it's it's I mean, I love it for the absurdity.
What's interesting is that the related items always tend to
be the other ones that have similar ridiculous reviews, which
(09:48):
means that even then always tend to include horse mask.
Horsehead mask. You know, yeah, people like you bought this thing, right,
but at any rate, the I wonder if you click
on that, if you also get like the Godfather as
a suggestion, you've seen horsehead mask? Right, I've seen horsehead mask. Yes,
(10:08):
I've used the internet, so yes, I have seen it.
But yeah, that is another example. Right. Amazon uses this
in order to make more sales, because the thought is,
if you are interested in this one particular kind of product,
then you're probably interested in these other products, especially if
there's a history of other people having bought those together,
(10:29):
or at least you know, I bought them at some
other point in their and their history. And then you
can you can start pouring that information not just into
what are people going to buy next, but into why
are people buying this thing, and start tracking things like
flu outbreaks. Right, Okay, so this is this is This
is one of those things that I thought was really,
(10:51):
uh an interesting example of using big data in a
way that you wouldn't necessarily first think about. This was
something that Google wrote up a white paper on there's
actually a full paper about Google using information to detect
influenza outbreaks. And the way that they did it was
they essentially found search queries that people were putting in
(11:14):
that indicated that someone was feeling sick, especially things like
various symptoms and stuff. And then by relating that information
to specific regions in the world and seeing multiple people
requesting this information from say a particular city, they could say,
this looks like this is an outbreak of the flu.
(11:35):
They said that there was a reporting lag of about
one day, so a day after a certain you know,
a large enough sample size of people are looking for this,
Google could say there's a potential flu outbreak in this
very specific area of the world. Maybe we need to
you know, by a learning something like the Center for
(11:57):
Disease Control the CDC, They could say, we need to
head this off before it becomes some sort of pandemic, right,
which basically says to me that that that because of
Google and big data we're going to be able to
uh prevent the inevitable zombie. But exactly the scary thing
is will probably never even find out about it, right
(12:18):
because of stuff like this. They'll get to the government
before the public knows. No, there there has to be
enough queries of dead uncle trying to eat my face
for at least a few people to take notice. If
you happen to be checking Google trends on dead uncle
turned on Twitter long before. Yeah, you know, just the
(12:40):
tumbler alone would be But this is funny. Actually, Google
trends is a great, really simple, really straightforward example of
how big data is really interesting, like looking at um
the popularity of a search term over a period of time.
I mean, it's so cool. You can get lost in
these bore to see is watching the spikes when you
(13:01):
know different movies or books or cultural events happened. Yeah,
when Kanye West does something embarrassing and immediately shoots to
the top. One I love is when you can look
at the historical ones because they have every book ever written, right,
They've scanned that in and then they can you say,
historical ones. I just said, they're thinking like bad play.
(13:22):
My American cousin just think like Google trends throughout the centurial.
You can look at you can look at like back
to eighteen hundred in the books they've scanned, right, um,
and you can you can chart changes in spelling. Right.
You can compare spelling a word one way versus spelling
it another way, and watch one go down with while
the other goes up one one data artists that I'm
(13:44):
going to talk about in a little bit created a
graph of the use of hope versus despair and in
recent years, just just watching the times when when despair
overlapped hope. Interesting, beautiful. I can't wait to talk about that. Well,
and and you know, let's start with other applications. Well,
I was going to mention that, you know, when you
think about Google, Google's mission statement is all about big data,
(14:08):
because they're about organizing the universe's information, which when you
think about that, you know they want to index and
organize all the information everywhere that we ever encounter. That
is big data. That's that's as clear as you can get. Yeah,
And so the fact that they're able to demonstrate the
(14:29):
usefulness of this proves the the the the utility of
their company, right because otherwise if if all they did
was index this and there was no uh, actually useful, Yeah,
then you'd be like, well, this company is just not
going to stick around. So besides education or predicting a
flu outbreak, you can actually use it to monitor cybersecurity
(14:51):
and check a network's health. So if you see a
spike in network activity, you can check it out and
make sure that it's not a d d o S attack,
a distributed denial of service attack, so that a hacker
hasn't said, hey, this website has raised my ire, I
shall direct my zombie computers to attack it. UM. You know,
being able to see that kind of stuff and respond
(15:11):
to it in real time is really useful. And you
obviously need to have a robust, robust system to deal
with a lot of information because it may just be
that it's a heavy amount of traffic for completely legitimate reasons.
So that's another implementation of big data. UH. It's also
part of what they're talking about when they talk about
the smart grid UM for electrical electrical companies to be
(15:34):
able to get energy to the right places at the
right times and prevent brownouts and blackouts. UM system overloads.
People have talked about using UM tracking the number of
ups packages sent to track how well the economy is doing. Interesting. Yeah,
I've definitely heard about the smart grid stuff. I mean,
there are a lot of utility companies that are running
at close to full capacity, and being able to to
(15:56):
see where a demand is going to be at any
given time means that you are reducing the demand on
any individual power company because they can all work in
concert together and that way you don't have these you
reduce the possibility of a brown out or a blackout. Um. So,
I mean that's clearly important, but that is a lot
(16:18):
of information. You're constantly getting feedback from all the different
meters essentially smart meters, and even if you get down
to it, you can have smart appliances that are very
specifically giving both you and the network more information about
power consumption. So that's all also important. Weather forecasting another
important part. Talk about gathering all the information from weather
(16:41):
sensors around the world and looking at the information and
detecting patterns because our forecasting abilities, don't know if you noticed,
not so great. Sometimes it's hilarious because when you think
about it, we have so much power and technology devoted
to produce acting the weather and sometimes we're still so
(17:04):
it's such a complex night. The other night, the hourly
weather on online was telling me zero percent chance of precipitation.
We had a storm that was knocking limbs out of trees.
It was like you couldn't see ten feet for the rain. Yeah. Yeah,
And it's hilarious when you take a look and say, wait,
they forecast ten days out, How how reliable is that
(17:25):
tenth day? There wrong about what's happening right now. Well,
and beyond that you have things like uh fraud detection,
and also governments can use big data for tax collection purposes,
looking at trends in taxes and the way that people
are paying taxes, and maybe comparing the way people are
(17:47):
paying taxes versus what they supposedly, oh in Texas, and
finding out if there are big gaps there, because right now,
the way it tends to work is it's after the fact,
right people file their taxes, and then a certain number
of those taxes tax reports are picked to be looked
over in more careful detail, and it's only if they
start to detect a pretty uh significant pattern that they'll
(18:12):
look at any individual's taxes specifically, unless you're part of
some political controversy which we won't get into, but this
big data thing would allow you to take a look
at a much larger scale and focus in on particular problems,
as opposed to just hoping that the sheet of reports
that you just pulled from the printer includes people who
(18:35):
are not paying their fair share. So I've got a question. Yeah,
now that we're talking about the government using big data
to predict near duells, and uh, I think I see
where this is going. Yeah, y'all seen that movie Minority Report,
documentary Minority Report. Well, okay, so I want to explain
(18:57):
a little bit. In that movie, they've got a they've
got a division of law enforcement called pre crime. Where
they are now in the movie, it's kind of they've
got these like psychic prelugs. But let's just say replace
the psychics with really, really really powerful computers, right that
look at trends pattern com make extremely accurate predictions about
(19:21):
what's about to happen. I can definitely foresee a future
where it might not be all that impossible for computers
to predict when somebody is very likely to commit a crime?
Could I can? I can tell you. Let me give
you a little more, a little more insight. From my perspective,
I don't know that we're going to get to a
(19:41):
point where we're going to be able to predict when
a specific individual is likely to commit a crime. We
can definitely get a little more probabilistic, you know, sit
there and say, what is the probability of any person
at any given time to commit a crime? Um, there
are some things that we can say. For example, there
are law enforcement agencies there that are now using big
data in order to predict crime trends. So not a
(20:05):
specific person not saying, you know, yeah yeah ne'er dowell,
Johnny today is gonna knock over the liquor store. They
are not doing that. What they are doing is saying,
looking at this big data, I'm seeing this trend where
this particular part of town tends to be a target
for vandalization. In burglary, let's say those those are two
(20:26):
crimes that often tons of factors, you know, based on
weather or right. Apparently things like burglaries, um kind of
go in rashes. Yeah. That's another thing is that if
a place is hit by burglars, then there is there
tends to be a increased risk of the same thing
happening in and in the sanginal area. Yeah, so if
(20:48):
there's a successful burglary attempt in one particular home, for example,
other homes in that neighborhood could be also um prone
to being hit by burglars. So that's one example that
law enforced we can use. They can use it as
a reactionary thing, saying all right, well, because we know this,
we should end up increasing patrols in this area for
the time being so that we can discourage any other
(21:10):
crime or catch the criminals before they're able to hit
another another house or another business. Another thing is that
for crimes like burglary and vandalism, those are crimes that
generally go down when you increase patrols. They they are
considered low intensity but high frequency crimes. So if you
(21:31):
were to adjust patrols so that there is a more
frequent patrol of police through that area, you reduce the
likelihood of those crimes being committed. And by using big
data and and and really analyzing where these crimes are
taking place within a city, you can redraw patrol routes
so that police are taking the most efficient patrol they can,
(21:55):
so they're not having to patrol an area that's way
larger than what they're capable of doing in a in
a given shift, and you also will hit the areas
that are most likely to be targeted and help reduce crime.
That way, you're preventing it from happening. So you're not
going out and arresting someone for a crime they haven't
committed yet. That's not the same thing at all. But
(22:17):
you can help attack those sort of crimes, things like
murder much less you know, much less prone to any
sort of pattern that you can predict. It's that's something
that's a high intensity but low frequency crime as opposed
to low intensity, high frequency like vandalism and burglary. So
(22:38):
they don't tend to take that kind of crime into
consideration when they're looking at this big data in this sense,
other than to uh perhaps say that, you know, this
particular area of town needs to have a stronger police
presence in order to help can back to what we
were talking about with sample size, Yeah, and uh, Like
in Santa Cruz, California, police use this approach to and
(23:01):
identify homes that were more likely to be hit by
a burglar so they could redraw their patrol roots to
take that into consideration and prevent that from happening. Now,
when we do talk about criminals and the likelihood of
someone to to commit a particular crime, there is some
statistical evidence to suggest that people who are who have
(23:23):
committed a crime are more likely to commit another crime
than someone who has never committed a crime like that,
there's like a fort recidivism rate. But part of that
is due to the way that we handle criminals and
how we try to reintroduce criminals to society. So it
may not be that people just have this statistical likelihood
(23:45):
of committing a crime again once they've already done. So
some of its institutionalize, right, It's a social construct, not
a personal propensity. Right, So therefore that wouldn't it wouldn't
be the issue what the cause was. It would just
be like that, you see it. Well, no, there's an
issue about what the cause was, because if you can
treat the cause, then you remove the I'm saying that
(24:07):
criminals matter, and Joe, you're just throwing them away. I
think you know, you know, I'm talking about what the
cause was. Wouldn't affect how how how well? Just like
I was, I was teasing Joe, but we were very
clearly talking about two different things of the same problem. Yeah,
And and what is scary about all of this is
(24:28):
the thought that someone could say, well, all of all
of this is is reactive in looking for this kind
of crime, and why can't we be proactive and well
and getting into that scary minority. And that's and and
I think, I mean, I'm not saying that we'll never
get to a point where we where where statistical models
(24:48):
won't give, at least again, a probabilistic approach of how
likely is person A to commit a crime versus person B.
And you take everything into account, and you compare that
against all the information you've ever gathered and come up
with a probability that's probably gonna happen at some point.
But I don't think that we're ever going to act
on that. I'm just saying that, I think if a
computer can predict that Jonathan Strickland is likely to buy
(25:12):
a horsehead mask, it can also probably predict that Jonathan
Strickland is more likely than the average person to Robert
Jimmy Johns. But what was what horrifying how accurate you are?
But there's a Jimmy Johns with then walking distance of
this office. What we should always keep in mind is
even if computers are that good, we shouldn't ever let
(25:34):
that prejudice our approach to Jonathan Strickland, because he may
very well not buy a horsehead mask, and he may
very well not Robert Jimmy Johns. That's true. That was
my point. Okay. So yeah, So while while Minority Report
definitely had this sort of scary science fictionary approach to
you know, uh, stopping people arresting people for crimes they
(25:58):
had not yet committed, but we're going to come it. Uh.
And and they, you know, they had the benefit of
having psychics who are apparently infallible, except they're not. When
you watch the movie spoiler. Yeah for a movie that's
that old. I'm sorry anyone who's listened to this. Yeah.
(26:19):
So anyway, Uh, I don't think we're ever gonna I
don't think we're ever going to get to a point
where big day is when pre crime comes up. Vote
no right to your representative, say no to pre crime things.
You got to vote no one. Vote no one pre
crime and vote no on giving artificial intelligence the right
to vote. Those are the two things you have to
make sure deals. Yeah, those are two big strikes, Lauren,
(26:40):
can you tell us something happy? I can? Well, okay, So,
so part of what part of what is scary about
all this data is that it's really hard for us
to understand what's out there, what we're generating, what it's
being used for, and what all that looks like. I mean,
because you know, like we were talking about like at
a certain point, we're like, oh, sure, a quadrille, what's
(27:01):
what's the number. It's a lot, Yeah, it's it's a
it's a one with fifteen zeros. And that's a lot
of zeros. And and there are a group of data
artists out there. Are you giggling at more than a
dozen zeros? That is more than a dozen zero? Let's
(27:22):
thank you. Let's not let Joe talk anymore. Take the
X away from Joe. I haven't been touching it. Please
please go on. Um, they're there are a group of
data artists out there who are working to to put
all of this into some kind of meaningful and and
also culturally meaningful unit that that we can process and
(27:45):
u And there there's there's one particular fellow by the
name of Jared Thorpe who used to be the data
artist in residence at the New York Times and has
as of I think December or January of UM gone
off and found did the Office for Creative Research as
as it's being called a company of his UM and
(28:06):
and he he posits that that this data art is
going to help people understand what all of this data
means and what it's being used for. UM and he's
got some really interesting just personal projects that he did
a TED talk that's that's pretty pretty terrific UM or
you can you can see, you know, he's he's taken
Twitter data from from people saying good morning and putting
(28:28):
it into this kind of gorgeous bouncy map of of
just of just tracking when people are waking up and
saying good morning to Twitter. He also shows what time
they say good morning based upon the color of the
block that appears. So if they say good morning earlier,
it's a green block, and the later they say good morning,
it goes into goes into the reds. And so he
(28:49):
also could show trends that way, like around the world,
showing trends of when people would say good morning and
uh in general, Let's say the West Coast wakes up
at around eleven am and the East coast we're early risers,
not only because the sun gets to us first, but
because the west coast is sleeping in. It's all those
(29:11):
actors who say good morning at two pm. You're an actor,
that's true, I am, but I get up at you know,
five in the morning. The trick is you don't say
good morning, just a grump coffee everyone. That's pretty much
meet hate everyone? That what what when am I not
(29:33):
tweeting that I hate everyone? So so that's an interesting
example of data visualization right right, and and and that's
that's what they're working for, is that visualization of getting
something down to a graphic scale where we can go like, oh,
that's still a way too huge for me to comprehend,
but at least it looks kind of pretty and I
(29:54):
get it now. I can totally see that how that
would help people understand what data means. H um he
was talking about in one UM one talk that he
gave it pop tech. I believe about about how people
have been saying that data is the new oil, and
and how kind of grandiose and lovely that sounds. For
a second, because people are thinking like oil, oil is money, money,
(30:17):
is good. But but but it's how how terrifying that
is in a certain way because because oil for for
you know, a very specific example has been a resource
that has been so misused and is so poisonous and
terrible global instability and war and and all of this.
(30:38):
But and that that you know, similarly, this data could
be used or misused rather for um, you know, not
very good purposes like like we were talking about. But
if we you know, if if we use these kind
of resources, and by resources, I mean people who are
processing this, um too, get away from all of the
(31:02):
capitalism that the capitalism is terrible. But but but using
this data for the greater common good, for some things
like predicting influenza outbreaks and being able to respond quickly
before it becomes a pandemic. I mean, clearly you're talking
about benefiting potentially millions of people. We've seen flu outbreaks
affect millions of people, and if you're able to respond
(31:25):
fast enough so that you could contain that, then that
would be an obvious, you know, benefit to everybody. So yeah,
that's and that's just one example. There's some that are
more like, well, this makes my life easier. The traffic
stuff for example. But even even in the bigger scheme,
if you talk about traffic, that seems kind of trivial.
You know, all it means that I don't have to
(31:45):
spend you know, extra time sitting in traffic. That also
means you're spending less time running a gasoline power at
engine I mean, unless you have an electric vehicle or whatever.
But and the stress levels which relate to your to
your heart rate and health, um, the your productivity at work.
If you could get everyone in Atlanta to work in
half an hour less than they currently spend on the road,
(32:05):
I mean, we would probably just be looking at pictures
of cats on the internet anyway. But um, but either way, yeah, yeah, no,
I agree entirely. So there are and you know, I
like the I like the artistic vision of showing this
as a way to demonstrate this is just one way
of looking at the information. And uh, and you know,
the ways that you've heard of are just the tip
(32:27):
of the iceberg. We haven't even really explored the full
extent of what we can use this data for. And
in some cases it may be truly transformative. We won't
have to necessarily reinvent or or invent brand new technology
to make the world a better place. We may have
all the tools already, it's just in that information we
(32:49):
have to feagure. Yeah, and then part of that is
getting people interested in this field and creating a culture
around it. Yeah. Yeah, agreed. Well that's awesome. I mean
it's I've only seen one of those, uh those examples.
I saw the good morning example for Twitter. It was
a spinning globe and all the little uh pop ups
(33:11):
of showing where people had said good morning. Um. It
also made me feel better about my tweets because I don't.
I don't tend to say good morning. No, I don't know.
I also don't LaVar Burton does. Is LaVar Burton not
good enough for you? No, LaVar Burton is good enough
for me. Um. You know, I I like to take
He was data. That was terrible. His data's best friend
(33:33):
was about to say, uh, what was this just the
worst next generation? Well, yeah, okay, So LaVar Burton can
say good morning, that's fantastic. I don't. I don't have
enough followers to say good morning. I do occasionally quote
half of a song lyric here's a there's a shock
just to see how many people who follow me know
what I'm quoting and see. Yeah, so if you know
(33:57):
the end of the sky is blue and all the
grass is green, my heart's as full as a baked potato.
You let me know, all right. Well, I think that
wraps up our discussion about applications of big data and
and what we're using it for. And again, it's just
kind of a hint at what big data will be
used for. And while yes, there are certainly examples of
(34:19):
how companies, governments could abuse big data in a way
that are legitimately scary, there are also some truly amazing
uses that could be very beneficial. So I don't think
we should shy away from it because of the uh,
the possibility of things being used in a scary way.
We just need to be aware of it and be
(34:39):
and make sure that we don't go down that pathway
because the benefits are too great for us just to ignore. Well,
of course, it can go either way. I mean, it's elemental,
it's knowledge and know. Yeah, it's a tool, and you
know a tool is it's going to be used the
way the person who's using the tool wants to use it,
So like a drill, so a third so so not
(35:02):
nice you really carried it home there, We're just going
to I'm just gonna end here, guys. If you have
suggestions for future episode topics or you want to tell
me the end of the song lyric I quoted rite
us let's know. Are you know? Just as FW Thinking
at discovery dot com or go to f W thinking
dot com. Check out our blogs, check out the podcasts,
(35:23):
check out the videos. We've got some really fun ones
up there. I think you guys will really like it,
and we'll talk to you again really soon. For more
on this topic in the future of technology, is it
Forward Thinking dot Com Brought to you by Toyota. Let's
(35:51):
go Places,
Brought to you by Toyota. Let's go places. Welcome to
Forward Thinking. Hey there everyone, and welcome to Forward Thinking,
the podcast that looks at the future and says, here's
a little story. I've got to tell about three bad podcasters.
You know, so well, I'm Jonathan Strickland and I'm Joe McCormick,
(00:24):
and you just you don't dig the BC boys they're
Lauren No, no, I do that. That was That was lovely,
Thank you, terrific, thank you. Uh, and we want to
talk a little bit more about big data. You know,
we talked about big data. It's okay, I'm going to
use both pronunciations. Okay, fair enough, so big info, uh no,
(00:45):
big data because we're all you are talking about computer
information here and not the android from Star not the
android from Star Trek. Yeah, just get that all the way.
So we're we wanted to talk more about applications of
what what different organizations, companies, governments are using big data
for what they're mining out of this huge amount of
information that we are generating every day. Now. You may
(01:07):
remember in our last podcast we said that we're generating
about two point five quintillion bytes of information per day
and not just humans, but you know, sensors, things that
are uh indirectly yeah, internet things, stuff that's connected to
the Internet that we're not directly in putting data into.
And for those of us who missed the last podcast,
(01:28):
what's the difference between this paradigm of big data and
just say a lot of data. Big data? We're talking huge, huge,
enormous amounts of information. When we're talking two point five
quintillion bytes, that's half of all the spoken words that
humans have uttered since the dawn of language. So we're
(01:49):
talking about in two days, you generate as much information
as all the words we've ever spoken ever of all
the people. So there's this issue of volume, but also
these characteristics we talked about, like velocity and variety. So
it's not just the amount of data, but it's interacting
exactly extremely fast. We're gathering it at an incredible pace.
(02:13):
I mean, you're you're gathering data at an unprecedented pace.
You are. It's rich and intense and everywhere. Yeah, and
it's all different types of information. And also there's a
fourth v that we can mention, which is voracity, which
is the quality of the information truthiness. Yeah, yeah, there
you go. Uh we're gonna tell you some strategicies that
(02:35):
people use with big data. Uh it does mean truthiness.
I mean it means that the data is essentially high quality. Right. Yeah.
Voracity is really kind of their way of saying, how
good is your information? And sometimes you don't know how
good your information is, because again, you've got a lot
of it. Until you analyze it, you don't really know
if there's anything useful there. Uh. IBM is in the
(02:58):
business of of leveraging big data and helping other companies
leverage it. And uh and and they say very clearly
on their website and in their white papers that the
important part here is that you have to figure out
what your goal is before you start just looking at
big data and saying we need to be part of this.
(03:18):
Because whatever your goal is, that's going to end up
informing your approach to using that information in a way
that makes sense. Otherwise, you're just talking about an enormous
resource that may not be directly useful to you. You're
just kind of looking at it and thinking, I want
to make use of that information. I just it's too
big a problem for me to even get a grasp
(03:41):
on how I want to use it. So you can't
just run at it and say, give me some ones
and zeros, I'm gonna make magic happen. You have to
have a plan in place first. But I've got a
lot of really clever people have figured out really cool
stuff to do with this information. Sure. In the last podcast,
we talked a little bit about traffic analysis, which was
(04:02):
a very kind of you know, you know, it's an
easy to understand application of big data, right, So let
me give you an example using Google's approach. So the
way Google would generate traffic on Google Maps. If you
were using Google Maps on a mobile device and you
wanted to try and get from point A to point B,
(04:22):
and you wanted traffic to be part of that that equation,
that route, then what it would do is it would
start looking at information of other Google users. Uh. There
are other systems that use this, the Dash system, which
doesn't really exist anymore, but uh, it used a very
similar approach where it would send anonymous data about vehicles
(04:44):
that were moving through a particular region and the speed
at which they were moving. It was just sampling the vehicle, yeah,
sampling the vehicle's location. It would get the GPS coordinates
and it would just sample it over a certain amount
of time and derive how fat that vehicle was moving
down the street based upon the information, right saying, okay,
well it was at this point at this time, and
(05:06):
it was at this other point a little bit later,
and this other point a little bit later. Therefore, that
means traffic is moving at this speed down this particular street,
and then it's extrapolates that sends it out to everyone
so that you know which routes have the heaviest traffic.
Now the Yeah, it's it's a very simple approach to
big data in the sense that it's just taking real
(05:29):
time information, analyzing it, and sending the results back very quickly.
It's not storing information, it's not trying to be transformative
with the information. It's just trying to make sense of
all these different pieces of information that are coming in
and then making it meaningful to the people who are
using the service. So that's one example, but that's just one.
(05:49):
You can actually see some pretty interesting patterns when you
get huge amounts of information. You can see patterns where
you might have thought there was just chaos before. So
you you can look at a system that you might say, well,
from the outset, it just looks like stuff is happening.
But now when I see all this information, it's broken
(06:10):
down like this, I can actually see trends where before
I just saw stuff. So education is a good example
of this. So let's say you're a teacher and you're
teaching a class, and you have your classes submitting schoolwork
in a into a system that can then analyze the
school work. So you're grading the kids. You you might
(06:32):
actually just be inserting the grades into the system. In fact,
it may not have any connection with the kids directly
at all. You might just be the teacher in the system.
The system would be able to, if you're using a
very sophisticated approach, be able to start detecting trends in
each individual student's progress. So you might be able to say, oh,
(06:53):
while while student A isn't failing, the trend indicates that
the student is beginning to struggle. So I need to
adjust my way of reaching this student so that I
am not leaving the student without any support. Oh, I wonder,
so would that involve comparing uh little signals with millions
(07:15):
of other students, Like if we we've seen that when
these things start to happen, statistically, that means like we're
heading towards failure. It would mean that It would also
mean that on a larger level, you might see that
an entire classroom is having some issues, which would tell
the teacher I need to change my approach. I I
you know this, this concept that I've tried to teach.
(07:37):
Obviously this has not worked out. So I need to
find a new way of getting this across in a
way that makes sense to my students, or perhaps help
an entire school system figure out how to how to
grade and test better. Exactly. Yeah, you know how if
you take a survey um and your survey has just
a hundred people in it, well, it's probably not very
(07:57):
representative of the entire population, but the big but you
can play family feud, right if you have a thousand people,
it's better. If you keep increasing your sample size, your
statistics become better and better at representing stronger trends that
you want to look for. And this is the same
thing you'd see. It's why big data is great. Right,
you're increasing your sample size, right, Yes, As you increase
(08:20):
that sample size, then you can actually start to recognize
things that are truly trends and not just a one off.
They're less likely to be anomalies. Yes, exactly, And this
has tracked a lot in especially in consumer segments. I mean,
you know, like every time you buy something on Amazon,
it it collects a little group of other things that
people who have bought that thing have also bought in
case you want to buy that thing, um and and
(08:42):
can be very useful not just for not just for
that basic you know, like we want to sell more stuff,
but uh yeah, I mean with Amazon sometimes it's creepy
how how on spot it is? Or it's creepy how
not on spot it is, and you wonder if they're
something you don't know about yourself. My my favorite, my
(09:02):
favorite is that if you start to find the products
that are on Amazon that have the ridiculous reviews, like
the ones that are just you know, it's the product
itself is absurd for or the gallon of milk there
they're there are examples of products that are on Amazon
that people have written like novellas in review and the
(09:26):
novellas are hilarious. Like you get to a point where
it's like it's like a soap opera that opens up
and it is this whole thing, and then the last
line will be some throwaway review of the product or whatever,
and it's it's I mean, I love it for the absurdity.
What's interesting is that the related items always tend to
be the other ones that have similar ridiculous reviews, which
(09:48):
means that even then always tend to include horse mask.
Horsehead mask. You know, yeah, people like you bought this thing, right,
but at any rate, the I wonder if you click
on that, if you also get like the Godfather as
a suggestion, you've seen horsehead mask? Right, I've seen horsehead mask. Yes,
(10:08):
I've used the internet, so yes, I have seen it.
But yeah, that is another example. Right. Amazon uses this
in order to make more sales, because the thought is,
if you are interested in this one particular kind of product,
then you're probably interested in these other products, especially if
there's a history of other people having bought those together,
(10:29):
or at least you know, I bought them at some
other point in their and their history. And then you
can you can start pouring that information not just into
what are people going to buy next, but into why
are people buying this thing, and start tracking things like
flu outbreaks. Right, Okay, so this is this is This
is one of those things that I thought was really,
(10:51):
uh an interesting example of using big data in a
way that you wouldn't necessarily first think about. This was
something that Google wrote up a white paper on there's
actually a full paper about Google using information to detect
influenza outbreaks. And the way that they did it was
they essentially found search queries that people were putting in
(11:14):
that indicated that someone was feeling sick, especially things like
various symptoms and stuff. And then by relating that information
to specific regions in the world and seeing multiple people
requesting this information from say a particular city, they could say,
this looks like this is an outbreak of the flu.
(11:35):
They said that there was a reporting lag of about
one day, so a day after a certain you know,
a large enough sample size of people are looking for this,
Google could say there's a potential flu outbreak in this
very specific area of the world. Maybe we need to
you know, by a learning something like the Center for
(11:57):
Disease Control the CDC, They could say, we need to
head this off before it becomes some sort of pandemic, right,
which basically says to me that that that because of
Google and big data we're going to be able to
uh prevent the inevitable zombie. But exactly the scary thing
is will probably never even find out about it, right
(12:18):
because of stuff like this. They'll get to the government
before the public knows. No, there there has to be
enough queries of dead uncle trying to eat my face
for at least a few people to take notice. If
you happen to be checking Google trends on dead uncle
turned on Twitter long before. Yeah, you know, just the
(12:40):
tumbler alone would be But this is funny. Actually, Google
trends is a great, really simple, really straightforward example of
how big data is really interesting, like looking at um
the popularity of a search term over a period of time.
I mean, it's so cool. You can get lost in
these bore to see is watching the spikes when you
(13:01):
know different movies or books or cultural events happened. Yeah,
when Kanye West does something embarrassing and immediately shoots to
the top. One I love is when you can look
at the historical ones because they have every book ever written, right,
They've scanned that in and then they can you say,
historical ones. I just said, they're thinking like bad play.
(13:22):
My American cousin just think like Google trends throughout the centurial.
You can look at you can look at like back
to eighteen hundred in the books they've scanned, right, um,
and you can you can chart changes in spelling. Right.
You can compare spelling a word one way versus spelling
it another way, and watch one go down with while
the other goes up one one data artists that I'm
(13:44):
going to talk about in a little bit created a
graph of the use of hope versus despair and in
recent years, just just watching the times when when despair
overlapped hope. Interesting, beautiful. I can't wait to talk about that. Well,
and and you know, let's start with other applications. Well,
I was going to mention that, you know, when you
think about Google, Google's mission statement is all about big data,
(14:08):
because they're about organizing the universe's information, which when you
think about that, you know they want to index and
organize all the information everywhere that we ever encounter. That
is big data. That's that's as clear as you can get. Yeah,
And so the fact that they're able to demonstrate the
(14:29):
usefulness of this proves the the the the utility of
their company, right because otherwise if if all they did
was index this and there was no uh, actually useful, Yeah,
then you'd be like, well, this company is just not
going to stick around. So besides education or predicting a
flu outbreak, you can actually use it to monitor cybersecurity
(14:51):
and check a network's health. So if you see a
spike in network activity, you can check it out and
make sure that it's not a d d o S attack,
a distributed denial of service attack, so that a hacker
hasn't said, hey, this website has raised my ire, I
shall direct my zombie computers to attack it. UM. You know,
being able to see that kind of stuff and respond
(15:11):
to it in real time is really useful. And you
obviously need to have a robust, robust system to deal
with a lot of information because it may just be
that it's a heavy amount of traffic for completely legitimate reasons.
So that's another implementation of big data. UH. It's also
part of what they're talking about when they talk about
the smart grid UM for electrical electrical companies to be
(15:34):
able to get energy to the right places at the
right times and prevent brownouts and blackouts. UM system overloads.
People have talked about using UM tracking the number of
ups packages sent to track how well the economy is doing. Interesting. Yeah,
I've definitely heard about the smart grid stuff. I mean,
there are a lot of utility companies that are running
at close to full capacity, and being able to to
(15:56):
see where a demand is going to be at any
given time means that you are reducing the demand on
any individual power company because they can all work in
concert together and that way you don't have these you
reduce the possibility of a brown out or a blackout. Um. So,
I mean that's clearly important, but that is a lot
(16:18):
of information. You're constantly getting feedback from all the different
meters essentially smart meters, and even if you get down
to it, you can have smart appliances that are very
specifically giving both you and the network more information about
power consumption. So that's all also important. Weather forecasting another
important part. Talk about gathering all the information from weather
(16:41):
sensors around the world and looking at the information and
detecting patterns because our forecasting abilities, don't know if you noticed,
not so great. Sometimes it's hilarious because when you think
about it, we have so much power and technology devoted
to produce acting the weather and sometimes we're still so
(17:04):
it's such a complex night. The other night, the hourly
weather on online was telling me zero percent chance of precipitation.
We had a storm that was knocking limbs out of trees.
It was like you couldn't see ten feet for the rain. Yeah. Yeah,
And it's hilarious when you take a look and say, wait,
they forecast ten days out, How how reliable is that
(17:25):
tenth day? There wrong about what's happening right now. Well,
and beyond that you have things like uh fraud detection,
and also governments can use big data for tax collection purposes,
looking at trends in taxes and the way that people
are paying taxes, and maybe comparing the way people are
(17:47):
paying taxes versus what they supposedly, oh in Texas, and
finding out if there are big gaps there, because right now,
the way it tends to work is it's after the fact,
right people file their taxes, and then a certain number
of those taxes tax reports are picked to be looked
over in more careful detail, and it's only if they
start to detect a pretty uh significant pattern that they'll
(18:12):
look at any individual's taxes specifically, unless you're part of
some political controversy which we won't get into, but this
big data thing would allow you to take a look
at a much larger scale and focus in on particular problems,
as opposed to just hoping that the sheet of reports
that you just pulled from the printer includes people who
(18:35):
are not paying their fair share. So I've got a question. Yeah,
now that we're talking about the government using big data
to predict near duells, and uh, I think I see
where this is going. Yeah, y'all seen that movie Minority Report,
documentary Minority Report. Well, okay, so I want to explain
(18:57):
a little bit. In that movie, they've got a they've
got a division of law enforcement called pre crime. Where
they are now in the movie, it's kind of they've
got these like psychic prelugs. But let's just say replace
the psychics with really, really really powerful computers, right that
look at trends pattern com make extremely accurate predictions about
(19:21):
what's about to happen. I can definitely foresee a future
where it might not be all that impossible for computers
to predict when somebody is very likely to commit a crime?
Could I can? I can tell you. Let me give
you a little more, a little more insight. From my perspective,
I don't know that we're going to get to a
(19:41):
point where we're going to be able to predict when
a specific individual is likely to commit a crime. We
can definitely get a little more probabilistic, you know, sit
there and say, what is the probability of any person
at any given time to commit a crime? Um, there
are some things that we can say. For example, there
are law enforcement agencies there that are now using big
data in order to predict crime trends. So not a
(20:05):
specific person not saying, you know, yeah yeah ne'er dowell,
Johnny today is gonna knock over the liquor store. They
are not doing that. What they are doing is saying,
looking at this big data, I'm seeing this trend where
this particular part of town tends to be a target
for vandalization. In burglary, let's say those those are two
(20:26):
crimes that often tons of factors, you know, based on
weather or right. Apparently things like burglaries, um kind of
go in rashes. Yeah. That's another thing is that if
a place is hit by burglars, then there is there
tends to be a increased risk of the same thing
happening in and in the sanginal area. Yeah, so if
(20:48):
there's a successful burglary attempt in one particular home, for example,
other homes in that neighborhood could be also um prone
to being hit by burglars. So that's one example that
law enforced we can use. They can use it as
a reactionary thing, saying all right, well, because we know this,
we should end up increasing patrols in this area for
the time being so that we can discourage any other
(21:10):
crime or catch the criminals before they're able to hit
another another house or another business. Another thing is that
for crimes like burglary and vandalism, those are crimes that
generally go down when you increase patrols. They they are
considered low intensity but high frequency crimes. So if you
(21:31):
were to adjust patrols so that there is a more
frequent patrol of police through that area, you reduce the
likelihood of those crimes being committed. And by using big
data and and and really analyzing where these crimes are
taking place within a city, you can redraw patrol routes
so that police are taking the most efficient patrol they can,
(21:55):
so they're not having to patrol an area that's way
larger than what they're capable of doing in a in
a given shift, and you also will hit the areas
that are most likely to be targeted and help reduce crime.
That way, you're preventing it from happening. So you're not
going out and arresting someone for a crime they haven't
committed yet. That's not the same thing at all. But
(22:17):
you can help attack those sort of crimes, things like
murder much less you know, much less prone to any
sort of pattern that you can predict. It's that's something
that's a high intensity but low frequency crime as opposed
to low intensity, high frequency like vandalism and burglary. So
(22:38):
they don't tend to take that kind of crime into
consideration when they're looking at this big data in this sense,
other than to uh perhaps say that, you know, this
particular area of town needs to have a stronger police
presence in order to help can back to what we
were talking about with sample size, Yeah, and uh, Like
in Santa Cruz, California, police use this approach to and
(23:01):
identify homes that were more likely to be hit by
a burglar so they could redraw their patrol roots to
take that into consideration and prevent that from happening. Now,
when we do talk about criminals and the likelihood of
someone to to commit a particular crime, there is some
statistical evidence to suggest that people who are who have
(23:23):
committed a crime are more likely to commit another crime
than someone who has never committed a crime like that,
there's like a fort recidivism rate. But part of that
is due to the way that we handle criminals and
how we try to reintroduce criminals to society. So it
may not be that people just have this statistical likelihood
(23:45):
of committing a crime again once they've already done. So
some of its institutionalize, right, It's a social construct, not
a personal propensity. Right, So therefore that wouldn't it wouldn't
be the issue what the cause was. It would just
be like that, you see it. Well, no, there's an
issue about what the cause was, because if you can
treat the cause, then you remove the I'm saying that
(24:07):
criminals matter, and Joe, you're just throwing them away. I
think you know, you know, I'm talking about what the
cause was. Wouldn't affect how how how well? Just like
I was, I was teasing Joe, but we were very
clearly talking about two different things of the same problem. Yeah,
And and what is scary about all of this is
(24:28):
the thought that someone could say, well, all of all
of this is is reactive in looking for this kind
of crime, and why can't we be proactive and well
and getting into that scary minority. And that's and and
I think, I mean, I'm not saying that we'll never
get to a point where we where where statistical models
(24:48):
won't give, at least again, a probabilistic approach of how
likely is person A to commit a crime versus person B.
And you take everything into account, and you compare that
against all the information you've ever gathered and come up
with a probability that's probably gonna happen at some point.
But I don't think that we're ever going to act
on that. I'm just saying that, I think if a
computer can predict that Jonathan Strickland is likely to buy
(25:12):
a horsehead mask, it can also probably predict that Jonathan
Strickland is more likely than the average person to Robert
Jimmy Johns. But what was what horrifying how accurate you are?
But there's a Jimmy Johns with then walking distance of
this office. What we should always keep in mind is
even if computers are that good, we shouldn't ever let
(25:34):
that prejudice our approach to Jonathan Strickland, because he may
very well not buy a horsehead mask, and he may
very well not Robert Jimmy Johns. That's true. That was
my point. Okay. So yeah, So while while Minority Report
definitely had this sort of scary science fictionary approach to
you know, uh, stopping people arresting people for crimes they
(25:58):
had not yet committed, but we're going to come it. Uh.
And and they, you know, they had the benefit of
having psychics who are apparently infallible, except they're not. When
you watch the movie spoiler. Yeah for a movie that's
that old. I'm sorry anyone who's listened to this. Yeah.
(26:19):
So anyway, Uh, I don't think we're ever gonna I
don't think we're ever going to get to a point
where big day is when pre crime comes up. Vote
no right to your representative, say no to pre crime things.
You got to vote no one. Vote no one pre
crime and vote no on giving artificial intelligence the right
to vote. Those are the two things you have to
make sure deals. Yeah, those are two big strikes, Lauren,
(26:40):
can you tell us something happy? I can? Well, okay, So,
so part of what part of what is scary about
all this data is that it's really hard for us
to understand what's out there, what we're generating, what it's
being used for, and what all that looks like. I mean,
because you know, like we were talking about like at
a certain point, we're like, oh, sure, a quadrille, what's
(27:01):
what's the number. It's a lot, Yeah, it's it's a
it's a one with fifteen zeros. And that's a lot
of zeros. And and there are a group of data
artists out there. Are you giggling at more than a
dozen zeros? That is more than a dozen zero? Let's
(27:22):
thank you. Let's not let Joe talk anymore. Take the
X away from Joe. I haven't been touching it. Please
please go on. Um, they're there are a group of
data artists out there who are working to to put
all of this into some kind of meaningful and and
also culturally meaningful unit that that we can process and
(27:45):
u And there there's there's one particular fellow by the
name of Jared Thorpe who used to be the data
artist in residence at the New York Times and has
as of I think December or January of UM gone
off and found did the Office for Creative Research as
as it's being called a company of his UM and
(28:06):
and he he posits that that this data art is
going to help people understand what all of this data
means and what it's being used for. UM and he's
got some really interesting just personal projects that he did
a TED talk that's that's pretty pretty terrific UM or
you can you can see, you know, he's he's taken
Twitter data from from people saying good morning and putting
(28:28):
it into this kind of gorgeous bouncy map of of
just of just tracking when people are waking up and
saying good morning to Twitter. He also shows what time
they say good morning based upon the color of the
block that appears. So if they say good morning earlier,
it's a green block, and the later they say good morning,
it goes into goes into the reds. And so he
(28:49):
also could show trends that way, like around the world,
showing trends of when people would say good morning and
uh in general, Let's say the West Coast wakes up
at around eleven am and the East coast we're early risers,
not only because the sun gets to us first, but
because the west coast is sleeping in. It's all those
(29:11):
actors who say good morning at two pm. You're an actor,
that's true, I am, but I get up at you know,
five in the morning. The trick is you don't say
good morning, just a grump coffee everyone. That's pretty much
meet hate everyone? That what what when am I not
(29:33):
tweeting that I hate everyone? So so that's an interesting
example of data visualization right right, and and and that's
that's what they're working for, is that visualization of getting
something down to a graphic scale where we can go like, oh,
that's still a way too huge for me to comprehend,
but at least it looks kind of pretty and I
(29:54):
get it now. I can totally see that how that
would help people understand what data means. H um he
was talking about in one UM one talk that he
gave it pop tech. I believe about about how people
have been saying that data is the new oil, and
and how kind of grandiose and lovely that sounds. For
a second, because people are thinking like oil, oil is money, money,
(30:17):
is good. But but but it's how how terrifying that
is in a certain way because because oil for for
you know, a very specific example has been a resource
that has been so misused and is so poisonous and
terrible global instability and war and and all of this.
(30:38):
But and that that you know, similarly, this data could
be used or misused rather for um, you know, not
very good purposes like like we were talking about. But
if we you know, if if we use these kind
of resources, and by resources, I mean people who are
processing this, um too, get away from all of the
(31:02):
capitalism that the capitalism is terrible. But but but using
this data for the greater common good, for some things
like predicting influenza outbreaks and being able to respond quickly
before it becomes a pandemic. I mean, clearly you're talking
about benefiting potentially millions of people. We've seen flu outbreaks
affect millions of people, and if you're able to respond
(31:25):
fast enough so that you could contain that, then that
would be an obvious, you know, benefit to everybody. So yeah,
that's and that's just one example. There's some that are
more like, well, this makes my life easier. The traffic
stuff for example. But even even in the bigger scheme,
if you talk about traffic, that seems kind of trivial.
You know, all it means that I don't have to
(31:45):
spend you know, extra time sitting in traffic. That also
means you're spending less time running a gasoline power at
engine I mean, unless you have an electric vehicle or whatever.
But and the stress levels which relate to your to
your heart rate and health, um, the your productivity at work.
If you could get everyone in Atlanta to work in
half an hour less than they currently spend on the road,
(32:05):
I mean, we would probably just be looking at pictures
of cats on the internet anyway. But um, but either way, yeah, yeah, no,
I agree entirely. So there are and you know, I
like the I like the artistic vision of showing this
as a way to demonstrate this is just one way
of looking at the information. And uh, and you know,
the ways that you've heard of are just the tip
(32:27):
of the iceberg. We haven't even really explored the full
extent of what we can use this data for. And
in some cases it may be truly transformative. We won't
have to necessarily reinvent or or invent brand new technology
to make the world a better place. We may have
all the tools already, it's just in that information we
(32:49):
have to feagure. Yeah, and then part of that is
getting people interested in this field and creating a culture
around it. Yeah. Yeah, agreed. Well that's awesome. I mean
it's I've only seen one of those, uh those examples.
I saw the good morning example for Twitter. It was
a spinning globe and all the little uh pop ups
(33:11):
of showing where people had said good morning. Um. It
also made me feel better about my tweets because I don't.
I don't tend to say good morning. No, I don't know.
I also don't LaVar Burton does. Is LaVar Burton not
good enough for you? No, LaVar Burton is good enough
for me. Um. You know, I I like to take
He was data. That was terrible. His data's best friend
(33:33):
was about to say, uh, what was this just the
worst next generation? Well, yeah, okay, So LaVar Burton can
say good morning, that's fantastic. I don't. I don't have
enough followers to say good morning. I do occasionally quote
half of a song lyric here's a there's a shock
just to see how many people who follow me know
what I'm quoting and see. Yeah, so if you know
(33:57):
the end of the sky is blue and all the
grass is green, my heart's as full as a baked potato.
You let me know, all right. Well, I think that
wraps up our discussion about applications of big data and
and what we're using it for. And again, it's just
kind of a hint at what big data will be
used for. And while yes, there are certainly examples of
(34:19):
how companies, governments could abuse big data in a way
that are legitimately scary, there are also some truly amazing
uses that could be very beneficial. So I don't think
we should shy away from it because of the uh,
the possibility of things being used in a scary way.
We just need to be aware of it and be
(34:39):
and make sure that we don't go down that pathway
because the benefits are too great for us just to ignore. Well,
of course, it can go either way. I mean, it's elemental,
it's knowledge and know. Yeah, it's a tool, and you
know a tool is it's going to be used the
way the person who's using the tool wants to use it,
So like a drill, so a third so so not
(35:02):
nice you really carried it home there, We're just going
to I'm just gonna end here, guys. If you have
suggestions for future episode topics or you want to tell
me the end of the song lyric I quoted rite
us let's know. Are you know? Just as FW Thinking
at discovery dot com or go to f W thinking
dot com. Check out our blogs, check out the podcasts,
(35:23):
check out the videos. We've got some really fun ones
up there. I think you guys will really like it,
and we'll talk to you again really soon. For more
on this topic in the future of technology, is it
Forward Thinking dot Com Brought to you by Toyota. Let's
(35:51):
go Places,
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Joe McCormick
Lauren Vogelbaum
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