Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says, I'm an
Speaker 1: ordinary guy burning down the house. I'm Jonathan Strickland, I'm Lauren,
Speaker 1: and I'm Joe McCormick. Hey Jonathan, Hey Joe. Do you
Speaker 1: remember the very first episode of Forward Thinking the videos
Speaker 1: I'll never forget. That was a long day of filming videos. No. No,
Speaker 1: we only did three that day. We wanted to do five.
Speaker 1: So before we get into this topic, just so in
Speaker 1: case you're curious about what this experience was like. I
Speaker 1: had shot plenty of videos in the past, but never
Speaker 1: one with a full crew. And we had a full
Speaker 1: crew that first day. So we had sound people, camera person,
Speaker 1: we had wardrobe and makeup, we had lighting, and we
Speaker 1: had um producers who were there for the first time
Speaker 1: to to like the executive producer types who were actually
Speaker 1: there to watch this happen. There was a lot of
Speaker 1: pressure on Uh. No teleprompter. I never used a teleprompter
Speaker 1: for any of the Forward Thinking videos. UH might explain
Speaker 1: a few of them to you, because I get a
Speaker 1: little wacky and a little off script, so, uh intern't
Speaker 1: knowet things was one of the three videos we shot
Speaker 1: that day. I remember three D printing was one of
Speaker 1: the other, but I don't remember what the third one was.
Speaker 1: Oh it was it was a car one I think,
Speaker 1: um self driving cars. Self driving cars. Yeah, yeah, because
Speaker 1: we actually had a a a car in the studio
Speaker 1: that we used. It was not an actual self driving car,
Speaker 1: but we treated it as such. Uh So, anyway, Internet
Speaker 1: of Things has a long, long, long history with The
Speaker 1: Forward Thinking Show, and of course we talked about on
Speaker 1: the podcast too. All the time, it comes up a
Speaker 1: lot smart devices all around you. We've talked about smart
Speaker 1: devices in your kitchen, smart devices in your in your
Speaker 1: body right, and the whole idea of just having sensors
Speaker 1: that are collecting data and devices that can use that
Speaker 1: data to change your environment in some way. This is
Speaker 1: not something that you know is a new topic, obviously,
Speaker 1: but we wanted to talk about a specific implementation of
Speaker 1: this where you incorporate directly into architecture. And this is
Speaker 1: an area of research that IBM is conducting. They call
Speaker 1: it cognitive buildings. Yeah, so I would say this relates
Speaker 1: to the Internet of Things in that it's sort of
Speaker 1: like the Internet of Things, but a top down, integrated
Speaker 1: approach to it, right, right, Yeah, this is an idea
Speaker 1: of we have the capability of incorporating all these different
Speaker 1: types of sensors and systems into a building piecemeal, but
Speaker 1: what if we were to do it from the design
Speaker 1: of a building and incorporated, uh directly integrate everything so
Speaker 1: it's into a parable And this is a big idea
Speaker 1: that could potentially have uh some some interesting benefits down
Speaker 1: the road. So it's something I wanted to explore a
Speaker 1: bit more. Um, It's a pretty pretty simple idea that
Speaker 1: I think a lot of people can grasp early on, right,
Speaker 1: Like the idea of a building that has all of
Speaker 1: these things integrated in it and then essentially has like
Speaker 1: a pretty hefty processor to take in all that data,
Speaker 1: crunch it, and then uh respond in whatever way is
Speaker 1: most appropriate, right right. And the process that IBM is
Speaker 1: is using for this kind of concept is cognitive computing, yes,
Speaker 1: which involves lots of different topics We've talked about before,
Speaker 1: artificial intelligence, pattern recognition, a natural language processing, data mining,
Speaker 1: machine learning, all of that is part of cognitive computing.
Speaker 1: Generally speaking, cognitive computing is trying to use computers to
Speaker 1: simulate the way we think, not necessarily in a like, uh,
Speaker 1: granular way where a processor is behaving the way a
Speaker 1: brain is, but so that the output is similar to
Speaker 1: what you would have if you asked a very smart
Speaker 1: person to do the same sort of stuff. So it's
Speaker 1: a problem solving kind of technique, and it's using some
Speaker 1: of the data processing that we've talked about in our
Speaker 1: episodes about deep learning. Yes, yes, so if you can
Speaker 1: imagine the same sort of stuff that produces incredibly trippy
Speaker 1: paintings could be running your home in the future, driving
Speaker 1: you mad over a series of entertaining days. I just
Speaker 1: realized that we we may be able to put together
Speaker 1: in the future tutor a tutorial on how to overclock
Speaker 1: your house. That's actually it's not too far off from
Speaker 1: from the possible reality, right, So uh, ideally, what this
Speaker 1: would result in is a system that would learn about
Speaker 1: you as you move through it. So whether it's your
Speaker 1: office that you work in or the home that you
Speaker 1: live in, if it's a cognitive building, it actually learns
Speaker 1: about you, your preferences, your needs, and is able to
Speaker 1: respond to that in a way that is seamless, so
Speaker 1: that you don't have to even think about it. You
Speaker 1: don't even necessarily have to voice a command UH if
Speaker 1: it's learned enough. This is not that far off from
Speaker 1: some systems we have now, Like the Nest thermostat is
Speaker 1: an example. It's easy to point to because it's been
Speaker 1: around for a few years. So Nest thermostat can detect
Speaker 1: when you are home using motion sensors UH. It can
Speaker 1: have some pre program temperatures that you could tell it like, well,
Speaker 1: at night, I like it to be around this cool.
Speaker 1: During the day, you can turn the temperature up a
Speaker 1: certain amount. It might even have have other parameters saying, well,
Speaker 1: while the person is gone, we can even crank that
Speaker 1: up a little bit more so that we're saving energy.
Speaker 1: You know, the a C is not blowing when no
Speaker 1: one's home, and we also know that typically the person
Speaker 1: gets home at around this time, so we can turn
Speaker 1: the a C back down UH an hour before they
Speaker 1: get home, so that way the house is nice and
Speaker 1: cool by the time you walk through the front door.
Speaker 1: That's a pretty easy example, and it's one tiny system,
Speaker 1: but in a cognitive building, it would be one part
Speaker 1: of an overall integrated system, and it would just be
Speaker 1: one element of that. Imagine a building able to measure
Speaker 1: pretty much everything you're able to do inside that building
Speaker 1: and respond in a way that helps you save energy
Speaker 1: so you're not wasting it and also makes the experience
Speaker 1: pleasant and comfortable for you. That's kind of the basic concept,
Speaker 1: but it's way harder than what it south. Yeah. Yeah,
Speaker 1: we're not really quite there yet to the point where
Speaker 1: in most buildings we are prepared to do that. Like,
Speaker 1: like right now, if you if you want to integrate
Speaker 1: all of these different Internet of Things type systems you're uh,
Speaker 1: your smart lights in your in your nest, and your
Speaker 1: eye toaster, you're gonna need to get a third party
Speaker 1: thing that's going to connect up with all of them, right,
Speaker 1: because otherwise what you're talking about is having a phone
Speaker 1: with eighty thousand apps on it. Like I need to
Speaker 1: turn off my lights. Okay, let me just scroll through
Speaker 1: all right, it's an this folder. Okay, let me scroll
Speaker 1: down through here, there's my light folder. Like it's ridiculous. Right.
Speaker 1: If you if you go through a single service provider
Speaker 1: or product manufacturer that makes lots of different stuff in
Speaker 1: that space, then maybe you could get away with two
Speaker 1: or three systems all using an integrated app. But I
Speaker 1: don't think anyone makes all the different types of home
Speaker 1: automation systems that you can run into. So if you
Speaker 1: really wanted like the automated home of the future, the
Speaker 1: downside is you're going to have all these different interfaces
Speaker 1: that you have to work with in order to make
Speaker 1: that happen. Uh. The alternative, as Laurence said, is to
Speaker 1: get a third party object that is able to be
Speaker 1: kind of like a liaison between you and each of
Speaker 1: your disparate systems, one app to rule them all. Yeah.
Speaker 1: So Amazon's Echo is a great example that has Alexa,
Speaker 1: the the voice activated digital personal assistant. And Alexa can
Speaker 1: work with lots of different systems, right, It can work
Speaker 1: with Nest, it can work with certain like I think
Speaker 1: Phillips Hugh light bulbs. So with Alexa, you could tell
Speaker 1: Alexa what you want and then Alexa would communicate with
Speaker 1: the appropriate system on whatever terms are necessary for it
Speaker 1: to get this done, and it would happen. This is
Speaker 1: an inelegant way of going about this because you still
Speaker 1: have to have that intermediary. This is essentially translating your
Speaker 1: commands so that whatever you want to have happen happens.
Speaker 1: It would be it would be nicer to have a
Speaker 1: fully integrated system that does this all automatically because it
Speaker 1: fully understands you not just what temperature you like, but
Speaker 1: what light level you like, and what music you like
Speaker 1: and uh, you know, even things like what you prefer
Speaker 1: to eat, because this could be incorporated all into the
Speaker 1: appliances of your house, not just the lighting and the
Speaker 1: climate system. And all of these integrat sans can ultimately
Speaker 1: result in energy conservation, which is I think, I would
Speaker 1: argue is the biggest point of concern for the IBM
Speaker 1: research team. Uh So, designing this building not the easiest
Speaker 1: thing in the world to do. You have to have
Speaker 1: all these different sensors to detect all sorts of stuff,
Speaker 1: you know, temperature, lighting, electricity, water flow. Perhaps. Uh it's
Speaker 1: a big job. And in fact, the IBM research center
Speaker 1: that they have, they've got a lab with more than
Speaker 1: three thousand sensors integrated into it. It's about a four
Speaker 1: ft by five ft cubicle. No, I don't I don't
Speaker 1: know how big the lab is, but it's not it's
Speaker 1: not as far as I know. It's in Dublin, Ireland. Um,
Speaker 1: it's not as large as like a full building as
Speaker 1: far as I'm aware, Um, but still, I mean, and
Speaker 1: if you think about that, if you think, well, if
Speaker 1: it's not as large as a full building, then a
Speaker 1: full building would need probably even more sensors to get
Speaker 1: comprehensive data. All right, But I mean think think about
Speaker 1: how many different factors you're you're trying to integrate here
Speaker 1: and and this is in every single room in the
Speaker 1: entire building. So you know, you're you're you're looking to
Speaker 1: detect motion, perhaps have facial recognition, uh, detect light levels
Speaker 1: like natural light levels to detect temperature, uma, detect water
Speaker 1: use to detect everything that's going on, and all of
Speaker 1: the pipes to detect what the sewage pipes are doing. Yeah,
Speaker 1: there's a lot of sensors for every single room, and
Speaker 1: and you think about that. You you ultimately have all
Speaker 1: of that data sent to a central processing unit a
Speaker 1: a computer of some sophistication to make sense of what
Speaker 1: that information means. It may mean that certain choices in
Speaker 1: order to conserve energy could at first appear counterintuitive because
Speaker 1: it may be that it's taking into consideration a lot
Speaker 1: of factors that you, as a human being inside that building,
Speaker 1: aren't necessarily aware of at the time. So it's like
Speaker 1: in a closed room, the amount of sunlight that the
Speaker 1: roof is getting somewhere near you could definitely affect the
Speaker 1: temperature that you're experiencing. Yeah, that's a that's a simple example,
Speaker 1: and there are so many out there. Everything like if
Speaker 1: if the room happens to have a whole bunch of
Speaker 1: pipes that water flows through, that might affect things as well.
Speaker 1: It's it's really complicated stuff, and so that's why you
Speaker 1: need like the really powerful processing ability on the other
Speaker 1: end to make sense of that and turn it into
Speaker 1: actionable items that the house can can then go through. So,
Speaker 1: like I said, saving energy, that's like the primary purpose
Speaker 1: for this project, and a cognitive building could keep track
Speaker 1: of all the rooms that are in use which ones aren't.
Speaker 1: Essentially it's like the very complicated way of telling people, Hey,
Speaker 1: turn off the light when you leave the room, except
Speaker 1: now the building is doing it for you, and also
Speaker 1: maybe adjusting the the air conditioning for that room. If
Speaker 1: if there's like different air conditioners for different floors or
Speaker 1: different even different sections of the building. It can manage
Speaker 1: it on a very grand, a little granular level. Ah,
Speaker 1: And it's h it's pretty intense to do all this.
Speaker 1: It's is an intense processing and intense data gathering system.
Speaker 1: It's really taking that big data approach on a building
Speaker 1: by building or collection of building kind of scale, which
Speaker 1: is also crazy because IBM research is looking not just
Speaker 1: at making one cognitive building, but making groups of them.
Speaker 1: Oh sure, yeah, not not just like personal homes, Like
Speaker 1: it's kind of easy to think about this on a
Speaker 1: single home kind of level. But but of course they're
Speaker 1: trying to expand that out. Yeah yeah, So, uh, let's
Speaker 1: say that what where can you where can you go
Speaker 1: besides energy conservation? I mean, that's obviously a very important element,
Speaker 1: but what else could you do with this? One thing
Speaker 1: that IBM is proposing is a system that could detect
Speaker 1: problems before they become so great that you have to
Speaker 1: have like an emergency response to it. So, let's say
Speaker 1: that your smart sensors detect that your air conditioner is
Speaker 1: working harder than it normally has to in order to
Speaker 1: maintain the temperature. It may mean that you need to
Speaker 1: change your air filter, right, it may be something as
Speaker 1: simple as that, or maybe that uh oh, we're detecting
Speaker 1: a decrease in pressure in the tubes and your your
Speaker 1: free on or other your coolant is leaking somewhere. It
Speaker 1: can then send you an alert. You get alert alert
Speaker 1: on your phone saying hey, by the way, I've detected
Speaker 1: this drop in efficiency. Um, the diagnosis I have is
Speaker 1: such and such. If it's something like switching out of filter,
Speaker 1: you could probably do it yourself. If it's something a
Speaker 1: little more complicated, might say that you might need to
Speaker 1: call someone, and may even bring you give you suggestions
Speaker 1: on who to call, depending upon the way you've designed
Speaker 1: this building. Same thing could be true with plumbing. It
Speaker 1: could say, well, I detected that they're the the waste
Speaker 1: pipes aren't flowing as properly as they should. It may
Speaker 1: very well be that the vent that allows air to
Speaker 1: pass through the the waste pipes has been clogged with something,
Speaker 1: so you need to just run some water down the
Speaker 1: vent to clear it out. And then you don't have
Speaker 1: to bring a plumber in to do an expensive job
Speaker 1: when you can just do it yourself. This is something
Speaker 1: that I actually had to go through myself recently. I
Speaker 1: had to I didn't have a computer tell me. It's
Speaker 1: just one of those things where I thought this drains
Speaker 1: backing up. I sure hope the venice claude because that's
Speaker 1: an easy solution. Turned out it was, But it would
Speaker 1: be great to have a system that tells me that
Speaker 1: oh sure, yeah, you know it's something that's that's telling
Speaker 1: you where you've got a a leak in a pipe
Speaker 1: or a leak in electricity flow. Sure a you know
Speaker 1: if if if you're if you're in an area where
Speaker 1: your pipes might freeze during the winter. Um, something that
Speaker 1: can automatically regulate the temperature in problematic sections at the
Speaker 1: house to not let your pipes freeze, yeah, or or
Speaker 1: an automated tap of some sort that can just let
Speaker 1: enough water flow to prevent it from freezing up right right, Yeah,
Speaker 1: it is Yeah, that that that would be great because
Speaker 1: again it saves you money in the long runs. It
Speaker 1: uh ends up also preventing further damage being done two
Speaker 1: elements of the home or the building. It would be
Speaker 1: a huge benefit sure And speaking of money, hey, in
Speaker 1: multi home buildings like condos or apartments or whatever, you
Speaker 1: could track who's using the most resources and adjust the rent. Wow,
Speaker 1: So you'd have to pay for the number of times
Speaker 1: you flush the toilet and your landlord knows Yep. I mean,
Speaker 1: I mean a lot of a lot of a lot
Speaker 1: of apartments and condos that I know of have there.
Speaker 1: You don't have a have a collected waste removal kind
Speaker 1: of fee. You know, I think it's only fair starting
Speaker 1: to get into brazil territory. And here's my receipt for
Speaker 1: your receipt. Uh. Yeah. And beyond this, like I said,
Speaker 1: this does actually remind me strangely of Brazilian Yeah that really? Yeah,
Speaker 1: will this cognitive building have ducks? Will There'll be lots
Speaker 1: of ducks? How man? Yes, require duct repair people, not
Speaker 1: side ducks. I've already been yelled at for talking about
Speaker 1: Pokemon Go too much. Uh. As I mentioned earlier, it
Speaker 1: doesn't have to just be a building and can be
Speaker 1: a collection of buildings. The example that they gave was
Speaker 1: imagine a college campus that could actually track the movement
Speaker 1: of students not just within individual buildings, but across different buildings.
Speaker 1: And then it starts getting into a really complicated kind
Speaker 1: of approach where if if you're designing it in such
Speaker 1: such a way so that uh it's as a seamless
Speaker 1: and experience for the people in there, but also one
Speaker 1: that conserves the most energy possible, and it's as busy
Speaker 1: as like a college campus that gets pretty tough to
Speaker 1: do too. Um. I was really impressed when I was
Speaker 1: reading about this, and I was curious, like, how could
Speaker 1: they even process this information? And then I found out
Speaker 1: that the back end, the brains behind this is essentially
Speaker 1: IBM Watson, Like, oh, that would do it. I don't
Speaker 1: happen to have access to one of those, but I
Speaker 1: can understand how that would help. Um. But it's not
Speaker 1: just not just convenience or energy conservation in these cases
Speaker 1: that could be helped out too. Oh yeah, stuff like
Speaker 1: response to a security or emergency situations could be streamlined
Speaker 1: because of the way that everything is connected and the
Speaker 1: way that it knows who's coming and going, which gets
Speaker 1: back into weird Brazil territory. There's some some privacy issues obviously, sure,
Speaker 1: but beyond that even you know, like they would never
Speaker 1: run out of your favorite soda in the vending machine
Speaker 1: or out of toilet paper in a bathroom. Now you're
Speaker 1: speaking my language. There's nothing like just taking that casual
Speaker 1: glance over and then thinking I have committed to an
Speaker 1: action that I regret now. Wow, yeah, thanks for taking
Speaker 1: us there with you, hey, you know, I was speaking
Speaker 1: in a circumspect kind of way. I was trying to
Speaker 1: be as gentlemanly about it as possible. Hey, how about
Speaker 1: another example of a cognitive building that they actually talked
Speaker 1: the IBM team is talked about hospitals. Yeah, and this
Speaker 1: is a this is a really kind of inspiring one.
Speaker 1: So not just energy conservation here either, but also trying
Speaker 1: to improve the health and safety of the staff and
Speaker 1: patients in the hospital. One of the biggest challenges facing
Speaker 1: hospitals is managing infection and contagion. I mean you hear
Speaker 1: people say this all the time, like I don't want
Speaker 1: to go to the hospital sick people are there. Yeah,
Speaker 1: and we talked a little bit about this in our
Speaker 1: episode about antibiotics. Yes, so yeah, hospital acquired infections are
Speaker 1: real things. Yeah, absolutely, And so it's it's you know,
Speaker 1: it's a it's a dangerous environment to be and even
Speaker 1: when people are very carefully following protocols. So a smart
Speaker 1: building version of a hospital might be able to help
Speaker 1: in that way by improving the airflow and even uh
Speaker 1: controlling air pressure in individual rooms to help maintain some
Speaker 1: safe between different patients, to contain areas that might have
Speaker 1: contagious uh pathogens in them or uh some other form
Speaker 1: of of infectious material, so that you minimize the risk
Speaker 1: of this spreading to either staff or other patients. Also,
Speaker 1: just knowing which rooms are occupied and which ones aren't,
Speaker 1: I mean you can save energy that way too. You know,
Speaker 1: one thing that occurred to me when I was thinking
Speaker 1: about the example of the hospital is what if your
Speaker 1: buildings in a sense could actually do science for you,
Speaker 1: like doing data analysis, And by that I mean taking
Speaker 1: the kind of data the buildings like this would naturally
Speaker 1: be collecting, and then pairing that with some other kind
Speaker 1: of data and cross referencing to see if any correlations emerge.
Speaker 1: So in a hospital environment, for example, that might mean
Speaker 1: you you're pairing this integrated environmental data like you know
Speaker 1: room temperature and where the lights are on, all that junk,
Speaker 1: you know, the hundreds of variables the buildings collecting just
Speaker 1: about the environment and what appliances are being used, and
Speaker 1: you pair that with linked data about health outcomes. You know,
Speaker 1: we might not find anything interesting. It probably wouldn't surprise
Speaker 1: us if environmental data about the building had no real
Speaker 1: correlation with health outcomes. But what if in some cases
Speaker 1: it did in ways that we wouldn't have even thought
Speaker 1: to look for if we had to do all these
Speaker 1: analyzes ourselves. Just like what if patients in rooms with
Speaker 1: lower air conditioning temperatures had fewer complications during surgery or
Speaker 1: something like that. Yeah, and and this could also help
Speaker 1: us us out what's going on with a sick building syndrome,
Speaker 1: which is which is the phenomenon in which there tend
Speaker 1: to be these these just kind of breakouts of long term,
Speaker 1: low grade illness among office workers who work in certain environments.
Speaker 1: I can imagine that I've worked with people who have
Speaker 1: made me feel sick before. It's it's suspected that it's
Speaker 1: usually due to like air quality of some kind or another,
Speaker 1: either like surrounding pollutants or mold and mildew or etcetera
Speaker 1: flickering fluorescent light that gives you a brain cloud, but
Speaker 1: it can also be march. Why march another thing that
Speaker 1: you have. When you talked about the building itself gathering data,
Speaker 1: it occurs to me that you could even incorporate the
Speaker 1: actual like you would probably want to scrub this of
Speaker 1: any personal information, but you could you could incorporate things
Speaker 1: like the actual nature of diseases of patients in actual rooms,
Speaker 1: and the building itself could become a data collection point
Speaker 1: and perhaps be an early indicator for something like, Hey,
Speaker 1: you need to be on the lookout for an epidemic,
Speaker 1: because based on the statistics that we're seeing here and
Speaker 1: the number of patients who have come in with this
Speaker 1: similar or identical condition, there could be a source nearby
Speaker 1: that's making people sick. It could give people the information
Speaker 1: they need to actually address the source of an issue
Speaker 1: before it gets even worse. Yeah, that's kind of I
Speaker 1: never even thought about that, but that is really cool. Yeah. Also, um,
Speaker 1: what about what about factories? Um? I mean these these
Speaker 1: are places where things are already fairly streamlined, but a
Speaker 1: cognitive factory could could monitor the waar too. Again to
Speaker 1: like every piece of equipment and alert staff if the
Speaker 1: if the heat levels or the sound of the moving
Speaker 1: parts or whatever it is, um indicates that something is
Speaker 1: about to wear out and and get in there and
Speaker 1: replace it to to prevent a costly and time time
Speaker 1: consuming or or even a hazardous breakage. Right. You know,
Speaker 1: factory environment seems susceptible to the same kind of automated
Speaker 1: scientific analysis that I was just talking about. With the hospital,
Speaker 1: where you could have the building just ambiently collecting all
Speaker 1: of these variables. And whereas in the hospital you'd compare
Speaker 1: all that data to health outcomes, in the factory, you
Speaker 1: could compare it to productivity or you know, other business.
Speaker 1: It turns out that oh, what do you know when
Speaker 1: we when we have more lights on in the building,
Speaker 1: people have a ten more productive day. Yeah, and it
Speaker 1: could even limit downtime. I think that's kind of what
Speaker 1: Lauren was saying. It could limit downtime, which time is
Speaker 1: money for a factory. So just for this kind of application, uh,
Speaker 1: it could be invaluable. You know, it means that you're
Speaker 1: you're you're constantly producing or you're producing as much as
Speaker 1: you know your factories, as opposed to saying, well, an
Speaker 1: unanticipated breakage stop production for four days, that's four days
Speaker 1: lost productivity. So a system like this could prevent that
Speaker 1: from happening and thus increase profit. And and that's true
Speaker 1: I think for for other businesses as well. I mean,
Speaker 1: we are all really just producers of content, are we not?
Speaker 1: So so okay, So, so imagine an office building wherein
Speaker 1: you're an exchangeable human cog with a mobile computer and
Speaker 1: when you get to work, you could just be shuffled
Speaker 1: off to anonymous workstation seventeen s w D eight and
Speaker 1: all your coworkers are are organized to save your corporate
Speaker 1: overlords from having to to heat or cool half empty floors. Beautiful, right, Okay,
Speaker 1: it's a glorious vision and I welcome it. Or or
Speaker 1: at least your elevator banks could could know when people
Speaker 1: are approaching and thus minimize your wait times. That's a
Speaker 1: glorious vision and I highly anticipate that one. Or or
Speaker 1: by tracking WiFi connectivity throughout the building, your I T
Speaker 1: kids could maximize the placement of your tech resources so
Speaker 1: that your computer never runs into the problem that we
Speaker 1: run into in our Sometimes this has been inspired by
Speaker 1: actual events. Yeah, here's the thing. Now, would would some
Speaker 1: of the data collection in the building just be microphones
Speaker 1: listening for whether people are cussing about what they can't get?
Speaker 1: Why by you know, it's like, if you hear these
Speaker 1: combinations of words, we need to do something about the
Speaker 1: connectivity situation. I'd be i'd before that kind of privacy breach. Honestly,
Speaker 1: if if it would lead to better WiFi, well we're
Speaker 1: gonna talk about something kind of similar to that in
Speaker 1: just a moment. Actually, But but when we're one more example,
Speaker 1: uh shopping, shopping, y'all. Uh, when when you when you
Speaker 1: go out to a store, or if you're managing a store,
Speaker 1: you know, this is another kind of business and in
Speaker 1: which this kind of technology could could be pretty great.
Speaker 1: You know, like Okay, so, like you might be saying,
Speaker 1: but Lauren, bar codes and QR codes and wireless scanners
Speaker 1: and integrated checkout and warehouse systems, like all of that
Speaker 1: stuff is already given stores a pretty incredible power to
Speaker 1: to harness big data. But a cognitive building could could
Speaker 1: help the staffing and the stocking and just tracking how
Speaker 1: many people are in a store in any given time,
Speaker 1: tracking what those numbers look like over the course of
Speaker 1: a day, a week, a year, um. Correlating purchases to
Speaker 1: weather trends, hunting down items that customers have infuriatingly put
Speaker 1: on a different shell, you know, just all all of
Speaker 1: that little stuff to make Yeah, time is money. And
Speaker 1: I can just imagine that that you're you're building, you
Speaker 1: own a shop, like a like a sporting goods type
Speaker 1: thing and or maybe just general goods type of thing,
Speaker 1: and that your building says Hey, about two hours, it's
Speaker 1: gonna start raining. Put the umbrellas out right, which I
Speaker 1: mean that people do that, but you know, take advantage
Speaker 1: of that for some reason, also sell all the grind cups.
Speaker 1: It's just just it's like, man, every day I opened
Speaker 1: up my email message number one. Well, as I mentioned, sorry,
Speaker 1: you said sporting goods store. I was confused why. I
Speaker 1: was just thinking of like a place that would possibly
Speaker 1: have umbrellas to sell like some places do in some
Speaker 1: places don't. I wouldn't say. You go into a pet
Speaker 1: store and it says put out the umbrellas, fair enough. Oh,
Speaker 1: but those are the two things. I guess. I know
Speaker 1: that sporting stores umbrellas and basketballs. I suppose I probably
Speaker 1: they've got a lot of sports ball stuff in there.
Speaker 1: I've seen it. So as we were saying the brains
Speaker 1: of this IBM s Watson, Yeah, and we've taught so
Speaker 1: much about Watson and other episodes, Watson could also help
Speaker 1: them with the cafeteria menu. Can you just see people
Speaker 1: walking into work every day saying, man, I dread lunch. First,
Speaker 1: grill your lettuce, Yeah, that's for your lettus, free burgers.
Speaker 1: Rill you're olive paste, yeah, Watson's chef Watson is still
Speaker 1: I still maintain that one day we're going to have
Speaker 1: to bring in food that we've we've cooked based off
Speaker 1: a Chef Watson recipe, have a taste test, probably not
Speaker 1: on microphone because that's gross. We're gonna we're gonna keep
Speaker 1: saying this every time Watson comes up until it happens. Yeah, so, hey,
Speaker 1: we we have that snack stuff. That's true, that's true.
Speaker 1: We do a live stream where we occasionally subject employees
Speaker 1: of how stuff works to eating things that are unusual,
Speaker 1: sometimes delicious, sometimes not so much, sometimes painful. We've got
Speaker 1: a hot pepper one coming up pretty soon. I think, yeah,
Speaker 1: I'll be exciting. Oh I wasn't invited for this, the
Speaker 1: hot pepper one. You're totally invited if you want, because
Speaker 1: there's there was an email change. We're talking about ghost peppers,
Speaker 1: and then I said, well I had a Carolina Reaper
Speaker 1: the other day, which is the hottest pepper so far,
Speaker 1: and uh, that kind of precipitated into this macheesemo thing
Speaker 1: about hot peppers that apparently I've been pulled into. So
Speaker 1: not I'm not protesting anyway, I've got off track. So
Speaker 1: Watson yes, it is the brains behind this, but beyond that,
Speaker 1: they've used Watson to do some interesting analysis. They actually
Speaker 1: collected about five million tweets about different buildings around the world,
Speaker 1: and they were looking for people describing something in those buildings,
Speaker 1: either in a positive or negative way. They aggregated all this,
Speaker 1: they analyze the data, and they started crunching the information
Speaker 1: to find out who has the best and worst features
Speaker 1: of various things in various buildings around the world, like
Speaker 1: like what what ideas can we steal and what should
Speaker 1: be definitely avoid for building the best type of building
Speaker 1: we possibly can. So apparently the best elevators in the
Speaker 1: world according to people commenting about this on Twitter. And
Speaker 1: remember these are just casual tweets. It wasn't like someone solicited, Hey,
Speaker 1: who do you think has the best elevators? This was
Speaker 1: just based off people tweeting the information on their own.
Speaker 1: The best elevators, according to this analysis, would be the
Speaker 1: elevators in the Empire State Building in New York City,
Speaker 1: which I was in once when I was thirteen and
Speaker 1: haven't been in since. So why are they the best?
Speaker 1: That's just because they were the most positive comments about
Speaker 1: these elevators in all the in all the data that
Speaker 1: they crunched. Yeah, it seems like that might be affected
Speaker 1: by things like you know, what are the elevators people
Speaker 1: are based on the site? Most absolutely, I would argue
Speaker 1: this is not that different from something like Rotten Tomatoes,
Speaker 1: where you look at the score and some people say, oh,
Speaker 1: the score is indicative to the quality of the movie.
Speaker 1: But really, what the score just tells you is what
Speaker 1: percentage of critics gave it a positive review versus negative review.
Speaker 1: It doesn't give you an indication of the overall quality
Speaker 1: of whatever that thing is or how much you'll enjoy it. Yeah. So,
Speaker 1: at any rate, all three of us like very very
Speaker 1: terrible movies. We do not always not always the same
Speaker 1: ones though, uh so. Ibms Watson says that according to Twitter,
Speaker 1: Empire State Building is the best elevators, and according to Twitter,
Speaker 1: the worst air conditioning in any major building in the
Speaker 1: world is in the Louver. That makes sense to me.
Speaker 1: Pretty stuffy in there and very French. Uh. And if
Speaker 1: you need to take a bathroom break, the best place
Speaker 1: to do it is in the Sydney Opera House. I
Speaker 1: don't know if that just means that the bathrooms are
Speaker 1: the nicest, or maybe they pipe in the opera nice
Speaker 1: and loud so you can do your business and not
Speaker 1: worry about anyone hearing what's going on in there. Yeah,
Speaker 1: you've thought this through. Yeah, I mean, I mean it's fine.
Speaker 1: I mean, that's that's a great thing to know. But
Speaker 1: other than the kind of like kitch factor of all this,
Speaker 1: like what what are we really looking at it? It's
Speaker 1: sort of like identifying the best practices for various features
Speaker 1: and saying, well, if people really like the way this
Speaker 1: one thing works in this one building, maybe we should
Speaker 1: pay attention to that. Maybe we should try to incorporate
Speaker 1: that same style, that same design, whatever it is that
Speaker 1: makes that thing the best in its class, so that
Speaker 1: we can uh make future buildings awesome And for all
Speaker 1: the ones that are identified with negative features, we can
Speaker 1: avoid those so that we don't make really awkward, awful
Speaker 1: buildings unless we want to do a really weird art
Speaker 1: project where everything is terrible. But if they keep going
Speaker 1: just by what people tweet the most joyfully about, every
Speaker 1: building is going to have an ikea monkey. Well, this
Speaker 1: is again, this is just one part. Don't see the downside.
Speaker 1: It's just one part of the overall approach. So it's
Speaker 1: one of those things where I saw it and I thought, well,
Speaker 1: that's kind of interesting that they're not It's not just
Speaker 1: cold analytics and and uh, personality free data. It's also
Speaker 1: taking into account you know, what are people passionate about, what,
Speaker 1: what do they find comfortable or awesome or cool or interesting.
Speaker 1: What do they find uncomfortable or inefficient or aggravating, and
Speaker 1: and what are the specific elements that go into that,
Speaker 1: so that when we designed the buildings of the future,
Speaker 1: we can eliminate as much of the negative stuff as
Speaker 1: we possibly can, incorporate as much of the positive stuff
Speaker 1: as we possibly can do it in a way that's
Speaker 1: as energy efficient as we possibly can make it, and
Speaker 1: may get responsive to all of our needs so that
Speaker 1: it's a fantastic place to be in all the time.
Speaker 1: And as soon as my house does all this, I
Speaker 1: will never leave it again, so it'll just collect data
Speaker 1: on you night and day. Is my my house is up?
Speaker 1: My house is buddy, Buddy with the n essay, are
Speaker 1: you kidding me? Um? One of the other things I
Speaker 1: didn't put it in the notes, but one of the
Speaker 1: other things I thought was interesting is they're talking about
Speaker 1: incorporating a R and VR into various buildings in a
Speaker 1: very uh like uh like into the not literal foundation
Speaker 1: of the building, but into the building itself, so that
Speaker 1: you're talking about a holidack. You could have something like
Speaker 1: that like there there are there are never went well, look,
Speaker 1: we only saw the episodes where the holiday didn't work.
Speaker 1: We didn't see any of the episodes where the holiday
Speaker 1: was perfectly fine. It's like the van is always at
Speaker 1: the corner. You only notice that when it's at the corner,
Speaker 1: and when it's not at the corner, you don't even
Speaker 1: think about it. Um No, really, that holidack was a
Speaker 1: terrible idea, But now they I have seen like amusement
Speaker 1: uh like an amusement part type thing where there is
Speaker 1: the incorporation of augmented reality or virtual reality and physical
Speaker 1: objects within the environment that are mapped to the virtual environment,
Speaker 1: so that you can pick up things that are appearing
Speaker 1: to you in the virtual environment, but because there's a
Speaker 1: physical object that's mapped to you're actually holding a thing.
Speaker 1: Oh my god, I just had a great idea for
Speaker 1: a home augmented reality application. It would be to haunt
Speaker 1: my house, app that it's your own house. But this
Speaker 1: just puts some ghosts in it. I was about to say, like,
Speaker 1: if you could, like I keep saying, imagine wearing something
Speaker 1: like Microsoft Hollow Lens and playing something like pt that
Speaker 1: playable trailer that came out for the Silent Hills game
Speaker 1: that never happened. Imagine walking down your own hallways and
Speaker 1: seeing that stuff. Nope, nope, never sleep again. I think
Speaker 1: you'd be pretty awesome. I'd do it at once, Like
Speaker 1: I'd be like, honey, um, I'm never sleeping in the
Speaker 1: bedroom again. Enjoy I'll be I'll be asleep in the
Speaker 1: office see later. Certainly not I was. I was just imagining,
Speaker 1: because I'm equally terrible, but in a different bent, that
Speaker 1: you could you could have like a physical clippy that
Speaker 1: could just help you tour around unknown places. Cool. Yeah,
Speaker 1: I see that you're totally lost. Would you like to
Speaker 1: be for me to direct you to the specific location?
Speaker 1: And if it's a physical object, the best part is
Speaker 1: that you could literally throw it out a window. I
Speaker 1: like the idea of being in your house and they're
Speaker 1: just Pokemon everywhere already happened. That's crazy. But oh yeah,
Speaker 1: you're just looking around, you see him, and you're like,
Speaker 1: I gotta catch that one, and it's like mapp to
Speaker 1: your pet. But then you have to wrestle your pet.
Speaker 1: My dog is fast. It would be a challenging and
Speaker 1: fun experience for the both of us. But I don't
Speaker 1: want you to put your dog in a poke ball.
Speaker 1: He'd be fine with it, he'd love it. No, Seriously,
Speaker 1: be kind to your pets and be kind of mine too.
Speaker 1: He's awesome. Guys. If you have any questions or suggestions, comments,
Speaker 1: that sort of thing and you want to let us
Speaker 1: know about it, here's what you can do, and you
Speaker 1: can send it an email form and use the address
Speaker 1: f W Thinking at how Stuff Works dot com, or
Speaker 1: drop us a line on Twitter. Our handle is fw thinking,
Speaker 1: or on Facebook you can search f W Thinking in
Speaker 1: the search bar. Our profile will pop right up. You
Speaker 1: can leave us a message there and we will talk
Speaker 1: to you again really soon. For more on this topic
Speaker 1: in the future of technology, visit forward thinking dot com,
Speaker 1: brought to you by Toyota. Let's go places,