Speaker 1: Get in tech with technology with tech Stuff from how
Speaker 1: stuff works dot com. Hey there, and welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer with
Speaker 1: how Stuff Works and I love all things tech. This week,
Speaker 1: I've got a series of special shows in addition to
Speaker 1: our normal episodes that I am happy to bring with you.
Speaker 1: You may notice that things sound a little different than
Speaker 1: they usually do. That is because I am not currently
Speaker 1: in the massive, wonderful, underground, super secret studio at how
Speaker 1: stuff Works. Instead, I am on the road. I'm actually
Speaker 1: in Las Vegas, Nevada, where I am attending the inaugural
Speaker 1: IBM Think Conference. It's taking place between March nineteenth and
Speaker 1: March ten and it's kind of a big industry conference
Speaker 1: for people who are in the text space, who work
Speaker 1: with computers and software and hardware. Kind of a place
Speaker 1: for them to meet, to network and to learn a
Speaker 1: lot more about some bleeding edge technologies. They have lots
Speaker 1: of different activities going on all week, and I am
Speaker 1: going to be here attending some of the events, talking
Speaker 1: to people, getting more information, and recording special episodes for you,
Speaker 1: my beloved listeners. So I hope you enjoy this special
Speaker 1: series and I can't wait to really dive in and
Speaker 1: learn more about these topics. It looks really interesting. Now.
Speaker 1: One thing I will say before I jump in and
Speaker 1: give you kind of an overview of what I have
Speaker 1: to expect this week is that it's crazy busy, y'all.
Speaker 1: I mean, the conference has got so many people attending.
Speaker 1: I haven't seen any numbers about how many people are
Speaker 1: actually at this thing. But it's taking place at Mandalay
Speaker 1: Bay as one of the resorts and casinos here at
Speaker 1: Las Vegas, and it has a very large convention center.
Speaker 1: During ce S, you may have heard me do several
Speaker 1: episodes about ce S over the years. This is the
Speaker 1: place where the press events happened before the show floor opens.
Speaker 1: At the Las Vegas Convention Center at Mandalaid Bay, you
Speaker 1: would have all the different rooms booked up for the
Speaker 1: various press conferences that companies would use to unveil their
Speaker 1: latest and greatest products they're coming out over the next
Speaker 1: year or two. Well, at IBM think that's what is
Speaker 1: being used as the meeting space and the keynote space,
Speaker 1: presentation space for all sorts of different I T computer
Speaker 1: topics and activities. It's kind of overwhelming, to be honest.
Speaker 1: In fact, I am now kind of unwinding in my
Speaker 1: hotel room before I have to go back out and
Speaker 1: jump right back into I'm going to go to a
Speaker 1: session where they're going to talk about five big topics
Speaker 1: in science and technology. So I'm really looking forward to it.
Speaker 1: There's some very interesting people who are here presenting, and
Speaker 1: I can't wait to learn more. Some of the areas
Speaker 1: that we're going to talk about this week will include blockchain. Now,
Speaker 1: if you've been listening to text stuff over the past
Speaker 1: few months, you've probably heard a recent episode I did
Speaker 1: about blockchain. Blockchain is the technology that underlies cryptocurrencies like bitcoin,
Speaker 1: and in fact, I would argue bitcoin is probably the
Speaker 1: most famous use of blockchain technology, but blockchain is not
Speaker 1: only good for bitcoins and cryptocurrencies. That's one use for it,
Speaker 1: but not the only one. In fact, a lot of
Speaker 1: people argue that blockchain is going to be the next
Speaker 1: um next evolution of the web. So you might remember
Speaker 1: people were talking about web two point oh more than
Speaker 1: a decade ago, and blockchain would be like the next step.
Speaker 1: So what does that even mean? Well, what one point
Speaker 1: oh was the sort of websites you first saw, primarily
Speaker 1: when the web first launched, that would be websites that
Speaker 1: were pretty much static. They did not change, They were
Speaker 1: not really interactive. They did not have any capacity to
Speaker 1: have users add to or change things in any meaningful way.
Speaker 1: And so it was kind of like looking at a
Speaker 1: newspaper or a magazine, something that's in a fixed format
Speaker 1: that doesn't have any real um, any real, any real
Speaker 1: special thing about it. Right, there's nothing that gives it
Speaker 1: any sort of uh sense that you your presence matters,
Speaker 1: or that that's even registering at all, unless there was
Speaker 1: one of those little helpful counters, as was often the
Speaker 1: case in early websites, where it would tell you how
Speaker 1: many visitors had been to that website above. From that,
Speaker 1: there really wasn't anything that indicated that you mattered at all.
Speaker 1: So that was web one point. Oh and a lot
Speaker 1: of those websites ended up kind of just being uh momentary.
Speaker 1: They didn't stick around because there was no point in
Speaker 1: going back and visiting after you've seen them once. They
Speaker 1: weren't ever going to change. If they did change, you
Speaker 1: didn't necessarily know about it, and so you would just
Speaker 1: have to go back and visit and see if anything
Speaker 1: had changed since the last time you were there. It
Speaker 1: was kind of inconvenient. Web two point oh arguably was
Speaker 1: when websites started to incorporate interactive features where users could
Speaker 1: come into the website and things would change, they'd be dynamic.
Speaker 1: Sometimes this meant that there were animated aspects to the
Speaker 1: web page. Sometimes it just meant that there was a
Speaker 1: way for users to leave feedback. For example, some people
Speaker 1: point to Amazon as being an early web dew point
Speaker 1: oh style web page because you could leave user reviews
Speaker 1: on the site, so you could actually impact what happened
Speaker 1: with products on Amazon dot Com. And then the course
Speaker 1: later on, Amazon was also incorporating things like recommendation engines
Speaker 1: that would try to engage users and and encourage them
Speaker 1: to spend more money and to buy more things, and
Speaker 1: that was also and indication that was web two point oh.
Speaker 1: It was something that was more than just visit website,
Speaker 1: read some information and leave. Blockchain is supposedly going to
Speaker 1: be the next step, and really it comes down to
Speaker 1: the very nature of blockchain itself, which is a peer
Speaker 1: to peer technology within a network. So you're not necessarily
Speaker 1: talking about Internet wide. It's a network within the Internet.
Speaker 1: Now it might be a network that spans multip pole networks,
Speaker 1: because it's all peers that are connecting to one another
Speaker 1: and using a method that creates blocks of data. Thus
Speaker 1: the block in the block chain, and each block is
Speaker 1: sequentially added to a chain of other blocks, and it
Speaker 1: all dates back to the very first block in the chain,
Speaker 1: and there's information within each block that can be traced
Speaker 1: all the way back to that first one. Uh. The
Speaker 1: transactions that happen within a block are recorded within that block,
Speaker 1: and as other computers inside the peer to peer network
Speaker 1: verify and validate those transactions, whatever they may be, they
Speaker 1: then are able to create the next block in the blockchain.
Speaker 1: With cryptocurrency, you get a reward for this. That's what's
Speaker 1: called mining. It's when in bitcoin your computer participates in
Speaker 1: solving a particularly tough math problem essentially, and if you
Speaker 1: do end that ends up being the validation for previous transactions.
Speaker 1: The transactions all are codified as a block added to
Speaker 1: the end of the chain, and everyone in the peer
Speaker 1: to peer network has a has access to a ledger
Speaker 1: that is updated across the entire network. So the ledger
Speaker 1: is the full record of all transactions dating back to
Speaker 1: the very first one. They're going to be very deep
Speaker 1: dives on blockchain technology here this week, as people talk
Speaker 1: about the different ways to use it beyond cryptocurrencies, and
Speaker 1: how it could serve as a backbone to future web interactions. Honestly,
Speaker 1: I'm very eager to hear more about that, because while
Speaker 1: I've just described kind of a very high level of
Speaker 1: what blockchain is, I I am really curious about how
Speaker 1: this is going to be used to actually, uh form
Speaker 1: the the spine of the web in the future. Was
Speaker 1: it going to be besides a way of keeping track
Speaker 1: of transactions, like whether it's currencies or property or anything
Speaker 1: along those lines, what can it do beyond that? And
Speaker 1: I honestly don't fully understand that, so I look forward
Speaker 1: to learning more while I'm here. That's just one of
Speaker 1: the aspects that will be covered during this conference. Another one,
Speaker 1: a big one, is artificial intelligence. Now I've talked about
Speaker 1: that a lot on tech stuff as well. Artificial intelligence
Speaker 1: A lot of people when they hear that, I think
Speaker 1: they immediately go to the idea of a machine that
Speaker 1: can quote unquote think like a person. It is mimicking
Speaker 1: the way brains think, or it somehow possesses human like intelligence,
Speaker 1: or perhaps even superhuman intelligence, where it's able to think
Speaker 1: even better than humans are. But artificial intelligence is a
Speaker 1: much more broad category than that. That would be a
Speaker 1: very specific niche definition of what artificial intelligence is. Artificial
Speaker 1: intelligence encapsulates multiple disciplines, and it is also uh something
Speaker 1: that involves lots and lots of different parts of intelligence,
Speaker 1: not just this cognition that we think of, but think
Speaker 1: of things like image recognition with computers. Teaching a computer
Speaker 1: to identify a specific kind of thing to extrapolate from knowledge,
Speaker 1: it's tricky. You might be able to teach a computer,
Speaker 1: for example, that a mug is a mug like a
Speaker 1: coffee mug. You've got a red coffee mug has got
Speaker 1: sort of a curved cut mug. It's not, you know,
Speaker 1: just straight up and down. It's got kind of kind
Speaker 1: of bowls out a little bit. And you take images
Speaker 1: of it from a certain angle with certain lighting, and
Speaker 1: you feed it to a computer and you let them
Speaker 1: You have your computer breakdown the image so it can
Speaker 1: I identify where the all the borders are, what are
Speaker 1: what is the mug versus, what is the background versus
Speaker 1: what is the platform it's on? And you Essentially, you
Speaker 1: teach the computer, hey, this is a mug. Computers don't
Speaker 1: magically then understand that all other containers that have that
Speaker 1: same basic sort of shape um are mugs. If you
Speaker 1: showed another picture of that same mug with different lighting,
Speaker 1: the computer might not be able to tell you that
Speaker 1: that's a mug. Or it's at a different angle. Maybe
Speaker 1: you turn it so that the handle is facing a
Speaker 1: different way, and then you showed the computer the image,
Speaker 1: it may not be able to figure out that that
Speaker 1: is also a mug. If it's a different color, if
Speaker 1: it's a different shape. All of these things are tricky
Speaker 1: for machines. Like as human beings, we can see an
Speaker 1: example or two of something and then we're pretty good.
Speaker 1: We then can say, all right, I now I get
Speaker 1: the general idea of the things that that are the
Speaker 1: constitute a mug. And when I encounter something else, even
Speaker 1: if it is of a different shape, a different color,
Speaker 1: different lighting, on a different surface, maybe it's even got
Speaker 1: a different type of liquid inside of it, I still
Speaker 1: can figure out that that thing is a mug. Because
Speaker 1: I can extrapolate. I can take what I've learned and
Speaker 1: extend it beyond just the few instances that I have encountered.
Speaker 1: And then if I walk into a mug store, I
Speaker 1: don't look at just one and say that's a mug,
Speaker 1: but I have no idea what the rest of these
Speaker 1: things are. I can actually say, oh, these are all
Speaker 1: different types of mugs and different sizes and shapes and colors.
Speaker 1: Computers are not good at that generally speaking. This is
Speaker 1: one of the reasons why you may have heard about
Speaker 1: that story of a computer being fed thousands upon thousands
Speaker 1: of images of cat pictures so that the computer could
Speaker 1: learn what a cat is. Because without all of that,
Speaker 1: without the this huge body of examples, the computer simply
Speaker 1: can't extrapolate and figure out what is is not a cat.
Speaker 1: And even after all of those images were fed to
Speaker 1: the computer, it's still had some issues. It wasn't like
Speaker 1: it had magically understood what a cat was. It did
Speaker 1: not have an innate grasp of cat nous, and by
Speaker 1: that I mean the qualities that make a cat not
Speaker 1: a character from Hunger games. So artificial intelligence, again is
Speaker 1: very much a multidisciplinary thing, image recognition being just one
Speaker 1: tiny part of it. There are numerous other aspects to
Speaker 1: artificial intelligence, including things like voice recognition and natural language processing.
Speaker 1: Natural language processing, of course, is where a computer starts
Speaker 1: to learn what we mean when we say things a
Speaker 1: certain way. Now, again, just like with our ability to
Speaker 1: extrapolate based upon the limited examples we have seen of
Speaker 1: any one object, with image recognition, natural language, we can
Speaker 1: have different ways of saying the same thing and still
Speaker 1: mean the same meaning. So I might say it's raining,
Speaker 1: or it's pouring outside, or it's coming down like cats
Speaker 1: and dogs. Those are all different ways of me saying
Speaker 1: that water is falling from the sky in the form
Speaker 1: of precipitation, and you will get wet if you walk outside.
Speaker 1: And people who have had just a limited amount of
Speaker 1: exposure to these sort of ideas can pick up on that,
Speaker 1: but computers, again do not unless you expressly tell them. So.
Speaker 1: The way we say things, the word order we choose,
Speaker 1: the syntax, the the accent we may speak with, the
Speaker 1: dialect if you prefer that we might speak with, the
Speaker 1: emphasis we would place on different words, the speed at
Speaker 1: which we speak. All of these different factors make it
Speaker 1: very challenging for computers to understand us. That being said,
Speaker 1: voice recognition and natural language processing has come a far
Speaker 1: away in a short amount of time. Over the last
Speaker 1: couple of decades, it has really advanced quite a bit,
Speaker 1: which is why we see all of these digital personal
Speaker 1: assistance that we can talk to like Siri and Alexa
Speaker 1: and Google Assistant, and they have become pretty good and
Speaker 1: understanding us. They're not perfect, but they're pretty good at
Speaker 1: figuring out what we want, even if we say things
Speaker 1: in different ways. But that's another aspect of artificial intelligence.
Speaker 1: All of these sort of things are the kind of
Speaker 1: topics that will be talked about during the IBM Think Conference,
Speaker 1: And again they're gonna go into much greater detail and
Speaker 1: talk about real world applications for AI, not just theory,
Speaker 1: not just how far we've come, but how we can
Speaker 1: put it to use. There's more to come about the
Speaker 1: IBM Think Conference, but before I get much further into it,
Speaker 1: let's take a quick break to and our sponsor IBM Watson.
Speaker 1: You may have heard of it as the computer that
Speaker 1: defeated former Jeopardy champions. Well, it's much more than that.
Speaker 1: Although that definitely was a big publicity stunt. You could
Speaker 1: argue it was the equivalent of when Deep Blue went
Speaker 1: up against Kasparov in the chess competitions. But IBM s
Speaker 1: Watson is sort of in that natural language processing and
Speaker 1: understanding what people are saying and being able to pull
Speaker 1: data based on that. That is sort of an example
Speaker 1: of that. So in Jeopardy it was really put to
Speaker 1: the test because with Jeopardy, you don't just get clues
Speaker 1: and then you have to come up with the right answer.
Speaker 1: Sometimes those clues are puns or rhymes, or they are
Speaker 1: very circumspect, like it's very circuitous the kind of logic
Speaker 1: you have to use to figure out what the actual
Speaker 1: answer is in the form of a question, and Watson
Speaker 1: did really well with it. You may remember that the
Speaker 1: way Watson worked was that it had a massive database.
Speaker 1: It was not connected to the Internet for the purposes
Speaker 1: of Jeopardy. It just had a big database of information
Speaker 1: and it would process whatever the clue was. It would
Speaker 1: reference its database, and it would look for potential answers
Speaker 1: and assign each potential answer a probability, sort of a
Speaker 1: confidence probability. If Watson's confidence probability probability was high enough,
Speaker 1: and as I recall, it was somewhere in the eight
Speaker 1: percentile range, like it had to be at least sure
Speaker 1: that that was the right answer. It would then put
Speaker 1: forward that as its answer in jeopardy. If none of
Speaker 1: the answers that came up with met that level of confidence,
Speaker 1: Watson would not answer. It would not attempt to see
Speaker 1: if perhaps it could take a wild guess and at
Speaker 1: the right answer, and it turned out to work. Watson
Speaker 1: ended up winning that game of Jeopardy. But of course,
Speaker 1: Watson isn't just about showing up former champions. It's about
Speaker 1: all sorts of stuff IBM SUH. Early applications of Watson
Speaker 1: were to include it with medical procedures so that it
Speaker 1: could help doctors diagnose and treat patients. It was not
Speaker 1: replacing doctors. It was not meant to be a robo
Speaker 1: doctor that would treat you based on your symptoms and
Speaker 1: then you just go to the robo pharmacist. But rather
Speaker 1: it was acting as an assistant to help either confirm
Speaker 1: what a doctor was thinking or perhaps narrowed down some
Speaker 1: options of what the cause of any particular symptoms might be.
Speaker 1: And it has seen a lot of use in that field.
Speaker 1: It's gone well beyond that, though some of the other
Speaker 1: applications have been a little silly. You may remember if
Speaker 1: you listen to the Forward Thinking podcast We did an
Speaker 1: episode about Chef Watson that was an application that IBM
Speaker 1: created where Watson would design a meal for you based
Speaker 1: off of ingredients you told it. We're at your disposal.
Speaker 1: So you might say, hey, I have chicken, and I've
Speaker 1: got rosemary, and i have some potatoes, and I've got
Speaker 1: some rice, and i've got some green beans. What can
Speaker 1: I make with these? And it would say, all right, well,
Speaker 1: let me come up with a recipe for you, and
Speaker 1: would generate a recipe and it probably includes some ingredients
Speaker 1: that you might not have on hand. It would be
Speaker 1: stuff that you would have to go shop for. But
Speaker 1: the interesting thing was it was dynamically creating these recipes.
Speaker 1: It wasn't accessing a database of recipes and pulling from
Speaker 1: the ones that included the ingredients you mentioned. Instead, it
Speaker 1: would look at how flavors had been combined in the
Speaker 1: past by looking at a huge library of recipes that
Speaker 1: had been fed to Chef Watson. So just imagine a
Speaker 1: library is worth of cookbooks fed to this computer, and
Speaker 1: the computer says, well, based upon what I can see
Speaker 1: from all these recipes, roseberry and chicken do pair well together,
Speaker 1: So we're going to make a recipe that uses those
Speaker 1: two ingredients, and based upon all the different ways I've
Speaker 1: seen to prepare chicken, I think a baked chicken dish
Speaker 1: would be the perfect one to go with, and so
Speaker 1: on and so forth. But it's not pulling a recipe,
Speaker 1: it was creating one based upon its knowledge. It's like
Speaker 1: a chef, not a cook. Uh. This did not always
Speaker 1: necessarily work out great. If you listen to that episode
Speaker 1: of Forward Thinking, you'll hear some of the experiences we
Speaker 1: had as we were testing out Chef Watson. I remember
Speaker 1: a cauliflower fricacy recipe in which the cauliflower was listed
Speaker 1: as optional, which I thought was a bit unusual. Also, Uh,
Speaker 1: the other interesting thing was that you could feed the
Speaker 1: exact same ingredients into Chef Watson multiple times and you'll
Speaker 1: get different results each time. Again because the recipes are
Speaker 1: created dynamically, so it's not again not not looking at
Speaker 1: a recipe it's already written. It's writing a new one
Speaker 1: for you each time you ask it. It's kind of interesting.
Speaker 1: There was also a an example recently where Watson was
Speaker 1: being used as a platform for the Weather Company, where
Speaker 1: if you use the Weather Company's app, Watson was helping
Speaker 1: power that so it's kind of like an an A
Speaker 1: p I. So different developers can use Watson as a
Speaker 1: platform to build different applications, and it all depends on
Speaker 1: what your application needs whether or not Watson is a
Speaker 1: good fit. It doesn't necessarily mean that every single application
Speaker 1: is going to benefit from using Watson. If you want
Speaker 1: to create a game that's like Angry Birds, Watson may
Speaker 1: not be of much use. But if it's anything where
Speaker 1: you have someone asking questions or asking for data, then
Speaker 1: Watson might end up being helpful. And so that Obviously,
Speaker 1: there's a lot of different roundtable discussions and breakout sessions
Speaker 1: all about IBM Watson and how to use it. One
Speaker 1: thing that I think is gonna be particularly interesting in
Speaker 1: a very sensitive subject given extremely recent news, is the
Speaker 1: idea of self driving cars. There are some sessions that
Speaker 1: are supposed to be about self driving cars. The reason
Speaker 1: I say sensitive is because the day I'm recording this,
Speaker 1: on March nineteen, two thousand eighteen, UH there was also
Speaker 1: very tragic news out of Arizona, and that news was
Speaker 1: that Uber uh has a fleet of self driving vehicles
Speaker 1: that they've been testing in different markets, including in Arizona,
Speaker 1: and one of those self driving vehicles and SUV ended
Speaker 1: up striking and killing a pedestrian, Elaine Hertzberg. She was
Speaker 1: trying to cross the street. She was walking a bicycle
Speaker 1: across the street in Tempe, Arizona, on the night of
Speaker 1: March eighteen, two thousand eighteen, and she was struck by
Speaker 1: this SUV. And the SUV was an autonomous mode. There
Speaker 1: was a driver in the vehicle because while Uber has
Speaker 1: been testing this autonomous vehicle technology, they have been asked
Speaker 1: to keep an operator behind the wheel of the car
Speaker 1: to take over just in case something goes wrong and
Speaker 1: the vehicle is unable to cope with it. And I'm
Speaker 1: as of the recording of this podcast, I'm not sure
Speaker 1: exactly what happened, whether or not the driver was aware
Speaker 1: of what was about to happen, was unable to respond
Speaker 1: in time. I don't know what the sequence of events was,
Speaker 1: specifically in regards to that operator. But the tragic story
Speaker 1: is that this autonomous car, the self driving car, struck
Speaker 1: and killed a pedestrian. And as far as I can determine,
Speaker 1: it is the first fatality due to an autonomous car.
Speaker 1: Obviously not the first accident. There have been a few
Speaker 1: others and a few that have been attributed specifically to
Speaker 1: the autonomous cars and not to human drivers. But this
Speaker 1: is a terrible, terrible story, and obviously that's going to
Speaker 1: affect the conversations that go on here, So I hope
Speaker 1: to attend some of those and hear what experts in
Speaker 1: the field have to say about this and what is
Speaker 1: the best course of action. Obviously, you want to be
Speaker 1: respectful of the victim and her family, and you want
Speaker 1: to be realistic. You do not want to dismiss this.
Speaker 1: Obviously that would be horrible, it'd be unthinkable. But how
Speaker 1: do you move forward when there's so much momentum technologically
Speaker 1: speaking behind the movement to go to autonomous cars? And
Speaker 1: I'm I want to find out what people's thoughts are
Speaker 1: on this. It may very well be that there aren't
Speaker 1: any prepared sessions to cover this because the news is
Speaker 1: so recent, but I'm sure there will be questions about it.
Speaker 1: There's a bit more I expect to see here at
Speaker 1: the IBM Think Conference, and I'll tell you about it
Speaker 1: in just a minute, but I've got to take a
Speaker 1: quick break to thank our sponsor. Another area that is
Speaker 1: going to get a lot of coverage here at the
Speaker 1: conference is all about cloud and data issues, and oh boy,
Speaker 1: there's a lot to talk about there too, because again,
Speaker 1: recent news has been pretty rough in regards to data mining.
Speaker 1: So I guess I should talk quickly about what this
Speaker 1: is the first place cloud computing In case you were
Speaker 1: not aware, is this this model of computing in which
Speaker 1: you have powerful computers connected to a network that are
Speaker 1: doing computation for you, more specifically, for your computer. It
Speaker 1: might be just computation, it might be storage, It might
Speaker 1: just be storage, it might be both. But the idea
Speaker 1: is that instead of your computer doing all the work,
Speaker 1: a computer on a network is doing all the work
Speaker 1: for you and sending you the results. So your computer
Speaker 1: is just receiving some information. It's not having to crunch
Speaker 1: any numbers. This could be really useful if you wanted
Speaker 1: to do something that was well beyond your computers processing abilities.
Speaker 1: It's also a great way to distribute work across a
Speaker 1: network of computers instead of depending on just one processor.
Speaker 1: Even if it's a massive, multi core processor, it can
Speaker 1: still be more efficient to distribute that workload cross a
Speaker 1: network of computers. An example of this would be the
Speaker 1: many at home projects like SETI at home. These are
Speaker 1: projects where computers uh in a centralized location received massive
Speaker 1: amounts of data. So with SETI, it's data about radio
Speaker 1: frequencies radio waves, and it's generally mostly noise. The vast
Speaker 1: majority of that information is mostly noise, but there could
Speaker 1: be some signal in that noise. That's the whole point
Speaker 1: at SETI at Home is to look for any potential
Speaker 1: signal that could have originated away from us, extraterrestrial in nature, aliens,
Speaker 1: and other words. But to do that, you have to
Speaker 1: siphon through an awful lot of radio frequencies that either
Speaker 1: came from Earth and just bounced around, so we're just
Speaker 1: picking up stuff that we sent out, or they came
Speaker 1: from natural occurring phenomena like pulsars or some other celestial body.
Speaker 1: And to do that would just take a regular computer
Speaker 1: way too much time. We're constantly gathering more of this information,
Speaker 1: so you would fall behind very quickly, and you would
Speaker 1: never be able to catch up because every time you're
Speaker 1: solving a little bit of that problem, you're getting a
Speaker 1: hundred times over more information every minute, so you would
Speaker 1: never be able to keep up with it. Steady at
Speaker 1: Home divides that data into chunks and sends it out
Speaker 1: across its network to people's computers, and their computers will
Speaker 1: work on parts of those problems while the processor would
Speaker 1: otherwise be idle. So let's say that you've got your
Speaker 1: computer on your CPU is working at capacity. Well, if
Speaker 1: you had one of these programs on it, you could
Speaker 1: dedicate a lot of that unused CPU processing power to
Speaker 1: solving these problems, and you would only be solving a
Speaker 1: teeny tiny fraction of the overall work, and other computers
Speaker 1: would be working on the same stuff and sending all
Speaker 1: that data back to the main center of computers, which
Speaker 1: would verify by the results and then continue dividing up
Speaker 1: the job and sending it out to other computers. This
Speaker 1: is kind of a method of grid computing or cloud computing.
Speaker 1: Cloud storage is very similar. You probably have used it.
Speaker 1: If you haven't used it on your computer, you've definitely
Speaker 1: used on your smartphone. This is where you store information
Speaker 1: on servers that belong to someone else. So you might
Speaker 1: have photo albums that are sitting on someone else's computer
Speaker 1: by someone else, I usually mean a corporation like Apple
Speaker 1: or a Google or Facebook or something like that, and
Speaker 1: you have instances of those images, perhaps on your smartphone
Speaker 1: but they also exist on other computers. That's cloud storage,
Speaker 1: and it's very useful if you want to be able
Speaker 1: to store more stuff than what your device can hold.
Speaker 1: That's fantastic. It it's great to be able to turn
Speaker 1: to that. But it's also somewhat limited because um, someone
Speaker 1: else has your your your file, your work, your images,
Speaker 1: and that means that if they change their policies, you
Speaker 1: may no longer have access to it, or you may
Speaker 1: not have full control over it. You may have surrendered
Speaker 1: control over the things that you generated to the entity
Speaker 1: that is now storing it. You might be compromising your
Speaker 1: own privacy. Uh. It is a tricky situation. It's it's
Speaker 1: got a lot of factors to it, and it's a
Speaker 1: big big deal here at IBM. Think recently there was
Speaker 1: a big news story and I'm going to do a
Speaker 1: full episode about this later, but there was a big
Speaker 1: news story about a company called Cambridge Analytica, which used
Speaker 1: an enormous amount of data that it mind from primarily Facebook,
Speaker 1: in order to influence elections, to get information about voters
Speaker 1: and potential voters, and to help push them in a
Speaker 1: specific direction when it came to elections. It is an
Speaker 1: enormous story and developing scandal really, and because of that story,
Speaker 1: I feel like that's going to end up generating some
Speaker 1: questions here at the conference as well, not just about
Speaker 1: the viability of cloud services and data mining, but the
Speaker 1: ethics of it. What is ethical, what is not? And
Speaker 1: how should we codify that, How should we define those
Speaker 1: ethics and how how do we hold ourselves accountable to
Speaker 1: ethical standards to make sure that the technologies that we
Speaker 1: have at our disposal are used in a responsible manner,
Speaker 1: because some would argue that so far that has not happened,
Speaker 1: that we have had multiple instances of violations of privacy security. Uh.
Speaker 1: Another example of of that sort of thing is all
Speaker 1: the different data breaches we have seen over the years
Speaker 1: where companies have not done a good job at protecting
Speaker 1: customer data. And since that data is very much important
Speaker 1: to us as individuals, this is a big concern. In fact,
Speaker 1: that's another area at IBM THINK. It's all about data security.
Speaker 1: How do we keep that data safe? How do we
Speaker 1: protect against cyber attackers? And I'm sure I will see
Speaker 1: a lot of information about that. Another big area of
Speaker 1: discussion at IBM THINK is all about infrastructure. How do
Speaker 1: you incorporate this technology into existing infrastructures. How do you
Speaker 1: design new infrastructures with this technology? And that could be anything.
Speaker 1: It could be anything from the infrastructure of a building
Speaker 1: to a city, to a country, to all sorts of stuff.
Speaker 1: But there are a lot of interesting discussions that are
Speaker 1: on the schedule about the Internet of Things, about integrating
Speaker 1: this technology into buildings and cities and making it a
Speaker 1: seamless part of the infrastructure, not an overlay, but an
Speaker 1: integral part. So when we talk about things like smart
Speaker 1: homes and smart cities, that's what this is all about.
Speaker 1: How can this technology actually improve things, not just be
Speaker 1: a gimmick, but be something that ends up becoming absolutely necessary,
Speaker 1: so that it is so seamlessly entwined with our infrastructure
Speaker 1: that it is, uh, we can't imagine our lives without
Speaker 1: it moving forward. Obviously, that also raises other questions, mostly
Speaker 1: pertaining to things that I've already talked about, like privacy
Speaker 1: and security. How can you make sure that once you
Speaker 1: have this infrastructure, it's safe from bad actors who would
Speaker 1: perhaps try to damage or otherwise leverage that information in
Speaker 1: malicious ways. So there's a lot to talk about their
Speaker 1: Internet of Things has brought up a lot of interesting
Speaker 1: questions about security. You may remember when we had Shannon
Speaker 1: Morse on the show. She talked a bit about this,
Speaker 1: how there are a lot of companies out there that
Speaker 1: are springing to market with these Internet of Things products
Speaker 1: that maybe haven't been fully fleshed out, especially when security
Speaker 1: comes into play. And there's also quantum computing. That's another
Speaker 1: discussion that's going on here at IBM. Think there's talk
Speaker 1: about quantum computing emerging from labs and going into practical use.
Speaker 1: Quantum computers are interesting things. They make use of cubits.
Speaker 1: Cubits are quantum bits. A bit obviously for those who
Speaker 1: have been listening you know all about this. Bits are
Speaker 1: the basic units of information. They can either be a
Speaker 1: zero or a one, and that is you can think
Speaker 1: of as a note or a yes, or an off
Speaker 1: and an on, and using it's and chaining bits together,
Speaker 1: you can represent all sorts of different types of information. Ultimately,
Speaker 1: computers are processing information in bits. A cubit. A quantum
Speaker 1: bit can be in superposition, which means it can inhabit
Speaker 1: all possible states, which means it can be both a
Speaker 1: zero and a one and everything Technically in between simultaneously.
Speaker 1: Now that does not necessarily mean anything for every single
Speaker 1: type of application, but for certain types of computational work,
Speaker 1: that would make it much easier to process information rapidly. Specifically,
Speaker 1: any anything that was using parallel processing, cubits would be
Speaker 1: pretty good for that. Not all the computational problems would
Speaker 1: benefit from quantum computing, but the ones that would. The
Speaker 1: processing would take a fraction of the amount of time.
Speaker 1: I mean that a fraction of a fraction of the
Speaker 1: amount of time that classical computer would take. One of
Speaker 1: the big things that that cubits could do is make
Speaker 1: decryption really easy, which is kind of terrifying because encryption
Speaker 1: is how we keep a lot of data safe. Basically,
Speaker 1: the way you're you're your base level encryption works is
Speaker 1: that you you take a really really really big prime number.
Speaker 1: That meaning that it's a number that is only divisible
Speaker 1: by itself. You it doesn't have any other factors. You
Speaker 1: cannot uh find anything else in there too too, uh
Speaker 1: divide it by and get a whole integer. So, for example,
Speaker 1: the number five, you can't divide five by anything other
Speaker 1: than itself and get a whole integer. Except instead of
Speaker 1: using the number five, you would use a digit that
Speaker 1: are a number that is maybe you know, hundreds of
Speaker 1: digits long, but still prime number. Then you take another
Speaker 1: incredibly long prime number, a really really really really really
Speaker 1: long one, and you multiply both of those together, and
Speaker 1: then you have a product. That product ends up being
Speaker 1: the crux of your encryption. If someone tries to break
Speaker 1: your encryption, they can look at that product and they
Speaker 1: see what the product is, but they don't know which
Speaker 1: two numbers you use to multiply together to get that product,
Speaker 1: so they have to start trying to figure it out,
Speaker 1: and they do this by going through all the known
Speaker 1: prime numbers to see if you can divide the product
Speaker 1: by that prime number, and if so, is the other
Speaker 1: factor also a prime number. This can take a really
Speaker 1: long time. For a classical computer, it takes ages because
Speaker 1: it has to go through every single possible solution before
Speaker 1: it can try and, you know, find the right one,
Speaker 1: or at least every possible one leading up to the
Speaker 1: right one. With a quantum computer, because you can have
Speaker 1: cubits in superposition, they can process this information much more quickly.
Speaker 1: It's like they're doing numerous processes, numerous solutions simultaneously, because
Speaker 1: all the cubits can be both zero and one at
Speaker 1: the same time, So as long as you have enough
Speaker 1: cubits enough to be able to process the request you
Speaker 1: have in theory, you could do that kind of computational
Speaker 1: problem in an instant as opposed to perhaps years or
Speaker 1: decades or centuries, depending upon the complexity of the computational problem. Now, again,
Speaker 1: that's only for a specific set of computational problems. For
Speaker 1: that set, quantum computers will be amazing. But if you
Speaker 1: wanted to play a game on a quantum computer, it
Speaker 1: wouldn't necessarily run any better. In fact, we probably run
Speaker 1: worse than on a classical computer because you have to
Speaker 1: have enough cubits two at least equal what the classical
Speaker 1: computer could do. Cubits also are very tricky keeping bits
Speaker 1: in superposition. Keeping anything in a quantum state is tricky
Speaker 1: because the slightest thing can cause it to decohere too
Speaker 1: for the whole system to sort of fall apart and
Speaker 1: then just become a classical computer. And since most quantum
Speaker 1: computers have a relatively small number of cubits, they end
Speaker 1: up becoming very dumb computers. If you were to disturb
Speaker 1: your typical quantum computer and reverted to classical computer status.
Speaker 1: It would probably be less powerful than your average smart watch.
Speaker 1: But there's gonna be a lot of discussion here at
Speaker 1: IBM think about quantum computing and how it will start
Speaker 1: to become a practical thing and not just something that's
Speaker 1: been worked on in laboratories and research facilities. There are
Speaker 1: a lot of interesting speakers here as well. Obviously IBM
Speaker 1: has a lot of their experts here on things like
Speaker 1: cognitive machine learning, artificial intelligence, virtual reality, augmented reality. That
Speaker 1: both of those subjects are also represented here at the conference. Uh.
Speaker 1: They're going to be doing breakout sessions all week long,
Speaker 1: and I hope to talk to some of them this week.
Speaker 1: But they're also representatives from other companies that are taking
Speaker 1: on sessions, uh, Folks from like American Airlines or Nvidia,
Speaker 1: or even companies like ticket Master. There's also some celebrities here, uh,
Speaker 1: some people who are famous for their commentary on science
Speaker 1: and their contributions to science, like astrophysicist Neil deGrasse Tyson
Speaker 1: is here, futurist Michio Kaku is here. So they will
Speaker 1: be giving keynote presentations as well on various subject matters.
Speaker 1: I can't wait to hear some of those. I probably
Speaker 1: won't be able to talk to them because they're booked
Speaker 1: pretty solid, but I do hope to talk to at
Speaker 1: least some of the experts in these various fields and
Speaker 1: get their insight everything from what do they think is
Speaker 1: cool about their area of study, what are some of
Speaker 1: the most recent developments that have them excited, How did
Speaker 1: they get into their field in the first place, And
Speaker 1: if someone else is interested in that field, what should
Speaker 1: they do, how should they pursue it. I want to
Speaker 1: ask all those sorts of questions. So I hope to
Speaker 1: present to you guys several bonus episodes this week, all
Speaker 1: about this conference and the people I talked to and
Speaker 1: the things I encounter and learn, and hopefully that will
Speaker 1: all be useful to you guys and you'll enjoy it.
Speaker 1: And uh, the regular episodes will also publish, so we're
Speaker 1: going to have a whole bunch of tech stuffs in
Speaker 1: a short amount of time. But I also hope to
Speaker 1: do more of these in the future, where I go
Speaker 1: to certain events and create special episodes just for those experiences,
Speaker 1: so I can bring you some more current events and
Speaker 1: and cool news on top of the normal tech Stuff episodes,
Speaker 1: so the show is not changing. We haven't completely revamped it.
Speaker 1: This is just sort of a mini series of specialness,
Speaker 1: so you're just getting more of what you love. I
Speaker 1: hope now I'm gonna focus really heavily on IBM Think
Speaker 1: this week, but I'll be back also in the office
Speaker 1: next week doing my regular tech stuff stick, which means
Speaker 1: I need to hear from you guys. If you have
Speaker 1: suggestions for topics that you really want to hear more about,
Speaker 1: send me a message. You can get in touch with
Speaker 1: me via email. The address is tech Stuff at how
Speaker 1: stuff works dot com, or you can drop me a
Speaker 1: line on Facebook or Twitter. The handle for both of
Speaker 1: those is text stuff hs W. We've got an Instagram account.
Speaker 1: You should be following that because all sorts of cool
Speaker 1: behind the scenes information gets posted to that all the time.
Speaker 1: And if you want to see me record live, although
Speaker 1: not this week, but on normal weeks, you can go
Speaker 1: to twitch dot tv slash tech Stuff. I record on
Speaker 1: Wednesdays and Fridays. I stream my recording sessions live so
Speaker 1: you can watch as I record an episode of tech
Speaker 1: Stuff and watch as I make silly mistakes and have
Speaker 1: to stop myself and go back and fix it, and
Speaker 1: you can even chat with me. There's a chat room
Speaker 1: in there, and I welcome all people who want to
Speaker 1: chat and tell me about their favorite episodes of tech
Speaker 1: stuff or things they would like me to cover. I
Speaker 1: welcome that. I hope to see you in there, and
Speaker 1: I will talk to you again really soon for more
Speaker 1: on this and bathos of other topics because it how
Speaker 1: staff works dot com