Speaker 1: Get in touch with technology with tech Stuff from how
Speaker 1: stuff works dot com. Hey there, welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer at
Speaker 1: how Stuff Works and I love all things tech. And yeah,
Speaker 1: I am at the IBM Think two thousand eighteen conference,
Speaker 1: which is why this sounds a little different than normal.
Speaker 1: I am in a hotel room over at the Excalibur Casino,
Speaker 1: and I wanted to talk a little bit about what
Speaker 1: I saw and some of the talks that I went to,
Speaker 1: and I learned a lot of interesting things. Now, one
Speaker 1: thing to say is that the THINK conference it's all
Speaker 1: about IBM and ibm S partners and customers. And unlike
Speaker 1: a lot of companies that we deal with on a
Speaker 1: day to day basis, IBM doesn't really have consumer facing businesses.
Speaker 1: In other words, it's not like you go to the
Speaker 1: store and you go buy IBM stuff. IBM mostly makes
Speaker 1: things for other companies and as such, we don't necessarily
Speaker 1: have to uh, we don't necessarily encounter it directly. We
Speaker 1: encounter IBMS products because they are inside other things that
Speaker 1: we are using. So uh, it's interesting to go to
Speaker 1: these events and to hear these talks, because a lot
Speaker 1: of it is stuff that is very much relevant for
Speaker 1: business leaders or for I T. Professionals, or for UH
Speaker 1: infrastructure engineers that kind of thing, but less so for
Speaker 1: the general public unless you step back a little bit.
Speaker 1: Even so, there were some really interesting talks that talked
Speaker 1: about UH where the future is headed as far as
Speaker 1: very big, broad technologies, and I thought that that would
Speaker 1: be the best way to kind of tackle this, to
Speaker 1: talk about these sort of trends that have been identified
Speaker 1: and these predictions that have been made about these kinds
Speaker 1: of tech, because those are the sort of things that
Speaker 1: are going to affect us moving forward, us being, you know,
Speaker 1: the average person as opposed to people who are running
Speaker 1: a tech company. One of the things that they talked
Speaker 1: about UH both at the the keynote speech that was
Speaker 1: technically the very first big keynote speech that was a
Speaker 1: Jenny Romti. Jenny Rometti is the CEO of IBM. She
Speaker 1: got up and spoke very directly to IBMS partners and customers.
Speaker 1: She talked about how there are different laws that we
Speaker 1: have created more like observations really that UM that have
Speaker 1: described the way technology has developed over the years. Now.
Speaker 1: The most famous one is one I've talked about numerous
Speaker 1: times on this show. That would be Moore's law, Moore's law,
Speaker 1: which was proposed by Gordon Moore. Of course he didn't
Speaker 1: call it Moore's law. He just made an observe. Sian
Speaker 1: was about how every eighteen months or so year and
Speaker 1: a half to two years, the number of discrete components
Speaker 1: meaning transistors at that time on a microchip we're doubling.
Speaker 1: And this observation wasn't about necessarily our technological capabilities, like
Speaker 1: the ability to make things that small. It was more
Speaker 1: about the fact that economics demanded that this was the case,
Speaker 1: that there was enough of a demand two in to
Speaker 1: give an incentive to manufacturing facilities that made these microchips
Speaker 1: to try and make ever smaller components to make more
Speaker 1: powerful processors. So, in other words, it wasn't so much
Speaker 1: that we had this these egghead scientists locked in the
Speaker 1: laboratory coming up with new ways to make transistors smaller.
Speaker 1: It was more like we had money in wheelbarrows out side,
Speaker 1: and we can only get that money if we made
Speaker 1: smaller transistors. And so it was really an economic driven law.
Speaker 1: But the effect that we have on us, it doesn't
Speaker 1: really matter. The economic part we can kind of ignore.
Speaker 1: What we look at is the fact that our processing
Speaker 1: power effectively doubles every eighteen months or so. So every
Speaker 1: year and a half to two years, the machines were
Speaker 1: using are twice as powerful as the ones that were
Speaker 1: two years ago. Uh, And that's kind of cool. It
Speaker 1: means that we keep getting these incredibly sophisticated machines on
Speaker 1: a regular basis, and a lot of the technology sectors
Speaker 1: businesses depend upon the continuation of Moore's law. Later on,
Speaker 1: I was at a talk with Dr Michio Kaku, who
Speaker 1: is a famous physicist and futurist. He talked a little
Speaker 1: bit about the end of the era of Moore's law.
Speaker 1: He did not give a specific prediction as to when
Speaker 1: it would end, but he did say that based just
Speaker 1: purely on physics alone, it will end. What he meant
Speaker 1: by that is, More's law depends on us shrinking these
Speaker 1: components down more and more and more. Once you get
Speaker 1: to the point where the quantum world comes into play,
Speaker 1: this gets really tricky and I've talked about this before,
Speaker 1: to the fact that if you were to create logic
Speaker 1: gates that are so thin that an electron could potentially
Speaker 1: exist on the other side of a logic gate, then
Speaker 1: sometimes an electron is going to be on the other
Speaker 1: side of the uh the electron gates sort of like
Speaker 1: it had tunneled through, except it had not physically tunneled
Speaker 1: through the wall. It's just that it had the probability
Speaker 1: of potentially being on the other side of that wall.
Speaker 1: And as long as there's a probability, it means that
Speaker 1: sometimes that does happen. Even though that you know, in
Speaker 1: the classical world we would say, well, there's a barrier there.
Speaker 1: You can't just go through. A barrier didn't go through.
Speaker 1: It just appeared on the other side because there was
Speaker 1: a chance it could. And if there's a chance, then
Speaker 1: sometimes that does happen. Well, even beyond that, even if
Speaker 1: you say, well, we'll keep figuring out ways to counteract
Speaker 1: this quantum effect so that we can keep having microprocessors
Speaker 1: that are accurate even with quantum tunneling being an issue,
Speaker 1: you eventually get down to the point where you're at
Speaker 1: the atomic scale, meaning the components you're creating are made
Speaker 1: out of atoms themselves. At this stage, you really it
Speaker 1: would be really difficult to counteract those quantum effects and
Speaker 1: you would have to abandon this particular approach to computer
Speaker 1: science and computer architecture, or else it would just collapse
Speaker 1: in on itself. So Moore's law, while it was incredibly important,
Speaker 1: and it continues to be incredibly important right now. Um Ever,
Speaker 1: since you know the transistor was invented, it it it
Speaker 1: only represents the first kind of wave of laws. The
Speaker 1: next law that they talked about was one they called
Speaker 1: Metcalf's law. Uh. Metcalf's law is actually pretty commonly referred
Speaker 1: to law, just not necessarily among you know, regular folks
Speaker 1: like me and you. But Metcalf's law is about the
Speaker 1: value of a network. So how do you measure how
Speaker 1: valuable a network is? Like if you look at a
Speaker 1: network of devices, and then you look at a different
Speaker 1: network of devices, how could you say which one is
Speaker 1: is quote unquote worth more. Metca Cat's law gives you
Speaker 1: that that measurement. It states that the value of a network,
Speaker 1: of a telecommunications network is proportional to the square of
Speaker 1: the number of connected nodes in the system. So, however
Speaker 1: many nodes are there, and the node can be any
Speaker 1: connected device. They could be a computer, it could be
Speaker 1: a smartphone, could be a table, it could be a
Speaker 1: game console. Those nodes collectively end up determining the value
Speaker 1: of the telecommunications network. When you square the number of
Speaker 1: those nodes. It's those interconnections that make the network of
Speaker 1: a valuable. This is incredibly important again in the world
Speaker 1: of business, less so probably for for me and you.
Speaker 1: The third one, the third law that they were proposing,
Speaker 1: would be what they were cheekily referring to as Watson's law. Watson,
Speaker 1: of course, is not just and artificially intelligent platform for
Speaker 1: IBM and for IBMS customers and partners. Watson also refers
Speaker 1: to the founder of IBM what was his name, his
Speaker 1: last name, But Watson's law would be about how the
Speaker 1: amount of data in a system can be leveraged to
Speaker 1: get the amount of knowledge out of that data. This
Speaker 1: sort of as as data grows exponentially, our ability to
Speaker 1: leverage knowledge from that data grows exponentially. So what the
Speaker 1: heck does that mean? Well, think of data as just
Speaker 1: points of information that are not necessarily connected to one another.
Speaker 1: They're not structured necessarily. This would be as if I
Speaker 1: recorded a podcast and I just started to say random
Speaker 1: words into the microphone, and I did that for forty
Speaker 1: five minutes to an hour. And Okay, smart Alex, you
Speaker 1: might think that's how I do it now, but you're
Speaker 1: you're just mean, you're meaning heads. That's not how I
Speaker 1: do it. I actually think this stuff out and I
Speaker 1: structure my data so that I create a foundation and
Speaker 1: then I build upon it. That's a very easy way
Speaker 1: to get knowledge, right. You have the structured format, you
Speaker 1: can digest it, you can synthesize it. You can then
Speaker 1: use that yourself. But if the data is unstru shirt,
Speaker 1: and the data is about a lot of seemingly unconnected things,
Speaker 1: and it's spread across multiple types of files, Let's say
Speaker 1: that you've got an enormous folder, uh, and that folder
Speaker 1: contains files that are video files, they are documents, their presentations,
Speaker 1: their spreadsheets, they're all these different things that that on
Speaker 1: casual glance don't have any connectivity to them. How can
Speaker 1: you make that useful so that you can actually leverage
Speaker 1: that data and do stuff with it? And that's kind
Speaker 1: of what IBM was focusing on. And that's really where
Speaker 1: they were talking about Watson quite a lot. It wasn't
Speaker 1: a lot of people think of Watson as this, uh,
Speaker 1: this the supercomputer that played on Jeopardy, which is not accurate.
Speaker 1: Watson is not a supercomputer. The machine that ran Watson
Speaker 1: was just a machine. It was not the entity itself. Uh.
Speaker 1: If you want to do to get a little metaphysical
Speaker 1: with this, you could actually think about a human being
Speaker 1: and you ask, well, what is the human Is the
Speaker 1: human being the body, the physical form, or is it
Speaker 1: the mind? The person, the personality, the emotions, the memories,
Speaker 1: the things that are that inhabit the body and also
Speaker 1: that control the body. Is that the person? And you
Speaker 1: might argue, well, it's actually the collective. It's the body
Speaker 1: and the mind, and I think that's a valid argument.
Speaker 1: You could also argue that Watson ultimately is a platform
Speaker 1: and the physical machine that runs that platform. I probably
Speaker 1: wouldn't argue with you too much there either, except I'd
Speaker 1: say that the platform is more important than anything else
Speaker 1: in this in this particular case, And by platform I
Speaker 1: really just means set of rules, set of algorithms that
Speaker 1: Watson uses in order to process information, to look for meaning,
Speaker 1: to look for results. So let's take that Jeopardy example, Uh, Jeopardy.
Speaker 1: In Jeopardy, Wat's and played against two former champions, one
Speaker 1: of whom now Records podcast for How Stuff Works. So
Speaker 1: that's kind of awesome. And Watson was playing by looking
Speaker 1: at a clue. We're looking quote unquote. It was the
Speaker 1: clues being fed to Watson and then going through its
Speaker 1: massive amount of data and trying to use that to
Speaker 1: figure out what the answer is. And it wasn't just
Speaker 1: looking at a list of trivia or facts. It's not
Speaker 1: like it's looking at an enormous table and every cell
Speaker 1: in that table is filled with a different fact, like
Speaker 1: George Washington was the first President of the United States. Instead,
Speaker 1: it's looking at a massive library of information and pulling
Speaker 1: bits and pieces of information together to formulate an idea
Speaker 1: of what the answer is. And if that formulation reaches
Speaker 1: a certain threshold of confidence, Watson would then ring in
Speaker 1: and present that answer. So it's it's not that it's
Speaker 1: looking at, uh you know, a very long trivia book.
Speaker 1: It's looking at all this information and drawing conclusions from
Speaker 1: it the way similar to how a human being would
Speaker 1: not not completely analogous, but similar and uh so, using Watson,
Speaker 1: you could leverage your unstructured data. You put Watson into
Speaker 1: work at this, and Watson would start to look for
Speaker 1: meaningful connections between data points and pulling relevant information about
Speaker 1: any given query. So then Watson becomes an agent that
Speaker 1: you could interact with. And this agent's job is kind
Speaker 1: of like a reference librarian. It's to go to the
Speaker 1: massive amount of information that's at its disposal and return
Speaker 1: to you the relevant points of information. This is not
Speaker 1: that different from the way people were thinking about web
Speaker 1: three point oh when that was a big discussion. H
Speaker 1: you may remember that like people to talk about how
Speaker 1: right now? If you use a search engine, typically the
Speaker 1: way it works as you type something in the search
Speaker 1: engine and it pulls up a list of websites that
Speaker 1: may or may not have what you're looking for on
Speaker 1: those websites. So if you might you might be looking
Speaker 1: for a let's say it's a um A history of
Speaker 1: the Crusades, and you type that into the search engine
Speaker 1: and it pulls for you a bunch of different sites
Speaker 1: written by different people. Some of them might be very
Speaker 1: easy to read and understand. Some of them might be
Speaker 1: less easy to read, but they might be more accurate
Speaker 1: and more uh unbiased. With the information you don't necessarily
Speaker 1: know at the top of it. You have to go
Speaker 1: through and read all that yourself. But the web three
Speaker 1: point of search engines. This was something that Will from
Speaker 1: Alpha was trying to be would pull the relevant information
Speaker 1: not websites, but the relevant information from those websites and
Speaker 1: present it to you. And that way you could look
Speaker 1: over the important bits of information, you skip over everything
Speaker 1: else you're given the correct context. In theory, you could
Speaker 1: even have an agent like this that could learn about
Speaker 1: you and your learning styles and thus present the information
Speaker 1: to you in a way that is most helpful to you.
Speaker 1: So it's a very big difference between the way we
Speaker 1: do searches now and the way that this proposed method
Speaker 1: would work. And that's kind of what Watson is doing.
Speaker 1: So you've got this this user facing aspect of Watson.
Speaker 1: It's kind of like a chat bot, and you can
Speaker 1: send that chat bot requests and then the chat bot
Speaker 1: will try and pull the information for you, or you
Speaker 1: can use it to generate reports. Let's say that you
Speaker 1: are a business owner and you want to look at
Speaker 1: some information that's gonna pull things from presentations, predictions, results.
Speaker 1: Maybe you've got like a end of the quarter report.
Speaker 1: Maybe you want to take a look at formation from
Speaker 1: reports from your supply chain. All this kind of complicated
Speaker 1: stuff and Watson could go out, curate and present this
Speaker 1: information in a way that has meaning to you, that
Speaker 1: where you can understand what's going on and you can
Speaker 1: draw conclusions. Uh. This actually was a pretty interesting concept
Speaker 1: to me. I mean, I've seen some implementations of Watson
Speaker 1: that do this, and they do it in such a simple,
Speaker 1: seemingly simple way that's deceptive. You start to forget that
Speaker 1: there is a very powerful computer algorithm that is controlling
Speaker 1: all of this because the implementation itself might be pretty straightforward.
Speaker 1: So for an example, I went to the Weather Company
Speaker 1: last year in TV and while I was there, I
Speaker 1: had a chance to talk to a team that was
Speaker 1: using Watson in a lot of different implementations, and uh,
Speaker 1: you know, they were using it as is the basis
Speaker 1: of a customer service platform or to respond to requests.
Speaker 1: And when you first look at that, it looks deceptively simple.
Speaker 1: You're asking, well, what's the weather going to be like?
Speaker 1: And you get results, Uh that that doesn't seem like
Speaker 1: it's that hard. You would figure that, oh, well, they're
Speaker 1: just gonna pull whatever the record is for my location
Speaker 1: for tomorrow and present it to me. But a lot
Speaker 1: more could be going on behind the scenes, and I
Speaker 1: think that's part of the problem that IBM has been
Speaker 1: dealing with and kind of one of the reasons why
Speaker 1: they've made such a big deal of it at this conference.
Speaker 1: It's because the perception of what Watson is maybe a
Speaker 1: little too narrow, a little too uh uh focused on
Speaker 1: little aspects of what Watson does and ignores the big picture.
Speaker 1: So they've they've definitely doubled down on that. I went
Speaker 1: to a talk called Journey to AI that was really
Speaker 1: all about this, and they talked all about the the
Speaker 1: different variations of artificial intelligence, and uh one of the
Speaker 1: things they mentioned was the very different views of what
Speaker 1: AI is. For example, you've got simple AI. Simple AI
Speaker 1: would include some of the stuff I talked about in
Speaker 1: a previous episode about the little aspects of intelligence that
Speaker 1: are very very narrow, just to slice the pie of intelligence,
Speaker 1: but they do represent what intelligence is in in just
Speaker 1: a very specific application. So image recognition is an example
Speaker 1: of that, or voice recognition or natural language processing even
Speaker 1: as part of that. These are all aspects of intelligence.
Speaker 1: You would not call a machine that lacks one of
Speaker 1: these things truly intelligent, but you also wouldn't call a
Speaker 1: machine that only has one of these things truly intelligent.
Speaker 1: So if I have a smartphone and the smartphone is
Speaker 1: able to recognize uh images, so i'm I'm I point
Speaker 1: my smartphone at something and it even labels what that
Speaker 1: thing is. Maybe it says, oh, well, that's a specific
Speaker 1: model and make of car, or maybe it says that
Speaker 1: building is a historic landmark, or this park is going
Speaker 1: to have a concert uh at at it the next day,
Speaker 1: or something along those lines. That's cool. That image recognition
Speaker 1: is really cool, but I wouldn't call my smartphone intelligent. Similarly,
Speaker 1: if my smartphone happens to have one of those digital
Speaker 1: assistance on it, and it does, I've got an Android phone,
Speaker 1: so I've got the Google Assistant on there. Um, I
Speaker 1: can talk to that and it can retrieve information for me.
Speaker 1: It can do tasks for me. I can use it
Speaker 1: to make calls, I can use it to send text messages,
Speaker 1: or I can use it to search for information on
Speaker 1: my phone or on the internet. I still wouldn't call
Speaker 1: my phone intelligent. It has an aspect of intelligence. Similarly,
Speaker 1: if I had a supercomputer that could listen to voice commands,
Speaker 1: respond in natural language, and do these other things, but
Speaker 1: it couldn't do any image recognition. I would feel I
Speaker 1: would I would notice that lack, and I wouldn't call
Speaker 1: that intelligent. On the other side of the scale, you
Speaker 1: have general AI, where you know, the classic image of
Speaker 1: this is you've got a big machine. They can do
Speaker 1: uh that can do general thinking, like thinking that's analogous
Speaker 1: to human thinking. It can process information, it can draw conclusions,
Speaker 1: that can synthesize data. It can um innovate. It may
Speaker 1: even be self aware, although the weather or not self
Speaker 1: awareness is directly tied to intelligence is a matter of
Speaker 1: philosophical debate. Talking about general AI, I mean, that's that's
Speaker 1: a hard, hard goal to hit. We honestly don't know
Speaker 1: what it will take to get there. It may be
Speaker 1: that we are thirty years away from having a true
Speaker 1: general AI, It may be much longer than that, it
Speaker 1: maybe a century away, or it may even be impossible
Speaker 1: for us to do based upon our technological abilities. Right now,
Speaker 1: most technologists think that it is attainable, but they don't
Speaker 1: know exactly what it's going to take to get there.
Speaker 1: So there's some argument about the timeline, But there are
Speaker 1: a lot of interesting things that can happen between those
Speaker 1: simple versions of AI, and that that crazy general AI
Speaker 1: that that you know, science fiction writers write about and
Speaker 1: warn us about. And that's where this this ability to
Speaker 1: deal with unstructured data comes in and h designing AI
Speaker 1: is part of that problem. But as they mentioned in
Speaker 1: multiple presentations here at IBM, it's not just building the
Speaker 1: artificial intelligence to do this that's a challenge. It's also
Speaker 1: incorporating that artificial intelligence into existing work practices because, as
Speaker 1: most businesses have existed for a while now, it's not
Speaker 1: like you can just slot AI n necessarily. It's not
Speaker 1: like a module you plug in and everything works properly.
Speaker 1: You might have to reevaluate and redesign work processes in
Speaker 1: order to make this happen. And again, this gets a
Speaker 1: little little dry and technical if you're not really into
Speaker 1: the business side of things. But when you start thinking
Speaker 1: about you realize, yeah, it's not enough to just build
Speaker 1: a tool. You have to figure out how's the best
Speaker 1: way to use that tool with respect to the things
Speaker 1: you're already trying to do that. They started talking about
Speaker 1: impotence match. The engineers were chatting, chatting all about impotence
Speaker 1: match between man and machine to get machines to process
Speaker 1: human language and commands and to return information that would
Speaker 1: be useful to humans, and to eventually get rid of
Speaker 1: that boundary between man and machines so that decisions can
Speaker 1: be made together and implemented together. So this gets into
Speaker 1: that concept of augmented intelligence, not that we are trying
Speaker 1: to create a supercomputer that is incredibly intelligent, and we
Speaker 1: will then reference the supercomputer as if it were an
Speaker 1: oracle or a deity, instead talking about creating machines that
Speaker 1: would work right alongside people, and the machines could help
Speaker 1: fill in the gaps that would be there because of
Speaker 1: the human failings that are in all of us, and
Speaker 1: humans could provide all the bits that machines are not
Speaker 1: good at, and together we could be better. And that
Speaker 1: we have to get to a point where we have
Speaker 1: to trust the machines as a an assistant, and the
Speaker 1: machines have to quote unquote, trust us as teachers. By
Speaker 1: trust us, they don't necessarily mean that the machines are
Speaker 1: going to be harboring doubts, but rather that humans are
Speaker 1: the ones designing these machines, and we have to make
Speaker 1: certain that we do so in a way that is responsible,
Speaker 1: that is ethical, that is inclusive. Otherwise we end up
Speaker 1: with bad machines. And it's not that the machines themselves
Speaker 1: were inherently wicked, but rather they were poorly designed. I've
Speaker 1: got more to say about the Journey to AI presentation
Speaker 1: at IBM THINK, but before I go into it, let's
Speaker 1: take a quick break to thank our sponsor. The folks
Speaker 1: over at IBM are arguing that every single industry across
Speaker 1: the world is going to be affected by this sort
Speaker 1: of transformation of of data and knowledge. They started referencing
Speaker 1: things like retail optimization, or the oil industry, or automotive
Speaker 1: industries shipping. All of these things they said, we're going
Speaker 1: to transform dramatically over the next few years due to
Speaker 1: this kind of technology. Uh. And they talked about how
Speaker 1: the one field you can look at right now that
Speaker 1: is undergoing such a transformation is healthcare. All healthcare is
Speaker 1: is transforming because we are seeing not just advanced tools
Speaker 1: come into hospitals and doctors offices, but also these programs
Speaker 1: like Watson where a doctor can actually turn to Watson
Speaker 1: as a colleague, almost like someone up here who can
Speaker 1: provide more information a second opinion, if you will. In fact,
Speaker 1: IBM brought up some representatives from the American Cancer Society
Speaker 1: and some very prestigious cancer research hospitals to talk about
Speaker 1: this and about how cancer is a really really difficult problem.
Speaker 1: It is, uh, it is a complicated disease. Really, when
Speaker 1: you think about cancer is a family of diseases. It's
Speaker 1: not just a single illness, but rather a whole, a
Speaker 1: whole suite of illnesses. There are hundreds of different types
Speaker 1: of cancer. Now, to make it more complicated, there are
Speaker 1: different methods for diagnosing and treating all these different types
Speaker 1: of cancer, and that obviously means that you have to
Speaker 1: be very careful when you're an oncologist, a cancer specialist
Speaker 1: to correctly identify, to diagnose, and to treat specific types
Speaker 1: of cancer, because a treatment for one type may not
Speaker 1: be effective for a different type, and not every place
Speaker 1: in the world has access to incredibly gifted, educated oncologists.
Speaker 1: If you happen to be fortunate and a lot enough
Speaker 1: to live in a major city in a well developed nation,
Speaker 1: then you may live close to a teaching hospital, in
Speaker 1: which case you have the access to incredible specialists who
Speaker 1: have dedicated their lives to learning and fighting cancer. But
Speaker 1: if you live in a small town and you don't
Speaker 1: have that access, then you your your options are severely limited. Well,
Speaker 1: IBM and Watson. One of the first problems they were
Speaker 1: looking at tackling outside of you know, once developing the platform,
Speaker 1: was using Watson to help doctors treat cancer. And the
Speaker 1: way Watson works, the way it's effective, is that you
Speaker 1: have to feed it information. Without the data, Watson is useless.
Speaker 1: Watson is good at analyzing data, curating data, and producing results,
Speaker 1: but in order to do that, you have to give
Speaker 1: it data. So what the IBM did was they reached
Speaker 1: out to the American Cancer Society and they talked with
Speaker 1: them about feeding Watson data about cancer. American Cancer Society
Speaker 1: had millions of data sets and clinical records that they
Speaker 1: used to help train Watson to understand how the diagnosis
Speaker 1: and treatment processes for different types of cancer actually went.
Speaker 1: So this was like Watson getting a crash course in
Speaker 1: oncology and from that information which is constantly being refreshed
Speaker 1: with new research, with new experiments, with new treatments, that
Speaker 1: also can then go to Watson. Watson is able to
Speaker 1: look at a huge set of data points and look
Speaker 1: at the effectiveness overall of any given diagnosis method or treatment. So,
Speaker 1: in other words, you might have conducted a series of
Speaker 1: experiments and determined that one particular approach is the most effective,
Speaker 1: and that's why you that's your go to approach for
Speaker 1: looking at that type of cancer. Watson, however, can look
Speaker 1: across the higher set of data points, not just from
Speaker 1: your experiments and your work and your research, but everyone
Speaker 1: else is that has been part of the American Cancer
Speaker 1: Society's work, and then Watson can say, you know, yeah,
Speaker 1: that that method, out of all the ones you've tried,
Speaker 1: has worked best for you. But there's this other methodology
Speaker 1: that is even more effective that you have not yet tried,
Speaker 1: that you didn't even know about. But because I have
Speaker 1: access to all the information, which is far far greater
Speaker 1: than what any human can navigate, I can tell you that,
Speaker 1: based upon the success rate of all those cases, this
Speaker 1: is something you should try. And thus Watson becomes that
Speaker 1: cancer specialist who can provide a second opinion. Uh, this
Speaker 1: is a very powerful tool, something that can legitimately save lives,
Speaker 1: and it is of a real consequence to those of
Speaker 1: us in the audience who are not just trying to
Speaker 1: create a business or I shouldn't say just but are
Speaker 1: trying to create a business or trying to figure out
Speaker 1: how to uh streamline our our back end processes as
Speaker 1: we try to do whatever it is we do. This
Speaker 1: is life and death for millions of people around the world. Uh,
Speaker 1: it's a really interesting case study too. I mean that
Speaker 1: so far Watson is being used in more than two
Speaker 1: hundred hospitals across the world. More than ten thousand patients
Speaker 1: are able to take advantage of this using Watson to
Speaker 1: help make decisions. Really, it's the physicians who are using
Speaker 1: Watson to kind of guide themselves and get that second
Speaker 1: opinion which may or may not confirm what the original
Speaker 1: physician had concluded, help refine approaches, help give options to patients,
Speaker 1: which obviously is also really important. And when you consider
Speaker 1: that this year alone, in one point seven million Americans
Speaker 1: will be diagnosed with cancer, you realize this is a
Speaker 1: very big deal. And of course that's just the United States. Obviously,
Speaker 1: global numbers will be much higher. And again, if you
Speaker 1: happen to live in a country like the United States
Speaker 1: and you're near a learning hospital, you then might have
Speaker 1: access to people who are the leading practitioners, the leading thinkers,
Speaker 1: leading researchers in cancer. But if you live in a
Speaker 1: developing nation where you have a much worse ratio of
Speaker 1: doctor to patients, then you would really want to have
Speaker 1: access to this deep level of expertise. That's the whole concept.
Speaker 1: So uh they all the folks up on stage, the
Speaker 1: representatives from Memorial Sloan Kettering, which is a cancer treatment center,
Speaker 1: and also of the American Cancer Society. We're citing some
Speaker 1: really interesting uh um statistics. So in the United States,
Speaker 1: where we have a lot of oncologists, a lot of
Speaker 1: cancer specialists, on average, every oncologist has about one patients,
Speaker 1: which you know, that's that's a lot of patients. But
Speaker 1: if you think about you realize, well, that might be
Speaker 1: manageable for a single oncologist. But in other parts of
Speaker 1: the world, it's more like the number. You look at
Speaker 1: the number of oncologists versus the number of people who
Speaker 1: are dealing with cancer, and it becomes ten thousand patients
Speaker 1: to one oncologist. At that scale, it is impossible, no
Speaker 1: matter how gifted and intelligent and educated you are, to
Speaker 1: be able to handle that enormous amount of of work
Speaker 1: without help. And so again that was where they were
Speaker 1: citing use of Watson as a way to help offload
Speaker 1: some of this this very difficult work that the oncologists
Speaker 1: do and get guidance from expertise from around the world.
Speaker 1: And again, this is not Watson coming up with new treatments.
Speaker 1: This is an artificially intelligent platform for a very narrow
Speaker 1: definition of AI looking at an enormous data set that
Speaker 1: was generated by humans, by human beings. So we're not
Speaker 1: saying that there's a computer doctor out there that's better
Speaker 1: than human doctors, that it's smarter than we are. Moreover,
Speaker 1: it's more like saying we have the world's best librarian
Speaker 1: that is looking at the mass collected knowledge base on
Speaker 1: a very specific subject and returning the results that are
Speaker 1: relevant to any given query to help with human decisions.
Speaker 1: So that's where that augmenting intelligence comes in. It's not
Speaker 1: that you've got a robo doctor. It's that you've got
Speaker 1: a robo reference librarian who is able to reference all
Speaker 1: the human doctors and see what has worked the best.
Speaker 1: That's a good way of looking at Watson in general
Speaker 1: when you want to understand what it does and what
Speaker 1: it could do in lots of different contexts. It's again
Speaker 1: something that could help with handling any large set of
Speaker 1: data points. It wouldn't have to be medical, although that's
Speaker 1: an easy way to understand how that could be an
Speaker 1: effective use. Another possible use of Watson would be for
Speaker 1: the purposes of augmented reality, where you are using something
Speaker 1: like a smartphone, let's say, to take images of whatever
Speaker 1: it is you're looking at, and you're asking Watson to
Speaker 1: give you guidance on how to deal with the situation.
Speaker 1: So imagine that you are an auto mechanic and you
Speaker 1: have a vehicle come in that is not not frequently
Speaker 1: found in your area, so you haven't had a lot
Speaker 1: of experience working on it. You you know, you have
Speaker 1: good working knowledge of automobiles in general, but you don't
Speaker 1: know the particulars of this specific make and model. And
Speaker 1: you lift up the hood and you're looking at the engine,
Speaker 1: and you're looking at different parts, and you see one
Speaker 1: particular part that you believe is the problem, so you
Speaker 1: take up photo of it, and then you have a
Speaker 1: Watson assistant that's working with you on an app that's
Speaker 1: specifically written for your line of work. So, in other words,
Speaker 1: Watson is really just looking at a data set that
Speaker 1: is relevant to auto mechanics. It's not like it's the
Speaker 1: world's it's not looking at all the information across the
Speaker 1: Internet or anything like that. This is a specific implementation
Speaker 1: of the platform. And then Watson references it's information, returns
Speaker 1: the results to you, and explains what that part is.
Speaker 1: What are some of the common problems, what is you know, basically,
Speaker 1: what was the problem that you have encountered specifically, how
Speaker 1: do you address it? Do you have repairs you can make?
Speaker 1: Do you need to replace the part? If you do
Speaker 1: need to replace the part, where would you get it?
Speaker 1: How long will it take to get there? Essentially all
Speaker 1: the amation you need as a mechanic in order to
Speaker 1: fix the problem and also to alert your customer. Hey,
Speaker 1: here's what's going on. Here's how much it's gonna cost.
Speaker 1: Here's how long it's gonna take. Um, And you can
Speaker 1: even answer why. You could find out where the delays
Speaker 1: are if it's gonna be something that's gonna take like, well,
Speaker 1: it's gonna take two weeks. Why, Well, because here's the
Speaker 1: obscure part that I need to order, and here's the
Speaker 1: really complicated supply chain of how it's going to have
Speaker 1: to get to me. And I can't speed that up
Speaker 1: because I have no control over it. If you're able
Speaker 1: to actually explain that to the customer, then you can,
Speaker 1: you know, maybe take some of the heat off. And
Speaker 1: you can also probably say, hey, next time, buy a
Speaker 1: car that's not so uh, you know, exotic. It's something
Speaker 1: that I can work on. No, no, don't victim blame.
Speaker 1: That's not cool, but you could at least explain the
Speaker 1: context of what's happening. And I found this really interesting.
Speaker 1: They also talked about how Watson could also work with
Speaker 1: companies that have much smaller data sets that you know,
Speaker 1: obviously you have different scales here. If you look at
Speaker 1: all the information on a consumer facing business where they're
Speaker 1: collecting information about the people who use the product, then
Speaker 1: the data sets could potentially be enormous. A good example
Speaker 1: of this would be Facebook, which of course is is
Speaker 1: going through a massive scandal right now due to a
Speaker 1: company that collected data and then tried to leverage it
Speaker 1: in a way that was unethical at best. So Facebook
Speaker 1: has more than a billion users, and people use Facebook
Speaker 1: a lot. People who are using Facebook a ton are
Speaker 1: sharing a lot of information about themselves, either directly or indirectly.
Speaker 1: So you have this massive amount of data that Facebook
Speaker 1: is collecting and sitting on top of and using a
Speaker 1: device like or a an API platform like Watson to
Speaker 1: go through all that data and pull meaningful information from
Speaker 1: it could create ate some really powerful strategies. You could
Speaker 1: figure out trends and be able to leverage them, and
Speaker 1: you could do them in ways that were maybe helpful
Speaker 1: or maybe exploitative, probably the second. But you would have
Speaker 1: a huge amount of data. That's really the point I'm
Speaker 1: getting at is because you've got an engaged user base
Speaker 1: that is enthusiastically handing information over, you would have an
Speaker 1: enormous data set. But you could also use a tool
Speaker 1: like Watson for internal processes like let's say that you
Speaker 1: are a company, and let's say that you're part of
Speaker 1: a shipping company. So you need to be able to
Speaker 1: keep track of all the suppliers, the destinations, the the
Speaker 1: way that you're actually moving product from point A to
Speaker 1: point B. It's a lot of moving parts, law logistics,
Speaker 1: but it's on the whole. If you look at all
Speaker 1: the data and you were to say, like let's fill
Speaker 1: up you know, two containers with raw information, it would
Speaker 1: be a fraction of the size of something like Facebook. Like, yeah,
Speaker 1: there are a lot of data points and it's complicated.
Speaker 1: It's too complicated for humans to navigate easily. But it's
Speaker 1: not like it's the huge amount of data that's generated
Speaker 1: on a daily basis from Facebook. Watson still, however, has
Speaker 1: the capability of learning even from smaller data sets. So again,
Speaker 1: this was IBM talking to their partners and their customers saying, Hey,
Speaker 1: I know that we're talking about using Watson for these
Speaker 1: really really big ideas and these really world changing applications
Speaker 1: that are relying upon millions and millions of records, but
Speaker 1: Watson could also work for you. That was kind of
Speaker 1: a message, uh, and you know that was a very
Speaker 1: compelling one. They were. They they brought up several people
Speaker 1: to talk about how this has been used. For example,
Speaker 1: they brought up the CEO of Orange Bank. Orange is
Speaker 1: a telecommunications company, and the telecommunications company to I did
Speaker 1: that they were going to create a financial institution as well,
Speaker 1: so an actual bank, and they the bank had decided
Speaker 1: that one of the things they wanted to do was
Speaker 1: create a an interface for their customers that would make
Speaker 1: it very easy to deal with routine sort of problems
Speaker 1: and questions and uh and provide information without the need
Speaker 1: to reference that customer up to a human customer service representative,
Speaker 1: which is a delicate thing to do. You want to
Speaker 1: make sure that you are serving your customers properly. You
Speaker 1: don't want to turn them off. You don't want them
Speaker 1: to log again. They see a chat bot and they say, oh, well,
Speaker 1: no one cares about me. They just put me in
Speaker 1: touch with a robot. Uh. But at the same time,
Speaker 1: you don't want to have to deal with uh, you know,
Speaker 1: customer service representatives answering the same mundane questions over and
Speaker 1: over again. That makes it hard to have an engaged
Speaker 1: and and happy workforce. So there's a delicate balance here.
Speaker 1: What Orange decided to do was create a virtual advisor.
Speaker 1: They named the virtual advisor Jingo d J I, N G,
Speaker 1: G O, and Jingo uses Watson as the the foundation
Speaker 1: for what it does. And as the CEO explained, it's
Speaker 1: the customer's first point of contact for the bank, and
Speaker 1: Jingo can respond to a lot of different common queries
Speaker 1: and they could be very general ones that are sort
Speaker 1: of bank wide kind of questions, or they could be
Speaker 1: very specific to the individual. And they said that Jingo
Speaker 1: is the most effective agent they've seen, and that Jingo
Speaker 1: also never has to take a break. Jingo can work
Speaker 1: seven and is never tired and can respond to most
Speaker 1: requests without the need to funnel customers to other agents.
Speaker 1: So this was an example of an industry that has
Speaker 1: a relatively small data set compared to something like Facebook,
Speaker 1: and a bank, even with a lot of customers, is
Speaker 1: going to be dealing with the same volume of information
Speaker 1: as a social media network would. What else can we
Speaker 1: expect when AI starts to insinuate its way into our
Speaker 1: daily lives. Well, I'll tell you about it in just
Speaker 1: a minute, but first let's take a quick break to
Speaker 1: thank our sponsor. IBM also chatted about how AI could
Speaker 1: help out in the field of human resources. That HR
Speaker 1: is another one of those those departments in most companies
Speaker 1: that has to field a lot of the same questions
Speaker 1: over and over, and it may be that there are
Speaker 1: lots of different policies that the HR representative has to
Speaker 1: go through and find the relevant information. And while the
Speaker 1: HR representative might have access to all that, he or
Speaker 1: she may not automatically know the answer, and so it
Speaker 1: takes time and effort to hunt down to and serve's
Speaker 1: that employees might have. For HR professionals, so IBM had
Speaker 1: also kind of I mentioned that Watson would be an
Speaker 1: ideal tool for that as well. So if you need
Speaker 1: to ask about specific forms or policies or uh compensation packages,
Speaker 1: all the sort of things that HR folks have to
Speaker 1: deal with, you could have an artificially intelligent platform do
Speaker 1: that on your behalf. Which was also kind of interesting.
Speaker 1: So there were several other folks that they brought up
Speaker 1: on stage to chat about, you know, their experiences implementing
Speaker 1: Watson in different ways. It was very much all about here,
Speaker 1: here's what this this API is really for and how
Speaker 1: you might use it, and not you know, trying to
Speaker 1: get away from Watson is the the computer program that
Speaker 1: one on Jeopardy or Watson was this quirky platform that
Speaker 1: could come up with dynamically created recipes based upon the
Speaker 1: ingredients who fed to it. The whole idea was to
Speaker 1: create something that would have multiple use cases on multiple scales,
Speaker 1: and I found it. I found it helpful to get
Speaker 1: a better grip on exactly what Watson is and is not.
Speaker 1: Um It was a fascinating discussion. We saw a lot
Speaker 1: of interesting people. We saw the CEO of Nvidio come
Speaker 1: out and talk about partnering with IBM to pair GPUs
Speaker 1: and CPUs together to create the most powerful machines that
Speaker 1: are able to process enormous amounts of information in a
Speaker 1: very short amount of time. They talked about how uh,
Speaker 1: this is the sort of of technology that's powering the
Speaker 1: next generation of machines like autonomous cars. They also even
Speaker 1: acknowledged the fact that this is still a young field
Speaker 1: and a knowledge the the tragic accident that happened in
Speaker 1: Arizona when a an autonomous suv that was that belonged
Speaker 1: to Uber struck and killed a pedestrian as she was
Speaker 1: walking her bicycle across the street. They took some time
Speaker 1: to actually talk about this and say, this is a
Speaker 1: horrible tragedy and nothing should distract us from the fact
Speaker 1: that you know this, this person passed away and her
Speaker 1: family is dealing with the the aftermath of that, and
Speaker 1: it's terrible, and it also forces us to acknowledge that
Speaker 1: these things were working on our life and death situations.
Speaker 1: They are not trivial, They're not something that are It's
Speaker 1: not just an engineering problem, it's not just a kind
Speaker 1: of a hypothetical situation. These are are technologies that could
Speaker 1: potentially save or end lives if the technology is implemented
Speaker 1: one way or another, so it behooves us to be
Speaker 1: extremely careful to figure out how to do it properly. Uh.
Speaker 1: The CEO of Nvideo also talked about just how complicated
Speaker 1: this whole process is for for vehicles and mentioned that,
Speaker 1: you know, some people might think that a car is
Speaker 1: just sort of processing one big stream of data and
Speaker 1: making decisions on how to proceed based on that, because
Speaker 1: that's kind of how humans do it, right, Like we
Speaker 1: perceive stuff and then we have to respond to it,
Speaker 1: We have to react to it. But machines do this
Speaker 1: in a different way. They're they're collecting different individual streams
Speaker 1: of data, and each of those streams needs to be
Speaker 1: analyzed and processed, and then the collective information needs to
Speaker 1: be analyzed and processed so that the right reaction can
Speaker 1: take place. So it's it's almost like you can think
Speaker 1: of each sensor as sending its information to a centralized location,
Speaker 1: and then all of those collective information streams from all
Speaker 1: of those sensors has to be synthesized and analyzed, and
Speaker 1: then the reaction has to take place. So it makes
Speaker 1: it sound way more complicated than you might originally imagine,
Speaker 1: I certainly felt that way. We got to watch a
Speaker 1: video of a an eight minute drive of an autonomous
Speaker 1: car down country roads in New Jersey, showing how it
Speaker 1: would navigate down the roads, even properly navigating when there
Speaker 1: were no road signs available, making certain that the car
Speaker 1: was behaving the way it was supposed to. And as
Speaker 1: they were pointing out, like even the in this scenario
Speaker 1: it was nice weather, it was during the daytime. Uh,
Speaker 1: even in that scenario, it's a complicated thing to make
Speaker 1: a machine do that properly. And then you start imagining
Speaker 1: all the different additional complications that could arise, like bad
Speaker 1: weather or night driving, or heavier traffic, and or even
Speaker 1: things like wildlife running across the street. We realized this
Speaker 1: is a lot more difficult than just sensing a potential
Speaker 1: obstacle on the road and taking the right course of
Speaker 1: action to avoid hitting it. In fact, according to the CEO,
Speaker 1: he said that every car needs about a hundred servers
Speaker 1: to process all the information. And uh they were using
Speaker 1: a fleet of around a hundred cars, so or two
Speaker 1: hundred cars, so they had a thousand to two thousand
Speaker 1: servers dedicated just to processing information in order to develop
Speaker 1: this technology in the first place, so it becomes an
Speaker 1: incredibly difficult thing to do well. That was kind of
Speaker 1: the overall story of the journey to AI. This this
Speaker 1: discussion of being in this this middle period between developing
Speaker 1: these very hyper focused tools and artificial intelligence and the
Speaker 1: goal of getting general and artificial intelligence. The idea of
Speaker 1: using AI as kind of an assistant to performing very
Speaker 1: complicated tasks, complicated from a computational standpoint, also complicated from
Speaker 1: just just from how much data is there. Again, if
Speaker 1: you if you put a human being in charge of
Speaker 1: going through all that information to find the most relevant
Speaker 1: and useful information, it would take hours or days or years,
Speaker 1: depending upon the data set, whereas artificially intelligent, properly designed
Speaker 1: program can do it in a fraction of that time,
Speaker 1: and do it dynamically, request after request after request, and
Speaker 1: can continuously update its answers based upon fresh information coming
Speaker 1: into the data set. I found it really interesting and
Speaker 1: it gives me a lot of hope for the future
Speaker 1: for various implementations of this type of technology, whether it's
Speaker 1: Watson or some comparable technology. I really think it's going
Speaker 1: to be interesting for all sorts of different applications, some
Speaker 1: of which we as consumers will interact with directly, whether
Speaker 1: it's a customer service agent or maybe it's a personal assistant,
Speaker 1: something that gets to know us and our routines. We're
Speaker 1: starting to see that a little bit in some of
Speaker 1: the personal assistants like Google Home, uh Sirie, Alexa, that
Speaker 1: kind of thing. You see a little bit there, But
Speaker 1: it'll continue to grow more sophisticated and more proactive to
Speaker 1: the point where we can have kind of like a
Speaker 1: It's almost like having an AI life coach right at
Speaker 1: your disposal. So I found it all very fascinating and
Speaker 1: I hope to learn a lot more about lots of
Speaker 1: different topics while I'm here at the THINK conference. I
Speaker 1: can't wait to chat with you guys more about quantum computing.
Speaker 1: I actually got to see a a model of what
Speaker 1: a quantum computer looks like, and boy, halldy, it does
Speaker 1: not look like a normal computer. But I'll definitely do
Speaker 1: an episode about that to talk more about what quantum
Speaker 1: computers are, how they work, why they are important, and
Speaker 1: where we might be going with it, and maybe talk
Speaker 1: a little bit more about some of the the stuff
Speaker 1: Dr Michio Kaku said, maybe some of the stuff that
Speaker 1: Neil deGrasse Tyson said. I went to his talk as well,
Speaker 1: and uh, they were very fascinating. They weren't quite as
Speaker 1: tech oriented as I would like to do a full
Speaker 1: episode like a recap on them, but I might touch
Speaker 1: on some of the themes they talked about and their
Speaker 1: meaning to me as just a person who loves tech
Speaker 1: and the tech sector in general, because they both gave
Speaker 1: very fascinating presentations. If you guys have suggestions for future
Speaker 1: episodes of tech Stuff, whether it is a technology, a company,
Speaker 1: a person, maybe there's someone you want me to interview,
Speaker 1: let me know. Send me a message. The email address
Speaker 1: for the show is tech Stuff at how stuff works
Speaker 1: dot com, or you can drop me a line on
Speaker 1: Facebook or Twitter. The handover both of those is text
Speaker 1: Stuff hs W. Remember you can follow us on Instagram.
Speaker 1: That account is always showing interesting behind the scenes information,
Speaker 1: so make sure you go check that out. And on
Speaker 1: Wednesdays and Fridays typically I record live. I stream my
Speaker 1: recording sessions on twitch dot tv slash tech Stuff, so
Speaker 1: you can come and watch me record one of these episodes.
Speaker 1: There's a chat room there. You can jump in there
Speaker 1: and chat with me live as I'm recording, although I
Speaker 1: don't respond until I hit a break because otherwise I
Speaker 1: find it too distracting and I ramble and that does
Speaker 1: not make for good podcasting. But please come on buy
Speaker 1: say hello. I would love to see you there, and
Speaker 1: I'll talk to you again really soon. For more on
Speaker 1: this and thousands of other topics because at how stuff
Speaker 1: Works dot com