Speaker 1: Get in text with technology with tech Stuff from stuff
Speaker 1: works dot com. Hey there, and welcome to tech Stuff.
Speaker 1: I'm your host job in Strickland. I'm an executive producer
Speaker 1: with How Stuff Works in my Heart Radio and I
Speaker 1: love all things tech. And if today's episode sounds a
Speaker 1: little different one, it's a special episode two. I'm recording
Speaker 1: it on location in San Francisco, California, so you might
Speaker 1: occasionally hear some traffic noises, some hotel noises. Maybe you'll
Speaker 1: hear a bell of the famous San Francisco trolley. Perhaps
Speaker 1: you'll even hear bagpipers, because we did. But I'm here
Speaker 1: in San Francisco for a specific reason. IBM invited me
Speaker 1: to fly out here and attend the Think two thousand
Speaker 1: nineteen conference and really get an up close and personal
Speaker 1: view of some of the innovations and services the company
Speaker 1: is rolling out all to their clients, their business partners,
Speaker 1: and I really wanted to share with you my own
Speaker 1: takeaways from this event. Now, before I jump into all this,
Speaker 1: IBM is a business to business entity, meaning that if
Speaker 1: you're an average Jonathan like me, you rarely deal directly
Speaker 1: with IBM. But the company is one of those leading
Speaker 1: entities that provides the tech that other companies use in
Speaker 1: order to do their business. So while it may or
Speaker 1: may not be obvious, there's a lot of stuff that
Speaker 1: we encounter in our day to day lives that's powered
Speaker 1: by IBM. This episode is the first of four special
Speaker 1: episodes about the conference and the technology and innovations that
Speaker 1: are at the bleeding edge of deployment, and today we're
Speaker 1: going to focus on artificial intelligence, something that you could
Speaker 1: argue is almost synonymous with IBM. So you guys know
Speaker 1: that AI is one of my favorite topics to talk about,
Speaker 1: and it can be easy to fall into the trap
Speaker 1: of thinking about AI as some sort of nebulous intelligence
Speaker 1: living in a machine, But when you strip away the
Speaker 1: veil of mystery, you'll see that AI is just another
Speaker 1: part of computer science. It might rely on one architecture
Speaker 1: over another, or it might require an artificial neural network approach,
Speaker 1: depending upon the application, but really it just comes down
Speaker 1: to a series of special algorithms designed to handle information
Speaker 1: in a way to allow a computer to make decisions.
Speaker 1: It's sophisticated and it's fascinating, but it's not magic. However,
Speaker 1: you might be forgiven for thinking of it as magic
Speaker 1: if you happen to witness the exchange between IBM s
Speaker 1: AI System Project Debater and Grand Champion debater Harish Natarajan.
Speaker 1: The debate between man and machine happened a day before
Speaker 1: the official start of the conference. The two participants of
Speaker 1: the debate we're not told about their topic until fifteen
Speaker 1: minutes before the debate was to begin, and then they
Speaker 1: were given their stance on what that topic was. They
Speaker 1: each had four minutes to establish their positions on the subject.
Speaker 1: Then after a short break, they had another four minutes
Speaker 1: to offer rebuttal of their opponent's stance, and then one
Speaker 1: short break later they had two minutes to summarize their arguments.
Speaker 1: The debate topic turned out to be preschools should be subsidized.
Speaker 1: Project Debater, who, by the way, has a gender. Project
Speaker 1: Debater is a she. She was given the pro stance
Speaker 1: on that particular argument and Mr Natarajan got the counter
Speaker 1: stance the the the idea that preschools should not be subsidized.
Speaker 1: I am not going to go through a blow by
Speaker 1: blow of the debate. For one thing, you can actually
Speaker 1: listen to it yourself. Intelligence Squared, which is a show
Speaker 1: dedicated to civil debate on a wide array of topics
Speaker 1: played host to this particular special debate. I urge you
Speaker 1: to seek out that podcast or a video of the
Speaker 1: debate if you want to see how it unfolded for yourself,
Speaker 1: bit by bit. I really just want to talk more
Speaker 1: about the process that was involved. So to debate, no
Speaker 1: matter what you are, whether you're human or machine, you
Speaker 1: need to have an understanding of what it is you're
Speaker 1: either arguing for or against, which is a pretty obvious statement,
Speaker 1: but I feel like I have to lay it out
Speaker 1: that way. You need to be able to form an argument,
Speaker 1: and you have to be able to support that argument logically.
Speaker 1: You want to build your argument so that one part
Speaker 1: leads inevitably into the next part and it all supports
Speaker 1: the stance you have, whether it be for or against
Speaker 1: a particular proposal. This is a non trivial task for
Speaker 1: a human being, and it is an incredible challenge for computers.
Speaker 1: Project debater Or has about ten billion sentences worth of
Speaker 1: data stored in its memory, So when a gets a topic,
Speaker 1: first it has to scour all of the information that
Speaker 1: is in its memory banks and look for relevant information
Speaker 1: related to that topic. Then it has to go a
Speaker 1: step further. It can't just pull up any random information
Speaker 1: about the topic. It has to understand that the information
Speaker 1: actually supports its argument. That is, the computer has to
Speaker 1: make sure it is picking information that is aligned with
Speaker 1: its debate position and not actually against its debate position.
Speaker 1: This falls into the field of natural language processing, and
Speaker 1: I've talked a lot about this too, but in short,
Speaker 1: this describes the area of computer science in which we
Speaker 1: try to find ways for machines to suss out the
Speaker 1: meaning from actual human language. At the basic level, computers
Speaker 1: communicate in machine code and we communicate in human languages.
Speaker 1: Machines don't natively understand human language, just as machine code
Speaker 1: would appear to be nonsense to us. The journey to
Speaker 1: creating powerful systems that use natural language processing to figure
Speaker 1: out the meaning of words, whether they're written or they're spoken,
Speaker 1: it's been a really long one, and many people have
Speaker 1: made advancements, sometimes from completely different perspectives. We've got a
Speaker 1: lot better at this in general, but it's still a
Speaker 1: challenging problem. So think about Google Search for a second.
Speaker 1: When you search for a topic, you type your search
Speaker 1: terms into Google, and then you look at the results.
Speaker 1: You typically get a pretty wide variety of responses. Some
Speaker 1: of them are going to be more relevant than others.
Speaker 1: You might even get a few that aren't relevant at all.
Speaker 1: Google's algorithms attempt to guess at which responses will be
Speaker 1: the most relevant based on your search and sometimes on
Speaker 1: some supplemental information like your search history. But sometimes the
Speaker 1: results aren't ordered in a way that you would prefer.
Speaker 1: You might get an okay response at the top and
Speaker 1: maybe a better one or more relevant one two or
Speaker 1: three spots down. Typically it ends up being on the
Speaker 1: first page, but sometimes it can even be buried lower
Speaker 1: down in the search results. Project debater can't just do
Speaker 1: a simple search and return on key terms, or else
Speaker 1: it might end up spouting out gibberish. It could string
Speaker 1: together two or three sentences that contradict each other that
Speaker 1: wouldn't do anyone any good. And that leads me to
Speaker 1: another point. It's not just good enough to grab relevant
Speaker 1: information that aligns with the argument stance. That's necessary, but
Speaker 1: it's not enough. Those statements have to be ordered properly.
Speaker 1: You have to build support for your stance. You have
Speaker 1: to have this logical progression. A good argument needs that
Speaker 1: from the opening to the closing, so you need to
Speaker 1: make sure there's a flow of information. Otherwise all you'll
Speaker 1: get is a series of relevant points, but they're in
Speaker 1: no particular order, and you have no transitions from point
Speaker 1: to point. It would be jarring and it would be ineffective.
Speaker 1: Project Debater could also support arguments with evidence, which is
Speaker 1: kind of cool. Throughout the debate, we heard the system
Speaker 1: site various studies and quote experts in the field to
Speaker 1: provide support for her stance. This was pretty compelling stuff,
Speaker 1: and this is where Project Debater could be incredibly helpful
Speaker 1: for people who want to argue for or against well anything. Really,
Speaker 1: it's the one area I would say that Project Debater
Speaker 1: had an enormous advantage over the human champion. Harrish. Natarajan
Speaker 1: understands how to create a logical, persuasive argument and how
Speaker 1: to find weaknesses in the arguments of opponents, but he
Speaker 1: can't research a library's worth of information in fifteen minutes
Speaker 1: in preparation for a debate, but Project Debater can. However,
Speaker 1: I wouldn't feel too badly for Harrish. He held the
Speaker 1: advantage in lots of other ways. For example, during the debate,
Speaker 1: he brought up a criticism of Project Debater's argument, pointing
Speaker 1: out that one of her conclusions was based without first
Speaker 1: establishing the evidence needed to support were in it. In
Speaker 1: a debate between human champions, you would likely hear during
Speaker 1: the rebuttal phase a response to this, perhaps including some
Speaker 1: of the evidence that might have been left out previously.
Speaker 1: Project Debater didn't really address that criticism. We also found
Speaker 1: out at the end of the debate that typically the
Speaker 1: Intelligence Squared format would include another round the moderator would
Speaker 1: hold around in which you would ask critical questions of
Speaker 1: each of the participants in order to test their arguments
Speaker 1: and their logic. This is done in the normal debates
Speaker 1: on Intelligence Squared, so if you listen to other examples,
Speaker 1: you would hear that round. But Project Debater, while it's
Speaker 1: really impressive, isn't quite up to the task of handling
Speaker 1: that sort of response just yet. And since this was
Speaker 1: really a showcase for the technology, that round was not
Speaker 1: included in this debate. I was really impressed by Project Debater,
Speaker 1: and as Harish pointed out after the exchange, the technology
Speaker 1: has the potential to really help people get a deeper
Speaker 1: understanding of complex topics. They can use it to help
Speaker 1: them support their arguments on any given stance, or and
Speaker 1: this is something I think is equally as important, they
Speaker 1: could use Project Debater to produce counter arguments to their
Speaker 1: own stances. Then they might either better learn how to
Speaker 1: argue against those who oppose them and anticipate the arguments
Speaker 1: they would put up against a specific stance, or it
Speaker 1: might actually change their own mind about the entire subject.
Speaker 1: It might be that you have a preconceived idea of
Speaker 1: what is right, and then you get the information from
Speaker 1: Project Debater and you start to question those ideas. You
Speaker 1: might change your mind. That leads me into the next
Speaker 1: section IBM's general philosophy about artificial intelligence. But before we
Speaker 1: get into that, let's take a quick break. A common
Speaker 1: thread that was once in science fiction and now tends
Speaker 1: to be in today's headlines is the impact that automation
Speaker 1: artificial intelligence will have on the workforce. On the most
Speaker 1: pessimistic side, there's a fear that these technologies are going
Speaker 1: to eliminate millions of jobs and that will be plunged
Speaker 1: into an economic crisis, perhaps requiring an entire overhaul of
Speaker 1: how we think about work and money. And there will
Speaker 1: no doubt be jobs that will become either completely automated
Speaker 1: or automated to the point that fewer humans will be
Speaker 1: needed to carry out that same amount of work over time.
Speaker 1: So there's definitely some validity to that fear, but many entities,
Speaker 1: IBM among them, say that we're probably not going to
Speaker 1: see anything quite so dramatic as a job apocalypse. Instead,
Speaker 1: IBM's vision is one in which artificial intelligence acts sort
Speaker 1: of like a super smart, super efficient assistant to aid
Speaker 1: us in our jobs. The tedious or difficult parts of
Speaker 1: jobs that humans find troubling could be handled by artificial intelligence,
Speaker 1: whereas the parts of jobs that are easy for humans
Speaker 1: but not so easy for machines would still need a
Speaker 1: person taking that position and fulfilling those parts of the
Speaker 1: job duties, and artificial intelligence will necessitate new positions in
Speaker 1: order to oversee the systems and to maintain them and
Speaker 1: grow them over time as businesses themselves grow. I had
Speaker 1: the opportunity to sit down with Rob Thomas, who is
Speaker 1: general manager of IBM Data and AI, to talk about this. Now.
Speaker 1: We're sitting in a lounge in the w Hotel in
Speaker 1: San Francisco for this interview, So if you hear some
Speaker 1: ambient noise that's just the sound of business people being
Speaker 1: business in the background. I'm sitting here with Rob Thomas,
Speaker 1: general manager of IBM Data and AI, and today we
Speaker 1: heard the announcement of Watson Anywhere, and I have to
Speaker 1: ask you what does that mean, Jonathan. It's an exciting
Speaker 1: day for us. Let's start with basics. I like to
Speaker 1: say there's no AI without I, A meaning information in architecture.
Speaker 1: AI is only as good as the data that you
Speaker 1: feed it. So that's a problem every company deals with
Speaker 1: and you can even see it in your consumer life.
Speaker 1: If you're using an app over and over again, it
Speaker 1: starts to know you a little bit. So your AI
Speaker 1: is only as good as your data. What we realized
Speaker 1: is companies have a lot of data, but they have
Speaker 1: a data in a lot of different places. It might
Speaker 1: be in one office location, might be data in a
Speaker 1: different office location. There might be data on a public cloud.
Speaker 1: They might have different cloud providers. We made the decision
Speaker 1: that we were going to bring the AI to the
Speaker 1: data and enable that to happen. So Watson Anywhere is
Speaker 1: about taking the best of what we've built in Watson
Speaker 1: and saying you can have that wherever you want it,
Speaker 1: which is normally wherever your data is. And this is
Speaker 1: going to be significant because this is what clients have
Speaker 1: been asking for and now we're making it really easy
Speaker 1: for them to consume Watson AI wherever they have data.
Speaker 1: So if if I'm understanding this correctly, you can look
Speaker 1: at this in very broad sense in two very different directions.
Speaker 1: You can look at it in the sense of I've
Speaker 1: got this big company and I have data spread out
Speaker 1: through multiple locations, and maybe I need to integrate that
Speaker 1: in meaningful ways. That's one way, but it may also
Speaker 1: mean I have a really large company and I've got
Speaker 1: offices in other states, other countries, perhaps where that integration
Speaker 1: may not be as easy or seamless. There might be
Speaker 1: specific laws, for example, when you need b are over
Speaker 1: in the UK, where I need to be very particular
Speaker 1: with how I'm handling data in this region. I might
Speaker 1: not be applying that somewhere else. In this way, if
Speaker 1: I'm taking the AI to where the data is, I
Speaker 1: can handle those different use case scenarios in the specific
Speaker 1: ways that are necessary. So it could be either way.
Speaker 1: It could be like integrating stuff, or it could be
Speaker 1: applying for specific implementations and I am I a lot
Speaker 1: of companies have different security policies for what they can
Speaker 1: do with their data. Like you say, some are worried
Speaker 1: about g d p R can't leave a certain country.
Speaker 1: So we're just saying take the best of the AI,
Speaker 1: put it wherever it is however you want to do it,
Speaker 1: which makes it really easier for them to access, which
Speaker 1: kind of brings up the idea of So what is Watson?
Speaker 1: What is AI? We can talk about that for a minute. Sure.
Speaker 1: I think there's two worlds of AI right now. One
Speaker 1: is people that want to build their own AI. So
Speaker 1: Watson is a way that you can build run your AI,
Speaker 1: manage that. We have a product called Watson Studio Watson
Speaker 1: Machine Learning that's basically how you build run manager AI.
Speaker 1: That's what the data scientists of the world do. They
Speaker 1: want to build something unique for their company. There's another
Speaker 1: world that says, I don't have those skills to build
Speaker 1: my own AI. I just want to use AI. We've
Speaker 1: built some Watson applications. We have Watson Assistant, which is
Speaker 1: basically a customer service agent encoded in software where we
Speaker 1: automate a lot of the decision making to make current
Speaker 1: customer service representatives a lot more effective and how they
Speaker 1: can support customers. And we've got another application called Watson Discovery.
Speaker 1: Any company with a lot of data in different places,
Speaker 1: they want to discover all of their data, what's in there,
Speaker 1: looking for the proverbial needle in a haystack. So there's
Speaker 1: kind of two worlds of a I I want to
Speaker 1: build my own and then I want to build for me.
Speaker 1: Just help me solve a problem I know I have.
Speaker 1: That's what Watson is today, what we're doing, and so
Speaker 1: you've got the with these two approaches. I also like
Speaker 1: the idea, and it's you sort of alluded to it
Speaker 1: that this AI is really all about augmenting us, not
Speaker 1: not that you're replacing any sort of human element, but
Speaker 1: that you're augmenting what we're already doing, making us more effective,
Speaker 1: more efficient, being able to find more meaning in that data.
Speaker 1: One of the stories we hear all the time is
Speaker 1: about just this concept of big data, this massive amount
Speaker 1: of information that we're constant only uh have at our fingertips,
Speaker 1: but it's so big of a problem that it's hard
Speaker 1: to tackle. This is another approach to doing that in
Speaker 1: a meaningful way, where you're actually able to h to
Speaker 1: create action plans based on all this information. You have
Speaker 1: to say, well, we've got all this info, what do
Speaker 1: we do with it? To me, that's a fascinating part
Speaker 1: of this as well, because obviously one of the boogeymen
Speaker 1: in tech is this concept of of AI. It's usually
Speaker 1: a misunderstanding what AI is, and ibm S approach has
Speaker 1: been No, this is really about enhancement, not about replacement.
Speaker 1: I like to say AI is not going to replace managers,
Speaker 1: but managers that use AI are going to replace managers
Speaker 1: that don't. And it actually it's a wave. It gives
Speaker 1: you a superpower if you're willing to use it. Give
Speaker 1: you an example, Royal Bank of Scotland, big retail commercial bank.
Speaker 1: They're trying to serve all of their ret hell banking customers.
Speaker 1: They're using Watson Assistant for customer service. They're getting faster answers.
Speaker 1: It's still going through their agents, but their agents are
Speaker 1: using the Watson Assistant to say do I understand that question?
Speaker 1: What they're really looking for? So they're getting faster answers,
Speaker 1: they're getting better answers, you get more satisfied clients. So
Speaker 1: that's why I go back to what I said, Managers
Speaker 1: that use AI have superpowers, and so I encourage everybody
Speaker 1: to be open to that because it makes you more effective.
Speaker 1: Means you have to do less of the boring work.
Speaker 1: We all have boring work we have to do. You
Speaker 1: can automate a lot of the boring work. So I
Speaker 1: think it actually makes jobs a lot more interesting, which
Speaker 1: is exciting. And as an average user and average person,
Speaker 1: the one of the results is that you get you
Speaker 1: just get better results when you're using these different services
Speaker 1: that are incorporating the AI, because you're getting the right answer,
Speaker 1: You're getting the right answer faster. Uh, you don't have
Speaker 1: to worry about as much follow up. So it's takes
Speaker 1: a lot of the frustration out of those interactions between
Speaker 1: customer and say, uh, you know a customer representative. I
Speaker 1: think I've been on both sides of that. I've been
Speaker 1: on the side as the customer who's frustrated, trying very
Speaker 1: hard not to let my frustration spill out with my
Speaker 1: interaction representative. And I've I've married to a woman who
Speaker 1: was a customer representative who would come home and I
Speaker 1: would I would hold her for an hour because she
Speaker 1: had been nailed at eight hours straight. So uh, that
Speaker 1: is something I think that a lot of people lose
Speaker 1: in this too, because when we have these discussions, we're
Speaker 1: talking about the enterprise level frequently, and the average person says, well,
Speaker 1: how does that affect me? It affects them because this
Speaker 1: ends up being incorporated into applications that are forward facing
Speaker 1: for customers in some cases. So I'm really excited by this.
Speaker 1: I think Watson is an incredible platform. I've had various
Speaker 1: interactions on various levels with Watson throughout the years, ray
Speaker 1: raging from seeing how it could be used in a
Speaker 1: customer service aspect to Watson Chef, which was my favorite
Speaker 1: implementation I've ever seen, just to get weird fun recipes
Speaker 1: generated by Watson Um. Last night I saw the debater presentation,
Speaker 1: which was fascinating to see this platform be able to
Speaker 1: actually put together a cohesive, coherent argument from lots of
Speaker 1: different points of data. That is a phenomenal achievement. People
Speaker 1: don't realize how incredibly difficult that is. What excites you
Speaker 1: most about where we are with AI and where you
Speaker 1: see us going in the future. Let me give you
Speaker 1: an example that I think hopefully everybody can relate to,
Speaker 1: which will kind of bring it to life for how
Speaker 1: AI is not just impacting businesses but individuals. AMC Networks
Speaker 1: is a big client of ours, so I'm sure some
Speaker 1: of your listeners have seen some of their TV shows.
Speaker 1: Breaking Bad was a popular one, but they've got many
Speaker 1: and AMC networks challenge was, how do we understand what
Speaker 1: viewers are responding to, what are they liking? And can
Speaker 1: we adjust plot lines based on what we're hearing and
Speaker 1: what they're liking. They also, you know, their businesses also
Speaker 1: around advertising, so can we give advertisers an idea of
Speaker 1: when to engage with the users, how to engage with
Speaker 1: the users. They're using our technology on cloud where they
Speaker 1: federate data coming from step top boxes companies like Nielsen
Speaker 1: third party data. They bring that behind their firewall and
Speaker 1: the company to manage that data. And then they're using
Speaker 1: things like Watson Studio to build models and say hey,
Speaker 1: you should go do this kind of thing to an advertiser,
Speaker 1: or even reaching out direct to a consumer saying we
Speaker 1: thought this show might interest you. So AI is behind
Speaker 1: the scenes actually everywhere, and I think sometimes you only
Speaker 1: notice that when you kind of have an aha moment
Speaker 1: where you're like, wow, that felt magical. It's it's actually
Speaker 1: AI is not magic, it's just computer science. But it
Speaker 1: is impacting every individual, whether they know it or not. Today.
Speaker 1: That's incredible. I only have one last question for you,
Speaker 1: which is are there any questions I should have asked you?
Speaker 1: I wish you would ask me why IBM and how
Speaker 1: is how is IBM relevant to this space going forward?
Speaker 1: I'm sorry I did have one more question. Okay, why
Speaker 1: IBM and why is IBM relevant in this space going forward?
Speaker 1: Great question. IBM has an amazing history over a hundred
Speaker 1: years old, and I think we've always been a steward
Speaker 1: of responsibility and integrity. And when you work with IBM,
Speaker 1: you know what you're going to get, which is you're
Speaker 1: going to be satisfied. This whole area of data AI
Speaker 1: make some people a little squeamish. They're worried about lack
Speaker 1: of transparency. They're worried about is my data being shared?
Speaker 1: The best part of working with IBM and being part
Speaker 1: of him is that you know your data safe, you
Speaker 1: know your models are safe. You know IBM not sharing
Speaker 1: this with anybody else. You know that we will be
Speaker 1: the stewards of responsibility and AI. That's why last year
Speaker 1: we came out with explainability and bias detection for AI.
Speaker 1: I think we're the first company to do that, because
Speaker 1: there's a lot of things that can go wrong in
Speaker 1: the world of machine learning or AI unless you're thinking
Speaker 1: about societal impacts, human impacts, and we spend a lot
Speaker 1: of time on that at IBM. So that's why I'm
Speaker 1: very optimistic. Absolutely, and yes, you definitely don't want something
Speaker 1: like artificial intelligence to become a black box technology where
Speaker 1: you have no idea how it's making its decisions behind
Speaker 1: the scenes, because obviously that breeds mistrust and unease. So
Speaker 1: I'm very happy to hear that as well. I remember
Speaker 1: at the Think Conference last year, I was at those
Speaker 1: those presentations and I took away I was really impressed
Speaker 1: by the discussions about bias and transparency as well. It's
Speaker 1: something that a lot of people have been arguing for,
Speaker 1: and to see a leader in a space take that
Speaker 1: very seriously is uh is a great relief and it
Speaker 1: gives me a lot of optimism about the future that
Speaker 1: Mr Thomas, Thank you so much for taking time to
Speaker 1: talk with me and my listeners. I really appreciate it
Speaker 1: great being here. Thank you appreciate it. Now up to
Speaker 1: this point, I haven't really mentioned Watson, but it's a
Speaker 1: good time to remind ourselves that Watson is an artificial
Speaker 1: intelligence platform. IBAM made a really big splashback in two
Speaker 1: thousand eleven when Watson competed on Jeopardy against two humans
Speaker 1: who were former long reigning champions, and you can see
Speaker 1: in Watson some of the same concepts that emerged more
Speaker 1: evolved in Project Debater. The system can parce language, including
Speaker 1: more subtle stuff like wordplay, figure out what is meant
Speaker 1: by that language, and then evaluate what the proper response
Speaker 1: should be. If the evaluation meets a certain threshold of confidence,
Speaker 1: then Watson will submit it. Otherwise it kind of keeps
Speaker 1: its electronic trap shut. Now, in real world deployments, Watson
Speaker 1: rarely has quite so difficult a task to perform as
Speaker 1: to compete in Jeopardy, which is all general knowledge. Typically,
Speaker 1: Watson is working within a fairly well defined set of
Speaker 1: parameters for its implementation. For example, a car insurance company
Speaker 1: using Watson to help with customer interactions wouldn't have to
Speaker 1: worry about someone asking what the capital of Belgium is
Speaker 1: or what's the best barbecue restaurant in Atlanta, So by
Speaker 1: showing off Watson's potential on the grand stage of Jeopardy,
Speaker 1: IBM was able to lay the foundation for a pretty
Speaker 1: convincing sales pitch. Yes, Watson could do all these amazing things,
Speaker 1: but imagine what it can do when it focuses on
Speaker 1: a very particular industry such as healthcare. That's really where
Speaker 1: IBM's focus has been. I have more to say, but
Speaker 1: first let's take another quick break. Another interesting thing that
Speaker 1: Mr Thomas brought up was the idea of taking artificial
Speaker 1: intelligence to the data as opposed to the other way around,
Speaker 1: and I think that's a pretty smart move. There have
Speaker 1: been so many high profile, high impact data breaches over
Speaker 1: the last few years that I imagine most companies are
Speaker 1: pretty reluctant to move mission critical information if they don't
Speaker 1: have to. The potential for something to go wrong, for
Speaker 1: some bad actor to find a vulnerability and exploit it
Speaker 1: and thus get access to private information, or perhaps worse,
Speaker 1: for the process itself to go wrong and to accidentally
Speaker 1: dump information into the public sphere without any need for
Speaker 1: outside interference. That's enough to make any company decline incorporating
Speaker 1: AI if it means porting data over to where the
Speaker 1: AI is so by making Watson available to companies to
Speaker 1: run on their own private cloud or on premises or
Speaker 1: on prem as they say here at Think two thousand nineteen,
Speaker 1: or in the public cloud. This is a huge deal.
Speaker 1: It removes that barrier of entry. Now companies that are
Speaker 1: interested in using AI with their services can do it
Speaker 1: without the worry of having to move their own data
Speaker 1: around and her keynote speech kicking off the IBM Think
Speaker 1: two thousand nineteen conference, Jenny Rometti, the CEO, president and
Speaker 1: chair of the Board for IBM, spoke about this. She
Speaker 1: outlined two general approaches to incorporating AI into services. One
Speaker 1: is what she would call the outside in approach. This
Speaker 1: is where companies would take their pre existing applications and
Speaker 1: then they would add a layer of artificial intelligence on
Speaker 1: top of those applications in order to make them work
Speaker 1: better and more efficiently. This is an approach companies might
Speaker 1: take if they lack the expertise or time to build
Speaker 1: out new apps entirely. But some companies might opt to
Speaker 1: do the reverse, the inside out approach. In other words,
Speaker 1: that's where they create all new applications and processes that
Speaker 1: incorporate AI into them from the beginning to try and
Speaker 1: maximize the value of having the artificial intelligence involved. So
Speaker 1: what the heck does all that mean? How does that
Speaker 1: impact us as average people? Well, largely, it means the
Speaker 1: service as we use, such as mobile apps or computer software,
Speaker 1: will work better, become more sophisticated, and they will incorporate
Speaker 1: more features. And this will become more important as companies
Speaker 1: continue to grow and place data in different data centers
Speaker 1: and clouds. As that happens, it becomes increasingly challenging to
Speaker 1: manage all that information and to coordinate between those centers
Speaker 1: of data and pull together meaningful results. It helps if
Speaker 1: I give you an example. So let's imagine that you
Speaker 1: have downloaded a travel app and it's all to help
Speaker 1: you plan and book a trip you want to take.
Speaker 1: Maybe you're traveling to another country in six months, so
Speaker 1: you're planning well in advance. The app helps you by
Speaker 1: consulting many different sources of information. It prices out flights
Speaker 1: through various airlines to help you find one that fits
Speaker 1: your budget and your schedule. It gives you information on
Speaker 1: hotels at your destination to have availability. It provides a
Speaker 1: list of possible activities you might want to do while
Speaker 1: you're there. Maybe the includes a calendar that's populated with
Speaker 1: cultural events that are going on at the location while
Speaker 1: you're there on vacation. There are options for restaurant recommendations,
Speaker 1: information on average weather during that time of year, so
Speaker 1: you know what sort of clothes you're gonna need to pack,
Speaker 1: and maybe more pieces. Maybe there are things that will
Speaker 1: populate over time so that each time you go back
Speaker 1: to consult your plan, it's update with the latest information.
Speaker 1: These various pieces of information don't all live on one
Speaker 1: server somewhere connected to that app. There's no business out
Speaker 1: there that has all of this information stored on some
Speaker 1: magical computer. Instead, the information is coming from numerous sources
Speaker 1: and organized in a meaningful way for your mobile device interface.
Speaker 1: The goal of an AI approach is to have an
Speaker 1: automated system in place that's able to do this kind
Speaker 1: of stuff quickly and without error, so that the end
Speaker 1: user you, in other words, ends up with a seamless
Speaker 1: and helpful experience, so that you get the information you need,
Speaker 1: when you need it and where you need it. And
Speaker 1: this is harder than it sounds. And the general message
Speaker 1: at think two thousand nineteen that I've been hearing is
Speaker 1: that it's not a question of if companies should start
Speaker 1: employing these sorts of AI approaches in their processes, but
Speaker 1: rather when they should. That if you don't do this,
Speaker 1: it means you're not going to be able to keep
Speaker 1: up with the demands of business and growth. So it
Speaker 1: sounds like we've got a future of artificial intelligent assistance
Speaker 1: ahead of us, and I think that's pretty fascinating, especially
Speaker 1: when we think of it in the context of augmenting
Speaker 1: what we humans can already do, not replacing what we
Speaker 1: can do. That's a pretty cool message and one that
Speaker 1: I really found inspiring while I was at Think two
Speaker 1: thousand nineteen. I'm going to have a lot more episodes
Speaker 1: coming out in the near future about some of the
Speaker 1: other things that I am looking into while I'm at
Speaker 1: the conference, including some more interviews with some really interesting people,
Speaker 1: So make sure you stay tuned and check out those
Speaker 1: when they published. They'll be coming out very soon. If
Speaker 1: you guys have any suggestions for future episodes of tech Stuff,
Speaker 1: whether it's about a specific company, a specific technology, maybe
Speaker 1: you have follow up questions about some of the stuff
Speaker 1: I'm talking about this week, make sure you reach out
Speaker 1: let me know about those The email address for the
Speaker 1: show is tech stuff at how stuff works dot com.
Speaker 1: You can also visit our website at tech stuff podcast
Speaker 1: dot com. There you're going to find an archive of
Speaker 1: all of our past shows, as well as links to
Speaker 1: how to connect to us on social media and to
Speaker 1: our merchandise store. And that's it for now, but I'll
Speaker 1: be back again in just a short while to talk
Speaker 1: more about the incredible stuff I'm seeing here at Thanks
Speaker 1: two thousand nineteen. Thank you very much, IBM, and I
Speaker 1: will talk to you again. Release soon for more on
Speaker 1: this and bathands of other topics, because it has to
Speaker 1: works dot com