Speaker 1: Get in touch 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 at
Speaker 1: how Stuff Works in I Love all Things tech, and
Speaker 1: welcome to episode nine hundred ninety nine of tech Stuff. Yep,
Speaker 1: we're gonna have a really big shin dig for the
Speaker 1: next one. And by shin dig, I mean I'm going
Speaker 1: to record another show. But today we're going to talk
Speaker 1: about the state of artificial intelligence because in late June
Speaker 1: Dr Nathan Bane and Ian Hogarth presented a report titled
Speaker 1: the State of a I, giving an update on the
Speaker 1: advancements that have happened in the field of artificial intelligence
Speaker 1: over the course of the last twelve months, so end
Speaker 1: of well summer of seventeen to the summer of eighteen essentially.
Speaker 1: So I thought it might be in using to talk
Speaker 1: about what they found as they researched the topic. But first,
Speaker 1: let's define a few terms, because artificial intelligence is one
Speaker 1: of those categories of topics that tends to encompass a
Speaker 1: lot of different ideas and unless you define what you're
Speaker 1: talking about early on, uh, you could have two people
Speaker 1: talking about different aspects of AI, and they don't realize
Speaker 1: they're talking about different aspects and they think they're disagreeing,
Speaker 1: but in reality they're actually agreeing. It's just they haven't
Speaker 1: defined their terms. So we're gonna do that first. So
Speaker 1: the term artificial intelligence dates to nineteen fifty five. John McCarthy,
Speaker 1: who was was a computer scientist, had coined the phrase
Speaker 1: while developing a plan for a conference that took place
Speaker 1: at Dartmouth, and it was supposed to happen in nineteen
Speaker 1: fifty six. So he came up with the term artificial
Speaker 1: intelligence about a year before the conference was to take place.
Speaker 1: And his was a definition of general intelligence, which means
Speaker 1: it would be a way to allow machines to reason,
Speaker 1: engage an abstract thought, do some problem solving, and also
Speaker 1: to pursue self improvement. And I think a lot of
Speaker 1: people still think of this when they are thinking of
Speaker 1: the phrase artificial intelligence. That means a computer that can
Speaker 1: process information in a way that is at least comparable,
Speaker 1: if not identical, to the way we humans process information.
Speaker 1: With the concept of self improvement included, there's at least
Speaker 1: an implication that such a machine would at least possess
Speaker 1: some degree of self awareness. It would have to know
Speaker 1: at least that it needed to improve upon something. It
Speaker 1: may not really have a full sense of self. However,
Speaker 1: that's not necessarily required in order to have self improvement,
Speaker 1: so uh, there'll just be a degree of it. Um. However,
Speaker 1: it could go as far as a machine that actually
Speaker 1: knows that it exists within a world it has a
Speaker 1: sense of self. That could be a potential uh way
Speaker 1: to interpret this concept. But as time has gone on,
Speaker 1: we have needed to narrow down various definitions of artificial
Speaker 1: intelligence because intelligence itself human intelligence is a very broad term.
Speaker 1: It encompasses many different things. It takes more than creating
Speaker 1: a machine that can process information at a faster rate
Speaker 1: than humans. We wouldn't say that a calculator is more
Speaker 1: intelligent than a person just because a calculator can, uh
Speaker 1: can perform a complex mathematical calculation in a fraction of
Speaker 1: the amount of time your average person could, right, because
Speaker 1: your average person can still do tons of stuff that
Speaker 1: a calculator can't do, so we wouldn't call the calculator intelligent.
Speaker 1: It takes more than just processing information at a very
Speaker 1: fast rate, so we have to go beyond just efficiency.
Speaker 1: If we want to talk about intelligence, the Encyclopedia Britannica
Speaker 1: defines AI as the ability of a digital computer or
Speaker 1: computer controlled robot to perform tasks commonly associated with intelligent beings. Uh.
Speaker 1: This isn't a bad definition, but it does require you
Speaker 1: to take another step back to consider what sort of
Speaker 1: actions would be considered intelligent versus those that are more instinctive. So,
Speaker 1: for example, if a fly sees that there is a
Speaker 1: hand coming toward it to squish it and it flies away,
Speaker 1: is that instinctual or is that intelligent? While you would
Speaker 1: argue it's more instinct, it's not a sign of intelligence. Uh.
Speaker 1: There have been a lot of experiments with insects in
Speaker 1: general to display that behaviors they have that appear to
Speaker 1: be intelligent because they involve complex behaviors, Uh, turn out
Speaker 1: to be instinctual because if you start messing with them,
Speaker 1: they will just repeat the exact same sequence over and
Speaker 1: over again. They don't have a way of retaining the
Speaker 1: information that just happen in order to build upon it,
Speaker 1: which is all part of learning. Right as human beings,
Speaker 1: when we encounter new information, we can incorporate that into
Speaker 1: our knowledge and then we can build upon that in
Speaker 1: future encounters with similar scenarios. We can even generalize from
Speaker 1: that situation and apply that knowledge to similar but different scenarios.
Speaker 1: This is something that differentiates intelligence from just instinct. AI
Speaker 1: is also a multidisciplinary field. It's not just one area
Speaker 1: of study. It's in fact, lots of different areas of study,
Speaker 1: everything from computer science to biology, to neuroscience, to psychology,
Speaker 1: to engineering and more. It's tons of different areas of
Speaker 1: study that all go into AI, and they're also a
Speaker 1: lot of different ways that we can categorize AI. One
Speaker 1: way is that we could just divide AI into two
Speaker 1: very large categories week AI and strong long AI. John
Speaker 1: Searle proposed this way of defining AI back in In fact,
Speaker 1: he argued that week AI is probably the best will
Speaker 1: ever do, will never have strong AI. Week AI refers
Speaker 1: to a simulation of human thought. That you could have
Speaker 1: a machine that appears to think like a person, but
Speaker 1: that's just an appearance. In reality, once you strip away
Speaker 1: all the different layers, it turns out it's just a simulation.
Speaker 1: The computer is not quote unquote actually thinking. Uh, it's
Speaker 1: mimicking the way we think, and it tends to be
Speaker 1: in a relatively narrow band of applications. It may perform
Speaker 1: those applications very well, it might even do so better
Speaker 1: than a human can, but it cannot act outside of
Speaker 1: that narrow band, or if it does attempt to act
Speaker 1: outside of it, it doesn't do so very well. So
Speaker 1: an example of this might be IBM's Deep Blue, which
Speaker 1: was the computer they designed to play against chess grand masters,
Speaker 1: and it plays chess really, really well, well enough to
Speaker 1: beat grand masters at chess, But you couldn't then tell
Speaker 1: it to sort a complex series of problems so that
Speaker 1: you could tackle them properly, or ask it about what
Speaker 1: the weather is going to be like three days from now.
Speaker 1: You couldn't set it to other tasks. It was programmed
Speaker 1: for a very specific application, and you could not just
Speaker 1: leverage that quote unquote intelligence to something else. Whereas with
Speaker 1: a person, you can have a person tackle all sorts
Speaker 1: of different problems, and even if the person has never
Speaker 1: encountered that problem before, he or she can apply the information,
Speaker 1: the knowledge that they have accumulated throughout their experiences an
Speaker 1: attempt to apply it as best they are able to
Speaker 1: the new task. They might not be very good at
Speaker 1: the new task, but they can at least try to
Speaker 1: do that based upon the information and the knowledge that
Speaker 1: they have gained in their past experiences. It doesn't matter
Speaker 1: how many games of chess Deep Blue plays, it's still
Speaker 1: not going to be good at doing other tasks. Strong
Speaker 1: AI would refer to an artificial intelligence that could actually
Speaker 1: think in a way that's at least analogous to the
Speaker 1: way we humans think, even if it uses a different
Speaker 1: methodology to do so, and it could apply that intelligence
Speaker 1: to any situation, not just a narrow set of situations.
Speaker 1: And that does not mean that it would immediately be
Speaker 1: great at everything. This isn't a definition of a super
Speaker 1: intelligent computer. It might also be pretty crappy at brand
Speaker 1: new tasks, but it can learn over time and improve
Speaker 1: over time, self improvement being an important concept in intelligence.
Speaker 1: So I can learn from mistakes and even generalize what
Speaker 1: it's learned into new, unrelated situations, so it would not
Speaker 1: be bound by those narrow set of applications like a
Speaker 1: week AI would. Cirl, By the way, is also the
Speaker 1: philosopher who proposed the Chinese room thought experiment to argue
Speaker 1: against strong AI. I've talked about the Chinese room thought
Speaker 1: experiment and other episodes. Basically, it's this this thought experiment
Speaker 1: where you imagine that you are in a room and
Speaker 1: the room only has a door, and the door has
Speaker 1: a little slot in it, and occasionally a piece of
Speaker 1: paper is shoved through the slot. You get the piece
Speaker 1: of paper, something is written in an alphabet that you
Speaker 1: don't you don't understand, you have you have no knowledge
Speaker 1: of this alphabet. It's just it looks like squiggles to you.
Speaker 1: But you have an enormous book, and that enormous book
Speaker 1: has a list of all these different pages of squiggles.
Speaker 1: So what you do is you consult the enormous book,
Speaker 1: you look for a page of squiggles that's identical to
Speaker 1: the one that was sent to you, and then you
Speaker 1: follow the instructions of what to do. When you get
Speaker 1: a page that has the squiggles, you follow the instructions,
Speaker 1: you put the output through the slot in the door,
Speaker 1: and things continue. Searl argued, this is essentially the same
Speaker 1: way that computers process information. They don't understand the information
Speaker 1: that's coming in. They look at the information, they look
Speaker 1: for the match of that information against what is programmed
Speaker 1: to do, and then they respond with the appropriate response.
Speaker 1: But they don't understand the process. Uh. You would say
Speaker 1: that the same thing is true of the Chinese room
Speaker 1: thought experiment. That if you did a piece of paper
Speaker 1: written in Chinese, and the person inside does not understand Chinese,
Speaker 1: they see the piece of paper, they have the book,
Speaker 1: they write the response, they send it back through from
Speaker 1: an external observer, it would appear that whoever is inside
Speaker 1: the room understands Chinese. But the truth of the matter
Speaker 1: is you don't understand Chinese. You're just following the instructions. Now,
Speaker 1: there have been a lot of responses to this particular
Speaker 1: thought experiment, but that's another episode, so I'm not gonna
Speaker 1: go into it here, but it's a very interesting philosophical notion.
Speaker 1: There was an assistant professor of integrative biology and computer
Speaker 1: science and engineering at Michigan State University named Erin Hints
Speaker 1: who lays out four types of AI uh in an
Speaker 1: article that he wrote for the Conversation dot com. His
Speaker 1: four types of AI start with type one, which are
Speaker 1: reactive machines. These base all operations on the current state
Speaker 1: of any given situation, but it cannot refer back to
Speaker 1: past events. Uh, As Hints points out in this piece
Speaker 1: Deep Blue, When I was mentioning earlier. It falls into
Speaker 1: this type one category. When Deep Blue played chess, it
Speaker 1: wasn't tracking moves. It wasn't saying, all right, well, the
Speaker 1: last five moves, my opponent did such and such, So
Speaker 1: I suspect based on that that I'm learning more about
Speaker 1: his style of play. Deep Blue would look at the
Speaker 1: state of the board, where all the pieces were, and
Speaker 1: it would do this as if it was a the
Speaker 1: first time Deep Blue had ever seen the chessboard. It's
Speaker 1: not referring to the previous moves. It's just looking at
Speaker 1: what is on the board at that moment and then
Speaker 1: starts to evaluate all the potential moves it can make,
Speaker 1: all the potential moves its opponent can make, and then
Speaker 1: chooses a move among that set. The next time it's
Speaker 1: Deep Blues turn, it does it all over again. It
Speaker 1: is not referring to its past experience. It's only looking
Speaker 1: at the current state of the board. So again, it
Speaker 1: can't analyze player behavior leading up to the turn and
Speaker 1: then base the decision off of that. So Deep Blue
Speaker 1: blade chess as if on every single turn it was
Speaker 1: the first time it ever scenes ever I had a
Speaker 1: chance to look at that board. Um Ronnie Brooks and
Speaker 1: AI researcher argued that this is really the only type
Speaker 1: of AI we should try to build, because any other
Speaker 1: AI would require some sort of internalized concept of the world.
Speaker 1: It would need to have some form of representation of
Speaker 1: the world in its for lack of a better word, mind,
Speaker 1: in order for it to be able to react off
Speaker 1: of that and be us. We humans are the people
Speaker 1: programming these machines, we would have to program that representation
Speaker 1: of the world, and no matter how carefully we do,
Speaker 1: that is never going to be as good a representation
Speaker 1: as the world actually is. Right, It's only going to
Speaker 1: be a weak simulation of what we see the world
Speaker 1: to be, and therefore any decisions such a machine makes
Speaker 1: based off of this imperfect representation of the world will
Speaker 1: in turn be imperfect. Moreover, if I were to program
Speaker 1: such a computer, I'm doing so based off my own
Speaker 1: concept of what the world is. But my concept of
Speaker 1: the world is different from someone else's concept of the world.
Speaker 1: Someone who comes from a very different background, with a
Speaker 1: very different set of experiences might have a very very
Speaker 1: radically different perception of what the world is. And if
Speaker 1: I were to design a machine off my perceptions, it
Speaker 1: might behave in a way that is completely alien to
Speaker 1: this other person, perhaps in a way that is harmful
Speaker 1: to this other person, and we'll get more into that later.
Speaker 1: That falls into the realm of bias. So there are
Speaker 1: some who argue that we shouldn't even try to go
Speaker 1: beyond type one because of the potential problems we could encounter.
Speaker 1: Type two. AI possesses some limited ability to remember. This
Speaker 1: is sort of like short term memory for humans, though
Speaker 1: perhaps it's more transient than short term memory. The memories
Speaker 1: never get converted into long term storage for these machines,
Speaker 1: but rather they serve to help a machine make immediate decisions.
Speaker 1: Hence points to self driving cars as an example of
Speaker 1: this type of AI. Hence says that, well, they have
Speaker 1: to identify and monitor other elements on the road that
Speaker 1: are constantly changing, such as other vehicles. They have to
Speaker 1: be able to tell how fast is another vehicle traveling,
Speaker 1: How close to your vehicle is this other vehicle, what
Speaker 1: direction are they traveling in? Uh Like, if you're going
Speaker 1: down the highway and there are other cars on the highway,
Speaker 1: your car needs to know how many there are, where
Speaker 1: they are in relation to you, how fast they're traveling.
Speaker 1: This is all ongoing information. So the alternative would be
Speaker 1: for your AI to look at the world as a
Speaker 1: series of snapshots. Right, But snapshots don't tell you things
Speaker 1: like speed. They would tell you the thing was here,
Speaker 1: now it's here. You could interpret it as speed if
Speaker 1: you knew how long it was between snapshots. But that's
Speaker 1: a lot of unnecessary processing power. It makes more sense
Speaker 1: to design AI that has the ability to at least
Speaker 1: hold information in short term to understand things like velocity.
Speaker 1: So uh, slightly different version of AI, slightly more sophisticated
Speaker 1: than type one. But you don't have to worry about
Speaker 1: other stuff, right, You don't have to worry about anything
Speaker 1: outside of whatever the AI's purposes. So a self driving car,
Speaker 1: for example, doesn't need to know how expensive a jug
Speaker 1: of milk is, right. It doesn't need to know any
Speaker 1: of that. It just needs to know rules of the road,
Speaker 1: needs to know how to identify things that it's places
Speaker 1: where a car can go versus where a car should
Speaker 1: not go. It has to be able to identify pedestrians, uh,
Speaker 1: these sort of things. But outside of that, the car
Speaker 1: doesn't need to worry about it, so it still has
Speaker 1: a fairly narrow set of parameters that it follows. Its worldview,
Speaker 1: in other words, is constrained, so you don't have to
Speaker 1: worry about creating a perfect representation of the world. You
Speaker 1: just have to create as perfect a representation of the
Speaker 1: specific part of the world the AI will inhabit as
Speaker 1: you possibly can to make sure it behaves properly. Type
Speaker 1: three AI should not only have an internal representation of
Speaker 1: the world, but also a concept of other entities that
Speaker 1: are within that world as and it doesn't just note
Speaker 1: the presence of other things within its environment, but recognizes
Speaker 1: which of those things have agency. Humans possess agency. We
Speaker 1: understand our own faculties. We can recognize that others possess
Speaker 1: similar abilities. If you and I were to have a conversation,
Speaker 1: we would do so knowing that the other person possesses
Speaker 1: at least some of the same abilities that we ourselves do. Right,
Speaker 1: so we know like you would know that I have motivations,
Speaker 1: that I have needs and wants. You would know this,
Speaker 1: and I would know the same about you. We might
Speaker 1: not know what they all are, but we recognize that
Speaker 1: the other person has them. A Type three AI would
Speaker 1: be able to recognize this and other entities It would
Speaker 1: not itself necessarily possess needs, wants, anything like that, but
Speaker 1: it would recognize that other entities do have those things,
Speaker 1: So it is not self aware, but is aware of others.
Speaker 1: So if we just assume that we are the only
Speaker 1: ones who possess these faculties, then any conversation we would
Speaker 1: ever have with anyone else would be akin to speaking
Speaker 1: to ourselves, because we would assume other people don't have
Speaker 1: those faculties, they don't possess the intelligence that we have.
Speaker 1: That would mean that every single episode I did of
Speaker 1: tech stuff would essentially just turn into Tacos and Lord
Speaker 1: of the Rings, because that's all I would care to
Speaker 1: talk about. But I assume you guys want to hear
Speaker 1: more than that, Sorry, wants to hear about Tacos and
Speaker 1: Lord of the Rings, or at least one or the other.
Speaker 1: I don't know which. I'm gonna guess Tacos if I
Speaker 1: have to guess. So this comes to the theory of
Speaker 1: the mind, which is also pretty close to what Alan
Speaker 1: Touring was talking about during the Turing Test. If we
Speaker 1: were to create a machine that could reliably mimic a
Speaker 1: human well enough so that your average person couldn't tell
Speaker 1: if the responses it was getting the human was getting
Speaker 1: were from a person or from a computer, You would
Speaker 1: say that computer passes the Turing test, and Touring would say,
Speaker 1: you might as well grant that the computer possesses intelligence,
Speaker 1: because you would do the same thing to another human being.
Speaker 1: Right if you talk to another human being, the human
Speaker 1: talks to you like a human being, you say, oh,
Speaker 1: this person has some of the same basic aspects of
Speaker 1: humanity that I have, like intelligence and motivations and needs
Speaker 1: and wants. Turing said, you might as well extend that
Speaker 1: to computers if they're able to mimic human interaction close
Speaker 1: enough so that you could not tell if it was
Speaker 1: a human or a computer. Even if the computer doesn't
Speaker 1: possess those things, we might as well assume it does,
Speaker 1: because we give that same consideration to other people. However,
Speaker 1: Hints would say that would really only apply to type
Speaker 1: four AI. Those are the types of artificial intelligence that
Speaker 1: have self awareness. This is an AI that not only
Speaker 1: recognizes there are other entities out there, but understands that
Speaker 1: it itself is an entity possessing intelligence, and AI in
Speaker 1: type three would recognize that humans have thoughts, feelings, and motivations,
Speaker 1: but an AI in type four would have those of
Speaker 1: its own. It would have its own motivations, its own needs,
Speaker 1: its own wants, its own loves and hates all the
Speaker 1: way in main not defined it in such terms. It
Speaker 1: would not only be able to recognize motivations, but also
Speaker 1: understand motivations. It could put itself in the place of
Speaker 1: another entity and say, this other entity wants to do
Speaker 1: X because entity is why. So it might be Jonathan
Speaker 1: wants to kick open the door because he's hungry for tacos,
Speaker 1: and the computer would be able to understand this concept,
Speaker 1: although it may not ever want to eat a taco.
Speaker 1: I consider that imperfect machine. So that is where we are.
Speaker 1: That's the definitions of artificial intelligence, so that you kind
Speaker 1: of understand, uh, the philosophical approach to what we consider
Speaker 1: a I. When we come back, we'll talk more about
Speaker 1: the actual report and what the two researchers found as
Speaker 1: they were looking into the advances that have been made
Speaker 1: in AI over the past twelve months. But first let's
Speaker 1: take a quick break and thank our sponsor. Much of
Speaker 1: the work and artificial intelligence that have been following has
Speaker 1: fallen firmly in the type one category I mentioned before
Speaker 1: the break. And that's not to say that the work
Speaker 1: is boring or it's not useful. The technology needed for
Speaker 1: type one AI is incredibly sophisticated, so it involves not
Speaker 1: just developing sensors for a machine to be able to
Speaker 1: observe its environment, but also the various programs algorithms necessary
Speaker 1: to process information in a meaningful way so the machine
Speaker 1: can react in the way that we wanted to react.
Speaker 1: Stuff like image recognition, voice recognition, depths, sen saying, all
Speaker 1: that kind of stuff sort of fall into that category,
Speaker 1: although they can also be incorporated into higher categories of
Speaker 1: artificial intelligence, but they are sort of their building blocks essentially.
Speaker 1: One of the first topics from the report focuses on
Speaker 1: machine learning and a concept called transfer learning, so we
Speaker 1: get to talk about what that means. Machine learning is
Speaker 1: an approach that involves a computer examining data, learning from
Speaker 1: that data, and then using what has been learned for
Speaker 1: future decisions. So, for example, let's take Amazon's shopping suggestions.
Speaker 1: When you buy something off Amazon, you'll see a recommendation
Speaker 1: for stuff that other people have bought when they were
Speaker 1: purchasing the same thing you just bought. So Amazon is
Speaker 1: using machine learning to try and up sell you more stuff.
Speaker 1: It's like saying, hey, when other people bought that thing
Speaker 1: of a jig, they also gotta do hicky. You probably
Speaker 1: also want to do hicky because you just buy that
Speaker 1: thing of a jig. That's a simple example of this.
Speaker 1: Then you have deep learning that's kind of a a
Speaker 1: subset of machine learning. Deep learning is sort of a
Speaker 1: self correcting branch in machine learning. You train a computer
Speaker 1: on sets of data, and occasionally you have to step
Speaker 1: in to make corrections and adjustments to make certain the
Speaker 1: computer is on the right track, that the computer is
Speaker 1: not making bad suggestions, which will happen just because it's
Speaker 1: work from large amounts of data, and sometimes it is
Speaker 1: making choices that to the computer seem logical but to
Speaker 1: an outside observer seem wackadoodle crazy, So you have to
Speaker 1: go in and tweak things. You might train an algorithm,
Speaker 1: for example, to recognize pictures of coffee mugs, and then
Speaker 1: occasionally you have to pop in and you see something
Speaker 1: that isn't a coffee mug that has been mistakenly identified
Speaker 1: as one. You have to tell the computer, no, computer,
Speaker 1: that is not a coffee mug, and then it learns
Speaker 1: from there. Deep learning depends upon artificial neural networks. These
Speaker 1: are neural networks that mimic the way brains process information,
Speaker 1: and they have algorithms that behave like neurons. Each algorithm
Speaker 1: processes some information, then assigns a weight to how correct
Speaker 1: it believes its conclusion to be, like I'm pretty sure
Speaker 1: this is right, all the way down to this might
Speaker 1: be right, but I don't know. And then it passes
Speaker 1: the data down to another layer of neurons, which then
Speaker 1: takes that information, processes it another way, passes it on, etcetera, etcetera.
Speaker 1: And then the system can look at all the different
Speaker 1: weightings of all the different potential answers and say, out
Speaker 1: of all the conclusions I've come up with, this one
Speaker 1: is the one I'm most confident is correct. So that's
Speaker 1: the answer we're gonna go with. We're not gonna go
Speaker 1: with any of the others because they are statistically less
Speaker 1: likely to be the correct answer. So the key here
Speaker 1: is that you have to train a deep learning network
Speaker 1: on a really large amount of data so that you
Speaker 1: can really get it to grasp the concept, and you
Speaker 1: also have to make sure you tweak the waiting situation
Speaker 1: the way it waits how confident it is in an
Speaker 1: answer in such a way that filters out bad conclusions
Speaker 1: early on. Uh So it's a little different, like you're
Speaker 1: you're actually tweaking its decision making process as opposed to
Speaker 1: looking at the decisions once they've already been made. And
Speaker 1: it gets really really tricky, but we're gonna leave it
Speaker 1: to that. You just you have to very gently guide
Speaker 1: the decision making process and then you let it go
Speaker 1: on its own, and then it ultimately starts to produce
Speaker 1: the best decisions if you've designed the system properly. So
Speaker 1: these are all non trivial problems, but they are surmountable.
Speaker 1: We do have deep learning systems out there now. Transfer
Speaker 1: learning is where you train a machine to do one
Speaker 1: thing and then transfer that learning to a new task.
Speaker 1: It might be semi related, or it might be completely
Speaker 1: unrelated to the original task. You can reapply the learning
Speaker 1: model you developed for the first task, and you this
Speaker 1: cuts down the time it would take to train algorithms
Speaker 1: to do something new. Moreover, computer models that have been
Speaker 1: trained on different problems will begin to build a more
Speaker 1: rich representation of the world, which I mentioned earlier is
Speaker 1: necessary for the higher forms of artificial intelligence. The report
Speaker 1: gives an example in Google Inception V three network, which
Speaker 1: was trained on image recognition and then retrained on recognizing
Speaker 1: skin diseases. The result was that the AI could actually
Speaker 1: outperform twenty one Stanford dermatologists when it came to making
Speaker 1: informed decisions such as whether or not a patient should
Speaker 1: get a biopsy. Next, the report acknowledges the importance of
Speaker 1: graphics processing units, also known as GPUs. Unlike most CPUs
Speaker 1: central processing units, a GPU is designed to process a
Speaker 1: lot of data in parallel, so that ends up being
Speaker 1: really useful when you're training computer models with enormous amounts
Speaker 1: of information. But it's not an approach that works for
Speaker 1: all types of computing because not all computational problems can
Speaker 1: be divided up to be solved in parallel, and a
Speaker 1: GPU would handle a problem that can't be divided up
Speaker 1: in parallel much more slowly than a very powerful CPU could.
Speaker 1: This is also true for quantum computers, meaning that when
Speaker 1: we get reliable, powerful quantum computers will likely see a
Speaker 1: real boom in training computers and machine learning. The report
Speaker 1: also argues that while GPUs are incredibly useful for training
Speaker 1: a model, the actual application of the model can rest
Speaker 1: on CPUs. So if you prefer, you'd want to use
Speaker 1: a GPU when you're putting your computer model through school,
Speaker 1: but when it's time for your computer model to actually
Speaker 1: do its job to pursue its career, you switch it
Speaker 1: over to CPU because the parallel part all comes in
Speaker 1: the training section, not in the application section. The report
Speaker 1: also identifies a few big challenges and pushing AI further. First, there's,
Speaker 1: of course, the technological barriers. The report points out that
Speaker 1: processor clock frequencies are are starting to plateau. So generally speaking,
Speaker 1: clock frequencies tell you how many operations a processor can
Speaker 1: complete in a second. That's really an oversimplification, but generally
Speaker 1: the higher the number, the more stuff your processor can
Speaker 1: do within a second amount of time. Advance as an
Speaker 1: AI will likely depend upon new microprocessor architectures to overcome
Speaker 1: this hurdle. We're reaching the limit of what we can
Speaker 1: do with the classical microprocessor design. So essentially we're getting
Speaker 1: closer to the end of Moore's law due to fundamental
Speaker 1: physical limits that we cannot overcome if we just stick
Speaker 1: with the way we've been making microprocessors for the last
Speaker 1: several decades. But that does not mean our our computers
Speaker 1: are never going to get more powerful. It may only
Speaker 1: mean that we have to innovate new architectures, new designs,
Speaker 1: new approaches to processing, which in turn could necessitate a
Speaker 1: new version of Moore's law. We might be on another
Speaker 1: astronomical expansion of processing, but it would require brand new
Speaker 1: architectures that haven't necessarily been proven yet. The researchers identified
Speaker 1: Google's tensor processing unit or TPU, as a possible successor.
Speaker 1: The TPU is a type of application specific integrated circuit
Speaker 1: and a s I C. This is a circuit that
Speaker 1: has made for a very specific application, as opposed to
Speaker 1: a general circuit like a CPU. A CPU is supposed
Speaker 1: to be able to handle lots of different data for
Speaker 1: lots of different applications, but a TPU is meant for
Speaker 1: a very specific application, for example, for artificial intelligence. Related
Speaker 1: to this is another barrier, which is financial. Harnessing really
Speaker 1: powerful processing technologies is expensive, so artificial intelligence R and
Speaker 1: D tends to be really costly progress and AI is
Speaker 1: limited in part by funding. In other words, it's not
Speaker 1: just technology, it's also where's the money coming from. As
Speaker 1: for how AI has been coming along, the researchers pointed
Speaker 1: to Google's Alpha zero, which taught itself how to play
Speaker 1: the game Go at superhuman levels, and it did that
Speaker 1: just by playing itself. It played games of Go against itself,
Speaker 1: repeat eatedly, with no human interaction. The system didn't have
Speaker 1: any historical data to pull from. It wasn't consulting historic
Speaker 1: games of Go and the strategies that people employed. It
Speaker 1: was developing strategies on its own, so it only had
Speaker 1: the basic rules of the game programmed into it, and
Speaker 1: then it just began playing thousands and thousands of games
Speaker 1: against itself and learned strategies. It would learn tactics, it
Speaker 1: would abandon approaches. It had forty days of training, and
Speaker 1: it reached levels of mastery that could foil even the
Speaker 1: best human players. The researchers also mentioned Open Ai that's
Speaker 1: a team that created Ai agents that could play the
Speaker 1: game Dota too. Dota two is a mobile that's a
Speaker 1: style of game in which two teams of players try
Speaker 1: to win a match that involves capturing certain spaces on
Speaker 1: a playing field and defeating the members of the other team.
Speaker 1: Like Alpha zero, the team used a self playing feature
Speaker 1: as a training mechanism. Every player on a team was
Speaker 1: controlled by a different AI agent. Now there was one
Speaker 1: computer that was generating all these AI agents, but each
Speaker 1: AI agent was acting as its own kind of individual.
Speaker 1: So each agent had its own neural network, and that
Speaker 1: meant that these different AI agents were having to collaborate
Speaker 1: with each other to work together to form these strategies
Speaker 1: in order to achieve goals. So this was not just
Speaker 1: a one computer that was controlling all the pieces simultaneously.
Speaker 1: It was almost like a separate computer system for every
Speaker 1: single player. And then they would talk to each other
Speaker 1: and coordinate with each other, which is really cool and
Speaker 1: also a little terrifying if you if you think about it,
Speaker 1: computers working together independently is kind of scary anyway. Another
Speaker 1: big development and AI is addressing bias in machine learning models.
Speaker 1: I mentioned this earlier. Here's the interesting thing. A computer
Speaker 1: can have a bias, and that's because computers are working
Speaker 1: off of algorithms that were ultimately designed by human beings,
Speaker 1: and human beings have bias. If I were to set
Speaker 1: out to create something, I'd be drawing on my own
Speaker 1: personal experiences and my own knowledge. But that is a
Speaker 1: tiny sliver of the spectrum of human experience. The same
Speaker 1: is true for people who design machine learning algorithms. As
Speaker 1: a result, those algorithms might overlook or misidentified data points
Speaker 1: that fall outside the experience of the architect who designed
Speaker 1: that machine learning tool, and depending upon the nature of
Speaker 1: the AI, that could be disastrous. So, for example, back
Speaker 1: in two thousand nine, Hewlett Packard had to deal with
Speaker 1: a scandal. They had these cameras that had image recognition
Speaker 1: software built into the camera, and it would identify a
Speaker 1: person's face so that it would focus properly on a
Speaker 1: on a the subject of your photo. Assume that if
Speaker 1: you had a person in the picture, that you wanted
Speaker 1: the person to be in focus. However, they failed to
Speaker 1: recognize dark skinned people. So that's a problem where your
Speaker 1: your computer tool is ignoring people because the person who
Speaker 1: designed it had designed it to recognize folks that were
Speaker 1: like themselves. They weren't necessarily thinking about it outside of
Speaker 1: their own realm of experience, which was obviously a pr nightmare.
Speaker 1: Now imagine you have that same issue. But now let's
Speaker 1: go beyond something that is just a public relations problem
Speaker 1: to something even worse than that. Uh, think about self
Speaker 1: driving cars. Self driving cars need to be able to
Speaker 1: recognize pedestrians who are crossing the street. But if you
Speaker 1: have a self driving car that doesn't recognize a dark
Speaker 1: skinned person, then that could lead to tragic results. You
Speaker 1: could have a terrible collision fatalities. These are non trivial issues.
Speaker 1: Biases may appear simply problematic on first blush, but they
Speaker 1: can lead to really catastrophic outcomes, and so in recent months,
Speaker 1: more work has been dedicated to creating systems that eliminate bias,
Speaker 1: and that's easier said than done. The researchers gave an
Speaker 1: example of a biased system with Google Translate as well.
Speaker 1: So Turkish is a language that does not have gendered
Speaker 1: pronouns like he and she. It just doesn't. In Turkish,
Speaker 1: all pronouns he, she, and it are represented by a
Speaker 1: single pronoun oh. The researchers translated she is a doctor
Speaker 1: and he is a nurse from English into Turkish, and
Speaker 1: Google Translate dutifully change the gendered pronouns in English to
Speaker 1: the Turkish genderless pronoun oh. But then they went to
Speaker 1: reverse the process, turned the Turkish phrases back into English.
Speaker 1: Google Translate assigned genders to the pronouns. You know, they
Speaker 1: were both genderless pronouns in Turkish, but in order to
Speaker 1: make it makes sense in English and not be it
Speaker 1: is a doctor and it is a nurse, it assigned genders,
Speaker 1: and it assigned he to the doctor phrase and she
Speaker 1: to the nurse, even though the original English phrases were
Speaker 1: she is a doctor, he is a nurse, translated from
Speaker 1: Turkish back to English into he is a doctor, she
Speaker 1: is a nurse. So the genders on the occupations swapped,
Speaker 1: and that reveals a gender bias in knee translation algorithm.
Speaker 1: That just assumes that if you're talking about a doctor
Speaker 1: and the gender is indeterminate in your original language, then
Speaker 1: it must be a man, which is more than a
Speaker 1: little problematic. So those are just simple examples, but it
Speaker 1: goes much deeper than that. Well, i'll tell you more
Speaker 1: about what the researchers found in their state of the
Speaker 1: AI and as well as an update that has happened
Speaker 1: since that report came out, But first let's take another
Speaker 1: quick break to thank our sponsors. Another really important concept
Speaker 1: and artificial intelligence that the researchers point out is transparency,
Speaker 1: because it's not really enough to have a computer get
Speaker 1: to the right answer. We need to know how it
Speaker 1: got to that answer, and it may turn out that
Speaker 1: the computer system is making a lot of incorrect assumptions
Speaker 1: before it arrives at the right conclusion. Those faulty assumptions
Speaker 1: should be addressed to avoid problems in the future, such
Speaker 1: as future conclusions that are wrong because they depend too
Speaker 1: heavily on faulty assumptions. So AI designers need to build
Speaker 1: in systems that help us check the work of the
Speaker 1: AI to make sure this is not happening. This is
Speaker 1: something that needs to be built into an AI system
Speaker 1: from the beginning, or else we get into what was
Speaker 1: called a black box situation. So a black box is
Speaker 1: where you have a system where all the processes that
Speaker 1: happen inside the system are hidden away from the average person.
Speaker 1: You don't know how the system got to its conclusion,
Speaker 1: and so you don't know if you can trust the
Speaker 1: conclusion or not. That's a problem. This always makes me
Speaker 1: think of the computer Deep Thought, which was the super
Speaker 1: Intelligent computer and Hitchhiker's Guide to the Galaxy. They asked
Speaker 1: the computer what is the meaning to life, the universe
Speaker 1: and everything? And the computer says forty two, Well, you
Speaker 1: don't understand why the computer got to its conclusion. Of
Speaker 1: forty two, because the computer doesn't tell you how it
Speaker 1: got to its answer, It just processes the information and
Speaker 1: then produces the answer. We don't want that situation with AI.
Speaker 1: There's also the danger of inserting changes in data to
Speaker 1: cause AI systems to make big mistakes. By inserting what
Speaker 1: the researchers referred to as adversarial patches, you can cause
Speaker 1: a system to fail. So, in other words, you purposefully
Speaker 1: introduced bad data to make the system start to have
Speaker 1: problems throughout the processing of information. They gave examples of
Speaker 1: image recognition software and showed that by inserting some extra data.
Speaker 1: They included one that was a sticker that had a
Speaker 1: particular design on it, you can override a computer's ability
Speaker 1: to correctly identify an image and cause it to misidentify it.
Speaker 1: In the example they used, they showed a sticker that
Speaker 1: if you put it down in the view of the
Speaker 1: the camera, then the AI would always identify it as
Speaker 1: a toaster, no matter what it was, because the sticker
Speaker 1: was enough to fool the computer into thinking what it
Speaker 1: was looking at was a toaster, even if there was
Speaker 1: a banana saying right next to the sticker. So that's
Speaker 1: a huge problem if you can fool computer vision into
Speaker 1: thinking it seeing one thing when it's something else. Again,
Speaker 1: if we look at the autonomous car example, and you're
Speaker 1: able to think of a way to have the vision
Speaker 1: system of the car, assuming that's relying solely on optics,
Speaker 1: then you've got a real problem on your hands. But
Speaker 1: even if it's relying on multiple sensors, if you find
Speaker 1: ways to fool those sensors or to misdirect them in
Speaker 1: some way, you will cause the technology itself to behave
Speaker 1: in a way that it shouldn't because it's acting on
Speaker 1: the wrong kind of information. The report then goes on
Speaker 1: to address the issue of talent who are working in
Speaker 1: the field of AI. They estimated that twenty two thousand
Speaker 1: pH D educated researchers and engineers are working on AI
Speaker 1: around the globe in some capacity. About five thousand of
Speaker 1: them are very high level researchers. The United States leads
Speaker 1: the world in open positions for jobs relating to AI
Speaker 1: research and development. Google is the leading employer of AI
Speaker 1: talent in the US, but China has produced more pure
Speaker 1: reviewed publications relating to AI than any other country. Next,
Speaker 1: the report looks at how AI has been rolled out
Speaker 1: in various industries, noting that medical imaging and liquid biopsies
Speaker 1: are two effective uses of AI applications to help diagnose patients.
Speaker 1: Healthcare in general is a large area of opportunity for AI.
Speaker 1: Another application of AI is a little less warm and
Speaker 1: fuzzy than healthcare. That would be how governments are starting
Speaker 1: to put AI at work in surveillance operations, such as
Speaker 1: incorporating it in CCTV software to include facial recognition technology.
Speaker 1: I also mentioned Project Maven and previous episodes of tech stuff.
Speaker 1: Project Maven was another example they cited. The report covers
Speaker 1: a ton of other industries from warehouse automation to autonomous vehicles,
Speaker 1: to security, to agriculture to finance, and essentially all industries
Speaker 1: are seeing increased AI roll out, but at different rates.
Speaker 1: So it's not like you're seeing AI suddenly flooding all
Speaker 1: industries at exponentials speed, but they are starting to get
Speaker 1: more of an inroad into every single industry. It's just
Speaker 1: some of them. It's faster than others, but they tend
Speaker 1: to improve efficiencies and they tend to reduce costs. But
Speaker 1: it's also hastening and era of automation that will make
Speaker 1: it imperative to figure out what the heck to do
Speaker 1: when it comes to employment, which the report actually does
Speaker 1: address a little bit later. They also mentioned briefly the
Speaker 1: recent focus on privacy and security in the wake of
Speaker 1: things like the Cambridge Analytica scandal over at Facebook, as
Speaker 1: well as the adoption of g d p R. UH.
Speaker 1: Those are, by the way, unrelated to one another, but
Speaker 1: I've also talked about both of them in recent episodes
Speaker 1: of Tech Stuff. I can't help but think that as
Speaker 1: AI becomes more sophisticated, protecting privacy will become a larger challenge.
Speaker 1: AI will be able to work through large data samples
Speaker 1: and potentially identify individuals within it with very little trouble.
Speaker 1: So in light of something like g DPR, this would
Speaker 1: make a lot more types of data sensitive. We would
Speaker 1: have to identify those is saying you need to have
Speaker 1: this classified under g d p R. This is not
Speaker 1: truly anonymous data because remember, Harvard professor only needed three
Speaker 1: points of data to identify of all adults in the
Speaker 1: United States. That was the person's gender, their birth date,
Speaker 1: and their ZIP code. That's all she needed, and then
Speaker 1: she could identify of the adult US population based on
Speaker 1: those three data points. So when you think about sophisticated
Speaker 1: computer algorithms, and they're intelligent, and they are able to
Speaker 1: work with large data sets very effectively and very quickly,
Speaker 1: you start to see the potential for fewer and fewer
Speaker 1: data points to point to a specific individual. And then
Speaker 1: the concept of anonymized data starts to get really, really fuzzy.
Speaker 1: It's hard to say if a piece of data truly
Speaker 1: is anonymous unless it's just swallowed up by huge amounts
Speaker 1: of other information and you've you've washed it completely of
Speaker 1: its individual status. Otherwise there may be a chance of
Speaker 1: tracing it back to a specific person, and then you
Speaker 1: have the issues of g d p R. After the
Speaker 1: industry section in the report comes a politics section UH
Speaker 1: and that one they look at some survey results that
Speaker 1: address issues relating to AI, including the employment question I
Speaker 1: mentioned earlier. According to the surveys that the researchers were consulting,
Speaker 1: seventy six percent of respondents felt that the inequality between
Speaker 1: the rich and the poor will become much worse than
Speaker 1: it is today as a result of AI and automation. Essentially,
Speaker 1: thinking those who own the systems that have AI roll
Speaker 1: out involved and those who own the businesses are going
Speaker 1: to profit. Uh and then those who are otherwise affected
Speaker 1: are going to find themselves out of work, and you
Speaker 1: will get this increasing gap between the halves and have nots.
Speaker 1: The found it unlikely that the economy will create new,
Speaker 1: better paying jobs as a result of AI and automation,
Speaker 1: so saying that people are pessimistic as being kind of
Speaker 1: an understatement. Also, based on the results cited in the report,
Speaker 1: it seems like most people don't think a universal basic
Speaker 1: income is likely to happen, not that it wouldn't work,
Speaker 1: but that it's not likely to get adopted. The results
Speaker 1: also seemed to indicate many people are concerned about AI's
Speaker 1: potential dangers, ranging from a loss of privacy to more
Speaker 1: existential threats, and that more people favor some form of
Speaker 1: regulation than a fully deregulated approach, with in favor of
Speaker 1: regulation opposed and for not really sure. Uh So, almost
Speaker 1: the same number of people think that AI should have
Speaker 1: some form of regulation attached to it, as I don't
Speaker 1: know one way or the other. That might be due
Speaker 1: to survey wording. We have to remember survey results aren't
Speaker 1: always in negative of how people really feel, because it
Speaker 1: often also relies upon the wording used in the survey
Speaker 1: and how it was administered. The report found that in
Speaker 1: the United States, unemployment is at a seventeen year low.
Speaker 1: Jobs are on the rise, but wages are lagging behind
Speaker 1: job creation. In fact, it found that in the United States,
Speaker 1: labor productivity has increased much more dramatically than compensation rates
Speaker 1: have increased. The researchers also found that many of the
Speaker 1: new jobs that have been created are low paying ones,
Speaker 1: so that's problematic. The researchers are quick to point out
Speaker 1: that you cannot necessarily correlate any of the labor statistics
Speaker 1: directly with the adoption of AI and automation because there
Speaker 1: are so many other factors that are also present. There's
Speaker 1: just not enough information or evidence to support any firm
Speaker 1: conclusions about the impact of AI and automation on jobs. Yet,
Speaker 1: and not only that, the report points out that there
Speaker 1: are quote unquote only two million industrial robots in the
Speaker 1: world right now, and then the US has fewer robots
Speaker 1: in factories compared to countries like Japan, Germany, and Korea.
Speaker 1: The researchers conclude that the report with a few predictions
Speaker 1: of their own. Uh They say that a Chinese research
Speaker 1: lab will produce a significant research breakthrough sometime within the
Speaker 1: next twelve months. A machine learning algorithm will be able
Speaker 1: to design a therapeutic drug that will produce positive results
Speaker 1: in clinical trials within that twelve months, and that US
Speaker 1: and China will scramble to sweep up tech companies in
Speaker 1: Europe and Asia as part of a trade war and
Speaker 1: AI race, kind of like the space race was in
Speaker 1: the sixties and seventies. Uh. And here's an interesting PostScript
Speaker 1: that was not in that initial report. The day I
Speaker 1: finalize these notes for this episode, I received a report
Speaker 1: from Riot Research about an AI bubble and how it
Speaker 1: is due to burst. So this is just on the
Speaker 1: finance side of things, not in the technological side of things.
Speaker 1: It suggested that the return on investments for AI will
Speaker 1: yield quote rather poor results, with this being akin to
Speaker 1: a bubble bursting end quote. It suggests that many smaller
Speaker 1: companies working in AI could end up folding, similar to
Speaker 1: when the VR bubble burst in the nineties, but a
Speaker 1: larger companies like Google will weather the storm and they
Speaker 1: will continue to do R and D work in AI. Essentially,
Speaker 1: the report serves as a warning to investors that they
Speaker 1: should consider carefully where they put their money with regard
Speaker 1: to AI applications, as the amount that they invest is
Speaker 1: going to be greater than the potential yield from those investments.
Speaker 1: They're essentially saying more money is going into artificial intelligence
Speaker 1: than is going to be produced from the results of
Speaker 1: that AI work. At least right now, that may change.
Speaker 1: But here's the weird thing is that are not really weird,
Speaker 1: But here's the kind of self fulfilling prophecy. Is that
Speaker 1: if investors start pulling their money in order to protect
Speaker 1: their investments, they don't want to invest in a in
Speaker 1: an industry that isn't going to return create a return
Speaker 1: on that investment, then as a result, we could see
Speaker 1: developments slow down in AI, and then we start to
Speaker 1: see it plateau, and so it becomes this kind of
Speaker 1: weird self fulfilling prophecy where in the short term you
Speaker 1: may not expect a really good return on investment. That's
Speaker 1: a big risk. But if you end up heating this
Speaker 1: warning and you pull your money from investing in such things,
Speaker 1: then it may never get the chance to prove itself
Speaker 1: in the long run, and it may just put off
Speaker 1: true advancements in AI much further than they would otherwise happen.
Speaker 1: It's a double edged sword kind of situation. Anyway, That
Speaker 1: is the state of artificial intelligence as of the summer
Speaker 1: of Who's to say what will happen in the next
Speaker 1: twelve months. It will be interesting to see where we
Speaker 1: are in twenty nineteen, what role AI is playing in
Speaker 1: various industries and in our lives, and whether or not
Speaker 1: UH it has truly advanced in a noticeable way, or
Speaker 1: if it's just a situation where we get incremental improvements
Speaker 1: and UH an increased rollout, in which case you might say, well,
Speaker 1: things have gotten better, but not to a point where
Speaker 1: you you know your socks are gonna get blown off.
Speaker 1: Who's to say. We'll find out a year from now,
Speaker 1: I suppose. In the meantime, if you have any suggestions
Speaker 1: for future episodes of tech Stuff, you should write me
Speaker 1: and let me know. The email address for the show
Speaker 1: is tech Stuff at how stuff works dot com, or
Speaker 1: draw me a line on Facebook or Twitter. The handle
Speaker 1: there is tech Stuff hs W. You can also follow
Speaker 1: us on Instagram and don't forget Our next episode is
Speaker 1: episode one thousand. I'm letting that sink in. I've done
Speaker 1: a thousand of these. I'm so tired, but I'll see
Speaker 1: you guys on the next episode, and I can't wait
Speaker 1: to talk to you for the next thousand, So I'll
Speaker 1: talk to you again really soon for more on this
Speaker 1: and thousands of other topics because at how stuff Works
Speaker 1: dot com