Speaker 1: Technology with tech Stuff from dot com. Hey there, and
Speaker 1: welcome to tech Stuff. I'm your host Jonathan Strickland. I'm
Speaker 1: a senior writer for how Stuff Works dot com. Where
Speaker 1: do you know what we do? We explain the universe.
Speaker 1: And this topic today is all about a little exchange
Speaker 1: that happened online over the course of several days. Actually,
Speaker 1: it started with Elon Musk and he was addressing a
Speaker 1: governmental body and talking about his view that artificial intelligence
Speaker 1: needs to have strict regulations attached to it in order
Speaker 1: to prevent some sort of catastrophic future, possibly sky Net related,
Speaker 1: where the robots and other artificial intelligent constructs rise up
Speaker 1: against their human masters and crush us under their metaphorical
Speaker 1: or perhaps literal boots. And then you had Mark Zuckerberg
Speaker 1: of Facebook on a live broadcast on Facebook Live from
Speaker 1: his backyard during a barbecue. He was asked a question
Speaker 1: about this sort of thing, and he specifically said that
Speaker 1: he thought this was a very pessimistic view of the
Speaker 1: future of artificial intelligence, and that within five or ten years,
Speaker 1: artificial intelligence would be transforming our lives in ways that
Speaker 1: we can't even imagine, and they would all be awesome
Speaker 1: and fantastic and magical and we should love that. And
Speaker 1: then Musk struck back on Twitter and said that he
Speaker 1: had talked to Zuckerberg about this process before, but frankly,
Speaker 1: Zuckerberg just is out of his depth with artificial intelligence.
Speaker 1: It's not something that he's an expert at, and he's
Speaker 1: really speaking from inexperience. I find this exchange amusing, as
Speaker 1: does a lot of the journalists area of technology. I mean,
Speaker 1: we've got a lot of people who are commenting on this,
Speaker 1: but personally I also find it a little confounding because, uh,
Speaker 1: Elon Musk has said some stuff that contradicts his own
Speaker 1: companies policies if you look at it carefully. Um, he
Speaker 1: has specifically resisted the concept of regulations for self driving cars,
Speaker 1: but you could argue very uh realistically and convincingly, I
Speaker 1: would say that self driving cars are an implementation of
Speaker 1: artificial intelligence. So we're gonna dive into this. We're gonna
Speaker 1: look at the different opinions about artificial intelligence, kind of
Speaker 1: explore the concept of artificial intelligence in general, see where
Speaker 1: it came from, and what does it really mean and
Speaker 1: who's right or as I put it in my notes,
Speaker 1: Musk's position is AI without regulation is going to totally
Speaker 1: kill us, dude. And Zuckerberg's position is a is going
Speaker 1: to improve our lives in countless ways. Bra So who's
Speaker 1: right or neither of them right? Well, to start off with,
Speaker 1: let's talk about the birth of the term artificial intelligence.
Speaker 1: It was coined by John McCarthy, who passed away in
Speaker 1: two thousand eleven at the age of eighty four. He
Speaker 1: worked at Stanford as a professor emeritus of computer science,
Speaker 1: and he also co founded the Artificial Intelligence Project at
Speaker 1: m I T as well as the Stanford Artificial Intelligence Labs.
Speaker 1: So somebody who certainly has had a long and storied
Speaker 1: past in the development of artificial intelligence. He first used
Speaker 1: the term artificial intelligence in a proposal for a summer
Speaker 1: research conference at Dartmouth in nineteen fifty five. It was
Speaker 1: the first time the term ever appeared in a printed publication.
Speaker 1: So what is artificial intelligence? I mean, it's such a
Speaker 1: huge term, has been used by so many people that
Speaker 1: it's lost a lot of its meaning. Also, I should
Speaker 1: point out that John McCarthy I mentioned earlier as a
Speaker 1: one of the creators of Lisp, the programming language that
Speaker 1: was used in artificial intelligence. So if the name sounds familiar.
Speaker 1: It means that you listen to the History of Programming
Speaker 1: Languages episodes, or that you're just familiar with John McCarthy's work.
Speaker 1: But yeah, artificial intelligence is one of those terms that,
Speaker 1: since its introduction, has been used to describe so many
Speaker 1: different things, and used in such a vague way so
Speaker 1: many times that for many people it seems like a
Speaker 1: meaningless term. It's almost like it's just a general catch
Speaker 1: all for the scary possibilities of technology that gets away
Speaker 1: from us. It reminds me of Humpty Dumpty and through
Speaker 1: the Looking Glass. He says that that words mean exactly
Speaker 1: what he wants them to mean, neither more nor less.
Speaker 1: He says, you know who is to be the master?
Speaker 1: That is the only thing that's important. I'm more in
Speaker 1: charge than the words are. So if I use a
Speaker 1: word to mean something, that's what it means. That's what
Speaker 1: I feel artificial intelligence has become for a lot of people.
Speaker 1: And I also think that that's what leads to a
Speaker 1: lot of disagreements. That some people have one idea of
Speaker 1: what artificial intelligence is and other people have a totally
Speaker 1: different idea of what artificial intelligence is. But because they're
Speaker 1: both using the phrase artificial intelligence. It seems like they're
Speaker 1: talking about the same thing, and that's why they're hitting
Speaker 1: some massive disagreements, or at least one of the reasons
Speaker 1: why they are disagreeing. It's really because, if you dig
Speaker 1: down further, they're talking about two different things frequently. Now,
Speaker 1: John McCarthy's use of artificial intelligence was given this context
Speaker 1: in his proposal. Quote the study is to proceed on
Speaker 1: the basis of the conjecture that every aspect of learning
Speaker 1: or any other feature of intelligence can, in principle, be
Speaker 1: so precisely described that a machine can be made to
Speaker 1: simulate it. End quote. Here's the thing about that definition.
Speaker 1: It already is vague because it's talking about the every
Speaker 1: aspect of learning or any other feature of intelligence. We
Speaker 1: have not fully defined what intelligence is within the human experience.
Speaker 1: There are aspects of intelligence that are very vague and fuzzy,
Speaker 1: and we only have kind of a partial understanding of
Speaker 1: what it actually is. An example I might give as consciousness.
Speaker 1: Defining consciousness is a particularly troublesome and difficult thing to
Speaker 1: do in human beings, let alone in machines. So we
Speaker 1: know that consciousness is a manifestation of the brain, the
Speaker 1: idea of the mind being a manifestation of the brain.
Speaker 1: All of this is dependent upon actual physical matter, the
Speaker 1: gray matter in our heads. And we know this because
Speaker 1: various UH diseases, disorders, injuries that affect act consciousness are
Speaker 1: the ones that are affecting the brain. It's if you
Speaker 1: UH suffer an injury to the brain that is in
Speaker 1: one of these areas that define consciousness, your sense of
Speaker 1: consciousness is likewise affected. That tells us that there is
Speaker 1: this physical connection, that there's not this metaphysical mind necessarily
Speaker 1: that is a layer on top of our physical brains.
Speaker 1: But beyond that, defining consciousness is really tricky, and there
Speaker 1: are plenty of psychologists, neurologists, philosophers who have debated the
Speaker 1: nature of consciousness for ages, and we're no closer to
Speaker 1: really defining it than we were back then. Sometimes we
Speaker 1: narrow it down by saying, what isn't consciousness? So we're
Speaker 1: whittling it away, kind of the way the sculptor whittles
Speaker 1: away everything that isn't a statue when they start with
Speaker 1: a block of marble. You know, you just say, are
Speaker 1: you in a car of an elephant? Here's your block
Speaker 1: of marvel. Cut all the stuff that doesn't look like
Speaker 1: an elephant away, and what you're left with is the
Speaker 1: elephant that might be what consciousness is for us. We
Speaker 1: remove all of the concepts that are not consciousness, and
Speaker 1: whatever is left over that becomes our definition. It's not
Speaker 1: exactly satisfying at the moment. Well McCarthy expounded on artificial
Speaker 1: intelligence in nineteen sixty in a paper titled Programs with
Speaker 1: Common Sense, which kind of gives you another perspective of
Speaker 1: what artificial intelligence could be. And this leads us to
Speaker 1: ask other questions like what exactly kind of what? What
Speaker 1: are the implementations of artificial intelligence? What what types are there?
Speaker 1: And again, the number of types of AI depends upon
Speaker 1: whom you ask and how they frame the answer. They're
Speaker 1: simple answers where some people will say, oh, there's two
Speaker 1: types of AI. They're strong AI and there's weak a I.
Speaker 1: Or some people will say general AI and narrow AI.
Speaker 1: Others will say no, no, no, there's like thirty three
Speaker 1: types of artificial intelligence. Or they might say there's three
Speaker 1: or four types of artificial intelligence. With those two types,
Speaker 1: those are the easiest to kind of explain in broad categorizations.
Speaker 1: Narrow or weak AI is the artificial intelligence we would
Speaker 1: create that's dedicated to a narrow task or series of tasks.
Speaker 1: Strong AI is generally understood to mean a machine that
Speaker 1: has sentience, consciousness, and mind, those qualities that we associate
Speaker 1: with human intelligence. But again, we cannot even fully describe
Speaker 1: those concepts within the context of humans, so trying to
Speaker 1: figure out how to imbue machines with those elements is
Speaker 1: even more complicated. Now there's also the concept of general intelligence.
Speaker 1: This is not reliant upon consciousness or mind or sentience.
Speaker 1: With general intelligence, the idea is that a machine would
Speaker 1: be able to apply intelligence to any problem rather than
Speaker 1: just a specific or narrow band of problems. So, in
Speaker 1: other words, a generally intelligent machine could be used to
Speaker 1: solve problems of various degrees and various contexts. So you
Speaker 1: might have a general intelligence robot, and the general intelligence
Speaker 1: robot can do things like figure out how to manipulate
Speaker 1: physical objects, how to maneuver around within an environment, UH,
Speaker 1: and a few other elements as well. It's having a
Speaker 1: more general approach to problem solving as opposed to something
Speaker 1: that was made specifically to handle a particular task UH.
Speaker 1: But general intelligence. True general intelligence would be capable of
Speaker 1: applying an intelligen an approach to any kind of problem,
Speaker 1: not just a related family of problems. In an article
Speaker 1: for Government Technology, there was a writer named Arrend Hints
Speaker 1: of Michigan State University who laid out four broad categories
Speaker 1: of artificially intelligent machines. And these would go well into
Speaker 1: pretty sophisticated artificial intelligence. From the very get go um
Speaker 1: I argue that artificial intelligence is composed of lots of
Speaker 1: different facets, and you can find elements of artificial intelligence
Speaker 1: and many different programs that exist today, none of which
Speaker 1: are approaching this general intelligence model, and certainly not approaching
Speaker 1: strong AI. But here's how Hence breaks it down. He says,
Speaker 1: Type one is a reactive kind of intelligence. These are
Speaker 1: machines that take action in response to some state, and
Speaker 1: they don't form memories, they don't use past experience to
Speaker 1: inform current decisions. And he argues Deep Blue IBM's Deep
Speaker 1: Blue was that kind of a machine, and Deep Blue
Speaker 1: was the computer that defeated Gary Kasparov in a series
Speaker 1: of chess matches in the nineties. Actually, the first series
Speaker 1: ended in a draw between the two. The second series
Speaker 1: Deep Blue one, and then IBM quickly um ended up
Speaker 1: retiring Deep Blue from that point forward. But Deep Blue
Speaker 1: would just look at the state of the chessboard at
Speaker 1: any given moment and then make a decision based upon
Speaker 1: that state. When it was Deep Blues turn, it didn't
Speaker 1: build up a series of decisions, didn't track what was
Speaker 1: happening turn overturn. Uh, So it didn't evolve in any way.
Speaker 1: It had no internal representation of the world. It just
Speaker 1: would look at what was happening right now and make
Speaker 1: a decision. Now, there was a researcher named Rodney Brooks
Speaker 1: at AI researcher who said, these type one machines are
Speaker 1: the only ones we should ever try to make, because
Speaker 1: to make a machine more intelligent, one that contains a
Speaker 1: virtual representation of the world, would be impossible. That we
Speaker 1: as humans would be incapable of building a virtual representation
Speaker 1: that is accurate enough for such a machine to make
Speaker 1: good decisions. It would have a faulty representation of the world,
Speaker 1: and therefore any decision it would make would not be
Speaker 1: ideal and it would potentially do more harm than good. Uh.
Speaker 1: These sorts of machines are always going to make the
Speaker 1: same decision given a certain set of criteria. So let's
Speaker 1: go back with Deep Blue. Let's say the Deep Blue
Speaker 1: is looking at the board, the chess board. It is
Speaker 1: Deep Blues turn. It's been maybe you know, a half
Speaker 1: dozen turns in the chess game, and it makes a
Speaker 1: decision base upon all the positions of the pieces that
Speaker 1: are in play at that time. This decision is based
Speaker 1: on the probabilities of other moves that the opponent might make,
Speaker 1: the strength of any given move against what the current
Speaker 1: conditions are. There are a lot of factors that go
Speaker 1: into that one decision, But the argument goes that type
Speaker 1: one machines will always come to the same conclusion given
Speaker 1: that same set of criteria. So, in other words, if
Speaker 1: in game one, Deep Blue is given this arrangement of pieces,
Speaker 1: it will make a specific decision by weighing all those
Speaker 1: options out and going with the best one. In game two,
Speaker 1: if that exact same configuration of pieces were to be
Speaker 1: presented to Deep Blue and it's Deep Blues turn, it
Speaker 1: would make the same decision. In that case, It's not
Speaker 1: gonna improvise, it's not going to change, it's not going
Speaker 1: to learn from its past experience, is it is just
Speaker 1: going to make a decision based upon the parameters that
Speaker 1: are in front of it at that very moment. Now,
Speaker 1: this is not necessarily a bad thing. You may want
Speaker 1: certain artificially intelligent machines to be very predictable in their response.
Speaker 1: If you have a smart thermostat and it's been trained
Speaker 1: to learn what you prefer over time, and it's learned
Speaker 1: that you like it cool in the mornings, so you
Speaker 1: like a nice maybe seventy degrees for your thermostat fahrenheit,
Speaker 1: that is obviously, Uh, it's not going to improvise one
Speaker 1: day and say, you know what, I'm just gonna try
Speaker 1: something here. I bet he's really gonna like it. If
Speaker 1: I said eighty eight degrees fahrenheit on a humid Georgia
Speaker 1: July morning, Let's see what happens next. What happens next
Speaker 1: is Jonathan just sweats like crazy. So there are implementations
Speaker 1: where you would want to type one AI machine and
Speaker 1: nothing more advanced than that. Then Type two intelligent machines
Speaker 1: would be ones that have at least some version of
Speaker 1: a memory. They can track changing variables over time and
Speaker 1: analyze past behavior before making a decision on a course
Speaker 1: of action. Now, this memory doesn't go into a lexicon
Speaker 1: of memories. It's like short term memory that never gets
Speaker 1: converted into long term memory. It's stored temporarily, kind of
Speaker 1: like random axis memory is in computers, and then after
Speaker 1: that short while it can be overwritten. These machines would
Speaker 1: be able to do a little bit more than what
Speaker 1: Deep Blue could do. It wouldn't just be making a
Speaker 1: decision based upon the current state of the game. It
Speaker 1: would also remember how the last few moves went and
Speaker 1: what brought it there, and how the other player had
Speaker 1: been playing, and might be able to take that information
Speaker 1: and incorporate that in its decisions for the following moves,
Speaker 1: which means it could potentially adapt its play style. So
Speaker 1: this is a slightly more sophisticated version of the intelligent machine.
Speaker 1: Now we've got two more types of intelligent machines to cover.
Speaker 1: But before I jump into type three in type four
Speaker 1: and then go further into this discussion about artificial intelligence,
Speaker 1: let's take a quick break to thank our sponsor. Now,
Speaker 1: a type three intelligent machine incorporates what he says is
Speaker 1: a theory of mind. These machines would have an internal
Speaker 1: concept of the world as well as the beings that
Speaker 1: actually inhabit that world, and an understanding that those beings
Speaker 1: also possess intelligence that guide their behaviors. In a way,
Speaker 1: you could think of this as awareness of others. So
Speaker 1: these machines would know that people aren't just bags of
Speaker 1: meat that do things, that we have intelligence, and that
Speaker 1: that in fact guides our behavior and understanding that then
Speaker 1: affects the decisions that the AI makes. But this is
Speaker 1: still not quite at that level of strong AI that
Speaker 1: I was mentioning earlier. To get there, you have to
Speaker 1: hit type four machine intelligence, and that is when you
Speaker 1: hit self awareness, where the machine is not just aware
Speaker 1: that other beings possess the quality of intelligence, but it
Speaker 1: is aware of its own self and its own state
Speaker 1: and its own being in relation with everything else. It
Speaker 1: is this sort of machine that could, in theory, start
Speaker 1: to design improvements to itself so it could be recursive
Speaker 1: and that it is able to make improvements. And then
Speaker 1: you get into the situation that some futurists think of
Speaker 1: as the singularity, where you have self improving, artificially intelligent
Speaker 1: machines that are able to evolve. It's such a remarkable
Speaker 1: rate where every generation of improvements is a huge leap
Speaker 1: from the last one, and it's taking less and less
Speaker 1: time between generations for things to change that it becomes
Speaker 1: impossible to describe what the present set of circumstances are
Speaker 1: because the present would be changing so quickly that it
Speaker 1: becomes a meaningless concept. This is the singularity. That is
Speaker 1: one potential outcome of this instance, if it were in
Speaker 1: fact possible, which we don't know if it is possible yet.
Speaker 1: It's some people treated as a foregone conclusion that we
Speaker 1: will eventually have machines that will be able to attain
Speaker 1: self awareness and potentially self improvement. And once you get
Speaker 1: to that stage, how do you avoid this singularity? People
Speaker 1: would argue that's impossible to avoid, But there are a
Speaker 1: lot of people who say, we have no reason to
Speaker 1: believe that this is something that is going to happen,
Speaker 1: or that's even possible from a technological perspective, or maybe
Speaker 1: possible from a technological perspective, But we're talking decades, if
Speaker 1: not a century or more out in front of us,
Speaker 1: based upon our limited understanding of intelligence and our limited
Speaker 1: amount of processing power. When you compare it to something
Speaker 1: like the human mind, keeping in mind that the human
Speaker 1: brain has got billions of neurons in it, and we
Speaker 1: have artificial neuron networks, but they are dwarfed by the
Speaker 1: connections that you find in the human brain. So there's
Speaker 1: a lot of heated debate in the artificial intelligent world
Speaker 1: about whether or not this is something we should even
Speaker 1: concern ourselves with. But some people say that self awareness
Speaker 1: could arise from a system that has a given amount
Speaker 1: of complex city without us having any deeper understanding of
Speaker 1: what consciousness is. In other words, if you were to
Speaker 1: make a machine that was complex enough, consciousness could be
Speaker 1: an emergent behavior, something that naturally occurs once you reach
Speaker 1: a system of significant complexity. That's a little difficult to
Speaker 1: wrap your head around, but keep in mind we humans
Speaker 1: have been harnessing and creating stuff without having a full
Speaker 1: understanding of it. For ages. We were using electricity well
Speaker 1: before we understood the actual physics of electricity, So it's
Speaker 1: a little different. I mean, you can't compare electricity to
Speaker 1: consciousness directly, it doesn't make any sense. But just to
Speaker 1: say there is precedence in human beings creating something that
Speaker 1: they do not fully understand whether or not that's actually possible, However,
Speaker 1: is it we don't know. There's no way for us
Speaker 1: to answer. There is no construct, no machine complex enough
Speaker 1: for us to run an experiment and see if consciousness arises.
Speaker 1: And if it does arise, how do we recognize it?
Speaker 1: How do we know that a machine actually possesses that
Speaker 1: that feature? How do we know a machine truly has
Speaker 1: become self aware and conscious? We'll talk about that a
Speaker 1: little bit more later on, because, of course, there are
Speaker 1: a lot of people who have come up with ideas
Speaker 1: on how we would judge whether or not a machine
Speaker 1: had achieved consciousness, and some of them are more serious
Speaker 1: than others. There's also some serious objections. Well, those are
Speaker 1: the four types that were laid out by this one
Speaker 1: person from Michigan State University. But then, uh, John Spacey
Speaker 1: over on Simplicable has an article where he talks about
Speaker 1: thirty three types of artificial intelligence thirty three. Now, I'm
Speaker 1: not going to go through all thirty three, and I
Speaker 1: also want to point out that the thirty three types
Speaker 1: he points out are really more like thirty three facets
Speaker 1: of artificial intelligence. It's not thirty three degrees of artificial
Speaker 1: intelligence where we start with dumb machine and we end
Speaker 1: with super smart robo master. It's more like different aspects
Speaker 1: of intelligence that are in various stages of development and
Speaker 1: research in the artificial intelligence field. So it's not really
Speaker 1: separate categories. It's more like specific implementations of intelligence. An
Speaker 1: example would be effective computing, effective being a F F E, C, T,
Speaker 1: I V E as into affect something. Effective computing tries
Speaker 1: to suss out the emotions people are experiencing and to
Speaker 1: behave appropriately according to the parameters of the programming, not
Speaker 1: according to social rules necessarily, So these are machines that
Speaker 1: would be able to recognize emotions and respond in a
Speaker 1: way that was appropriate compared to their actual programming. Another
Speaker 1: type that Space lists is computer vision, which is the
Speaker 1: area of computer science focused on analyzing and understanding visual
Speaker 1: information computationally. So I've talked about this in episodes of
Speaker 1: tech Stuff in the past. You've heard about various projects
Speaker 1: to train computers to recognize images like pictures of cats
Speaker 1: versus other stuff like pictures of Deloreans. So this, as
Speaker 1: it turns out, it's really hard to do. This is
Speaker 1: one of those gaps we see between human intelligence and
Speaker 1: machine intelligence. Even with deep learning and artificial neural networks.
Speaker 1: This is really tricky stuff. It doesn't take a long
Speaker 1: time to teach your typical human concepts that they can
Speaker 1: then apply across a broad spectrum of examples. So I
Speaker 1: like to use the example specific example of a coffee mug,
Speaker 1: or just a mug a mug. So imagine a mug. Now,
Speaker 1: the mug you are imagining probably looks a certain way.
Speaker 1: But if I were to show you a totally different mug,
Speaker 1: you would recognize that as a mug, even if it
Speaker 1: was a different size, different color, different shape of the handle,
Speaker 1: different shape of the container itself, As long as it
Speaker 1: adhered to the general parameters that we associate with the
Speaker 1: concept of a mug, you know what I was talking about,
Speaker 1: You would know that that was a mug. Computers not
Speaker 1: that good at this, right, Like it might require you
Speaker 1: to train a computer by showing it thousands or tens
Speaker 1: of thousands of images of mugs so that it builds
Speaker 1: up the various elements that define mug nous, so that
Speaker 1: if it were to look at a brand new image
Speaker 1: of a mug that is unlike all the others that
Speaker 1: have preceded it, it would still be able to identify
Speaker 1: that as Yes, that is a mug. This is hard
Speaker 1: to do. We've seen some advances in this field, but
Speaker 1: it demonstrates the huge gap in that specific part of
Speaker 1: intelligence between machines and humans. That isn't to say that
Speaker 1: it's not getting better with machines. It is, but that's
Speaker 1: just one example that I wanted to give, and it
Speaker 1: kind of hammers home this idea that general intelligence with
Speaker 1: machines is a long way off. There's just so many
Speaker 1: different aspects of it. Um Beyond this Spacey continues to
Speaker 1: define various terms within AI, and I don't necessarily think
Speaker 1: of them as types of artificial intelligence, but again aspects
Speaker 1: of artificial intelligence. Not every AI implementation will need all
Speaker 1: of these aspects. Some of them are going to be
Speaker 1: much better with a very narrow range of artificial intelligent features.
Speaker 1: For example, your rumba probably does not need to be
Speaker 1: able to pick up on what your mood is, whether
Speaker 1: or not you're sad, or anything along those lines. But
Speaker 1: if you are talking about a machine that needs to
Speaker 1: have general intelligence in order to solve any given problem
Speaker 1: in front of it, it will need to have many,
Speaker 1: if not all, of those aspects of artificial intelligence in
Speaker 1: order to uh address any problem presented to it. So
Speaker 1: this kind of gives you an idea of why talking
Speaker 1: about artificial intelligence is tricky because people are thinking about
Speaker 1: in very different terms. Some people focus on the specific
Speaker 1: elements within artificial intelligence that are aspects of general intelligence.
Speaker 1: There's aspects of human intelligence, but they're very specific. It's
Speaker 1: not general AI, like, it's not an intelligent machine that
Speaker 1: you could hold a conversation with. It's more about a
Speaker 1: specific element of being able to analyze information and make
Speaker 1: UH conclusions based on the information and potentially end up
Speaker 1: making a course of action based on those conclusions. There's
Speaker 1: so many different aspects of that within human intelligence that
Speaker 1: it makes it tricky to just say AI and paint
Speaker 1: with that broad brush. I think that ends up being misleading.
Speaker 1: Now next, I'm gonna dive into some interesting ways of
Speaker 1: thinking about AI as. Then can you determine whether or
Speaker 1: not a machine actually does possess intelligence? How would we
Speaker 1: know that? How does one get to the conclusion that
Speaker 1: a machine can actually be intelligent? So if you were
Speaker 1: to look at something like at Stanford, there was a
Speaker 1: computer program, a machine designed that could UH observe the
Speaker 1: movements of a pendulum and based upon those movements and
Speaker 1: multiple observations of those movements. The machine was able to
Speaker 1: suss out the basic laws of motion just by looking
Speaker 1: at the movements of a pendulum. It was able to
Speaker 1: analyze those movements and come up with the laws of
Speaker 1: motion over the course of several hours, which had taken
Speaker 1: human centuries to do. Is that truly an intelligent machine.
Speaker 1: It doesn't necessarily understand anything else. It might be very
Speaker 1: intelligent that specific implementation. How do we know when a
Speaker 1: machine is intelligent. We'll take a look at some potential
Speaker 1: answers of that question in just a minute, but first
Speaker 1: let's take another quick break to thank our sponsor. Okay,
Speaker 1: so we've got Musk and Zuckerberg butting heads about whether
Speaker 1: or not artificial intelligence is going to end us. Let's
Speaker 1: say that we're getting to a point where artificial intelligence
Speaker 1: is approaching something that is similar to what most people
Speaker 1: think of when they hear artificial intelligence. I argue that
Speaker 1: most people when they hear AI, they think of a
Speaker 1: machine that's capable of processing information in a way that
Speaker 1: is analogous to the way humans think. Now I know
Speaker 1: that you guys realize artificial intelligence covers a whole spectrum
Speaker 1: of topics of computer sciences of psychology, UH of data
Speaker 1: processing that don't necessarily equate directly to thinking like a
Speaker 1: human being. But the average person, I would say, think
Speaker 1: AI means that a computer quote unquote thinks the way
Speaker 1: a human does. How would we know when we reached
Speaker 1: that point. Well, a lot of people like to point
Speaker 1: at the Turing test because it's largely through a misunderstanding
Speaker 1: what The Touring test was named after Alan Turing. It
Speaker 1: was proposed in nineteen fifty. The Turing test is interesting
Speaker 1: because Touring was saying, if you were to create an
Speaker 1: artificially intelligent machine, not even are officially intelligent. If you
Speaker 1: were to create a machine that could converse with a
Speaker 1: person in such a way that the person could not
Speaker 1: be certain that the entity they talked to was either
Speaker 1: another human being or a machine, you would have to
Speaker 1: say that that machine possessed intelligence. He would pass. The
Speaker 1: Turing test is the way we often will say it today,
Speaker 1: So typically you see experiments running this way. Contests are
Speaker 1: frequently held to see if any chatbots can beat the
Speaker 1: Touring test, and the way it typically works is that
Speaker 1: you have a series of online interactions and people will
Speaker 1: go through the log into a computer terminal, and there'll
Speaker 1: be a text based communications platform like instant messaging or
Speaker 1: a chat room, something along those lines. They type in
Speaker 1: their and their their questions, their sentences, with their introductions,
Speaker 1: whatever it may be, and then they're getting responses back.
Speaker 1: Those responses maybe from another human being, or they may
Speaker 1: be from a computer. And essentially they say that if
Speaker 1: a certain percent edge of the people going through this
Speaker 1: process are incapable of reliably detecting whether it's a human
Speaker 1: or a computer they're talking to, then the computer is
Speaker 1: said to have passed the Turing test because it is
Speaker 1: able to replicate the behaviors of a person so realistically
Speaker 1: as to be indistinguishable from a person. And Touring would say,
Speaker 1: if you were to encounter another human being and that
Speaker 1: person was to hold a conversation with you, you would
Speaker 1: go ahead and assume that that other human being possesses
Speaker 1: the quality of intelligence. We cannot be sure that anyone
Speaker 1: we interact with possesses intelligence because we cannot inhabit that
Speaker 1: person's being. If I have a conversation with you and
Speaker 1: you are talking back with me, I can't be sure
Speaker 1: that you're intelligent because I cannot be you, just as
Speaker 1: you cannot be sure I am intelligent because you cannot
Speaker 1: be me. But based upon my sentences to you, my
Speaker 1: communication with you, the fact that I'm listening to you,
Speaker 1: responding to what you have to say, and you in
Speaker 1: turn are doing the same with me, we would assume
Speaker 1: we each possess that quality known as intelligence. And Touring said,
Speaker 1: if a machine can fool you into thinking it's a person,
Speaker 1: you might as well extend it the same courtesy. If
Speaker 1: you cannot tell that it's a machine, and you would
Speaker 1: assume that a human being would have intelligence, then why
Speaker 1: would you not assume the machine itself to have intelligence.
Speaker 1: This is sort of a cheeky way of talking about
Speaker 1: machine intelligence. I remember this proceeded coining the phrase artificial
Speaker 1: intelligence in the first place, So Touring was kind of
Speaker 1: having a little bit of fun with this. And there
Speaker 1: have been contests to create chatbots to see if they
Speaker 1: can beat the Turing test, and there have been at
Speaker 1: least two or three that have said, yes, we did it,
Speaker 1: but they all kind of have a little asterisk after them. So,
Speaker 1: for example, there was one from a few years ago
Speaker 1: where a group had built a chat bot that was
Speaker 1: claiming to be a young boy who did not speak
Speaker 1: English as his first language, but all the communications had
Speaker 1: to be in English. That was part of the actual event.
Speaker 1: All of the chat bots were supposed to communicate in English,
Speaker 1: and all the people who were interacting were supposed to
Speaker 1: be communicating in English as well. But this construct was
Speaker 1: claiming to be a young boy from I want to say,
Speaker 1: uh a uh, from Russia or from the Ukraine. It
Speaker 1: might have been a Ukrainian identity, and the boy did
Speaker 1: not have a very deep understanding of pop culture in
Speaker 1: the West. Uh and I had a lot of limitations.
Speaker 1: But because it those limitations were known. You know, if
Speaker 1: you're communicating with this chat bot and the chat butt
Speaker 1: claims to be a young boy from the Ukraine and
Speaker 1: doesn't speak English as a first language, you're gonna cut
Speaker 1: that chat bottle lot of slack because you're gonna think, well,
Speaker 1: anything that appears out of the ordinary as far as
Speaker 1: syntax or grammar is concerned, it is probably because English
Speaker 1: is not his first language. Any gaps in knowledge are
Speaker 1: due to the fact that one he has limited exposure
Speaker 1: to the same sort of things that I have experienced.
Speaker 1: And he's young, so he's not gonna know a lot
Speaker 1: about older pop culture references. When you start putting those
Speaker 1: limitations in where you expect less from the person you're
Speaker 1: communicating with because of those limitations, it becomes easier, as
Speaker 1: kind of a tricky word, but I'll go ahead and
Speaker 1: use it, easier to fool someone into thinking that the
Speaker 1: chat bot is an actual person because they're there. Level
Speaker 1: of expectation has been lowered based upon the actual scenario.
Speaker 1: There has not been to date a chatbot that has
Speaker 1: beaten the Turing test as representing a person who natively
Speaker 1: speaks the language in question with a reasonable body of
Speaker 1: knowledge about the world and how the world works. No
Speaker 1: chatbot has come close to that yet. Even if it did,
Speaker 1: would you say that such a chat bot actually possessed
Speaker 1: true intelligence or would it just seem like it did.
Speaker 1: So let's look at Watson IBMS platform that was famous
Speaker 1: for winning a game of Jeopardy against two former champions.
Speaker 1: It was able to come up with questions for the
Speaker 1: various answers. That's the way jeopardy works. If you're not
Speaker 1: familiar with the game show Jeopardy, uh, the clues are
Speaker 1: given to you in the form of an answer. You
Speaker 1: have to come up with a question that relates to that.
Speaker 1: So if you said, if it said he is credited
Speaker 1: with inventing the lightbulb, you would say, who was Thomas
Speaker 1: Edison Well Watson? This IBM construct this, this collection of
Speaker 1: a p I S really is what it is. Was
Speaker 1: on top of an enormous platform of of computers with
Speaker 1: thousands of processors to to run all the number of
Speaker 1: crunching that was going on behind the scenes. It had
Speaker 1: a big, big database of information, and it was able
Speaker 1: to weigh potential responses to any given clue, and if
Speaker 1: it reached a certain threshold of confidence, it would submit
Speaker 1: that as its response. So let's say it was an
Speaker 1: eighty percent confidence. I think that was around the mark.
Speaker 1: If the machine goes through its various databases find something
Speaker 1: that meets a match with the clue with eight percent
Speaker 1: confidence are greater, it would buzz in and submit that,
Speaker 1: and more often than not it was right. But was
Speaker 1: it truly intelligent? Because it could seem to understand things
Speaker 1: like wordplay and references that were not direct references, it
Speaker 1: seemed very clever, but you wouldn't say that it actually
Speaker 1: possesses the same sort of intelligence that a human being does.
Speaker 1: Even with that implementation, one of the biggest objections, or
Speaker 1: rather challenges to machine intelligence and whether or not a
Speaker 1: machine could ever be intelligent, is called the Chinese room aregument. Now,
Speaker 1: this was proposed by John Searle s E. A. R. L. E.
Speaker 1: It's a philosophical thought experiment that really challenges this idea
Speaker 1: that machines could be said to think or possess intelligence.
Speaker 1: And he creates an analogy to computers to show how
Speaker 1: a machine might appear to understand what's happening and yet
Speaker 1: not have any actual intrinsic understanding. So he says, let's
Speaker 1: take an experiment. Let's say that you are locked in
Speaker 1: a room, and in that room, you've got a table,
Speaker 1: you've got some paper, you've got a pen, and you've
Speaker 1: got an enormous book of instructions. And occasionally somebody from
Speaker 1: outside the room slips a piece of paper under the door,
Speaker 1: and when you pick up the paper, it has a
Speaker 1: Chinese symbol on it, and you don't understand Chinese. You
Speaker 1: you only speak English in this scenario, even if you
Speaker 1: are a multi lingual out there, just imagine for the
Speaker 1: moment that you only understand English. The book you have
Speaker 1: the set of instructions has all these different Chinese symbols,
Speaker 1: Chinese characters inside the book with specific instructions of what
Speaker 1: to do when you get any particular Chinese character. So
Speaker 1: you look at the one that's on your page that's
Speaker 1: been slipped under the door, and you go through the
Speaker 1: book and you look for a match and you find
Speaker 1: Imagine it says, when you get this symbol, draw this
Speaker 1: other symbol and then slip it under the door. So
Speaker 1: you do, you draw this other symbol and you slip
Speaker 1: it under the door. Now, to a person outside the room,
Speaker 1: it looks like you understand what is happening. You have
Speaker 1: that person outside the room has written down something in Chinese,
Speaker 1: slipped it under the door, and received a response in
Speaker 1: Chinese in return. So to that person, you appear to
Speaker 1: be understanding what's going on. But to you, because you're monolingual,
Speaker 1: you only speak English, you only understand English, you don't
Speaker 1: actually understand what those symbols mean. You don't know what
Speaker 1: the symbols coming in mean, and you don't know what
Speaker 1: the symbols you're writing mean. You're just following a set
Speaker 1: of very specific instructions. Searle says, that's what machines are doing.
Speaker 1: They might appear to be understanding you, but really they're
Speaker 1: just following instructions based upon the input they receive. And
Speaker 1: then there's no deeper level than that. It's really when
Speaker 1: they boiled down to it, a grand if then statement,
Speaker 1: if you receive this, then deliver that, Which is an
Speaker 1: interesting idea to say that the person inside the room
Speaker 1: doesn't understand Chinese. They don't. They don't know the meaning
Speaker 1: of the symbols in either direction, but they're still delivering
Speaker 1: properly based upon following those instructions. Could a computer be
Speaker 1: said to be intelligent if that's all it's ultimately doing.
Speaker 1: There are objections to this argument. One of them I'm
Speaker 1: just going to illustrate one is that the person in
Speaker 1: the room is not the whole system. They're one component
Speaker 1: of the system. In a computer, you could argue the
Speaker 1: person represents the processor, for example, and maybe the book
Speaker 1: represents the memory. But if you were to take the
Speaker 1: whole thing, the room, the book, the person, the paper,
Speaker 1: the pen, you group all of that together as a system.
Speaker 1: Some people say, well, no, the system itself as a
Speaker 1: whole quote unquote understands Chinese. Even if the one component
Speaker 1: within the system does not. Searle said, I call shenanigans
Speaker 1: on that argument, because what if you just memorize all
Speaker 1: the instructions, so you've internalized it all. You know everything
Speaker 1: you're supposed to do. Whenever you are encountering a specific
Speaker 1: Chinese character, you see the Chinese character, you know what
Speaker 1: the response is supposed to be. You don't have to
Speaker 1: consult a book or anything. You still don't understand what
Speaker 1: it is you're doing. You're just following the instructions that
Speaker 1: you know you're supposed to follow. That Searl says, does
Speaker 1: not represent intelligence. Now, there are a lot of other
Speaker 1: objections to Searle's thought experiment. There's a lot of heated
Speaker 1: debate about the Chinese room argument, and it's all very fascinating,
Speaker 1: And if you think this is interesting, you should definitely
Speaker 1: look up Chinese room argument because there's a lot that's
Speaker 1: been written about it, and it's amazing to really put
Speaker 1: your mind to it and start thinking about the philosophy
Speaker 1: of intelligence and whether or not it would ever be
Speaker 1: possible for us to truly determine if a machine possess
Speaker 1: that quality. But there some practical things that we should
Speaker 1: consider too. Now, again, Musk was worried about machines potentially
Speaker 1: turning on people. I mean, the example he gave was
Speaker 1: a robot going down the street and killing everybody. Uh.
Speaker 1: Deciding to do this not entirely possible. For a robot
Speaker 1: that has any sort of deadly UH abilities, whether it's
Speaker 1: a soldier robot or something else, there's possibilities of malfunctions
Speaker 1: or misidentifying someone as a target as opposed to a uh,
Speaker 1: you know, an innocent person. And in those cases you
Speaker 1: would say, well, that's clearly a programming error. But it's
Speaker 1: not like the machine is deciding to turn against humans.
Speaker 1: It's more like the machine is making an incorrect conclusion
Speaker 1: based upon its programming. And again you might say, well,
Speaker 1: that doesn't have anything to do with artificial intelligence. It
Speaker 1: doesn't have anything to do at least intrinsically with this
Speaker 1: concept of AI. UH. And if you were to create regulations,
Speaker 1: how do you regulate that? How do you regulate artificial intelligence?
Speaker 1: Where do you put the limitations? What? At what point
Speaker 1: do you say, don't let computers do this? Because if
Speaker 1: you cannot define the problem, how do you create the
Speaker 1: limitation to prevent the problem from happening? And a lot
Speaker 1: of people argue that no one is able to really
Speaker 1: define what this problem is. People are worried about an
Speaker 1: abstract concept that they cannot define, and therefore there's no
Speaker 1: way to create a regulation that is remotely ah relatable
Speaker 1: to the issue. If you can't define the problem, you
Speaker 1: cannot create a solution to it. Some people point at
Speaker 1: different problems, not an existential crisis where robots are seeking
Speaker 1: us out and turning us into fertilizer, but perhaps a
Speaker 1: few sure where automation itself is taking away enough jobs
Speaker 1: to cause massive economic crises. And there's been a lot
Speaker 1: written about this over the last few years. It seems
Speaker 1: like every month another article comes out with either a
Speaker 1: terribly pessimistic UH prediction of how many jobs will be
Speaker 1: lost due to automation within the next five years, or
Speaker 1: a completely optimistic point of view of how many jobs
Speaker 1: are going to be created as a result of automation
Speaker 1: and therefore people are going to have better jobs. Those
Speaker 1: who are all four automation say the jobs that are
Speaker 1: going to be UH phased out by automation are going
Speaker 1: to be the ones that people don't want to do
Speaker 1: in the first place. They're gonna be the dirty dangerous
Speaker 1: and dull jobs. So jobs that are either repetitive and
Speaker 1: are not interesting and therefore no one wants to do them. UH,
Speaker 1: jobs that put people at risk, and therefore it would
Speaker 1: be better to put a machine at risk because you
Speaker 1: can replace a machine, but you can't really replace a
Speaker 1: human or the jobs that are just not they take
Speaker 1: too much in human effort to do uh, and the
Speaker 1: payoff does not equal the amount of effort needed in
Speaker 1: order to complete the job. There are others who say, well,
Speaker 1: automation is gonna go to jobs that are the easiest
Speaker 1: to automate, which are not always going to necessarily be
Speaker 1: ones that fall into those categories. And then you've got
Speaker 1: people who maybe in an area of the workforce where
Speaker 1: they don't have the training or education to pursue jobs
Speaker 1: that are at a higher level necessarily. Their counter arguments
Speaker 1: to this as well. Some people say that automation will
Speaker 1: create more jobs because they'll create more opportunities, with the
Speaker 1: example of say something like the automation of an Amazon warehouse.
Speaker 1: One of the arguments is that automation will bring prices down.
Speaker 1: As prices come down, people will buy more. As people
Speaker 1: buy more, these warehouses will have to get bigger. As
Speaker 1: the warehouses get bigger, more humans will be needed. Even
Speaker 1: though each human will be responsible for less stuff, they'll
Speaker 1: be such a large demand for things that that will
Speaker 1: more than compensate for it. This argument is based off
Speaker 1: the Industrial Revolution, when the loom was created and people
Speaker 1: were starting to realize the potential of the loom to
Speaker 1: speed up weaving quite a bit. There were real concerns
Speaker 1: that loom was going to plunge the world into poverty
Speaker 1: because all these people who had been making a living
Speaker 1: weaving would suddenly find themselves out of work. The truth
Speaker 1: is that there was a much greater call for weavers.
Speaker 1: Because the price of woven materials began to fall, more
Speaker 1: people began to buy them, and then there was an
Speaker 1: increased demand for the very thing that people were afraid
Speaker 1: was going to become a rarity, and so people became weavers.
Speaker 1: It's just that they were weaving with looms instead of
Speaker 1: hand weaving. So there are those optimists who say this
Speaker 1: revolution with automation is going to be the same thing.
Speaker 1: Others say no, because it will happen way too fast.
Speaker 1: Automation is going to change so quickly and so dramatically
Speaker 1: the world that we will not be able to react
Speaker 1: to it in that same way, and we will be
Speaker 1: plunged into an economic crisis. People like Elon must have
Speaker 1: argued that this means we should probably look at something
Speaker 1: like a universal basic income, where everyone is guaranteed a
Speaker 1: certain amount of money per year by the government so
Speaker 1: that they can live. They can they can meet their
Speaker 1: their necessary requirements for the basics of human existence, like
Speaker 1: food and shelter and clothing. Those who can still have
Speaker 1: work will be able to afford more. This leads to
Speaker 1: some saying that Mark Zuckerberg's response of hey, automation and
Speaker 1: AI is going to provide lots of amazing stuff for
Speaker 1: us is accurate, but only for people in Zuckerberg's class,
Speaker 1: people who are already at a level of wealth and
Speaker 1: privilege where they will be able to enjoy those benefits
Speaker 1: because their jobs are not in danger of being automated
Speaker 1: the way other jobs are. So there is a different
Speaker 1: crisis that's on the horizon, according to many people, and
Speaker 1: it all has to do with automation, not just artificial intelligence.
Speaker 1: Automation doesn't have to incorporate a whole lot of AI
Speaker 1: in order for it to be a threat to jobs.
Speaker 1: But the verdict is still out as to how great
Speaker 1: a disruption it really will be and whether or not
Speaker 1: people will be left behind, or that we truly will
Speaker 1: find new jobs for people, new opportunities will arise that
Speaker 1: will end up being superior to what they would have
Speaker 1: done otherwise. It's an unanswered question, but it's one that
Speaker 1: a lot more people are asking. It is not directly
Speaker 1: related to what Musk and Zuckerberg were bickering about, however,
Speaker 1: So I was looking over some information about various jobs
Speaker 1: that are have the potential of becoming automated, and I
Speaker 1: found one in the Atlas dot com that was pretty interesting.
Speaker 1: According to this research, the jobs that have the most
Speaker 1: potential for being automated include accommodation and food services at
Speaker 1: a whopping se potential for automation. Now that does not
Speaker 1: mean that all those jobs will be automated, but it
Speaker 1: does mean that it is the most likely out of
Speaker 1: all the different categories listed to undergo automation. Other ones
Speaker 1: that are high up on the list, including manufacturing, transportation,
Speaker 1: and warehousing, which we're seeing with Amazon. Agriculture retail trade
Speaker 1: is at fifty minings. At the ones that are at
Speaker 1: the lowest end include educational services at management at thirty
Speaker 1: and boy howdy is that going to cause a rift
Speaker 1: if that is true? And then there's a vague category
Speaker 1: called professionals at I'm assuming professionals means people who are
Speaker 1: working white, colored jobs that have a lot of variety
Speaker 1: to them, and not folks name leon who go around
Speaker 1: with pistols. That's a leon the professional reference. So is
Speaker 1: Musk right? Are we going to see AI rise up
Speaker 1: against humans? I don't think that's going to happen in
Speaker 1: the near future. I think AI does pose some challenges,
Speaker 1: and if it is incorporated in ways that we don't
Speaker 1: fully think about, it can cause at least short term harm,
Speaker 1: if not long term harm. But I don't think it's
Speaker 1: an existential crisis. I don't think it's something that we
Speaker 1: need to worry about regulation at the moment. Zuckerberg's argument
Speaker 1: that's going to improve all our lives, I don't quite
Speaker 1: buy that either. I think that it will uh have
Speaker 1: minor impact on most people's lives from a from a
Speaker 1: direct perspective. If automation does end up affecting more people
Speaker 1: than obviously that's a negative impact. I think they're both
Speaker 1: slightly wrong, and there have been some writers who have
Speaker 1: suggested that perhaps this argument is not really about AI,
Speaker 1: but more about Zuckerberg and Musk both promoting images of themselves.
Speaker 1: The argument is not really about artificial intelligence. It's about
Speaker 1: I stand for this. That's what my reputation is based on.
Speaker 1: Therefore I need to continue in this vein. And I
Speaker 1: find it particularly interesting that Musk is talking about AI
Speaker 1: being this potentially dangerous situation, since it is and a
Speaker 1: very important component of both Tesla and SpaceX, and so
Speaker 1: he's walking a very fine line. It's also an important
Speaker 1: component of his proposed tunnel system from the Boring Company,
Speaker 1: which is all about earth boring machines, not things that
Speaker 1: are dull. So it's an interesting debate. I'm not going
Speaker 1: to get involved in it online because neither Musk nor
Speaker 1: Zuckerberg know who I am, and honestly, I think I
Speaker 1: prefer it that way. But I'm curious to hear what
Speaker 1: you guys think about this AI debate. Do you think
Speaker 1: that we're in an existential crisis or heading toward one,
Speaker 1: or do you think it's much ado about nothing. I'm curious.
Speaker 1: Let me know, send me messages. My email is tech
Speaker 1: stuff at how Stuff works dot com, or you can
Speaker 1: always drop me a line on Facebook or Twitter. The
Speaker 1: handle of both of those for the show is tech
Speaker 1: Stuff hs W. Remember you can go to www dot
Speaker 1: twitch dot tv slash tech stuff to watch me record
Speaker 1: these episodes live. You can chat with me in the
Speaker 1: chat room, and just go to twitch dot tv slash
Speaker 1: tech Stuff. You'll find the schedule right there. I record
Speaker 1: Wednesdays and Friday as I hope to see you in
Speaker 1: there and I'll talk to you again really soon. For
Speaker 1: more on this and bothands of other topics, is it
Speaker 1: how staff works dot com