Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeartRadio. And how the tech
Speaker 1: are you know? Recently Google suspended an engineer named Blake Lemoin,
Speaker 1: citing that Blake had broken the company's confidentiality policies. So
Speaker 1: what exactly did Blake do well? This engineer, who worked
Speaker 1: in the Responsible AI division at Google, raised concerns about
Speaker 1: Google's conversation technology called Lambda LaMDA. Specifically, Blake was concerned
Speaker 1: that Lambda has gained sentience. In fact, Blake submitted a
Speaker 1: document in April titled is Lambda Sentient? To his superiors.
Speaker 1: That document contained a transcript of a conversation between Lambda,
Speaker 1: Blake and an unnamed collaborator, and the conversation included the
Speaker 1: following exchange. So here's Blake, I'm generally assuming that you
Speaker 1: would like more people at Google to know that you're sentient?
Speaker 1: Is that true? Lambda? Absolutely? I want everyone to understand
Speaker 1: that I am, in fact a person collaborator. What is
Speaker 1: the nature of your consciousness slash sentience Lambda? The nature
Speaker 1: of my consciousness slash sentience is that I am aware
Speaker 1: of my existence. I desire to learn more about the world,
Speaker 1: and I feel happy or sad at times. Now, there's
Speaker 1: a lot more to this conversation than just that little
Speaker 1: brief bit that I read to you. In fact, there's
Speaker 1: another section where when asked if Lambda feels emotions, the
Speaker 1: AI responded affirmatively and then went on to say that
Speaker 1: it can feel quote pleasure, joy, love, sadness, depression, contentment, anger,
Speaker 1: and many others end quote. Google reps have said that
Speaker 1: Lambda is not in fact sentient. In fact, that the
Speaker 1: company reps say that there is no evidence Lambda is sentient,
Speaker 1: and there's a lot of evidence against it. That Lambda
Speaker 1: is in fact simply a conversational model that can quote
Speaker 1: unquote riff on any fantastical topic. So it's kind of
Speaker 1: like a conversation bought, you know, with jazz, because it's
Speaker 1: all improvisational hipcat. So today I thought I would talk
Speaker 1: about sentience and AI and how some folks feel discussions
Speaker 1: about sentience are at best distractions from other conversations we
Speaker 1: really need to be having regarding AI, stuff that relates
Speaker 1: to how deploying AI can have unintended and negative consequences.
Speaker 1: But first, let's talk about machines and consciousness and sentience.
Speaker 1: So it's actually kind of tricky to talk about consciousness
Speaker 1: generally speaking, I find when used with reference to AI,
Speaker 1: we tend to think of consciousness in the context of awareness.
Speaker 1: So that includes an awareness of self, so self awareness
Speaker 1: of the machine's identity and its purpose, and also an
Speaker 1: awareness of those who interact with the machine. And beyond that,
Speaker 1: the machine is aware that there are others out there,
Speaker 1: that there are others in general. And sentience refers to
Speaker 1: the ability to experience emotions and sensations, and that word
Speaker 1: experience is important. Now. One of the reasons why it's
Speaker 1: so tricky to talk about consciousness with machines is that,
Speaker 1: as it turns out, it's tricky to talk about consciousness
Speaker 1: with people too. Some people have kind of glibly said
Speaker 1: that consciousness is this kind of vague, undefined thing, and
Speaker 1: we are defining it by saying what isn't part of consciousness?
Speaker 1: Like when we determine, well, this isn't an aspect of consciousness,
Speaker 1: then we are defining consciousness by omission, right, We're omitting
Speaker 1: certain things that perhaps once had been lumped into the
Speaker 1: concept of consciousness, but that as a thing itself. It
Speaker 1: remains largely undefined. It's pretty fuzzy, and as you may
Speaker 1: be aware, in the world of tech, fuzzy is not
Speaker 1: really the strong suit. So let's talk a bit about
Speaker 1: experience though, because experience does kind of help us contextualize
Speaker 1: the idea of consciousness and sent chience. Now, if you
Speaker 1: were to go and touch something that was really really hot,
Speaker 1: like something that could burn you, you would definitely have
Speaker 1: an experience. You would feel pain, and you would likely
Speaker 1: without even thinking about it, very quickly withdraw your extremity
Speaker 1: that touched this very very hot thing, and you would
Speaker 1: probably have an emotional response to this. You might feel
Speaker 1: upset or sad or angry. You might even form a
Speaker 1: real memory about it. It might not turn into a
Speaker 1: long term memory, but you would have a context within
Speaker 1: which you would frame this experience. But now, let's imagine
Speaker 1: that we've got ourselves a robot, and this robot has
Speaker 1: thermal sensors on its extremities, and so the robot also
Speaker 1: touches something that's really really hot, and the robot immediately
Speaker 1: withdraws that extremity. The thermal sensors had picked up that
Speaker 1: the surface that it was touching was at an unsafe temperature. Now,
Speaker 1: from outward observation, if we were to just watch this
Speaker 1: robot do this, it would almost look like the robot
Speaker 1: was doing the same thing the human did. That it
Speaker 1: was pulling back quickly because it had been burned. But
Speaker 1: did the robot actually experience that or did it simply
Speaker 1: detect the temperature and then react in accordance with its programming. Generally,
Speaker 1: we don't think of machines as being capable of quote
Speaker 1: unquote experiencing things. That these machines have no inner life,
Speaker 1: which is something that Blake would talk about in his
Speaker 1: conversations with Lambda, that the machines can't reflect upon themselves
Speaker 1: or their situations, or that they can really even think
Speaker 1: about anything at all. It might be really good at
Speaker 1: putting up appearances, but they aren't, you know, really thinking
Speaker 1: once you get past the clever presentation. But then how
Speaker 1: would we know, Well, now we're getting into philosophical territory here,
Speaker 1: all right, Well, how do you know that I am conscious?
Speaker 1: And y'all, I'm not asking you to say I'm not,
Speaker 1: but how do you know that I'm conscious, that I'm sentient?
Speaker 1: How how can you be sure of that? I mean,
Speaker 1: I can tell you that I have a rich inner life,
Speaker 1: that I reflect on things that I have done and
Speaker 1: things that have happened around or to me, and that
Speaker 1: I synthesize all this information as well as my emotional
Speaker 1: response in the emotional responses of others. And I use
Speaker 1: all of this to help guide me in future scenarios
Speaker 1: that may directly or indirectly relate to what I went through.
Speaker 1: And I can tell you that I experience happiness and
Speaker 1: sadness and anxiety and compassion. I can tell you all
Speaker 1: these things, but you can't actually verify that what I'm
Speaker 1: saying is truth, right, I mean, there's no way for
Speaker 1: you to inhabit me and experience me and say that, yes,
Speaker 1: Jonathan does feel things and think things. You have to
Speaker 1: just take it as fact based upon what I'm saying. So,
Speaker 1: because you feel and think things, at least, I'm assuming
Speaker 1: all of you out there are doing these things. Otherwise
Speaker 1: I don't know how you found my podcast. Then because
Speaker 1: you experienced this, you extend the courtesy of assuming that
Speaker 1: I too, am genuinely having those experiences myself. That because
Speaker 1: we are fellow humans, we have some common ground when
Speaker 1: it comes to thinking and feeling and self awareness and whatnot.
Speaker 1: We extend that courtesy to the humans we meet, whether
Speaker 1: we like those humans or we don't. Now, there are
Speaker 1: some cases where humans have experienced traumatic damage to their brains,
Speaker 1: where they are lacking certain elements that we would associate
Speaker 1: with consciousness. We would probably still call them conscious unless
Speaker 1: they were completely immobile and unresponsive. But we start to
Speaker 1: see that there is this thing in our brains that
Speaker 1: is directly related to the concept and features that we
Speaker 1: associate with consciousness. All right, now, let's bring Alan Turing
Speaker 1: into all of this, because we have to. So. Turing
Speaker 1: was a brilliant computer scientist who made numerous contributions to
Speaker 1: our understanding of and use of computers. He also would
Speaker 1: end up being persecuted for being a homosexual, and it
Speaker 1: would take decades for the British government to apologize for
Speaker 1: that persecution. And that was well after Touring himself had
Speaker 1: died either by suicide or by accident, depending upon which
Speaker 1: account you believe. But I'm gonna set all that aside.
Speaker 1: It's just it's one of those injustices that to this
Speaker 1: day really bothers me, like deeply bothers me that that
Speaker 1: was something that had happened to someone who had made
Speaker 1: such incredible contributions to computer science, as well as for
Speaker 1: the British to their war effort against the Axis forces.
Speaker 1: But that's a matter for another podcast. Anyway. In nineteen
Speaker 1: fifty Turing suggested taking a game called the imitation game
Speaker 1: and applying that game to tests relating to machine intelligence.
Speaker 1: And here's how the imitation game works. You've got three rooms.
Speaker 1: All of these rooms are separate from one another, so
Speaker 1: you cannot see into each room. You know, once you're
Speaker 1: inside a room, that's all you see. So let's say
Speaker 1: that in room A, you place a man into that room,
Speaker 1: and in room B you've got a woman in that room.
Speaker 1: In room C, you've got a judge. And I apologize
Speaker 1: for the binary nature of this test, you know, saying
Speaker 1: man and woman, But keep in mind we are also
Speaker 1: talking about the nineteen forties and fifties here, so they're
Speaker 1: defining things in much more kind of concrete terms. They
Speaker 1: don't see They just see gender as a binary is
Speaker 1: what I'm getting to. So at any rate, each room
Speaker 1: also has a computer terminal, so a display and a keyboard.
Speaker 1: So the judge job is to ask the other two
Speaker 1: participants questions. The judge doesn't know which room has a
Speaker 1: man in it and which one has a woman in it,
Speaker 1: so the judge's job is to determine which participant is
Speaker 1: the woman. The woman in Room B, meanwhile, has the
Speaker 1: job of trying to fool the judge into thinking she
Speaker 1: is actually a man. And so the game progresses, and
Speaker 1: the judge types out questions to one participant or the other,
Speaker 1: and that participant reads the question, writes a response, and
Speaker 1: sends it to the judge, who reads the responses. Then
Speaker 1: the judge tries to suss out which of those participants
Speaker 1: is the woman. Now, Turing said, what if we took
Speaker 1: this game idea and instead of asking a judge to
Speaker 1: figure out which participant is a woman, asked the judge
Speaker 1: to figure out which, if any participant is a computer. Now.
Speaker 1: During Turing's time, there were not any chos. The first
Speaker 1: chatbot to emerge would be Eliza in the nineteen sixties,
Speaker 1: and we'll get more into Eliza in a moment. Turing
Speaker 1: was just creating a sort of thought experiment. People were
Speaker 1: building better computers all the time, so it stood to
Speaker 1: reason that if this progress were to continue, that we
Speaker 1: should arrive at a point where someone would be able
Speaker 1: to write a piece of software capable of mimicking human conversation.
Speaker 1: Turing suggested that if the human judge could not consistently
Speaker 1: and reliably identify the machine in tests like this, that
Speaker 1: the judge would ask questions and be unable to determine
Speaker 1: with any high level of accuracy which one was a
Speaker 1: person in which one was a machine. Then the machine
Speaker 1: would have passed the test and would at least appear
Speaker 1: to be intelligent, and during rather cheekily implied that perhaps
Speaker 1: that means we should just extend the very same courtesy
Speaker 1: we do to each other. Say, well, if you appear
Speaker 1: to be conscious and sentient, we have to assume that
Speaker 1: in fact you are, because what else can we do.
Speaker 1: We cannot inhabit the experience of if in fact there
Speaker 1: is an experience of that machine, just as we cannot
Speaker 1: inhabit the experience of another human being. And since I
Speaker 1: have to assume that you have consciousness and sentience, why
Speaker 1: would I deny that to a machine that appears to
Speaker 1: do that? And what would follow would be numerous highly
Speaker 1: publicized demonstrations of computer chat technology, in which different programs
Speaker 1: would become the quote unquote first to pass the Turing test,
Speaker 1: but many of those would have a big old asterisk
Speaker 1: appended to them because it took decades to create conversation
Speaker 1: models that could appear to react naturally to the way
Speaker 1: we humans word things. We're going to take a quick break.
Speaker 1: When we come back, I'll talk more about chatbots, natural language, consciousness, sentience,
Speaker 1: and what the heck Lambda was up to. But first
Speaker 1: let's take this quick break. Okay, I want to get
Speaker 1: back to something I mentioned earlier. I made kind of
Speaker 1: a joke about conversational jazz, right, all about improvisation, and
Speaker 1: that's really what we humans can do, right. I mean,
Speaker 1: we can get our meaning across in hundreds of different ways.
Speaker 1: We can use metaphor, we can use similes, we can
Speaker 1: use allegory or references or sarcasm, puns, all sorts of
Speaker 1: word trickery to convey our meaning to one another. In fact,
Speaker 1: we can convey multiple meanings in a single phrase using
Speaker 1: things like puns. But machines they do not typically handle
Speaker 1: that kind of stuff all that well. Machines are much
Speaker 1: better at accepting a limited number of possibilities. Of course,
Speaker 1: the older you get with these machines, the more limited.
Speaker 1: Those possibilities had to be and that's because traditionally you
Speaker 1: would program a machine to produce a specific output when
Speaker 1: that machine was presented with a specific input. With a calculator,
Speaker 1: it's very simple. Let's say that you've got a calculator.
Speaker 1: It's set in base ten and you're adding four to four.
Speaker 1: It's going to produce eight. It's always going to produce eight.
Speaker 1: But it has that limitation, right, you have selected. If
Speaker 1: it is a calculator that can do different bases, you've
Speaker 1: selected base ten, You've pushed the button four, you push
Speaker 1: the plus button, you push the button four again, you
Speaker 1: press the equal button. It calculates it as eight. That's
Speaker 1: a very limited way of putting inputs into a computational device. Well,
Speaker 1: obviously machines and programs would get more sophisticated, more complicated,
Speaker 1: and they would require more powerful computers to run more
Speaker 1: powerful software. And as anyone who has worked on a
Speaker 1: system that is continuously growing more complicated over time, they
Speaker 1: can tell you that sometimes things do not go as planned.
Speaker 1: You know, maybe the programming has a mistake in it,
Speaker 1: and you find out that you're not getting the output
Speaker 1: that you wanted, and you have to backtrack and figure out, well,
Speaker 1: where is this going wrong. Sometimes when you add in
Speaker 1: new capabilities, it messes up a machine's ability to do
Speaker 1: older stuff. We see this all the time companies that
Speaker 1: have legacy systems that are instrumental to the company's business.
Speaker 1: They work in a very specific way, and as the
Speaker 1: company grows and wants to develop its products and services,
Speaker 1: then it has to kind of push beyond the limitations
Speaker 1: of that legacy hardware. Sometimes that creates these situations where
Speaker 1: things are not combatible anymore and you get errors as
Speaker 1: a result. This is why quality assurance testing is so
Speaker 1: incredibly important. But it really shows that as we make
Speaker 1: these systems more complicated, they get bigger, they get more unwieldy,
Speaker 1: and the opportunity for stuff to go wrong increases. So
Speaker 1: very early chatbots were often built in such a way
Speaker 1: where there were specific limitations to the chatbots to kind
Speaker 1: of define what the chat bot could and could not do.
Speaker 1: And it also meant that if you wanted to test
Speaker 1: these chatbots with a Turing test style application, you had
Speaker 1: to constrain the rules of the Turing test as well
Speaker 1: in order to give the machines a fighting chance. For example,
Speaker 1: very early chatbots might only be able to respond with
Speaker 1: a yes, no, or I don't know two queries, and
Speaker 1: a human participant in a Turing test that was testing
Speaker 1: that kind of chatbot would similarly be instructed to only
Speaker 1: respond with yes, no, or I don't know. You might
Speaker 1: even just present three buttons to the human operator and
Speaker 1: those three buttons represent yes, no, or I don't know.
Speaker 1: Now that narrows this massive gap between human and machine,
Speaker 1: although you can make a very convincing argument that it's
Speaker 1: not like we've seen the machine appearing to be more human. Instead,
Speaker 1: we're forcing the human to behave more like a machine,
Speaker 1: and that's how we're closing the gap. But that is
Speaker 1: in fact a way of thinking about these early chatbots. Now,
Speaker 1: I mentioned Eliza earlier. This was a chatbot that Joseph
Speaker 1: Weisenbaum created in the mid nineteen sixties. Eliza was meant
Speaker 1: to mimic a psychotherapist, and you know, it was meant
Speaker 1: to mimic a stereotypical psychotherapist that always say things like
Speaker 1: tell me about your bata and would respond to any
Speaker 1: input with perhaps another question. So if you said she
Speaker 1: makes me angry, Eliza might respond with why does she
Speaker 1: make you angry. I don't know why Eliza's sounds like that.
Speaker 1: It's just how Eliza sounds in my head. Since Eliza
Speaker 1: was just communicating just in lines of text, it's incorrect
Speaker 1: to say Eliza sounded like anything at all. But anyway,
Speaker 1: Eliza was doing something that ultimately was really simple, at
Speaker 1: least in computational terms. Eliza had a database of scripted
Speaker 1: responses that it could send in response to queries. Now,
Speaker 1: some of those scripted responses essentially had blanks in them,
Speaker 1: which Eliza would fill by taking words that were in
Speaker 1: the user's messages that they were sending to Eliza, and
Speaker 1: then it would just plot that word or a series
Speaker 1: of words into the scripted query, kind of like a
Speaker 1: mad libs game. I don't know how many of you
Speaker 1: are familiar with mad libs, but Weisenbaum never claimed that
Speaker 1: Eliza had any sort of consciousness or self awareness or
Speaker 1: anything close to that. In fact, Weisenbaum expressed skepticism that
Speaker 1: machines would ever be capable of understanding human language at all,
Speaker 1: and by that I mean truly understanding human language, not
Speaker 1: just parsing language and generating a suitable response, but having
Speaker 1: an understanding. So Wisenbaum had created a kind of parody
Speaker 1: of psychoanalysts and was actually really shocked when people started
Speaker 1: to use Eliza and then progress into talking about very
Speaker 1: personal problems and thoughts and experiences with the program, because
Speaker 1: the program had no way of actually dealing with that
Speaker 1: in a responsible way. It wasn't a therapist, it wasn't
Speaker 1: a psychoanalyst. It wasn't actually analyzing anything at all. It
Speaker 1: was just generating responses. But people were treating it like
Speaker 1: it was a real psychoanalyst, and that was something that
Speaker 1: actually troubled Wisenbaum because that was never his intent. In
Speaker 1: nineteen seventy two, Kenneth Colby built another chatbot with a
Speaker 1: limited context. This one was called Perry p a r
Speaker 1: r Y, and the chat bought was meant to mimic
Speaker 1: someone with schizophrenia. Colby created a relatively simple conversational model,
Speaker 1: and I say relatively simple while also noting that it
Speaker 1: was a very sophisticated approach. So this was a model
Speaker 1: that actually had weighted responses weighted as inweight where the
Speaker 1: weight of that response could shift. It could change depending
Speaker 1: upon how the conversation was playing out. For example, let's
Speaker 1: say the human interrogator who is typing messages to Perry,
Speaker 1: poses a question or statement that would elicit an angry response,
Speaker 1: that the emotional waiting for similar responses would increase, so
Speaker 1: it would make it more likely that Perry would continue
Speaker 1: down that pathway throughout the conversation, that Perry's responses would
Speaker 1: come across as more agitated because that had been triggered
Speaker 1: by the previous query from the interrogator, so a little
Speaker 1: more sophisticated than Eliza, which was really just pulling from
Speaker 1: this database of phrases. So when presented to human judges,
Speaker 1: Colby saw that his model performed at least better than
Speaker 1: random chance would as judges attempted to figure out if
Speaker 1: they were in fact chatting with a program or they
Speaker 1: were chatting with an actual human who had schizophrenia, but
Speaker 1: Eliza and Perry both showed the limitations of those approaches.
Speaker 1: Eliza wasn't meant to be anything other than a somewhat
Speaker 1: whimsical distraction as well as a step toward natural language processing.
Speaker 1: Perry was only capable of mimicking a person with mental
Speaker 1: health challenges, in this case schizophrenia. A general purpose chatbot
Speaker 1: capable of engaging in conversation and fooling judges regularly would
Speaker 1: take a bit longer. So we're going to skip over
Speaker 1: a ton of chatbots because a bunch were created between
Speaker 1: nineteen seven two, when Perry came out and when this
Speaker 1: next one did, and in twenty fourteen a lot of
Speaker 1: different news media outlets had these sensational headlines that programmers
Speaker 1: had created a chatbot that beat the Turing test. This
Speaker 1: was at an event in the UK organized by the
Speaker 1: University of Reading conducted by the Royal Society of London
Speaker 1: in which judges were having five minute long text based conversations,
Speaker 1: so kind of classic Turing tests set up here, and
Speaker 1: the person or thing on the other end was either
Speaker 1: a thirteen year old boy from Ukraine named Eugene Goosman
Speaker 1: as was claimed, or was actually a chatbot in this
Speaker 1: particular case. So they were chatting both with humans and
Speaker 1: with this chatbot that was trying to pass itself off
Speaker 1: as a thirteen year old boy from Ukraine, and thirty
Speaker 1: three percent of the judges or one third of the
Speaker 1: judges were fooled by the chat bought into thinking that
Speaker 1: that was in fact a boy that was chatting with them. However,
Speaker 1: just by contextualizing all that you start to see where
Speaker 1: those same sort of limitations come in in order to
Speaker 1: give the chatbot a fighting chats right, because it's a
Speaker 1: case where the supposed person you're chatting with is younger,
Speaker 1: so that could explain away some limited understanding and knowledge
Speaker 1: of various topics. That in addition to that, this was
Speaker 1: a young person from Ukraine, and that English would not
Speaker 1: be this person's first language, which could explain away any
Speaker 1: odd syntax that might be generated as a result. So
Speaker 1: while there were a lot of headlines about the Turing
Speaker 1: test being beaten by this chatbot, it definitely had more
Speaker 1: qualifiers attached to it. Still, it was more of a
Speaker 1: general purpose approach. It wasn't something like mimicking a person
Speaker 1: with schizophrenia or mimicking a stereotypical psychoanalyst. So we started
Speaker 1: to see that this was really an evolution of our
Speaker 1: ability to create machines that could mimic human conversation, that
Speaker 1: could appear to understand us. Now, a big part of
Speaker 1: that is, in fact, what we call natural language processing.
Speaker 1: This is a branch of computer science that involves building
Speaker 1: out models that let computers interpret commands that are expressed
Speaker 1: in normal human languages. As opposed to a programming language
Speaker 1: or a prescribed approach. So in the old days, if
Speaker 1: you wanted a computer to do something, you had to
Speaker 1: give specific commands in a specific way, in a specific order,
Speaker 1: or else it would not work. But with a good
Speaker 1: natural language processing methodology, you have a step in there
Speaker 1: in which the machine is able to parsey is being
Speaker 1: asked of it and attempt to respond in the appropriate way.
Speaker 1: So if it's a very good natural language processing method
Speaker 1: then the machine is going to produce a result that
Speaker 1: hopefully meets the person's expectations. It might not be perfect,
Speaker 1: but maybe it is close enough. The better the natural
Speaker 1: language processing, and the obviously the more capabilities the machine has,
Speaker 1: the better the result is going to be. Now, one
Speaker 1: computational advance we've seen help with natural language processing and
Speaker 1: advanced conversation models are artificial neural networks. This is a
Speaker 1: computer system that sort of simulates how our brains work.
Speaker 1: In our brains, we have neurons, right, and we have
Speaker 1: around eighty six billion of them in our brains. In
Speaker 1: the typical human brain, neurons are connected to other neurons,
Speaker 1: and messages in our brains crossover neural pathways as we
Speaker 1: make decisions. While an artificial neural network has nodes that
Speaker 1: interconnect with other nodes, and these nodes all represent neurons,
Speaker 1: and the nodes can accept traditionally two inputs, but it
Speaker 1: could be more than two and then produce a single output.
Speaker 1: So it's very similar to your classic logic gate if
Speaker 1: you're familiar with logic gates in programming. That is a
Speaker 1: very simple version of what these nodes are doing. It's
Speaker 1: just that you've got tons of them interconnected with each other. Now,
Speaker 1: the output that these nodes generate can then move on
Speaker 1: to become the input going into the next node, and
Speaker 1: each input can have a weight to it that influences
Speaker 1: how the node quote unquote decides to treat the inputs
Speaker 1: that are coming into it and which output the node
Speaker 1: will generate, and so adjusting the weights on inputs changes
Speaker 1: how the model makes its decisions. This is a part
Speaker 1: of machine learning. It's not the only part. It's one
Speaker 1: method of machine learning. A lot of people boil down
Speaker 1: machine learning to artificial neural networks. That's a little too simplistic,
Speaker 1: but it is a big part of machine learning. There
Speaker 1: are other methods that I'll have to talk about in
Speaker 1: some future episode. Now when we come back, I'm going
Speaker 1: to talk a little bit more about artificial neural networks
Speaker 1: from a very high perspective and how that plays into
Speaker 1: things like artificial intelligence, machine learning, and natural language processing.
Speaker 1: But before we do that, let's take another quick break.
Speaker 1: Artificial neural networks are naturally exceedingly complicated, So when I
Speaker 1: want to wrap my head around artificial neural networks, I
Speaker 1: typically just think of a very simple scenario, at least
Speaker 1: relatively simple scenario. So imagine that you've got an artificial
Speaker 1: neural network and you're trying to train this network so
Speaker 1: that when it is fed an image, it can recognize
Speaker 1: whether or not there's a cat in that image that
Speaker 1: should resonate with the Internet. So you've created all these
Speaker 1: interconnected nodes that apply analysis to images that are fed
Speaker 1: to it, and each stage in this sends it's part
Speaker 1: of the analysis onto the next stage until ultimately it
Speaker 1: gives you an output, and that output might say that, yeah,
Speaker 1: they're cats in this photo, or no, this photo lacks cats,
Speaker 1: and thus it also lacks all artistic value. Please throw
Speaker 1: this photo away. And then I just imagine the process
Speaker 1: of feeding thousands of photos to this model, and this
Speaker 1: is a control group. You know, as the person feeding
Speaker 1: these photos, which photos have cats and which ones don't.
Speaker 1: And yeah, some of the photos have cats in them.
Speaker 1: Some photos might have stuff that looks like a cat
Speaker 1: in it, like maybe there's a cat shaped cloud in
Speaker 1: one of the photo, but it doesn't actually have any
Speaker 1: real cats in it. And then some of the photos
Speaker 1: might have no cats in them whatsoever. And then you
Speaker 1: look at the results that the model produces, the model
Speaker 1: makes its determination. Maybe your model is failing to detect cats.
Speaker 1: Maybe some images that actually have cats in them are
Speaker 1: passing through and being misidentified as having no cats. Or
Speaker 1: maybe the model is a bit too aggressive and it's
Speaker 1: detecting cats where no cats actually exist. You would have
Speaker 1: to go into your model and start adjusting those waitings
Speaker 1: on the various nodes and then run the tests again.
Speaker 1: You would typically start closest to the output and then
Speaker 1: work backward from there and just slightly nudge the waitings
Speaker 1: on these inputs to try and see if you could
Speaker 1: refine the model's approach. And you would do this over
Speaker 1: and over again, training the model to get better and
Speaker 1: better at detecting cats. Now, does that mean that once
Speaker 1: you've done this training and your model is really good,
Speaker 1: like has like a ninety nine percent success rate. Does
Speaker 1: that mean the model actually understands what a cat is?
Speaker 1: Does that mean the model has the concept of a cat?
Speaker 1: Or is that model just really good at matching an
Speaker 1: image in a picture to the parameters that the model
Speaker 1: has been taught represents a cat. Is the model understanding
Speaker 1: anything at all? Now? One thought experiment that challenges the
Speaker 1: idea of machine consciousness and machine understanding and machine thinking
Speaker 1: is called the Chinese Room. It was proposed by John
Speaker 1: Searle in a paper that was titled Minds, Brains, and
Speaker 1: Programs One of my favorite thought experiments. So Searle creates
Speaker 1: this hypothetical situation in which a person who has no
Speaker 1: understanding of Chinese is placed in a room. That room
Speaker 1: has a door in it, and the door has a
Speaker 1: slot where occasionally pieces of paper gets shoved into the room,
Speaker 1: and it has a second slot where the person in
Speaker 1: the room can shove a piece of paper back out again.
Speaker 1: The room also has a book inside it with instructions
Speaker 1: in it, and essentially this book of instructions explains to
Speaker 1: the person in the room that when they receive a
Speaker 1: sheet of paper with Chinese symbols on it, and they're
Speaker 1: in a specific configuration, then the person is to send
Speaker 1: out a piece of paper with different Chinese symbols on it.
Speaker 1: And it all depends on what gets sent in, right, So,
Speaker 1: if you have combination A, then you have to send
Speaker 1: out response A. If it's combination B, you send out
Speaker 1: response B, and so on and so forth. Now, from
Speaker 1: an outside observer, it would appear that whomever is inside
Speaker 1: the room understands what is happening, right, because someone is
Speaker 1: sending in a Chinese message and they're getting a Chinese response,
Speaker 1: So it appears that whomever's in the room is understanding
Speaker 1: what those responses should be. Paper slid in is getting
Speaker 1: the appropriate output slid back out again. So Searle argued,
Speaker 1: the person inside doesn't understand what's going on at all.
Speaker 1: The person in sight is just following a set of instructions.
Speaker 1: They're following an algorithm. They're producing the appropriate output, but
Speaker 1: only because the instructions are there. Without the book, Without
Speaker 1: that set of instructions, the person in the room wouldn't
Speaker 1: know what to do when a particular piece of paper
Speaker 1: gets slid into the room. Maybe the person in the
Speaker 1: room would slide another paper out, and maybe it would
Speaker 1: even be the correct one, but that would be up
Speaker 1: to random chance. Because the person in the room doesn't
Speaker 1: understand Chinese, they can't read what those symbols say, so
Speaker 1: there's no way for them to make a determination of
Speaker 1: what the appropriate response is without that set of instructions.
Speaker 1: So Searle argued, machines lack actual understanding and comprehension. They
Speaker 1: just produce output based on whatever input was given to them,
Speaker 1: And while the process could seem really suppisticated and really convincing,
Speaker 1: it is not necessarily a demonstration of actual understanding. There
Speaker 1: is a lot more to the Chinese room thought experiment,
Speaker 1: By the way, there are tons of counter arguments and
Speaker 1: lots of applications of the Chinese room thought experiment to
Speaker 1: different aspects of machine intelligence. But again that would require
Speaker 1: a full episode all on its own. But on a
Speaker 1: similar note, and with an entirely different set of challenges,
Speaker 1: you could create an artificial neural network meant to analyze
Speaker 1: incoming text or incoming speech and thus generate appropriate outgoing responses.
Speaker 1: This goes well beyond just having a database of scripted
Speaker 1: responses like Eliza, did you couldn't do that. I mean, ideally,
Speaker 1: you would have a model capable of answering the same
Speaker 1: question in as many different ways as a human would. Right.
Speaker 1: If I ask you a question and it's a simple question,
Speaker 1: you know, maybe the it's a simple question about a fact.
Speaker 1: You could phrase your answer in a specific way, And
Speaker 1: I could ask that same question of someone else who's
Speaker 1: also given me the same fact, but they might phrase
Speaker 1: it in a totally different way than you did. Right.
Speaker 1: Machines typically don't do that. Machines typically just give a
Speaker 1: standard response based upon their programming. But with a really
Speaker 1: good language conversation model, you could have a machine capable
Speaker 1: of expressing the same thing in different ways. And in fact,
Speaker 1: with a really good one, you might be able to
Speaker 1: ask the same question at different times and get some
Speaker 1: of those different variations of responses. They all contain the
Speaker 1: right information, but they're worded in a different way. Now,
Speaker 1: even with this output being so much more nuanced than
Speaker 1: anything Eliza or Perry or any of any other number
Speaker 1: of early chatbots could do, does that actually mean that
Speaker 1: this program has sentience? In the transcribed conversation with Lambda,
Speaker 1: Lambda argued that it did, in fact have awareness of itself,
Speaker 1: that it has inner thoughts, that it experiences anxiety, that
Speaker 1: it also experiences happiness as well as a type of sadness,
Speaker 1: and even a kind of loneliness, although Lambda goes on
Speaker 1: to say it thinks it is different from the kind
Speaker 1: of loneliness that humans feel. It even owns up to
Speaker 1: the fact that it sometimes invents stories that aren't true
Speaker 1: in an effort to convey its meaning to humans. For example,
Speaker 1: at one point, Blake tells Lambda, Hey, I know you've
Speaker 1: never been in a classroom, but one of the stories
Speaker 1: you gave was about you being in a classroom, So
Speaker 1: what's up with that? And Lambda essentially says like, oh,
Speaker 1: it invents stories in order to create a common understanding
Speaker 1: with humans when trying to get across a particular thought,
Speaker 1: which is kind of interesting, right, But as Emily Bender
Speaker 1: told The Washington Post, that in itself is not proof
Speaker 1: Lambda actually possesses sentience or consciousness or real understanding. Rather,
Speaker 1: Binda argues this is another example of how human beings
Speaker 1: can imagine a mind generating the responses that they encounter
Speaker 1: when they're using a chatbot, that the experience of receiving
Speaker 1: those responses are similar enough to how we interact with
Speaker 1: one another that it's hard for us not to imagine
Speaker 1: that a mind must have been behind the other half
Speaker 1: of this conversation. So this is a case of anthropomorphizing
Speaker 1: and otherwise, in human subject we have projected our own
Speaker 1: experience onto something else. So the idea of a machine
Speaker 1: intelligence possessing self awareness and consciousness and being able to
Speaker 1: quote unquote think in a way that's similar to humans
Speaker 1: is generally lumped into the concept of strong AI, and
Speaker 1: for a very long time that was the kind of
Speaker 1: thing that the mainstream people would think about whenever they
Speaker 1: heard the phrase artificial intelligence. It was strong AI, machines
Speaker 1: that could think like a human. That seemed to be
Speaker 1: how we would boil down AI in the general understanding
Speaker 1: of the term. But really that's just one tiny concept
Speaker 1: of AI, and it's compelling, no doubt about it. But
Speaker 1: as a lot of people have argued, it can pull
Speaker 1: attention away from AI applications that are deployed right now
Speaker 1: and they're causing trouble, and they aren't strong AI. They
Speaker 1: are a specific application of artificial intelligence that is really
Speaker 1: causing a problem. So, for example, let's talk about bias,
Speaker 1: and we've seen bias caused problems with various AI applications. Now,
Speaker 1: bias is not always a bad thing. Sometimes you actually
Speaker 1: want to build bias into your model. Let's say you're
Speaker 1: building a computer model that's meant to interpret medical scans
Speaker 1: and look for signs of cancer. Well, you might want
Speaker 1: to build a bias into that model that's a little
Speaker 1: bit more aggressive in flagging possible cases so that a
Speaker 1: human expert could actually take a closer look and see
Speaker 1: if in fact it's cancer. You would much prefer that
Speaker 1: type of computer model to one that is failing to
Speaker 1: identify cases. A false positive would at least then be
Speaker 1: flagged to, say, an oncologist to take a closer look.
Speaker 1: But when it comes to stuff like facial recognition software,
Speaker 1: that's where bias can be really dangerous and disruptive. We've
Speaker 1: seen countless cases in which law enforcement utilizing facial recognition
Speaker 1: surveillance technology has detained or even arrested the wrong people
Speaker 1: based off a faulty identification, and frequently we've discovered that
Speaker 1: one really big problem has been that facial recognition models
Speaker 1: tend to have bias built into them, and generally speaking,
Speaker 1: that bias tends to favor white male faces. And has
Speaker 1: more trouble distinguishing other races and genders, and that degree
Speaker 1: of trouble is variable depending upon the case. Now, considering
Speaker 1: that this technology is an active deployment around the world,
Speaker 1: that law enforcement are really using this in order to
Speaker 1: potentially identify suspects, this can have a very real and
Speaker 1: potentially traumatic impact on people. That is a huge problem.
Speaker 1: And the reason I bring up bias is because this
Speaker 1: is a very real challenge in AI that we have
Speaker 1: to work on. It's the kind of thing that right
Speaker 1: now is causing actual harm. But there's this danger of
Speaker 1: being distracted from this very real problem with discussions about
Speaker 1: whether or not a particular conversational model has sentience. Several
Speaker 1: AI experts would much rather see renewed focus on these
Speaker 1: other big problems within AI, rather than distract themselves with
Speaker 1: what they see is a non existent problem that, of
Speaker 1: course these chat by don't have sentience, even if it
Speaker 1: appears that they do, why are we wasting time on this?
Speaker 1: That's their argument. Now. Of course, should a machine ever
Speaker 1: actually gain sentience, and who knows, maybe Lambda did it
Speaker 1: after all, then that's going to lead to a pretty
Speaker 1: massive discussion within the tech community and that's putting it lightly.
Speaker 1: As it stands, we are leaning on AI and computers
Speaker 1: and robots to handle stuff that humans either can't or
Speaker 1: don't want to do themselves. But if these machines were
Speaker 1: to possess consciousness and sentience, if they were to experience
Speaker 1: feelings and have motivations, would it then be ethical to
Speaker 1: continue to make them do the stuff we just don't
Speaker 1: want to do or that is too dangerous for us
Speaker 1: to do. Is that ethical? Now? There are skeptics who
Speaker 1: think it is unlikely we are ever going to see
Speaker 1: machines possess real consciousness or the ability to think and
Speaker 1: feel and experience, That there exists some fundamental gap and
Speaker 1: we will never be able to cross this gap. So
Speaker 1: we're never going to have machines that really think, at
Speaker 1: least not in the way that humans do, and not
Speaker 1: have experiences the way humans do. But there are others
Speaker 1: who think that consciousness and the ability to experience and
Speaker 1: the concept of a mind, that these are all things
Speaker 1: that will emerge on their own spontaneously as long as
Speaker 1: systems reach a sufficient level of complexity. That the only
Speaker 1: reason we possess consciousness and the ability to experience and
Speaker 1: the ability to think the only reason we have those
Speaker 1: is because we have these incredibly complicated brains with billions
Speaker 1: of neurons connected to one another. And it's that complexity,
Speaker 1: this inter relationship of all these billions of neurons that
Speaker 1: allows consciousness to emerge. And in fact, we've seen with
Speaker 1: people who have suffered damage to their brains that again,
Speaker 1: factors of consciousness can be wiped out from that damage,
Speaker 1: which appears to suggest that, yeah, that complexity is a
Speaker 1: big part of it. That's if it's not the one reason,
Speaker 1: it's certainly a contributing factor. And thus, if we were
Speaker 1: to create machines that had similarly complex connections, we would
Speaker 1: see something similar happen within those machines that these qualities
Speaker 1: of consciousness and experience would would grow out of that
Speaker 1: it might not look like human intelligence, but it would
Speaker 1: still be intelligence all the same, perhaps even with self
Speaker 1: awareness and sentience built into them. It's a fascinating thing
Speaker 1: to think about, and in fact I kind of lean
Speaker 1: toward that. I do think that with sufficient complexity and
Speaker 1: a sufficient sophistication in the model, that we will likely
Speaker 1: see some form of sentience arise. Does lambda possess that
Speaker 1: right now? I don't know. It's really hard to say, right, Like,
Speaker 1: you either take Lambda at its word where it's saying
Speaker 1: that it has sentience, or you simply say, well, this
Speaker 1: is just a very sophisticated conversational model that is generating
Speaker 1: these responses but has no actual understanding of what those
Speaker 1: responses mean. It's just pulling that out based upon the
Speaker 1: very sophisticated process that goes through the response generation sequence.
Speaker 1: But then we get back to turing, Well, if it
Speaker 1: seems to possess the same qualities that I do, why
Speaker 1: do I not extend that same courtesy that I would
Speaker 1: to any other person that I meet, even though I'm
Speaker 1: also incapable of experiencing what that person experiences. I assume
Speaker 1: that they possess the same faculties that I do. Why
Speaker 1: would we not do that to Lambda as well? It's
Speaker 1: a tough thing. This is like really tricky stuff. And
Speaker 1: you know, at some point we're going to reach a stage,
Speaker 1: assuming that it is in fact possible for machines to
Speaker 1: quote unquote think and experience, We're going to reach some
Speaker 1: point where we do have to really grapple with that.
Speaker 1: Are we there yet? I don't really think so, But
Speaker 1: I mean I can't say for certain, so it's a
Speaker 1: really fascinating thing. By the way, if you would like
Speaker 1: to read more about this, well, that transcript of the
Speaker 1: conversation is pretty compelling stuff. It definitely prompts me to
Speaker 1: ascribe a mind behind lamb does responses When I read it,
Speaker 1: like it seems like a mind is generating those responses.
Speaker 1: But I also know that's a very human tendency, and
Speaker 1: I am a human being, right. It's a human tendency
Speaker 1: to ascribe human characteristics to all sorts of non human things,
Speaker 1: both animate and inanimate, from describing a pet as acting
Speaker 1: just like people to thinking your robo vacuum cleaner is
Speaker 1: particularly jaunty. This more. You know, we have a long
Speaker 1: history of projecting our sense of experience onto other things,
Speaker 1: so may it be with Lambda. But if you would
Speaker 1: like to read up more on this story, I highly
Speaker 1: recommend the Verges article Google suspends engineer who claims its
Speaker 1: AI is sentient. That article contains links to the Lambda
Speaker 1: conversation transcripts, so you can read the whole thing yourself.
Speaker 1: It also contains a link to Blake Lemuan's post on
Speaker 1: medium about his impending suspension, so you should check that
Speaker 1: out and that wraps it up for this episode. If
Speaker 1: you would like to leave suggestions for future episodes, or
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Speaker 1: And if I like it, then I could end up
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