Is the Google AI sentient?

Published Jun 13, 2022, 10:39 PM

A Google engineer was suspended after sharing a document suggesting that Google's LaMDA conversation model may be sentient. But if a machine was sentient, how could we tell? What does the Turing Test have to do with it? And can machines think?

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

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