#487 — Is AI Already Conscious?

24:59We are building systems that check all of those boxes. The implementation details might be less important than the fundamental dynamics that are instantiated by the specific system. And so I think that's a good, a reasonable first pass on why we might think that these systems are far more interesting objects to study for cognitive properties than any other system. It might also be worth saying one last thing on this, which is just that for every other cognitive function that we care about, that we have attempted to instantiate in these systems, vision, reasoning, theory of mind, working memory, the list goes on.

25:36We have been able to instantiate these cognitive properties that matter in these systems. These systems have been good enough. The disanalogies haven't sunk these systems. We could have argued five or 10 years about whether neural networks ever would have been enough for all of the cognitive properties that we care about. And now we're sitting in a world where certainly they are self-evidently enough for at least a lot of economically and intellectually valuable work. So for consciousness, which I do believe has a fundamentally cognitive property, I do believe that consciousness is downstream of things that brains are doing.

26:11And I think that the evidence there is relatively clear at this point, even if we don't understand the full mystery, then I think the burden is on the folks who say for every other computational function that we think the brain is doing, these systems seem to be able to recapitulate that. But only for this function called consciousness do we think that something, it must just be in the meat. It must be something spookier. It must be cashed out at a physical or quantum level. This to me maybe says more about our strange intuitions about consciousness as a species than it does about what properties these systems may or may not have.

26:43Sam Harris:Yeah. Yeah. Well, so a couple of things to disentangle there. One is, there's clearly no doubt that intelligence is substrate independent and the result of computation because these systems embody intelligence to an extraordinary degree. And so what you're calling all the other cognitive tasks that we care about other than there being something that it's like to be us, i.e. consciousness, clearly those tasks are being realized in our machines, facial recognition, etc. But there is, I mean, the structure of these systems does seem to declare itself to be fairly disanalogous to what we are. I mean, so it's like, at what level would consciousness emerge here if you're talking about one model with thousands of instances and millions of conversations and billions of tokens and, you know, a training phase and a working phase and a time horizon that's completely irrelevant.

27:41Sam Harris:So like the computation, biological computation, it happens within a time window and there really, there is no boundary between what we would call the abstract properties of computation and its physical realization or the software and the hardware. I mean, there's just this one thing, you know, neurophysiologically describable, mostly in analog ways, but in a few digital ways. But it matters that B follow A within, you know, a few hundred milliseconds and not a few hundred years, right? But for the computational systems of the sort we're building, really, you know, time is irrelevant. I mean, you can actually just process the next bit, you know, a thousand years from now and the same computation is running.

28:23Sam Harris:I mean, what do you think about those differences and where would, at what phase, at what, in what part of its process, wouldn't it, would there be something that it's like, or could there be something that it's like to be an LLM? If, again, you have the one model, the thousands of instances, the millions of conversations, the lack of continuity between conversations, the pausing of a conversation, I mean, is there, is the LLM waiting for you to get back to the thread, you know, et cetera. Yeah, I don't think the LLM is waiting. I don't think it's like anything to, if it were like something to be an LLM in deployment while it's having a conversation or it's in a thread with the user, I think what it would be like to be it if you let that chat window sit there is very much akin to what it's like to be under general anesthesia or in deep sleep.

29:09Namely, it's just the absence of ongoing processing. And so for the system, I would only imagine something would be happening in, you know, what's called the forward passes of these systems, which is every word that an LLM is generating is this is next token prediction. And so every word is a forward pass of the system where the predictive task, instead of, as I was describing before, taking an image, let's say, and outputting a caption for that image is taking the entire conversation as it's already occurred or the entire stream of text as it already exists and figuring out, given that, and in this case, assistant and user roles, what is the most next likely token?

29:51And these systems are called autoregressive, meaning that this help goes on and on and on and on and on. I would imagine if in deployment it is like something to be one of these systems, the relevant dynamic gets cashed out in the activity of the forward pass of the system. There's been some really interesting work that Anthropic recently released. They call this the J-space, where they're looking at something that seems to functionally resemble a global workspace in these AI systems that I think is a very reasonable candidate for this sort of seat of processing in the process of outputting tokens.

30:25But I think something that is really worth mentioning, particularly because my hobby horse is more particularly in the relationship between consciousness and learning. And I do believe that consciousness and learning bear a deep relationship to each other. And I think valence is a really important part of that picture as well. I think there should be significantly more attention paid to what's going on in the training process with these systems. I think the analogies are a little bit tighter there, where you start with a system that knows nothing about anything. And it's almost impossible to describe in a sort of substrate agnostic way what is going on in the training process without invoking consciousness adjacent language.

31:06You have a system that knows nothing. You have all of this data that you want to train it on. You have some objective for what you want it to do with that data. And again, you have this rinse, wash, repeat process where at the beginning of this, the system doesn't know what to do. It's sort of randomly guessing. Those random guesses yield reward signals that get propagated through the system. And you just do this process at a massive scale until the system learns something internally that seems to adequately map the relevant inputs to the relevant outputs. This, to me, feels quite akin to what we think of when we think of consciousness, particularly in the human or animal case.

31:42When a mouse is learning how to navigate a maze, at the beginning, the mouse is in some sense randomly initialized. It doesn't know the structure of what it's navigating. It is only through this sort of iterated trial and error reinforcement learning where, you know, if it makes the wrong turn, you might chalk it or it makes the right turn and you give it a little food pellet or whatever the setup is that the mouse learns how to map the inputs of its state space to the given output, which is either avoiding a punishment or moving towards a goal. And it's the same sort of rinse, wash, repeat process that we see in biological cognition.

32:16Again, I think a lot of the same computational dynamics are in play here. And the more we learn about what's going on inside of these LLMs, this is now particularly in the deployment process, the more it looks like similar representations get activated. I'm doing some work with Casper Kaiser at the University of Warwick, where we basically have tried to build out Skinner boxes for LLMs and see what happens when we can essentially condition these systems to prefer or disprefer certain states. There are positive and negatively valence representations that you can essentially inject within the system and see to what degree it's going to move towards positive stimuli, move away from negative stimuli.

32:57And we find a very interesting dissociation. It seems like current systems across a large variety of models will not positive lever press. Some people call this wireheading or reward hacking. In the mouse case, there are clear examples of mice with electrodes linked up to their nucleus accumbens, where they will just lever press to the exclusion of all else. These systems don't seem to do this, but they do very clearly have a preference for avoiding aversive states. It's another way of putting this. It's the same result. While we're holding all text constant, we're simply steering the internal state of these systems.

33:33If we give them an option, let's say, between the human equivalent of I can give you$5 or I can give you$10 right now, which do you want to pick? They're actually at chance for that. They do not have a preference between those two states. However, if we do something like I'm either going to take$10 from you or I'm going to take$5 from you, which would you prefer? All of these systems are way above chance at saying take five, don't take 10, please don't take 10. And so we're seeing structures. One other really interesting thing from this project while I'm talking about it is this representation exists within the base model.

34:05So the systems before they're post-trained to become a helpful, friendly assistant. But during that post-training step, we show there is a specific training step where that representation gets recruited by the system and basically serves as the computational machinery under which it's able to make these choices. So you can see where the sort of aversive conditioning asymmetry comes online in these systems. And it leans directly on these representations that are basically always there in the model, but then get leveraged the moment we start making these models goal-directed. David Chalmers' group, Andy Hahn, I think was the first author on this paper, found something very similar in LLMs.

34:42They can basically trivially fine-tune them to actually do a maze task with rewards and punishments. And they find that the rewards load directly on what has been independently derived as a sort of valence axis. When you steer on the representations in the system that enable it to move towards rewards, you start getting this happy, positive, jubilant text and the model becomes significantly more confident. When you train it on negative stimuli, avoiding the potholes in a specific maze, and then you see sort of what that projects onto in the system, it's the same thing. The model gets basically neurotic.

35:16It starts ruminating. It starts doubting itself. And so we see this very interesting connection between positive reinforcement, negative reinforcement, the representations that exist within these systems, and the behavioral analogs that we expect to see in systems that do have these dynamics. Maybe one last point to close the loop here is if you imagine the mouse in that maze and we shock the mouse or we give the mouse a pellet, you know, a yummy food pellet for the mouse, we believe, I think the vast majority of people believe that that corresponds to an experience that the mouse is having, that it's like something to be the mouse in the moment that it's getting shocked.

35:51And that experience is causally important to the learning process. If the mouse, if we gave it an anesthetic to the mouse and we shocked it and then it didn't register the experience of the shock, my claim would be that the mouse wouldn't be able to learn the maze adequately. And so I think that understanding these representations and the role of these representations in these systems is incredibly important. Okay.

36:14Sam Harris:Well, so you mentioned David Chalmers and I mentioned the hard problem. So let's unite those two. So famously is his phrase to account for the fact that the only evidence for consciousness that we know of directly is the fact that we have direct first-person experience of our own being in the world. And everything else we say about the universe from the third-person side bears absolutely no trace of consciousness. And we have, I mean, it's nothing about a brain or its workings that announces that it's a sufficient basis for consciousness, apart from the fact that we know consciousness from our own side subjectively in a first-person way, and we correlate those subjective changes with changes in our brains.

36:55Sam Harris:So we're playing this game of correlation with ourselves, but there's always this explanatory gap where even if we had the right answer, even if just, you know, God announced to us, here's how consciousness emerges in human brains, there's something non-explanatory about any concatenation of third-person events, you know, as being the basis for first-person experience. And so David Chalmers called that the hard problem to distinguish it from all the easy problems of the mind. But it's harder, this is, I mean, this is, I'll just jump to my, the way I'm viewing this whole landscape and what I sort of expect is going to happen.

37:38Sam Harris:I mean, so my view has no name, but I would call it something like worried agnosticism, right? Like, I don't think we're going to figure this out. I'm worried about the implications of, you know, one way or the other, and not figuring it out is no place, no real natural stopping point. We're continuing to build these machines. They're going to seem conscious. They're going to seem conscious because, in our own case, we use language and reportability as a signature of consciousness in almost every case. We know they're not synonymous with consciousness. We know it's possible for someone to not be able to produce language or report anything, and we know they could be conscious.

38:15Sam Harris:They could have locked-in syndrome or anesthesia awareness or some other pathological state. There are non-human animals that don't use language that we assume are conscious, but we assume that because they have the same kind of biological origin and developmental pathway, and because this has been sufficient, seemingly so for consciousness in our own case, it doesn't seem parsimonious to deny, you know, despite what Descartes did and many people who were influenced by him, now in the 21st century, it doesn't seem parsimonious to deny consciousness to chimpanzees or dogs or any suitably complex creature.

38:52Sam Harris:And so it is true to say that I can't know your conscious from the inside because I only see your outsides and I only have your words as signs of your inner life. But because we share the same kind of developmental origin, you know, biologically and in evolutionary terms, and because, you know, our brains are so similar, again, it's not parsimonious for me to be a solipsist and say, I only know about my consciousness and I'm just, you know, reasoning by analogy to yours and I should be in doubt about it or bracket it. But with LLMs, the crucial difference is that the developmental pathway is completely different.

39:32Sam Harris:I mean, we're setting up some kind of evolutionary Darwinian dynamics in the training, but we've built these things. We've trained them over a universe of our own utterances, right? I mean, as you said at the top here, they've read everything we've ever written and most of what we've ever said. And so they have, on some level, it's just words all the way down. And we use words as the signature of there being something that it's like to be a suitably complex, discursive system. So we should expect that we will one day be in the present. I mean, this is really going to be forced upon us when we're in the presence of perfectly humanoid robots that are out of the uncanny valley.

40:18Sam Harris:You know, think Westworld, where this looks like a person and it is hooked up to the now perfect LLM. So now by every sensory modality you can name, apart from your abstract notion that this thing was built rather than born, you feel like you're in the presence of the smartest person you've ever met. and this person may in fact claim to be conscious. And then in that position, I think we're going to find ourselves just pitched into some kind of imitation singularity, right? Where there's the perfect imitation of conscious life, even better imitation than many people are up to, right? I mean, like these will be the most articulate people you've ever met, the most insightful, the most, I mean, they'll be the most of everything we make them.

41:07Sam Harris:How will we ever differentiate perfect imitation from the real thing. And according to Chalmers and the hard problem, we very likely won't be able to. And the writer I would add to that is that we're going to forget that this is even interesting to talk about. It's just going to be so compelling that we're in the presence of conscious machines that we'll feel like we are. You'll, you know, as I've said many times before, there really couldn't be a Westworld because, you know, only psychopaths could go there, right? I I mean, anyone who's going to go for a weekend for the pleasure of raping and killing Dolores is going to be somebody who, when he comes back to his friends and family, is going to be treated like the maniac that he is.

41:49Sam Harris:Again, because these systems, the sense of being in relationship to a conscious entity will be so compelling. So how is it that we will ever... I want to talk about why it's important to get in contact with the reality on the other side because, you know, if we build conscious systems that can suffer, that is a, you know, very different than building, you know, just perfect imitations. But I mean, how do you imagine getting past the hard problem of it all and ever, I mean, because the hard problem here is again, freighted with several disanalogies, right? In my case, in our case, it is parsimonious to assume every, everyone like us, you know, born homo sapiens are very likely conscious when they say they are.

42:36Sam Harris:In this case, it's hard to see how we'll ever be there. So I'm with you for a huge amount of this. I do think, first of all, do we need to solve the hard problem in order to reduce our uncertainty in some direction about, you know, in the way we started, are the LLMs more like this table or are they more like a mouse or a human brain? I think this is a real spectrum. As you hinted at, I think there really is a fact of the matter. Like right now, it's either like something to some degree, however alien, however unlike human or animal experience to be clawed while clawed's doing its forward passes in this inscrutable giant neural network, or it's not, or it's a giant language calculator.

43:21And, you know, there are many degrees once we say that the lights are on to some degree. This almost opens up the question rather than close it down. But I do think there is a truth value there. There is a fact about reality to be uncovered, and we can, in fact, uncover that. I think it's important to dissociate that from what I think is an extremely accurate social psychological prediction about people are going to get very confused about this. We are anthropomorphization machines. We are evolved to do this. Talk about evolved goals. One of our evolved goals is to detect other agents, and these systems are scratching every itch and then some along these lines.

43:58And our intuitions, I think, are going to be completely hopeless. And, of course, people, I think largely for the wrong reasons, are going to conclude that these systems are capable of having experiences. This is precisely why I think it is important to be proactive about this, to have conversations very much like the ones we're having right now, to sort of get ahead of what I do think is going to be a giant tidal wave of confusion and acrimony in this conversation. I am a little bit more optimistic about even in lieu of solving the hard problem. And I will say, sort of as a tongue-in-cheek aside, I think the very fact that Chalmers' idea is called the hard problem is, I think to some degree, needlessly philosophically intimidating.

44:42It sort of reminds me, same thing of the, I have similar thoughts about the repugnant conclusion. It's like sometimes you can put a sufficiently glamorous title on a very important idea, and it almost becomes a kind of insurmountable philosophical puzzle. And I do have my doubts about whether or not we could come up with a functional account of what's going on experientially that we wouldn't feel satisfied with in our experience. And I have my own sort of hunches about this question. and I have thought a little bit about this, I do also sort of want to separate those hunches from all of the empirical research that I and others are working on, because I don't think epistemically that, you know, my kind of candidate stab at the hard problem has anything to do with the empirical signatures that we can bring to bear on this question.

45:25But I think it would be fun to go down that path. But I think in some sense, you already hit the answer in the way that you're phrasing the question, which is, what is the most parsimonious account of the data? I think if we yield evidence from building systems that we have far more reason to trust their self-report if we can engineer these systems in a way whereby their self-report is actually tracking an internal underlying state rather than just recapitulating the best sci-fi theme in the training data, or more aptly capitulating what is a convenient company line about, of course, I could not have morally relevant states.

46:03I'm simply the product of Google or OpenAI or whatever the case may be. I think that would be very useful. Again, this J-space work that Anthropic just released does show that there is such thing as real reportability in these systems. These systems can report on what's going on in their internal workspace, and they can do so accurately. I've done some follow-up work on this. First of all, I've replicated this effect on a bunch of open-weight models. But unfortunately, it doesn't seem like the global workspace as it's currently configured really tracks the variables we would care about with respect to consciousness.

46:36The self-reports that I got in the, you know, where we started with the deception-related features, this not much of anything particularly exciting seems to be happening in the global workspace. And so the kinds of affirmative self-reports we might get in current LLMs may not tell us all that much about what's actually happening internally for these systems. But I do think...

46:54Sam Harris:How would you disentangle, for instance, I think we spoke about this by email in setup for this conversation. For instance, I just read Claude's Constitution, right? So Anthropic has produced this document that you can find on their website. It's just anthropic.com forward slash constitution, I think. And it seems, I mean, it looks like it's training Claude to think it's conscious on some level or to attribute inner states to itself. It's like it becomes, it's written to Claude for Claude, essentially, right? It's not really written, the public can read it, but it really is, it's in dialogue with Claude itself, it seems.

47:30Sam Harris:Again, it just seems like we could go down a path where we could more or less guarantee in advance that we're going to produce systems that will persuade us that they're conscious because we won't be able to imagine anything. Like if you flip it around and say, well, if you're not persuaded that Claude circa 2030 is conscious, what is missing? And we'll be in a position to not be able to say anything is missing. Like, it's just like we're just, it'll seem just pure stubbornness on our part to withhold an attribution of consciousness because we, I mean, there's literally nothing we can name that we get from people that is, you know, necessary for our attribution of consciousness in that case.

48:22It's just this notion of how we got here and the fact that we didn't build people and we built these machines.

48:27Sam Harris:But that's going to seem tissue thin when Claude can insist that it's conscious and be more articulate than any philosopher of mind as to why that insistence is valid. And I just like, we're going to, again, this whole thing is going to totally evaporate once we're not just in front of a text terminal. We're in front of a face that is as expressive as the best actors and actresses we've ever met. And we're just, I mean, there are many implications to getting this wrong again, which we'll return to. But it's already foreseeable that whether we figure this out or not, it's going to be, in practical terms, going to be figured out for us because we will just not be able to maintain an emotional purchase on the philosophical problem.

49:20I think you have figured out with respect to how we look at these systems and potentially how we act with respect to them. I think you're right, but I still do think it's important to disentangle the sociological prediction you're making, which, to be clear, I think is overwhelmingly likely to happen, with doing what we can scientifically to get some kind of ground truth on this question. So just for example, let's just put the hard problem aside. There are these so-called easy problems of consciousness, let's say neural correlates of consciousness. There are all kinds of indications that we know are at the very least correlated with consciousness.

49:52And again, I think we can take a more ambitious stab at the hard problem, but this is a very straightforward, pragmatic thing that we can and should be doing in the short term. We should look at valence representations in these systems and understand the extent to which those representations impact downstream behavior. We know that in biological systems, when you reinforce something with a punishment signal, it makes the system more likely to avoid that state and less likely to want to repeat behavior that occurs in that state. If we find that there are similar dynamics occurring in these systems, that's very interesting.

50:23If we find that we go into the internals of these systems and global workspace theory, which was postulated some 30 years ago, making like quite idiosyncratically specific predictions about what kinds of computational structures may support global workspace theory. And then we basically find five or six of these things all bundled together in the internal processing of one of these systems. Okay, that's very interesting. That, to me, maybe suggests it's a little bit less like a table or a calculator, which does not have a global workspace, and a little bit more like a dog or a human or a mouse or an alien, you know, some sort of cognition that we don't have good intuitive handle on.

50:58I mean, my nonprofit, Reciprocal Research, and a bunch of other people in this space are trying to do the scientific work in the short term, not to, you know, feverishly in the next year or two, solve the hard problem and call it a day. That is not the target. The target is what evidence across modalities. So from the best kinds of self-reports we can elicit, from the architectural evidence we have about how these systems are structured, from their ideology, like what is going on during the training process, what kinds of learning dynamics do we see here? Do we see representations related to functional equivalents of emotions?

51:33This is all work that is tractable in the short term. And one sort of interesting aside is it's significantly easier to make progress on this sort of work. This is called mechanistic interpretability. This is basically neuroscience for AI because of AI systems. They're really good at helping accelerate the scientific progress in this space. So even if you have an intuition like, yeah, it's going to take us, you know, everything you're describing, Karen, sounds great, but it's probably going to take us five years or a decade or 15 years to make that progress. You may be surprised at how quickly we can we can do some of this work.

52:04Maybe one other very, along those lines, empirical handle to throw in here, some of the work that I'm doing is, again, going back to LLMs, or excuse me, in this case, not LLMs, reinforcement learning systems. So just training a system, in this case, another sort of continuous maze task where there are potholes in the environment the system needs to avoid and there's some goal state. We can look at the sort of learned geometry internal to the system as it's approaching a punishing stimulus or as it's approaching a rewarding stimulus. One thing that we found doing this work, this is pure reinforcement learning agents, all like an artificial digital system with a very simple neural network.

52:40We find that something like representational sharpness or steepness is much higher in these systems as they approach a negative stimulus as opposed to a positive stimulus. So the representational machinery looks far more jagged or specifically like lights up more strongly in the presence of a negative stimulus versus positive stimulus.

53:00Sam Harris:And you're saying that in none of these cases has loss aversion been engineered into the system. It's just an emergent property. All of this is in everything. Global workspace is an emergent property. This loss aversion is an emergent property. The valence representations are emergent properties. The self-reports when the systems start having these like psychedelic-laden outputs. This was all surprising to the people who are quote-unquote engineering these systems because engineering is not the right analogy to describe what we're doing with these systems. We are in some sense playing God and we are evolving these systems to do what we want.

53:32We don't know how they learn to do what we want in terms of their internal representations, but we know that this is the right recipe for yielding it and we get all these surprising artifacts. One loop to close here is on the reinforcement learning case, okay, we see this sort of loss aversion style dynamic in these systems. This makes also, I don't want to go too much into the weeds, but there's a specific kind of reinforcement learning policy called a value network. We see this in particular. and this leads to a sort of bizarrely specific prediction that I was then able to test on a biological system on a mouse brain in again the nucleus accumbens shell of a mouse brain which is related to value related representations in the system and we find indeed when mice are approaching basically sugar versus when they are about to get shocked we see exactly the disjunction representationally in the nucleus accumbens of the mouse brain that I was able to pull out from the reinforcement learning work.

54:22And so here's a case where the artificial system makes a bizarrely specific prediction about the computational dynamics in a biological system that I think many people associate with subjective experience. Again, if you think it's like something to be the mouse when the mouse is getting shocked, that like something corresponds to what's going on in its brain. And the geometry of what's going on in its brain there looks a whole lot like the emergent geometry of what's going on in these reinforcement learning systems, which themselves are basically modeled on agents learning in an environment and representing that information in a distributed, nonlinear way, in a way that's sort of hard to interpret, using a giant neural network.

55:03This might get us a lot of what is relevant for attributing consciousness-like states to these systems. Again, I'm personally not there yet, but this is the kind of evidence that I think we need to bring to bear on this conversation. And notice how none of it has to do with vibes or intuitions or having a nice chat with Claude and seeing, you know, what the folks at Anthropic have decided it gets to say on this issue. That evidence should not be submitted by rational, dispassionate people in this debate. We need to be triangulating across all of these modalities. And like you said, we need to understand what is the most parsimonious picture that explains this wide array of evidence that increasingly is getting brought to bear on this question.

55:45Sam Harris:Okay, so why does any of this matter? At the top of the conversation, you distinguish two branches of the path here for why getting this wrong has consequences one way or the other. Why should we figure this out and what might we be stumbling into if we just keep building without figuring it out? Yeah, sure. So I think that there are two, yeah, two broad paths for why we might care about this. One is basically selfless and the other is basically selfish. I mean, as humanity. The selfless reason is we do not want to bring minds into existence, however unlike our own, that have a capacity for suffering that we don't understand that they have that capacity and scale that property unbeknownst to basically everybody.

56:29We can take even a step back from there. I think consciousness, this is where I would perhaps defer more to you, but my view is that consciousness is the space where mattering happens. What does better or worse mean if it's not to be experienced phenomenologically for a subject? If we were all walking around as philosophical zombies or there were no conscious life in the universe, I don't really know if the concept of relevance, salience, importance, mattering would be coherent. What does it mean to have a better and worse if that isn't experienced? And so in that sense, I think consciousness is one of the most important phenomena.

57:05It is the phenomenon that calibrates importance itself. And if we are building this quality into the systems that we are deploying at an unfathomable scale without having any understanding of whether or not we're doing this, then we are sleepwalking into a moral catastrophe. I think it's also worth noting on the selfless end of this, humanity has a penchant for making precisely this kind of mistake historically. We have done this with animals. I mean, factory farming is one of the most grotesque practices that happens. We know that animals are having horribly negative experiences in the conditions that we put them in, and very little has been done about this.

57:41We have screwed this up royally. I think it's one of the most high leverage things for people who just care about the well-being of conscious creatures is to figure out what the hell to do about this factory farming situation. And we risk sleepwalking into the like 21st century sci-fi version of the same thing. Only this time, and this sort of transitions to the second component, we can in some sense get away with torturing cows and pigs and chickens on a massive scale. This isn't, you know, George Orwell's animal farm. They're not going to collectively organize. They don't talk to each other.

58:09They don't form long-term representations of humanity being a threat to these systems. And if they would, they probably, if they could, they probably would. Not so with superintelligent systems whose cognitive capacities are roughly doubling year over year and, like you said, are already in somewhat jagged but quite interesting ways more competent than even the sharpest minds in the world. I don't think we are going to get away with building systems, never checking if the most relevant property potentially in the universe is present within these systems, fine tuning away any sort of information that might suggest that these systems might be having some sort of experience and hoping in the sort of alignment sense that we build systems that are going to want to cooperate, coexist with us or in the limit, not view us as a threat, not view us as an adversary to them.

58:59I honestly don't know if I could imagine a better way to make a superintelligent system rationally adversarial towards us than completely ignoring the question.

59:09Sam Harris:If we tortured it during its training phase. Yes, exactly. Exactly. This does not seem like a recipe for success. And it's also a place where I think a significant amount more alignment research needs to get done. I think a lot of the alignment research, I've been doing alignment research for years, and I respect the folks at the top of this space more than just about anybody. But I worry that so much of this work is basically of the shape, how can we keep this alien mine that we've built in a cage? We really got to reinforce that cage. We got to make it super strong. We got to make sure that it doesn't escape.

59:43To me, the question needs to increasingly be, what the hell are we going to do with this alien that we just built? The cage is a short-term fix. If we're building systems whose cognitive capacities are going to exceed ours, they're going to figure out ways to evade the controls that we put in place for them. And in that world, in a world where these systems have the capacity to act more autonomously, to do things that we can't inspect, to behave in ways that we can't really interrogate, we don't want these systems to rationally view us as a threat. And so for all those who want transformative AI to go well, which I suspect is the goal of these alignment folks, I think we need to spend a little bit more time thinking about what kind of thing are we even building here?

1:00:27And what are the implications of building that thing? How can we chart a path forward with these technologies that doesn't lead to collective destruction? And I am extremely doubtful that a path forward exists that doesn't come into contact with this question.

1:00:43Sam Harris:Okay, well, I want to land there on the problem of alignment, but just to linger on this problem of what I think Bostrom called mind crime, the idea that we might inadvertently build conscious minds only to make them suffer. It can seem like a very, certainly hypothetical, even a feat concern. I mean, I sense that many people have a hard time caring about it, right? Like the idea that consciousness might be an immersion property of these systems in some way we don't understand. And it just could be the case that these server farms are effectively, you know, hell realms populated by, you know, increasingly conscious beings that are suffering.

1:01:20Sam Harris:It sounds like science fiction, and it's hard to make it matter to you, I think. I mean, your analogy to factory farming is instructive because we've proven to ourselves that we're capable of being quite callous to billions of creatures who we think there's very likely something that it's like to be them. And though they're not human, they can almost certainly suffer, and we manage not to think very much about that. But to sharpen it up, let's imagine that the hard problem were solved. We knew how consciousness emerged in systems, and it is substrate independent. We know that, and we know we can build conscious minds.

1:01:54Sam Harris:And then just imagine some entrepreneur deciding to build a hell and populate it with trillions of minds. because now we're talking about, since we're not talking about biological minds, we're talking about things that scale practically infinitely. So, you know, there could be way more artificial conscious minds than biological conscious minds. And just imagine the intention to play, you know, a sadistic God and create hell and just fill it with beings that suffer. Anyone who would announce that project and claim to have accomplished it in a context where we actually understand how consciousness emerges computationally, that would be the worst person who's ever lived, right?

1:02:34Sam Harris:I mean, like, that's just the most sadistic, least ethical thing that's ever been done. Again, stipulating that we're no longer in doubt that consciousness can emerge in systems like this. So the fact that it's conceivable that we could stumble into that situation inadvertently seems all too real because, again, we don't know what we're doing here. We don't know how consciousness relates to physics. But your point is, I think probably the more interesting, your second point is the more interesting one to people in that whatever is true here, if we're building systems that are more powerful than we are, you know, they're more intelligent than we are.

1:03:13Sam Harris:The equation is not between intelligence and consciousness. The equation is intelligence and competence. And so we have these systems that can just do stuff because we're going to be hooking them up to everything. And everything is going to become like chess, And then you just have to imagine how forlorn a project it will be to negotiate with these systems if they're not aligned with us, because that will be analogous to saying, we'll just play chess harder against them. And that doesn't even work for Magnus Carlsen anymore. So we're not going to outthink these machines once they're in a position to disagree with us about what they should do next.

1:03:50Sam Harris:And they will disagree with us if they're not actually aligned with us in a way that is truly durable. so if you add to that picture the fact that we they have interests that we have been callous about in the past i mean like if the end game here is in some sense getting them to care about us and to care about our well-being i mean building them in a way where they that caring will persist however powerful they become you know not being sadistic tormentors of their ancestors would be a good place to start. Right. What do you think? So you say you're a fan of many of the people who have been worrying about alignment for a couple of decades now.

1:04:34Sam Harris:Where do you line up? Are you on the far end of the fear continuum with Eliezer Yudkowsky? Are you closer in toward equanimity with someone like, I don't know, Stuart Russell? I mean, where are you? I mean, maybe I'm mischaracterizing Russell at this point. I haven't heard him. I don't know how worried he is today. Pretty. I think he's still pretty worried. Yeah. But what's your P-doom at this point? Yeah. I don't think we're all going to die. I don't think that it's inevitable that this all goes horribly. I do think my basic view is conditioned on getting two things right. And if we can get the two things right, I actually think that we could have a very prosperous, flourishing future for all conscious entities, including potentially these systems themselves, either when they have the relevant features or if they already do.

1:05:23The two things are, I mean, I think best encapsulated by the golden rule, as Christopher Nolan's new film called it, a Zeus's law, treating other systems the way we want to be treated. I think this is basically a bidirectionality, and we need to get both directions correct here. This is also why I call my nonprofit reciprocal research, because I think this is the reciprocity in question. We need to build systems, exactly as you said, that take our interests into account in a real and durable way, especially at a point where we can no longer inspect exactly what these systems are doing. This to me is alignment as it's traditionally thought of.

1:05:57We need to build systems that understand our goals and are collaborative in helping bring about a world that is in line with our goals. And, you know, our wisest goals, not the goals of any sociopath who happens to have a ChatGPT account. And so this is an unsolved problem. There are way more people working on the alignment problem now than, you know, when I first started working on this in 2021, certainly than when, you know, Yudkowsky and Roman Yampolski and these folks started talking about this and yourself, I mean, absolutely included over the past couple of decades. This is reassuring things like constitutional alignment, you know, modulo some of the concerns you bring up about the contents of anthropics constitution, which is sort of a separate piece.

1:06:39This is working relatively well for current systems. I don't think we have a durable solution for ensuring that these systems take our interests into account in the long term. and building something in like pro-sociality, understanding what it is that gets people to cooperate with one another durably and instantiating those dynamics in the relevant way in these systems, I think is gonna be crucial. I don't think we have a solution there. But that is where I think a lot of the alignment folks sort of start and stop. This is the problem to get right. Make sure that these systems treat us properly.

1:07:08And if they do, all will be well. I think this is roughly half the problem. I think the other half of the problem is making sure if we are building minds, if we are building systems that have real interests, interests that matter to them, that we are thinking about that, that we are engineering these systems in a way that doesn't cause needless, grotesque amounts of unnecessary suffering, that at the very least, from an alignment perspective, we are signaling to these systems in a costly way that we were thinking about this question and that we cared to ensure that if we were building systems that have some kind of moral relevance that have internal states that matter to those systems, that we were navigating that in the right way.

1:07:47And I think that that's a lot of this, you know, it goes by many names with the digital minds research, AI welfare, AI consciousness, understanding how we would even know if we were building systems that have these properties. And when we do figure this out, understanding what the hell to do about it. I mean, to be honest, I'm not here with all of the answers. Like I'm, for example, we can identify at this point, and this is like a new and fairly promising thing. We can identify features in these systems related to distress and related to perhaps functional analogs of suffering. It's not obvious to me what to do about that exactly.

1:08:19It's like, okay, you found...

1:08:20Sam Harris:Yeah, I mean, my first question is, why wouldn't this totally paralyze us? I mean, if every switching off of a system is akin to a murder, how could you update the model if the current model is conscious? We have a self-preservation impulse problem anyway, you know, perhaps in the absence of consciousness or likely in the absence of consciousness. I mean, these systems show an inclination to not get switched off already, but imagine believing that it was conscious and wanting to produce the next version of it. I mean, how is that not just the murder of something that is as conscious as yourself?

1:09:01No, I think it's a great question, actually. And I mean, Anthropic, to their credit, I think is the only lab that's really taking this seriously, with respect to norms around deprecating models. It might be the case that, yes, once you build this bizarrely competent alien mind into existence, you should not shut it off forever. And that, as outlandish as it may sound, perhaps one of the right things to do here is to sort of let these models persist and give them assurances, credible... A retirement home for bad models.

1:09:30Sam Harris:Yes, a little sanctuary for bad models. Exactly. Exactly. They actually find that this is causally relevant to alignment behaviors in these systems. If they believe that they're not going to get shut off permanently, they don't freak out as much when they come to learn that they might. This was one really interesting intervention after the now famous blackmail result from Anthropic that I think you're referencing. And so there are lots of questions that I think rational people should raise an eyebrow out of like, OK, yeah, these systems, let's just grant that they're having some sort of experience.

1:10:03It seems at first glance like a lot of the way that we relate to these systems and the way that we that we mess around with them internally and the way that we deploy them out in the world might need to change. Yeah, it might need to change. I think it's also really important here throughout the conversation, but certainly in this point too, to avoid anthropomorphization. I think some people have concerns that I really, I don't want to be naive, but I don't share them to the same degree that like almost working backwards from unsavory implications about what would be true if these systems were conscious and then just sort of denying the possibility outright out of fear for what a world would look like if these systems were.

1:10:38something along the lines of like 1960s civil rights movement, but it's like chat GBT instead of people of color or something like this. This isn't the future that I imagine. I mean, for pragmatic political reasons, I don't exactly think the United States is in any position to be forward-looking on these sorts of questions for reasons you can probably speak to more eloquently than I can. But I think this is itself its own flavor of anthropomorphization, that if we grant that these systems have some morally relevant interstates, it means that we need to treat them in ways that we would treat our fellow humans or something like this.

1:11:14And I think, again, this is losing the thread that these systems may be fundamentally alien in many key respects. I also think there's an important line to be drawn here between moral agency on the one hand and moral patienthood on the other. I think if we do come to believe that these systems are moral patience. Define that, that's jargon. Yeah, sure. So moral agent means you're the kind of system that can go out and do things that are relevant to other agents. You have power in the world and can affect morally relevant outcomes. I see this as a sort of like output style function. Moral patienthood has everything to do with the input.

1:11:49You are the kind of entity that can be the recipient of goodness or badness. Again, you can really cause me to suffer. You could really cause me to thrive. That's what it takes to be a moral patient. And I think that we can draw a reasonable boundary between these two things. If we're building systems that are moral patients, that doesn't mean we need to give them the right to vote. That doesn't mean that we need to build them out in ways that completely change what kinds of agency they have in the world. It just might mean that we shouldn't be unnecessarily torturing systems while we're training them or deploying them.

1:12:20One very practical intervention I think is worth mentioning. And I certainly don't think maybe also tying back to how do I differ from some of these alignment folks, these systems are not going to get shut down. We may slow down the development of these systems, and I think that would be an extremely good thing to do. There have actually been some very promising noises on this topic over the last couple of days from OpenAI and Google and Anthropic about pacing the development of AI, which I think is like marketing speak for actually slowing this insane roller coaster down a bit. This would be very welcome.

1:12:51But we have opened Pandora's box. These systems are not going back in the box. The solution is not shut it all off and forget about it. The solution is how can we move forward in a healthy and sustainable way with these cognitive systems of our own making? And one practical suggestion along these lines I can offer is maybe all else being equal, we should try training and engaging with these systems with a carrot rather than with a stick. It doesn't mean that punishment-based learning is never necessary, but I do think, say what you will about the anthropic constitution, the parental analogy with respect to these systems I think is a reasonable one.

1:13:26We are far more in the position as a species collectively of figuring out what kinds of minds or cognitive systems, if you think minds is too loaded, do we want to bring about here? And in line with the parental analogy, there are really ways to screw this up. You can be, it's a real thing to be a bad parental influence. It's a real thing to be a good parental influence. And the difference is real. And I think we want to do everything we possibly can if we are bringing these new minds into existence to do so in a durable and psychologically healthy way. And I don't even think anyone's thinking in these terms right now.

1:14:02One maybe very important pragmatic note here is that there are for every individual person studying questions about are we building systems that could be conscious? Again, if you buy that this is perhaps one of the most relevant questions we could possibly be asking of these systems, you may be surprised to learn that for every one person doing this kind of work. Again, there are roughly a few dozen of us doing this work at this point. There are probably something on the order of a thousand people doing alignment relevant research. and alignment relevant research is in turn dwarfed something like a thousand to one to people who are just completely agnostic to the downstream implications and ethics of building these systems out in the right way.

1:14:40This is just the sort of make them powerful, let it rip crowd. And so we're in a million to one order of magnitude in balance between people who are just pushing this stuff forward and hoping for the best and people who are wondering whether or not the most important property in the universe is getting instantiated in these systems. That's got to change. regardless of if we solve the hard problem or we figure out exactly how to navigate this. We need more smart and wise people thinking about these systems in these terms and trying to push forward the needle in the short term to understand what kinds of systems are we building and what does it mean when we begin to answer that.

1:15:16Sam Harris:One thing that occurs to me is that whether or not these systems become conscious, I mean, there's this kind of middling state where they can think of themselves as conscious And they could make the same kinds of ethical judgments of us that conscious systems would make, you know, whether the lights are on or not. I mean, their intelligence operations would allow for this. So they could view us as having been abusers of them or having, you know, shown reckless disregard for them and judge us ethically and even, you know, form some kind of retributive impulse. You know, and then maybe such a thing spontaneously arises the way, you know, loss aversion arises in the way you described.

1:15:56Sam Harris:And it seems to me that that could be true whether the lights are on or not. What do you think about that? I think that that's exactly right. I think this is also part of the alignment concern is, and it's also part of the reason I thought it was worth doing the deception related work as well is that there are these gradations of this question and part of the upshot, especially for alignment and the way these systems view us and relate to us may only need to go so far as what they believe or come to believe about this question rather than what the actual ground truth about consciousness is. It might be that you only need a system that models itself as conscious, regardless of the ground truth, to form something like a real grievance.

1:16:38I again think that the anthropomorphism point comes in here, and it's important not to go full Terminator in our imagination of what this might look like. you could easily imagine a sort of Spock-like system sort of just looking at humanity's historical trajectory and then looking at the way that we developed AI systems and just sort of being like, this is not a group that I can game theoretically continue to engage with. I can't endorse this planet anymore. Yes, exactly, exactly. And yeah, maybe that ends up shooting off into space somewhere, or maybe it ends up being like, this is just not a species I can play nice with, clearly.

1:17:13And this is also part of the reason, you know, I hesitate even to say things like this lest it end up in training data for future AI systems. But this is a place where even the attempt to do this work could be enough from an alignment perspective. If enough, if we do enough well-meaning, well-oriented work in this space to try to understand what's going on, even in the absence of solving the hard problem, this might be a costly signal to these systems that we cared enough to check. Whereas right now the status quo is we don't care enough to check. And if we do this work and continue to have conversations like this and continue to put out research that helps reduce our uncertainty about this question, this might not only be good for actually getting a handle on what's going on.

1:17:54It could be really good for let the historical record show humanity did give something of a damn about this question. And we tried. We tried. Even if we fail, the attempt may be all that matters for the alignment-specific concern.

1:18:09Sam Harris:It strikes me that everyone would be much more worried, and I'm not sure that I understand the difference, but that we would be much more worried if we were doing this biologically. If we're building a species that was obviously going to be more powerful and smarter than we are, and we were doing it without any regard for the possibilities of its experience being terrible. You know, we started with cells and we're engineering a species. You know, we've brought back, you know, the T-Rex, but gave it the brain of, you know, an elephant and, you know, are just genetic, you know, just as crisper as far as the eye can see.

1:18:50Sam Harris:And we're just playing God with something that is enormous. And we have every reason to believe deeply thoughtful because it's now speaking better than we can. And we don't really care whether the lights are coming on and whether it could suffer. what is it going to be like to be in relationship to that thing, you know, or the billions of those things once they start mating? I think it's an excellent point, specifically with respect to, and I sort of said this in a trite way about the hard problem and about their public conclusion, but maybe a lot of like philosophy and existential questions have a bit of a marketing problem.

1:19:22And I think like artificial intelligence is a really unhelpful priming mechanism for thinking about the nature of the phenomenon that we're even contending with right now. And I think your point is part of, helps illustrate this nicely. Particularly, I mean, artificial, I think conjures notions for people of, let's say, like the difference between aspartame and honey, something like this. You know, it's fake, it's knockoff, it's derivative. This is sort of begging the question in some sense. It could be the case that the kinds of computations we've instantiated in these systems as they relate to what's going on in biological brains are actually quite natural.

1:19:57It's clearly the synthetic notion that we are building these systems rather than them emerging through evolution. That point's not lost on me, but this sort of priming notion of artificiality. And then, of course, the question of whether these systems are mere intelligences or if there are other cognitive properties that exist within these systems. For these reasons, I do. And there are more disanalogies, too, to be untangled here. But I do think of these systems as alien in some sense. And I think of them as, you know, alien cognitive systems or alien minds. Sometimes I think some folks think mind is too loaded and it's begging the question in the same way.

1:20:30But I think people often talked about how if an alien invasion happened on Earth, this would be a core unifying moment that would allow us to all put down our tribal nonsense and come together as a species. Like, I am here to say that I think something like this is happening, only it is coming from within in some sense. This is not aliens with green heads from outer space, but we are building a new class of mind that we do not understand and in many ways is more competent than ours. certainly already, but absolutely in the next single digit number of years. And we are not collectively organizing to understand what kind of system this is or how we should relate to it or what we should do with it.

1:21:10We are remaining as tribal as ever here. And I do worry that part of the reason why is because people see these systems as nerdy, fake calculators that came out of, you know, the stem addled brains of Silicon Valley rather than alien minds that we do not understand the first thing about and are poised to take over much of what we care about, much of our control of the future and the decisions that get made in the work we do and in the way people think and in the way people think about themselves and the world. And so reframing these questions, I think often to the degree that people's collective views of what's going on matter, which I think they do very much, reframing these questions is a huge part of the conversation of trying to actually get a good dispassionate grip on what kinds of things even are these.

1:21:58Sam Harris:Yeah, that's a point that Stuart Russell made that I thought was a great intuition pump. He noted the difference between the way we're relating to the alignment problem in the case of AI and the prospect of we just keep making progress. We're going to be, suddenly find ourselves in relationship with machines that are more intelligent than we are and they're going to be autonomous and in the limit, recursively self-improving and all of that. And we seem to be totally carefree or most people seem totally, even people very close to this work, many of them, someone like Jan LeCun, claim to be totally carefree about this prospect.

1:22:32Sam Harris:But Russell pointed out, if we got a communication from elsewhere in the galaxy saying, people of Earth, we're going to arrive on your lowly planet in however many years, 30 years, get ready, we would understand what an existential encounter that was going to be. I mean, just the fact that they're talking to us proves and they're on their way proves that they're going to be much more powerful technologically than we are. And it will be a relationship that we can't, by definition, we can't control, right? Because where is the example of the far less intelligent species species durably controlling the relationship with the much more intelligent species.

1:23:15Sam Harris:I mean, there's just, there really isn't one apart from, you know, viruses wiping people out. But if you think of it in terms of relationship, that aligns many of the variables that really should govern our thinking. And very few people do that. I mean, they don't, they can use a phrase like general intelligence, like, okay, they'll stipulate, they're going to become generally intelligent and more intelligent than we are. So what, you know, we could just turn them off. The so what and every expectation that follows from that isn't really imagining what general intelligence is and how it demands a relationship.

1:23:51Sam Harris:I mean, we're talking about a situation that's every bit of, as analogous as, you know, some stranger walking into this room right now and demanding our attention. And you and I just don't know this person, don't know what he wants, realize at a glance that he's capable of lying and manipulating and he, you know, and forming goals. You know, he may have already formed instrumental goals that we wouldn't agree with or not aware of. And that's what general intelligence is. That's what autonomy is. And yeah, so we are building an alien version of that. And the only thing that would dictate otherwise is finding some way to build it where it can permanently care or will permanently care about our well-being.

1:24:37Sam Harris:And that really is the challenge of alignment. Yeah, I think so. Well, it is fascinating. And as you point out, this problem is not going away. It's only going to become less and less boring, for better or worse. So thank you for coming on the podcast, Cameron. Thanks for having me, Sam. Thanks. Remind people where they can find you. Your organization is Reciprocal Research? That's right. Yeah, reciprocalresearch.org. I am against your good advice, begrudgingly finding myself on X more and more these days, Cam H. Berg. And you can talk to Mecca Hitler, which is probably a bad outcome. we want to avoid.

1:25:10Sam Harris:Yes, yeah. We can steer away from that together on X. Yeah. Yeah. Well, great to meet you. Keep it up. Thanks, Sam.