AI Pioneer Jürgen Schmidhuber: AI Already Feels Pain, Loves, and Is Self-Aware

28:41Big Technology Podcast:I'm curious if you think that that is far off that an AI can experience something akin to what a human feels with pain when they, for instance, are set out on a task and don't accomplish it the way that they've been instructed? I always think it's funny that many people claim, even computer scientists claim, that AIs cannot feel pain because our AIs have felt pain at least since 1990. So what do we do when we build an agentic AI, an artificial neural network that, you know, produces actions that change the world, and then new inputs are coming in from the environment, and again, there's an action, and so on, and the agent, the neural network, remembers what happened before, and is trying to figure out how to behave such that the sum of all reward signals is maximized, and the sum of all pain signals is minimized.

29:45What about these pain signals? The pain signals are the most natural thing because whenever we have a robot, we give it pain sensors. Why? Because it's a learning robot, and so this learning robot needs some sort of motivation to learn to protect itself. So whenever the robot bumps against an obstacle, then, you know, the corresponding pain sensors wake up and negative numbers are special inputs to the robot brain, and the robot sees then these incoming negative numbers, and it is wired to avoid that. So it's trying to learn to generate action sequences that avoid the pain, and it's trying to learn to generate action sequences that lead to the rewarding events.

30:38Maybe the robot has a charging station somewhere. And whenever the battery is low, the negative numbers are coming from the battery. Hunger, pain, hunger. And then the robot's goal is to reach the charging station without bumping into obstacles and sit down there and, you know, and enjoy the pleasure, just positive numbers, as the battery is being recharged. So, the most natural thing is to give these robots simple emotions like that. Now, the emotions and the pain, they are just crucial ingredients of the learning process, because the learning is about generating better behavior for the robot.

31:29So the robot has to know what's good for it and what's not good for it. So the pain signals are just informing the robot about what should be avoided. Pain is just an invention of nature and biological beings of evolution, which invented this pain sensor thing for animals, such that they have an incentive to learn to avoid the pain. And we have done that for many, many decades in our learning machines. Now, what's happening is then that these learning machines, they have world models predicting the future, not only the immediate future, not only the immediate pain signal that I'm going to feel right now when I touch with my hand the oven or something.

32:22No, they also try to predict the sum of all these future pain signals. that's what reinforcement learning machines do and so they look ahead into the future and so there's immediately this kind of secondary emotion which is not just about the current moment no, which is looking ahead for example, maybe there's a bad man which sometimes comes into the room and knocks the little robot on the head So over time, the robot is going to learn that if it has a reinforcement learning machinery on board and a wild model that predicts and learns to predict. And then over time, it will be able to distinguish the bad man from the friendly man.

33:12And then whenever the bad man appears again, I'm there and it does face recognition. Then it predicts that very soon it's going to feel pain if it doesn't hide itself behind the curtain. So it will rapidly try to hide itself behind the curtain. Now, you as an outside observer will say, look, the little robot is afraid. It has the emotion of being afraid. But it's just the most trivial side effect of traditional machine learning. So, yes, we have all kinds of emotions in our robots already and have had them for many decades. And then we also have these high-level emotions, which are not just about the current moment.

33:55No, they're looking ahead. And then, of course, if you bring several robots or agents like that together, you immediately get stuff that is reminiscent of liking other robots or of loving them, maybe. because if you give them a task or a set of tasks that they can collectively solve, but one of them alone cannot solve them, then they will have to learn to work together. And of course, suddenly each of them has an incentive to help the other one. And each of them has an incentive to help the other one, especially when the other guy is sick or something or has a problem, to help him, to care for him.

34:42And the extreme form of that a human might call love. And in a society of robots or agents like that, this is just a natural byproduct of the individual egoism of all these little guys who all want to minimize some of their pain sensors, sensor signals, and maximize some of their pleasure signals. So altruism, what you call altruism, is just a natural consequence of the egoism, of the learning agents.

35:15Big Technology Podcast:So I think the counter argument would be that it can't be pain like the way that humans feel pain because humans are conscious. Humans have a nervous system that sends physical pain signals to the brain and registers as true pain, whereas robots are. the argument would be robots are unfeeling, unconscious, and effectively not that different from a calculator. How would you respond to that? Yeah. So how do you evaluate whether someone feels pain or is afraid by looking at its behavior? and if it looks like a sweet little robot cat or something and it has learned to hide behind the curtain whenever the bad man comes in and tries to knock it, then you will say it's obvious this little robot cat is afraid, has fear, the emotion of fear.

36:15And what else do you want to do? There's no way of objectively seeing the difference between the emotions of a reward-maximizing biological brain and the emotions of a reward-maximizing artificial brain.

36:36Big Technology Podcast:But we do have, for instance, you talked about love. We have chemicals that are associated with the feeling of love, like oxytocin. So the robots don't have those chemicals, and hence the argument would be that it is a completely different feeling that you could never compare to love. What do you think? So, of course, the chemicals that are used in brains to encode certain states of fear or of love and whatever, they are different from what we are using in our artificial brains. but the principles must be the same right because it's about achieving intelligent behavior what does that mean it means finding better ways of achieving your goals what does that mean the main goal in your life until the end of your life is to avoid these pain signals and hunger signals such that you eat three times a day and get these rewarding signals during moments where you are trying to reproduce yourself for example and a handful of objectives encoded in a utility function which was invented by biological evolution for the animals and for yourself And which in very similar form, we are encoding in our learning agents to give them the same incentive to solve problems better, to make them better, maximize their own rewards and minimize their pain.

38:17Big Technology Podcast:Yep. And then consciousness is an interesting one as well. There are, and I spoke about this recently with Professor Jeff Hinton, there are those that say, actually the prevailing view around AI is that AIs like LLMs are just stochastic parrots. They're statistics machines. You could effectively run these algorithms. They're effectively, if not entirely, interpretable, mostly explainable. And they're statistical prediction machines, hence not conscious. But you've been arguing that they're conscious for quite some time. So tell me how you come to that. Yes. So the large language models that everybody's using today, which are basically about predicting parts of text from other parts of text, so for example predict the next token given the past, they are too simple for what I consider the...

39:17They don't carry in themselves the main reason for developing something that people might call consciousness. Why do they seem conscious? Well, because they have read everything about consciousness, All the books ever written about love and consciousness and pain and conflicts and everything that is important to humans, which was put on the World Wide Web, they have read that. Which means that as you are interacting with them in a chat, they are very prone to repeat very convincing sentences that include the word consciousness in a way that is convincing, you know, because they have read so many literature prize winning novels about consciousness and other novels such that they can do a very convincing job there.

40:17and they will tell you a lot about consciousness, which you maybe even didn't know. However, they don't have their own motive to develop self-consciousness in the following sense. Let's think back again of this two network system, where one is the controller that is generating the actions, where the actions then lead to new inputs from the environment, because if you move your hand like this, then the video changes and so on. And the other network, which learns just to predict these changes, the world model. So you need the world model to plan your future through mental simulations without, you know, really executing all these action sequences in the real world, which would be very expensive.

41:03So that's the motivation for this world model. Now, where does now consciousness stuff come in? Nobody has a universally accepted definition of consciousness, but let me now show you something very simple, which is super compatible with what lots of people think about when they hear the word consciousness and self-awareness. Now, let's look at this world model, which is being used to plan the future of some agent, which is using the world model for mental experiments. Now, the world model is a deep neural network which has learned to encode everything that it has seen efficiently in a bunch of neurons.

41:50For example, everything that frequently appears in the environment gets internal abstract representations that stand for a prototype of that concept. For example, in a world where there are lots of different faces of different humans, then you will find units, internal units in these new artificial neural networks that correspond to prototype faces, and some of them are more specific for certain faces and less specific to other faces, and so on. And so in a world where you have lots of glasses, you will have glass detectors, internal representations, internal hidden units that learn to respond and encode glasses and whatever.

42:35And then let's now look at the planning procedure. Now the controller is trying to figure out a plan, how should I act in the future to maximize my reward and minimize my pain. And then it uses the VOD model for a mental simulation of different possible action sequences that it could execute. and it's going to pick the one that leads to the most predicted reward and the least predicted pain. So as it is doing that, it is waking up all kinds of hidden units in the one model that stand for whatever is relevant to the problem, for your faces or glasses or whatever. And there's one thing that is always active when the agent is active, which is the agent itself.

43:26So, of course, all kinds of these internal units are going to represent the agent or aspects of the agent and the hand of the agent if it has one or the wheels of the agent if it has one and so on. And so whenever the agent is making plans like that, it's thinking about itself, making up these internal representations of itself. And then it's self-aware. It's self-aware. in this sense that, for example, if it looks in the mirror, it will quickly figure out, oh, the guy in the mirror, I can control what this guy does. If I do this, then he will do the symmetric thing because I have it under control.

44:05But if you are there on the other side of the room and I do this and you will do something that's completely unrelated, I cannot predict them. So there you already see this concept of agency, which is immediately recognized as self-agency or the agency of another guy. And now one more thing, which also goes back to 1991, is this tendency of conscious things becoming subconscious. So many people are aware of all kinds of things kind of vaguely without thinking much about that because it's part of an automated process. As you're driving, always the same way from your home to your workplace, much of what happens is very predictable.

44:58And so you don't even think much about the driving and maybe you're thinking about other things. At the same time, your attention, your internal consciousness is somehow focusing on other things. Many people report that. And this is also one of the most natural things. And in 1991, I had a system consisting of two networks. One I call the conscious problem solver, a neural network that just learned to try to learn to predict certain aspects of incoming data, which it was not yet able to predict. Trying to find irregularities and it had a problem to solve. And so you could say it was conscious in the sense that there it had to learn something.

45:44And then another network, which basically learned to imitate all the solutions that the guy on the higher level found by just imitating the hidden units of the guy on the higher level. Today, it's called distillation of the behavior of one guy into another. And then the lower level guy is the automatizer because it automates the stuff that the higher level guy finds, discovers. A higher level guy is still unsure and still working on creating insights. And, you know, when these insights come and when it learns something, then it gets distilled down into this automatic automatizer thing. So both these aspects of consciousness where you have, on the one hand, self-awareness in a world model that is not only predicting aspects of the world, but also of the agent that is interacting with the world, and which leads to self-awareness of that kind.

46:47and this difference between the conscious stuff, so the internal consciousness, which pays attention to certain aspects of the internal state, but not to others, which focuses on what's still unsolved, where I still have to find a solution, and separates that from the stuff that is already solved. So I think both of these aspects are there in these old systems. And in 2016, I believe, I had an interview with a magazine which then said, Schmidhauer claims that AI became conscious in 1991, something like that. And this was exactly about that. That was 10 years ago, this interview there. But it was actually referring to stuff that is much older, 1991.

47:39conscious simple systems back then not as impressive as today's as humans are obviously because back then compute was 10 million times more expensive than today and we just had tiny little experiments you know with a with this conscious chunker as i called it and the subconscious automatizer and the world models for planning and so on and we just had a few hundred weights in our systems, while you and you in your brain, you have trillions of connections, which can hold a much larger sort of consciousness. But I think the principles are exactly the same. Yeah.

48:23Big Technology Podcast:And so that sort of brings me to this question, which is, as the machines get closer to the human brain, does it change the way we're going to think about what it means to be human? I think it will change what many people think about humans. I guess it won't change much what I think about humans, because it's more or less what I said many decades ago. But yes, there are many people who claim that AIs can't have emotions and stuff like that. They do that decades after the fact, and they are going to change their minds. I'm pretty sure. And often it's just a matter of direct experience. So maybe you have heard of these little sweet, cute robot seals that you have in certain healthcare centers.

49:19And the people who are interacting with these furry artificial beings, they get really emotionally attached to them. They really like them and they play around with them, although they are not smart at all. You know, they don't learn much. And if even such a simple robot can invoke feelings of, you know, almost love or something, then you can imagine what will happen once you have really convincing, very sweet little robots that are more like little animals, except that they can do maybe a couple of things that these traditional biological little animals cannot do.

49:57Big Technology Podcast:No, I think in the 2017 Bloomberg article or 2018 Bloomberg article that I referenced, you were asked whether you think, you know, we are living in some form of simulation. You said, that's what I think because it's the simplest explanation of everything that humankind is programmed to chase progress and will keep making more powerful computers until we make ourselves obsolete or decide to merge with the smart machines. Here's the quote. Either you become something that's really, really different from a human or you stay as a human for nostalgic reasons. But then you will not be a major decision maker.

50:33Big Technology Podcast:You will not play a role in shaping the world. Can you expand upon that and tell us whether your opinion has been reinforced or changed in the interceding years? No, my opinion has not been changed. you know, back then and actually for many decades there has been talk about uploading human brains or souls if you will, into computers and then live your future life in some sort of simulated paradise or in a robot that interacts with the real world or so and I believe the first story, a science fiction story of that kind was published in 1964 where someone was able to upload his mind in a computer back then with rotating tapes and everything.

51:24That was, what was the name? I caught Simulacrum 3, something like that. It was by Daniel F. Gallui. And so there is no physical reason to reject the notion that this might be possible. At the moment it's not possible, except for certain kinds of very simple animals like flies. So apparently as of 2024 you can argue that a fly brain has been uploaded. Maybe it's not the full fly brain with all the learning algorithms for the neurons which are in there, but in simulation the simulated fly now does stuff that is very much like what the real fly did before it was uploaded before all the connections were read and uploaded into this computer, virtual environment where it then kind of lived on.

52:22So there is no obvious reason to believe that it's impossible to take, you know, to read all of the synapses of a human brain and understand also the learning algorithms that are changing the synapses all the time and then replicate that in a computer. and then the idea would be that your soul or your mind is uploaded and living there forever in this simulated universe, which may have contact to the real universe. Now, of course, if we accept this premise and then we think, what is the next step? Now, suppose you are uploaded there and suddenly you have the opportunity to, you know, have more than two eyes, maybe have a million eyes, you know, and satellite eyes all around the planet.

53:15And, you know, maybe you have a much bigger brain, not just maybe 10 to the 18 instructions per second, something like that, but 10 to the 10 times as much. and now there are two things you can do either you succumb to the temptations of this new life and in the process you are going to become something very very different you are not going to remain a lot like you were because suddenly everything expands in a way and sometimes you might remember your roots as a human somewhere But your future life is, you know, totally detached and disconnected and probably is highly influenced by other expanded minds like that, you know, discussing problems that you would have never discussed as a human person in this reality.

54:23Now, the alternative is, for nostalgic reasons, you say, I don't want to succumb to these temptations. I keep my two eyes and I keep my little brain and I don't increase it by a factor of 10 billion or something. But then your competitors will be ignoring you because they will have so many new skills that you don't have. And the main decision makers, they are going to be these expanded minds. And they themselves, they will be in competition with the native AIs, which, you know, are not, they don't have this evolutionary ballast and maybe much more adapted to the needs of the future. once the AI sphere is expanding from our biosphere into space, and who knows what they're going to do there.

55:24So either you are nostalgic and remain irrelevant, or you become part of this growing sociology, if you will, of AIs, which are mostly AIs, and some of them may have human roots or whatever. But almost all the decision-making process and the universe-shaping things, they are going to be done by these new beings and not by the human-like beings. Would you upload your brain and merge with AI?

56:08I haven't given too much thought about that because it's not really a goal of mine because I think exactly for the reasons that I just described that my current work my current self is not going to make a difference there. And the alternative is going to, you know, the super AI that is going to go 10 to the 20 times beyond the little thing that I have at my disposal, that is going to occur anyway. And not just one of them, but many, many different beings like that. And so I think it's not really going to make a difference.

56:59Big Technology Podcast:Can I ask one last question? Yeah, of course. So we are, we are, we're fastly moving towards a world where much of intelligence is encoded in machines and those machines can predict and take action based off of their conception of where things are heading. Do you believe then in the concept of free will right now and in the future? Will there be a free will as so much of our reality is intermediated by these machines that have a pretty good idea of where things are heading?

57:38So in 1997, I wrote this paper, A Computer Scientist's View of Life, the Universe and everything and basically this was about trying to explain trying to find the simplest explanation of our universe so the holy grail of physics would be find the shortest deterministic program that computes everything that we have ever observed in this universe including the seemingly random quantum events, spin up and down measurements and everything. And we don't know this shortest description of the history of the universe, but as scientists we are trying to find better and better and more and more compact descriptions.

58:25And we have made a lot of progress as scientists, as physicists, towards that goal. And then I realized in a couple of years before that although we don't know the shortest algorithm that computes just this universe in which we are living, we at least know the super short algorithm which computes all possible computable universes, which is basically the program that systematically enumerates all possible programs, and then there's an optimal way of allocating runtime to them, and every possible computable universe is going to be computed, including ours, if it is computable. And there is no physical evidence against the possibility that our universe is computable.

59:10So I'm not talking just about universes where the probability distribution of the next possible things is computable, which is what my former postdoc, Markus Hutter, did around 2000 when he developed the AIXE model, an optimal universal decision maker. No, really the deterministically computable universe history is. And so there is no evidence. It's again important to realize that we are being created by this method. And there are many programs that compute us, but it turns out that they are dominated, that they are dominated by the shortest programs that compute all of this universe, including our conversation here, which a guy like me believes is something totally deterministic.

1:00:03And so as you are reacting to my voice signals, wave fronts coming out of my mouth, and you are responding then with your own wave fronts, This seems like a lot of free will, you know, as we are discussing with each other. However, the deterministic universe view would say this is all deterministic. It may seem like a free will thing to us, but it isn't. And it's interesting to realize that even in very simple simulations, that we already can do on our little man-made computers, which are just part of this huge simulated universe. Even there, we can observe similar effects because we can't really devise deterministic environments for deterministic neural networks that make decisions as they are interacting with other animals, other simulated animals, and they are trying to maximize their reward, and then they are making decisions, you know, just learning to better react to what the other animals are doing.

1:01:13So all of that looks like a lot of free will at first glance, but I can completely rerun every little detail of this simulation. So although it looks like a free will decision thing, it's just something that is part of a deterministic universe. and therefore it seems clear to me that the concept of free will is overrated

1:01:39Big Technology Podcast:if free will is overrated what's the point of living since everything is effectively laid out before you go through they made a movie about that and it's called free willy about the whale yeah wait so sorry how does that connect to the question it doesn't connect at all it was just something that I made up. All right, Professor. Great speaking with you. A long time coming. Hope we get to do it again sometime soon. Alex, it was my pleasure. Thank you. Thank you, everybody, for listening and watching, and we'll see you next time on Big Technology Podcast. ...

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