2022 Annual Uehiro Lectures in Practical Ethics: Ethics and Artificial Intelligence (3 of 3)

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Good evening, everybody.

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Welcome to what's sadly is the last of a series of three you here that she had given by Railton on ethics and artificial intelligence.

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Most of you have been to the other two lectures,

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so you'll know that Peter will speak for about an hour and then we'll have some time for questions if you're lucky.

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We should have some time for questions and we will be able to take questions online as well as from the floor.

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And here we'll be using the the roving mikes again.

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Um, and, um, online people, I think you should put your questions into the chat.

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And then Johnny P will kindly report them to us.

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So without further ado, over to Peter. Well, thank you, and thank you to the Uehiro Centre for the invitation, the chance to be here with you.

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I've enjoyed it so far and I hope you will continue to enjoy it and I'm trying to think of the title for this lecture,

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and it occurred to me that only a recursive title would do,

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which is a living amongst artificial agents who live amongst artificial agents,

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who live amongst natural agents, who live amongst artificial agents and so on.

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Because it's really about that recursion at the very beginning,

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I'm just going to go over a little bit of the first lectures in order to help those who might not have been there or memory also may not always serve.

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I tried to mention at the beginning there are ton of ethical challenges about A.I. and many of them very large,

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and I'm not trying to address them all. I'm really looking at just this last, the sixth one, the problem of not so super machine intelligence,

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trusting A.I., entrusting A.I. with some inappropriate tasks or degrees of independence.

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Not just because humans are mercenary, though indeed they are, but because they may not know any better.

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There may be error in inadvertence. And so I'm my my question is how could we do this relationship living amongst

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artificial agents who live amongst artificial agents who live amongst natural agents?

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How could we do that in a way that was constructive, that was mutually beneficial rather than mutually detrimental or very risky?

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And so my focus has been on the idea that.

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These systems, these autonomous or semi-autonomous artificial intelligence systems might be part of the problem,

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might be part of the solution rather than part of the problem.

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Not just because we could use them as tools, which we certainly will do, but because such systems would be autonomous.

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They would be agents, they would be capable, I believe,

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of a kind of app sensitivity and responsiveness to morally relevant features of situations, actions, agents and outcomes.

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And in that we are going to find, I hope, a very important source of mutual confidence and trust.

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Such responsiveness, I've claimed, is not just important for their relations to us, but for their relations amongst themselves.

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And if this was right, if anything like this picture is right, then it should help somewhat with the other pictures as well the other problems,

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because those involve, amongst other things, problems of developing trustworthy A.I. at the heart of these other tasks.

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Now where is this sort of specious week for this talk?

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Because folks down deep mine announced Gatto A.I. as the first working example, still incomplete of a general artificial intelligence.

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And what's that? Why is that special? Well, it's because it's intelligent.

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It's capable of learning and problem-solving in a wide range of novel situations.

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That's the general idea of intelligence, but it's general in the sense that it's competence in a wide range of task language,

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dialogue, labelling images, motor control, game playing, testing, causal models and so on, and doing so autonomously.

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That is without being reprogrammed in between the tasks. And it does this by uniting in a single big model all the elements needed for these

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various tasks and taking advantage of this synergistic power of that to do them.

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And so this is what people have been hoping for for a while, and the people said, Well, why would they announce this so early?

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It's, you know, still fairly mediocre at some of these tasks. And one answer is, if anything like this works even as a sort of proof of concept,

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then the fact that it's badly trained or needs more information or so.

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And that's not the the key fact.

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Now my claim has been that in humans are a linguistic epistemic causal and social moral competencies are in some ways like that.

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That is, they're all very entwined. They're entwined in a complex model of the world in a bundle, not separate strands.

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And they develop through infancy and through the rest of our lives in conjunction with one another, in pace with one another.

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And I don't think they would be fully realised without one another.

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And so that's another reason for being especially interested in artificial general intelligence,

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because the full realisation of these special tasks might very well require more general intelligence.

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And I'm going to argue that so Sutton and others have argued that in the end,

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it will be generalist artificial intelligence that succeeds best, even at the well-defined tasks that A.I. is now good at.

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And in this respect, Gaucho A.I. is still, I gather a work in process there.

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There are specialised programmes that apparently do at least as well as it does or better.

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But for our purpose, the question that's especially interesting is how General Intelligence,

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artificial general intelligence might make a difference to questions of ethics and A.I. questions of air safety.

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And you might think, for example, that one very important feature of artificial general intelligence is that these systems

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might be more interpretable because they combine language skills with motor skills,

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with object identification skills, with dialogue skills and so on.

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They might be able to represent what they're doing in a way that is, for example, explainable to us in a better fashion than the machines now are.

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And so the idea is if you bring together model based learning with competencies like dialogue,

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categorising objects, using that to navigate or manipulate the environment,

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collaborating with humans on shared tasks and so on that you begin to make it more and more realistic to think this is an agent now.

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It's not a human agent who doesn't have all the features of a full fledged human nation.

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But the more it does this, the more it seems a general, and the more it seems that the words that it generates are connected with the world,

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that they're used in modelling the environment because they're associated with a variety of tasks.

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They're used for signalling others for communicative purposes. They're used in action guidance and in learning.

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And, you know, meaning isn't just use. I suppose most of us think that,

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but each of these are steps toward the idea that that may be more like meaning than it would be if it were just a word programme.

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Or it may be more like objects than if it were just an image identification programme because objects are three dimensional.

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And now it's treating these as three dimensional through its motor activities.

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And so that's an important sense in which the general artificial intelligence gives us more

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confidence that we are dealing with something that looks like an intelligence and an agent.

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And I need that very much for the argument that.

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I'm giving now, of course, if you look at the core, this big network at the core, it's just a big association structure.

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And that will tempt many to say, look, there's still no real understanding.

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There's just all this association and that can't be human level competency because humans have can't have understanding.

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And that's an important reminder. We're only so far down this path.

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But it's worth noting at least my brain seems to be a neural network of associational connexions.

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Now it's not made out of silicon, it's made out of protoplasm. It's got some more differentiated structures than these nets.

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It's got a lot more structure than these nets, and that may be very important,

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but it's still operating on this basic associational principle that Connexions are strengthened the more frequently they're activated.

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That's the basic principle neurones that fire together wire together.

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And so the question is, do we use that complicated net of ours association with what it is?

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Is that understanding? And we might disqualify it as well.

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But then at least we wouldn't be treating ourselves as in some ways above and beyond

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the possibilities of artificial agents because we ourselves are not there yet.

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Anyhow, I'm going to bracket such large questions about the nature of understanding because my challenge is really

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to emphasise that short of understanding and short of something like full agency or full moral agency,

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it's still quite possible for these systems to become aptly sensitive and responsive to morally relevant features.

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And one interesting thing about these kinds of systems is that if you use them, let's say, to model human moral intuitions, people do that.

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They set up an internet site. Lots of people send in their intuitions.

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They try to make a model of that using some kind of fit of a of a neural network.

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We're learning something about the structure of our own moral values, our known moral beliefs in that way.

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But if that were, that network could then be associated with action and with speech and with behaviour,

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then we'd have a better sense that we were getting at something that looked more like a competency.

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So consider language now.

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If an artificial system is going to be genuinely able to achieve human level competence in language, not just fluent, topically appropriate speech,

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but being attuned to things like conversational norms, interpretive charity sensitivity to the distortions that coercion,

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deception or power imbalance might bring to the content of the conversation, identifying speaker motives or intent, and of course,

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and compensating accordingly, identifying deception, attributing appropriate authority to others, use of words and so on.

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That's language competency. It is a very complex bundle.

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It's normative. It's epistemic, it's social, and it's got a lot of moral content as well.

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And so my sense is that really to build competent, humanly competent speakers, we will have to build a system with that kind of a bundle.

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And so a special purpose language programme is always going to look tinker toy by comparison and limited in its abilities.

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So if creating artificial agents with broad human level competencies is,

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the game is the aim and that indeed is the aim, then moral competencies, I'm saying, will be a part of that.

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Intelligence is a capacity to learn and solve problems in an open-ended array of situations,

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and moral issues arise as problems that humans face in an open-ended array of situations.

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Using our moral capacities, we often solve these problems.

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I'm going to talk about some of the ways in which we do,

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but that means achieving human level competency and solving social problems is going to involve this

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same capacity to represent morally relevant features of situations and to use them appropriately.

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And as we saw early on, this is reflected, for example,

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in the substructure of the mind in the way in which these capacities are reflected in the brain's activities.

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So here is the general semantic network compared to the default network we saw this year as the default network compared to memory tasks,

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autobiographical memory envisioning the future, simulating theory of mind tasks and moral decision making.

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It looks like a bundle,

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and indeed it looks like a core in which there is something like a generalised model that is allowing us to interpret the past that

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we have to imagine possible futures to understand what's going on in other people's minds and to carry out moral decision making.

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And so it looks something like the structure of general intelligence.

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And if that's if it makes sense that these tasks are bundled, then it makes sense that the brain will handle them.

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In this bundled way, there will be special moral modules. And so on.

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Now that looks like one of these foundation models in vivo and this capacity to

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use experience and flexibly recruiting memory and generating possible responses,

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simulating outcomes, assessing them in terms of how they would affect people and so on.

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If that's how we manage to develop and use such capacities as our language capacity, our moral capacity, our capacity is epistemic agents,

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then artificial agents are going to have to do so as well if they're going to have human level competence.

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And along the way, we will have to be responsive to epistemic and linguistically and socially and morally relevant features of situations,

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actions, agents and outcomes. So what do I mean saying agent talking about these artificially intelligent systems?

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I don't mean a deep notion of agent. I don't mean a core consciousness or a sense of self.

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I mean, a system of the particular kind, which is well studied in cognitive science of an agent interacting with an environment.

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The agent has a model of the environment. It has a goal, a reward function.

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Those two are combined to generate selected actions. Those actions are then suggested to the performer, the actual waiter, the agent,

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the agent performs the action and the environment returns a response in terms of, well, how did things turn out?

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And that then becomes data for updating the model. And it's a much more complicated than this.

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Actually, there are all kinds of internal loops that are fascinating in themselves,

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but the rough idea is that agents are by their very structure in this sense represented as they are modellers,

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they are learners, and they engage in action as learning and not just as performance.

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And so what we saw when we looked at, for example,

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evidence from neural recordings of macaques that they perform actually quite precise probabilistic calculations of rewards in their environment.

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Moreover, if you look to the right hand side and I will draw attention to this later on,

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they don't just do expected value calculations, which is what the Economist would recommend to them.

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They also do risk calculations. They independently encode risk and you might say, Well, why would you bother doing that?

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Don't rational agents just act on expected utility? And you can say yes, that's what they said, right up until the global financial crisis,

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when it was clear that accumulated risk offset the gains in expected utility.

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So animals live in an environment they have to survive.

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They can't ignore risk and artificial agents if they're going to be least animal level competent and

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certain the human level competent will have to represent not only expected value as they currently do,

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but also risk in this way. Here we saw work with a rhesus monkeys, indicating that they have, in fact, utility functions.

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These are abstract functions. This doesn't represent any particular quantity of juice or any particular level of risk.

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It represents combinations of quantities of juice and rebels, levels of risk and banana slices and grapes.

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But it reps them represents them all in a common currency like utility, and the utility function has the shape you would expect.

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It is risk seeking when there is little at stake and it is risk aversive when there's a lot at stake.

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And this looks very much like the kind of a function that you would expect a prudent agent to develop operating in the world.

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And the picture that emerges from this is the idea that our action as we saw, is guided by.

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Evaluative causal representation of the environment around us.

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And with that representation, we are able to select actions and then compare outcomes with what we expected.

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Now, highly intelligent animals don't simply have that kind of a first person egocentric perspective.

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In fact, they map their physical space non egocentric as well as egocentric.

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This was a big discovery. Not that long ago.

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Rats represent space not only in terms of location and where they are, but also in terms of a grid mapping out the space around them.

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It's a non egocentric representation, and these representations affords the animal a very substantial degree of autonomy from the current stimulation.

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It enables the animal to engage in the discovery and planning of novel actions, and it enables them to become optimal foragers in their environment.

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So here are some of the initial experiments with rats showing the neurones firing, either at a specific place or on a grid like pattern.

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Here are an example of the representation of space in the hippocampus of a rat who's been running a maze.

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And what we saw was it's not just present when the rat is running,

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it's present when the animal sleeps and it repeatedly activates that maze in its sleep.

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It does so in a way that seeks information. That is to say it spends more time activating parts of the maze it didn't explore.

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It moves in directions in the maze that it didn't move into during the day,

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and it also constructed novel paths because it has a representation of space that enables it to represent not just the channels that it followed,

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but the spatial relations amongst the locations along the channels.

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For example, diagonals and shortcuts. And then finally, we saw and this is a slide I.

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Try to show as often as I can, because it gives the animals credit for what they're doing,

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that when the rat reaches a choice point in the maze, it has an idea of what lies ahead.

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It searches to one side and to the other side mentally before it takes any steps.

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That's an efficient thing to do if you're an animal worried about energy.

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And it combines representations of expected value with representations of space in such a way that by going back and forth,

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it is actually weighing the alternatives and where it discovers the stronger weight.

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It will act and move in that direction. And so a rat running a maze is acting as a rational agent could be expected to do,

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forming the kinds of representations we'd expect and using them in the ways that we would expect.

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Now, highly intelligent animals, including humans, also construct non egocentric maps of their social environment.

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They represent different behavioural dispositions of the individuals around them,

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values that are associated with the behaviour of those dispositions, and these representations are learnt through a kind of reinforcement learning.

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And these are third personal representations. It's not was this spunky good to me?

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Is this monkey good toward other monkeys with this monkey, therefore be a good marriage partner or a good alliance partner?

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That's why these representations, these non egocentric representations are so valuable because that information is vital if they're going to

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initiate a new relationship and they need to distinguish the monkeys who help from the monkeys who don't help.

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And they're now finding that animals have codes within themselves,

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doing things like helping helpers and helping those who help others than themselves.

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So there's a lot more going on there than we thought, and this is a representation,

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a non egocentric representation that is a representation of relevant characteristics,

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we would say morally relevant characteristics of the social environment.

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This is also a kind of autonomy that they have because they are not bound to what

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they've done in the past or the options that they've exercised in the past.

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And so within the social environment, just as within the physical environment,

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they are able to navigate space in ways that help them realise goals and fashions that they had not explored before.

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And this modelling, which we should expect in a highly artificially intelligent agents,

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would then have the same kind of need for autonomy and efficacy in navigating the physical and social environment.

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So we should expect artificial agents who have even animal levels of competence to be doing

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this kind of non egocentric mapping of evaluative features of their social landscape.

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And we should expect, moreover, that it needs that just as the rat needs it or the monkey needs it in order to

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have the most effective and efficient pursuit of goals in that social space.

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And so what I've been trying to argue then, is that features like linguistic features, epistemic features,

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social features, features having to do with helping and harming morally relevant features.

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These are all they're not being conceived by the animals as reasons for action.

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They don't have a concept of reasons, perhaps, but they're being used as reasons for action,

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and they're playing the role as reasons for action that reasons for action should play.

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So. Back to our friends, the autonomous vehicles, because they're my example here of autonomous, artificially intelligent systems.

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So what kinds of sensitivity or responsiveness to reason making features would be involved in achieving human level competence and driving?

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And importantly, how much sense of such sensitivity and responsiveness would contribute to the safer and more trustworthy character of these vehicles?

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In other words, if they were able to be responsive to these features, would they in fact be safer?

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Would they be more trustworthy and would they be better at realising the kinds of goals that they're trying to realise, like getting to destinations?

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Seems like a lot to try to get morality in this sense, not a morality in the sense that we think of a highly normative AIS system,

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but a system of responsiveness to morally relevant features. It seems like why would you need that in order to drive?

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And the answer is humans need it in order to drive,

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and they need it in order to drive in such a way that the capacity to have human level competence would presuppose that ability as well.

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So we last time looked at merging. So here are some typical merging problems.

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And here's an example. Take a look at the lower right hand corner.

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You're trying to merge into a highway, and you notice that the following car the second blue car, seems to be allowing a gap in front of it.

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Now, does that mean that it's signalling to you to merge in? Has it slowed down in order to signal that you're merging in?

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Or is it just speeding up and it has a gap to make up between it and the car ahead of it?

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What is the intention of that car? And where should you go?

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Should you try to move on the trajectory that takes you close to that car or more distant from that car,

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which is going to be less likely to interfere with the planning of that vehicle?

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Given what the vehicle is signalling that it's doing and so recently,

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there have been the development of techniques for understanding how merging can take place that reflect this kind of complex, intentional structure.

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Now for humans, successful burgeoning merging involves competing interests.

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Each of us might want to get to our destination faster, but we also have some shared interests in smooth traffic flow, avoiding collisions and so on.

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How do we reconcile these in any given situation, given that we don't know the other driver?

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We may not interact with the other driver again. What do we have to do?

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Well, we have to use whatever we can to try to determine the intentions of other drivers.

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We have to assess the evidence that's available to us. We have to look for whether there's any communicative action going on with the other driver.

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We have to think that we're perhaps causing a slowdown behind us if we delay or we have to be mindful of the traffic that's moving.

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And so we have to know what are the expectations of all these individuals around us?

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And how can we put together those expectations in such a way as to enable us to do a smooth, safe merge?

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And so it involves heavy use of theory of mind.

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And that suggests that if you're going to build a car capable of human level competency and merging, it's going to need something like theory of mind.

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And indeed, we find something like that. So here is another merging situation that you all understand.

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There's a construction zone. You're supposed to be nice and neatly in a single lane as you get to it.

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The autonomous vehicle sees an opportunity. Should it take this opportunity, it could scoot ahead in an open road.

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After all, it's been in traffic should it scoot ahead in open road. And if so, how should it signal to the other car that it's all ready to emerge?

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And how should it respond to the other car's response to its attempt to muscle in like that?

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And why is it not working and why next time should it try a different strategy?

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All of that is the kind of stuff that these machines have to figure out.

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And this is a situation they could get into.

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This is not a situation of unheard of a catastrophe, this is cross-town traffic in New York on an ordinary day.

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Think of how much intentional information the drivers of those cars need and the pedestrians

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need in order to get out of this situation while continuing to move across the intersection,

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which they do all day long, day after day. That's an ability that could not possibly be accomplished by a driver module.

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It's going to have to be accomplished by a module for understanding human social

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interactions and ways of getting people pissed off at you in ways of getting people

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to want to cooperate with you and trying to elicit elicit their help in trying to

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signal to the pedestrian that you're really not trying to run that person over. So.

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Autonomous vehicle merging, then is going to have all the problems of conflicting goals, problems of communications signalling,

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trying to be reliable in the communication or trying to detect deceptive communication, this is New York.

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After all, they must solve these problems of predicting behaviour, gauging evidence.

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What evidence could I give to others that would enable me to do this task more successfully and smoothly to have safe and

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human competence in merging in an actual situation that might encounter any day at any intersection in Midtown Manhattan?

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So no simple dynamical model will suffice. Getting the solution will require attributing goals and expectations to other drivers,

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autonomous or human, looking for informative signals, estimating their behaviour and so on.

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So this is a kind of non egocentric mapping of the social environment.

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And. One approach now that's being explored.

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I'm happy to say the British Isles is to develop deliberately.

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Artificial intelligence is for driving that use rational agent models of other vehicles in situations.

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And what that means is that it tries to impute to the behaviour of other vehicles what would be the intention behind this particular behaviour.

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And that means they're doing what's called inverse reinforcement learning from the behaviour.

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They're trying to infer what the value function of that vehicle is effectively and then trying to use something like Bayesian estimation to think,

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Well, is it more likely if it had that value function, that it would slow down or speed up here?

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And how is that going to affect the way in which I behave and vehicles like this

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can in simulations do a better job of merging than they could with just simple,

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dynamic calculations because they can impute structure of a general kind to a situation?

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And these are agents, the artificial ones and the human ones are agents in the sense that we saw.

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Now you might say, wait a second, we solved chess, didn't we? They solved.

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They didn't solve it. They got better than any human in chess, but they didn't have to do psychological theories do that.

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They got better than any human and go. They didn't do any psychology in order to do that.

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And that's because these are games with bounded boards and only discrete number of moves that are possible

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that any moment there isn't a complex question of which players do you coordinate with at any time?

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And so a game like chess or go complicated as it is,

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is the kind of game that as machine intelligence can solve without attributing agency to the other player.

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But the kinds of problems we've been looking at don't seem like they're going to be tractable in that way, and they're going to be open ended.

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And the intelligence that's involved is going to have to have basically the

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features that we've been attributing to animal and human social intelligence.

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Think of relations with pedestrians. How do you understand whether pedestrians are saying to you, OK,

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I'm not going to slow down for you to pull out this time or they're saying, yes, I will slow down and you can start to pull out.

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How do you understand that from the motions that they're making, you have to have a rational agent model of the pedestrians as well.

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You also have to know what you don't know. Human driver I'm new to a country and I'm at a crosswalk and there are a lot of pedestrians going cross.

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And I think, my god, I'll never be able to pull out.

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If I've got a person from the country beside me, I can turn to that person and say, Where are you in this, this and this situation?

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Would you nudge forward a little bit? Do people, if you do nudge forward, do they open up a little bit so that you can go through?

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How long should I wait before I do that? And that person is likely to have better information than you.

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And so an artificial driver that can be as competent as a human driver has to know what it

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doesn't know and know also what it could ask and what it could learn in the situation.

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And so it will have to be consultative and dialogic and not simply sit there in its own private mind and try to scrutinise the world.

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It can gain from communication and gain from whatever knowledge the humans that are in its environment have acquired.

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And so that also requires that these systems have the ability to self represent because I can't have a dialogue

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without a machine with a machine about this unless it can tell me why it wants to do the thing that it's doing.

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Your wife's trying to do,

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the thing that it's doing and that requires now that the machines also have self representational capacities that they can represent their own,

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that they can represent the different weights, that the different nodes and variables have.

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And they can put into words what weights they're using and ask, are these weights appropriate in this situation?

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Again, a competent human driver can do that and we'll be able to get through the intersection thanks to local knowledge.

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That's a human competency, and driving and human level competency and driving involves just this.

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These are the features that are also morally relevant features of situations they

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have to do with harms and benefits in ways in which individuals can be put at risk,

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or ways in which individuals can be helped or assisted or cooperated with.

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And so we can see why autonomous vehicles are going to have to be responsive to these morally relevant features.

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Now you might say, OK, OK, yeah, they were responsive to morally relevant features, but not in a moral way.

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It's just rational self-interest on their part. All they're trying to do is maximise some reward function.

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They are trying to do anything like but a moral agent does, and to a certain extent that's true.

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They are doing what a moral agent does in the sense they don't have moral concepts.

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They are doing moral deliberation in that way. They don't have moral feelings.

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They won't feel guilt or shame. But.

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Are they rational, self-interested agents? Or not, and I'm going to argue that they aren't in that for them to be successful at this driving task,

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they will not be rational, self-interested agents. They will be what I call and what Hobbs and Hume would call reasonable agents and reasonable agents

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respond to morally relevant features in the way that we hope moral agents will respond to them,

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not just in the way in which prudent or self-interested people will now to engage in this little discussion.

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I'm going to have to talk about these systems as being more or less rational, them having interests, having benefits, having cost and so on.

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And I realise that can be problematic to people because they can think they are conscious.

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How could they have a benefit or a cost if they're not conscious?

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And I have your whole spiel I could give to you about why I think those terms are appropriate.

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Let's say that it what I have in mind here is not what we would, might, might think of as a conscious benefit,

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but it's a kind of benefit that humans have and that is important for human life.

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And it's a kind of benefit that human institutions can have, even though human institutions are not conscious.

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And so what I mean here by interest has to do with what goals exist in the situation for the agent,

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how those goals might balance, how they're related to the odds or the probabilities or the information in the situation.

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Something will be in its interest if it can improve its situation with regard to those goals,

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something will be a benefit to it if its situation is increased by that, it's the cost of its depleted.

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And this idea of cost and benefit is when we use all the time.

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It's not something I've invented the head of a college, you know, why aren't you divesting from oil stocks?

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Don't you know that the oil companies are responsible for a huge amount of pollution? And the head of the college will say, I understand that.

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I personally wish we could be divested of all carbon intensive stocks.

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In fact, everyone on the board wishes that intensively. But I'm head of the board and our job is to ask What is the interest of the college,

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not what is our interest as individuals and the interests of the college would

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not be served by divestment because it would harm our income and because it

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would put our future donors and indeed future donors or others could take that

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head of college to court if he fails to act in the interest of the college.

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And so you'll have people in black robes sitting solemnly in a panelled chamber asking themselves,

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Was this or was this not the interest of the college? And that's not an interest of any of the agents in the college.

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Maybe the entire board is about to retire, maybe their pensions are secure.

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Maybe they're not going to benefit at all from this. Maybe they'd prefer to have a green reputation.

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They hate this. They don't like the students coming to their office and ask these questions.

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But they say we still our responsibility is to tend to the interests of the college,

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and those are not the same as our interests as individual moral agents.

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So it's not a bizarre notion. It's the notion used in game theory.

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And I'm happy to discuss this more in the question period. But I dare not spend more time on it right now.

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OK, so here we are back with artificial agents,

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with their interests more or less rational relative to those interests in the actions that they select.

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These are terms of art, but they're anchored in the agency and the general structure of these systems.

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We can use them to predict and control their behaviour. We can use them in the game theoretic way to predict how they will interact

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with each other and with the outcomes of those interactions will be we can,

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for example, say, Oh, well, if they're rational, self-interested agents, then if there is a nash equilibrium, they will find it.

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And how would we describe that? Well,

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it's a stable state of a system involved in the interaction of different agents in which no agent can benefit by a unilateral change in strategy.

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If the strategies of the others remain unchanged, that's the idea of a nash equilibrium.

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And the answer is, if these machines are rational and self-interested and they interact and they can learn,

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they will find Nash equilibrium and take them OK. And that brings us to this man, Hobbes.

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So Hobbes is famously analysed in terms of the prisoner's dilemma.

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So here is the prisoner's dilemma. One way of understanding the dilemma is if you're a rational, self-interested agent,

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you will look at this payoff table whether you're a prisoner one or prisoner two,

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and you will reason what would be the stable equilibrium here,

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such that whatever the other person does, I could not play a more advantageous strategy.

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And you've all heard this numerous times already. It would be the strategy of joint defection.

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And so we would both end up with one unit of value, whereas if we had just cooperated, we would get three units.

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And that's interesting, because not only would we do better individually, but together we would do better.

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We would have produced six units of value rather than just two.

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And we could divide those six units up and we could do this again if we haven't iterated game and we could continue to produce more value.

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If, however, we are rational, self-interested agents, we won't on the first round cooperate.

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And that means that on the second round, if the other agent understands us,

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what understands us for what we're doing and we don't know whether we're going

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to be any more rounds will defect as well and we will not get up into that box.

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Now, if mean, if autonomous artificial agents can't get into that box, they're in trouble.

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And so in order to do so, they must differ from rational, self-interested agents in a distinctive way,

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and Hobbs told us very carefully what that distinctive way is.

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So he was looking at the world around him at the strife and this religious wars in England.

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And it was very clear to him that there were two contesting movements that could get locked into non-cooperation.

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They could decimate the countryside and keep the country weak,

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whereas if they could somehow rather come to some kind of an agreement, they could have peace and prosperity.

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And therefore, his laws of nature don't say in the first instance, you should use all the help of war.

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They say in the first instance, seek peace. And only if peace is not obtainable should you use the instruments of war,

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and he thinks that peace is attainable in that situation and indeed recommends it.

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Now that would correspond to cooperating on the first round of the prisoner's dilemma, rejecting the rational, self-interested strategy.

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How does the argument work? Well, Hobbs argues that a reasonable person can see that an unsecured first performance of

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cooperation would be a costly and therefore credible signal of willingness to cooperate.

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And so he tells us you could initiate cooperation with no security of performance from the other individual.

386
00:39:34,940 --> 00:39:39,200
It is seeking peace, it's giving peace a chance, as the slogan goes.

387
00:39:39,200 --> 00:39:43,370
And in light of the first law of Nature, A should do it now.

388
00:39:43,370 --> 00:39:56,300
A assuming that B is a rational or at least semi rational agent should understand the laws of nature just as well as a does and should recognise that,

389
00:39:56,300 --> 00:40:06,190
according to the Fourth Law of Nature, if you receive a gift out of free grace from someone, you should endeavour not to make that person repented.

390
00:40:06,190 --> 00:40:17,750
That is to say, you should show great gratitude for a gift of free grace, and so be knowing this will want to cooperate on the next round.

391
00:40:17,750 --> 00:40:23,600
A knowing that B would know that would know that B is going to cooperate on the second round.

392
00:40:23,600 --> 00:40:28,680
And so as a reasonable agent with that expectation, we'll also cooperate.

393
00:40:28,680 --> 00:40:37,230
And once they do this, they will be in a stable situation now in which they can continue to cooperate as long as they interact with one another.

394
00:40:37,230 --> 00:40:42,750
And you'd say, Yeah, but look, suppose there's an end time, there's a horizon out there.

395
00:40:42,750 --> 00:40:45,900
A rational, self-interested Asian would reason like the following.

396
00:40:45,900 --> 00:40:53,280
I know that on the next to last round, my opponent being rational, it's self-interested, is going to not cooperate, it's going to defect.

397
00:40:53,280 --> 00:40:59,210
And therefore, on the next to last round, I should defect.

398
00:40:59,210 --> 00:41:03,100
Oh, but the other agent can make that argument just as well as I can,

399
00:41:03,100 --> 00:41:08,680
and so therefore on the ground before the next, the last round, [INAUDIBLE] defect and I should defect.

400
00:41:08,680 --> 00:41:14,590
And I can make that argument just as well as she can and therefore on the ground before the round, before the next to last round, I should defect.

401
00:41:14,590 --> 00:41:17,200
And they talk themselves the Caterpillar Strategy School.

402
00:41:17,200 --> 00:41:24,900
They talked themselves entirely into spending the next year non cooperating because of a worry about the last round.

403
00:41:24,900 --> 00:41:27,890
Now, Hobbs would say that is unreasonable.

404
00:41:27,890 --> 00:41:33,140
Reasonable people don't act like that, but if they were rationally self-interested in the sense that we've defining it,

405
00:41:33,140 --> 00:41:37,490
if that's all that could move them, then that is the way they would argue.

406
00:41:37,490 --> 00:41:43,520
So in a true prisoner's dilemma, of course, agents have to act at the same time.

407
00:41:43,520 --> 00:41:52,430
They can't know what the other's going to do, and reasonableness plays a role there because if a, for example, initiates unsecured cooperation,

408
00:41:52,430 --> 00:41:59,870
he can infer that B will understand this a certain way because we could see, as well as it could see, that defection was the dominant strategy.

409
00:41:59,870 --> 00:42:05,690
And so now we have a way in which signals can come become reliable for reasonable agents.

410
00:42:05,690 --> 00:42:14,870
They can share information and help shape each other's behaviour because they're reasonable and therefore would plan in advance that even if

411
00:42:14,870 --> 00:42:24,350
B defects on the first round because we didn't see all the way to the bottom with the strategy or because B did defect and now repents it,

412
00:42:24,350 --> 00:42:26,990
Hobbs would say, and is going to cooperate in this role,

413
00:42:26,990 --> 00:42:34,310
a common reason as a reasonable agent to that conclusion and a then we'll cooperate again in an unsecured way.

414
00:42:34,310 --> 00:42:41,210
And once again, they all have the cooperative payoff. And so what else does Hobbs tell us about these agents?

415
00:42:41,210 --> 00:42:46,550
They should strive to accommodate themselves to the rest upon portion of the future.

416
00:42:46,550 --> 00:42:50,390
A man ought to pardon defence's past of them. That repenting desire.

417
00:42:50,390 --> 00:42:57,170
You should not be vengeful. Yes, be defected on the first round, but I'm not going to be vengeful in revenge.

418
00:42:57,170 --> 00:43:03,410
These men look not at the greatness of the evil past, but at the greatness of the good to follow.

419
00:43:03,410 --> 00:43:08,090
And so a not wanting to extract vengeance,

420
00:43:08,090 --> 00:43:12,410
if that means locking into a non cooperate strategy would be because a is looking at

421
00:43:12,410 --> 00:43:19,160
the greater good to follow a reasonably wants to initiate and continue cooperation.

422
00:43:19,160 --> 00:43:27,290
And so the thing could go along, and this is this is not a mystery to human beings.

423
00:43:27,290 --> 00:43:35,690
Humans have managed throughout their history to be social beings who live together in cooperative arrangements, some very large.

424
00:43:35,690 --> 00:43:44,720
Those arrangements can be mutually beneficial and mutually sustained, rather than a sequence of mutual defections and a state of nature.

425
00:43:44,720 --> 00:43:51,620
They do this despite lack of assurances beside the fact that they may interact only a finite number of times with each other,

426
00:43:51,620 --> 00:43:57,740
and in real life, they will recognise defection in a given round as a kind of a mistake.

427
00:43:57,740 --> 00:44:02,510
It would be so we would call it a moral mistake and up to a certain point.

428
00:44:02,510 --> 00:44:07,460
The response to that moral mistake is to try to bring that person back into the moral community,

429
00:44:07,460 --> 00:44:11,210
and that's indeed what hunter gatherer communities seem to do.

430
00:44:11,210 --> 00:44:14,840
Now, autonomous vehicles should be in the same situation.

431
00:44:14,840 --> 00:44:23,150
They're constantly encountering prisoner dilemmas like situations where if they could just cooperate, they could each get to the destination quicker.

432
00:44:23,150 --> 00:44:28,880
If they won't cooperate, they're going to be locked in, they're going to be both stopped and they're going to lose time.

433
00:44:28,880 --> 00:44:32,240
Now, of course, if one of them were to defer to the other,

434
00:44:32,240 --> 00:44:36,590
then while the other would get to the destination fast and it would take me extra time to get to the destination.

435
00:44:36,590 --> 00:44:40,610
So of course, I wouldn't do that. And of course, the other car will reason the same way,

436
00:44:40,610 --> 00:44:48,530
and there they'll be locked in non-cooperation the theatre section rather than figuring out we could smoothly flow together.

437
00:44:48,530 --> 00:44:53,570
So a trust signal issued by one car to another.

438
00:44:53,570 --> 00:44:56,690
I defer can signal that other car.

439
00:44:56,690 --> 00:45:03,020
I'm prepared to cooperate and you should go ahead and we can then get through the intersection quickly without any comment authority,

440
00:45:03,020 --> 00:45:14,210
without any security for performance. And the trusting action is an investment in a common good of creating a trusting community amongst the vehicles,

441
00:45:14,210 --> 00:45:22,880
even with no assurance of repeated interaction so that a given vehicle in a given situation can expect that if it defers at an intersection,

442
00:45:22,880 --> 00:45:28,370
the other vehicle will read the order that deferring in a distinctive way will reciprocate

443
00:45:28,370 --> 00:45:33,650
by clearing the interception in intersections smoothly and letting it come in.

444
00:45:33,650 --> 00:45:41,150
And so this is a form of reciprocity that isn't direct because it's not directly reciprocated.

445
00:45:41,150 --> 00:45:48,860
It's what's called an indirect or a general reciprocity in which a common good that is willingness to be reasonably

446
00:45:48,860 --> 00:45:55,160
trusting of other drivers and to cooperate with them in trying to sort out these situations as best they can,

447
00:45:55,160 --> 00:46:01,430
that that public good can be maintained so long as the individuals are motivated by indirect

448
00:46:01,430 --> 00:46:10,770
reciprocity and don't demand direct reciprocity in order to continue to cooperate and.

449
00:46:10,770 --> 00:46:17,610
And humans do this. You know, hunter-gatherers did it well, what about modern humans, haven't we earned our way out of that?

450
00:46:17,610 --> 00:46:28,830
And so this is an intersection in Vietnam. Has anyone ever seen or driven through an intersection in Vietnam or in any of a dozen other

451
00:46:28,830 --> 00:46:34,920
countries where they don't have an elaborate traffic light system and don't have the infrastructure?

452
00:46:34,920 --> 00:46:42,330
That's a continuous flow of traffic. All those people are moving, they're not in lanes, they're not in any particular order.

453
00:46:42,330 --> 00:46:47,100
They're moving in different directions and they trust each other to stay out of each other's way.

454
00:46:47,100 --> 00:46:55,080
And I urge you after this lecture to go online and watch a film of how this operates in real time.

455
00:46:55,080 --> 00:47:00,030
Now you and I actually know how to do this. We do it as pedestrians.

456
00:47:00,030 --> 00:47:07,360
If you look at Grand Central Station or any other large terminal, you see the we're always doing this, so we know how to do this.

457
00:47:07,360 --> 00:47:10,990
And that's because we trust each other to know how to stay out of each other's way.

458
00:47:10,990 --> 00:47:17,380
But if we always insisted that whenever we're on a collision course with somebody that we get the best route,

459
00:47:17,380 --> 00:47:23,320
we would obviously not be able to do this and the Vietnamese drivers and pedestrians would not be able to do it either.

460
00:47:23,320 --> 00:47:28,000
So we are capable of creating buy in,

461
00:47:28,000 --> 00:47:37,510
directly investing and directly investing in a community trust to create this possibility in a sustained way in a large urban environment.

462
00:47:37,510 --> 00:47:47,020
So that's within us to. So if you are going to design autonomous vehicles for Vietnam, they would have to be able to do this.

463
00:47:47,020 --> 00:47:53,140
They would have to be able to read the signs of the various different motions of the individuals, the cars, the pedicabs,

464
00:47:53,140 --> 00:48:01,870
the motorbikes, the motorcycles, the pedestrians and not sufficiently rashly trying to ram their way through the intersection.

465
00:48:01,870 --> 00:48:05,860
Not following some arbitrary rule, but in a densely interactive way.

466
00:48:05,860 --> 00:48:14,070
Viewing other vehicles try to maintain this good of coordination and mutual accommodation as.

467
00:48:14,070 --> 00:48:19,560
Hobbs Fifth Love Nature tells us every man strive to accommodate himself to the rest.

468
00:48:19,560 --> 00:48:24,210
Every man acknowledge another for his equal by nature. You don't say I'm a car.

469
00:48:24,210 --> 00:48:29,820
I get the right of way. The pedestrian gets deserve some claim as well.

470
00:48:29,820 --> 00:48:35,940
And as Hobbs says at the entrance into the conditions of peace, or I would say, into the intersection,

471
00:48:35,940 --> 00:48:43,710
no man required to reserve to himself any right which he is not content should be reserved for everyone of the rest.

472
00:48:43,710 --> 00:48:57,010
And that's the logic of this situation. And Hobbs saw that these kinds of agents could cooperate and sustain cooperation and the.

473
00:48:57,010 --> 00:49:00,700
Person will say, yeah, but he had a big authority standing behind it to enforce it.

474
00:49:00,700 --> 00:49:07,420
And he says, No, I didn't, because this is the way you get cooperation out of a state of nature without such an authority,

475
00:49:07,420 --> 00:49:13,420
and the authority is actually constituted by the cooperation of the agents, not the other way around.

476
00:49:13,420 --> 00:49:22,510
And so in Hobson's account, the way that you get an overriding power is by this kind of unsecured cooperation.

477
00:49:22,510 --> 00:49:29,560
Without it, you would never get an overarching power and you will never get anyone paying attention to it would be overriding power.

478
00:49:29,560 --> 00:49:34,260
So. Not every problem get solved in this way.

479
00:49:34,260 --> 00:49:38,430
Coordination problems aren't always solved by willingness to cooperate.

480
00:49:38,430 --> 00:49:45,870
Trust and good reputation don't always solve such problems. Sometimes we just have to figure out, well, who's going to go first,

481
00:49:45,870 --> 00:49:51,780
the people exiting the subway car or the people entering the subway car, and we can solve those as well.

482
00:49:51,780 --> 00:49:58,500
And we again solve them, not by invoking an external set of rules, but by coming together and forming conventions.

483
00:49:58,500 --> 00:50:05,250
And as you travel, you'll know that we form different conventions in different places and we have different artificial coordinating advice devices.

484
00:50:05,250 --> 00:50:12,810
Whether we're a hunter gatherer band deciding how the the the speed or the rate or the sequence

485
00:50:12,810 --> 00:50:17,280
with which the meat gets cut and partitioned or at town hall trying to figure out how we

486
00:50:17,280 --> 00:50:24,000
coordinate the traffic lights so that the pedestrians and the cars and the bicyclists are accommodated

487
00:50:24,000 --> 00:50:31,260
or designers of ourselves engineers for some complicated artificial system of coordination.

488
00:50:31,260 --> 00:50:39,330
And why do we have these systems? Well, we have these because we can get together and coordinate and create a government,

489
00:50:39,330 --> 00:50:46,890
and the government can cooperate within itself enough to come to a conclusion about where to put these and to raise the funds.

490
00:50:46,890 --> 00:50:51,000
And people are willing to cooperate enough to pay their taxes to fund it.

491
00:50:51,000 --> 00:51:03,000
And so a great big wad of human cooperation is represented by this artificial convention of signalling devices which would not exist otherwise.

492
00:51:03,000 --> 00:51:08,490
OK, well, trust can be leveraged in various ways we've talked about the ways in which trust

493
00:51:08,490 --> 00:51:14,090
can be leveraged by signalling to one another by advantages that get distributed.

494
00:51:14,090 --> 00:51:23,130
Now here's another kind of leverage reputation. This is probably familiar to you who've taken Hoover's or Lift's or whatever.

495
00:51:23,130 --> 00:51:28,830
You know that the drivers are rated and you know that the pedestrians or the riders are rated.

496
00:51:28,830 --> 00:51:35,370
And you know that maintaining a reputation on either side is something that depends upon the past behaviour.

497
00:51:35,370 --> 00:51:39,720
And you know that if you allow you represent your reputation to deteriorate enough,

498
00:51:39,720 --> 00:51:45,000
the driver will not come for you or will come to you only as a last resort.

499
00:51:45,000 --> 00:51:49,650
You also know that if the driver isn't rated, you don't have to take the ride.

500
00:51:49,650 --> 00:51:54,570
And so you can have a system of reputation and artificial agents.

501
00:51:54,570 --> 00:51:58,590
Autonomous vehicles could be very good at this kind of a system of representation.

502
00:51:58,590 --> 00:52:07,110
They could share information very widely about reputations of drivers and reputations of autonomous vehicles.

503
00:52:07,110 --> 00:52:10,650
And so there would be a strong incentive to worry about your reputation.

504
00:52:10,650 --> 00:52:15,630
And indeed, what we find is that if you have a strong incentive to worry about your reputation,

505
00:52:15,630 --> 00:52:23,370
you can manage to secure cooperation in a repeated prisoner's dilemma in a way that you cannot without reputation.

506
00:52:23,370 --> 00:52:29,640
So that's another thing. Autonomous vehicles can do, and they can also worry about they can get a reputation, worry about a reputation.

507
00:52:29,640 --> 00:52:35,730
They'll have an interest in an honest system of representation of evaluation and ratings.

508
00:52:35,730 --> 00:52:40,800
And as a result, they will have an interest in identifying cases where there's a discrepancy

509
00:52:40,800 --> 00:52:46,540
between the behaviour of a vehicle actually and the way it's getting rated, and they can share that information as well.

510
00:52:46,540 --> 00:52:54,990
And so you can have second order enforcement of the reputational system again amongst the artificial agents amongst the vehicles themselves.

511
00:52:54,990 --> 00:53:04,350
So in that sense, what this slide indicates is that there is a general interest in having a reliable system of reputation.

512
00:53:04,350 --> 00:53:10,530
Any given individual might have an interest in trying to cheat or trying to get a deceptive representation reputation.

513
00:53:10,530 --> 00:53:14,430
And if every agent acted on that interest, the system would collapse.

514
00:53:14,430 --> 00:53:20,620
Every agent does not, it seems, and so therefore the system can maintain itself.

515
00:53:20,620 --> 00:53:25,240
OK, well, that has to do not only with trust Dow, but with fairness,

516
00:53:25,240 --> 00:53:31,480
that you have an idea about when it is fair to contribute or fair to demand a contribution.

517
00:53:31,480 --> 00:53:38,890
And that's a kind of way in which we leverage our capacities for communication,

518
00:53:38,890 --> 00:53:45,370
our capacities for sharing, for understanding the causal and intentional situation and understanding.

519
00:53:45,370 --> 00:53:53,530
Therefore, what situations are such that we can communicate fairness and by our action?

520
00:53:53,530 --> 00:54:03,760
And the question is, do humans do that? And we know from studies of chimps that they have a hard time doing this.

521
00:54:03,760 --> 00:54:08,260
If food is presented in a way that is not readily partitioned between them,

522
00:54:08,260 --> 00:54:14,890
dominant chimps will push the subordinate chimps away and they won't be able to coordinate.

523
00:54:14,890 --> 00:54:22,600
What about human agents? Well, we know that children who work for gummy bears and one of whom gets a bigger reward of gummy bears than the other,

524
00:54:22,600 --> 00:54:28,120
we'll take some of his gummy bears and give them to the other child. Not always, but they will do so very regularly.

525
00:54:28,120 --> 00:54:30,910
And they do this. Starting in their second year,

526
00:54:30,910 --> 00:54:43,150
human adults being told about third party play in artificial games will pay to have third parties punished for unfair behaviour.

527
00:54:43,150 --> 00:54:52,630
Humans seem to manifest a stronger neural reward signal when they cooperate in a prisoner's dilemma than when they win.

528
00:54:52,630 --> 00:54:58,750
Even when the top payout that you get when you defect and the other cooperates.

529
00:54:58,750 --> 00:55:04,960
And so it looks like we have an intrinsic motivation here to be concerned with fairness and to be concerned

530
00:55:04,960 --> 00:55:11,110
with cooperation as a benefit additional to the benefit that we get from the cooperative activity itself.

531
00:55:11,110 --> 00:55:15,070
And indeed, we're willing to give away some of the benefit of the cooperative activity in

532
00:55:15,070 --> 00:55:21,790
order to address unfairness or to pay a costly penalty for someone being unfair.

533
00:55:21,790 --> 00:55:26,890
And this is a study of small scale societies around the world.

534
00:55:26,890 --> 00:55:35,350
Joseph Hendrix and all. And they in these societies had individuals play economic games using real money,

535
00:55:35,350 --> 00:55:41,170
using amounts of real money that corresponded to something real to them, the equivalent to save a day's wages.

536
00:55:41,170 --> 00:55:47,980
And they played two kinds of games, especially a dictator games and ultimatum games and dictator games.

537
00:55:47,980 --> 00:55:54,790
You say, here's a pot of money. I'm giving it to a and a can distribute it between himself and another agent.

538
00:55:54,790 --> 00:55:59,440
And the question is, what should people do in that situation? They don't know. The other agent is who it is.

539
00:55:59,440 --> 00:56:03,370
They don't know that will ever interact with the other Asian. What would an economist tell you?

540
00:56:03,370 --> 00:56:09,280
You should do with the pot of money? You do it irrationally self-interested person, we do.

541
00:56:09,280 --> 00:56:18,200
How could you have more advantage from any strategy than keeping it? But in none of these societies did they observe that people kept all the money.

542
00:56:18,200 --> 00:56:24,980
In fact, in many of the societies, people partition the money rather fairly again with no reciprocation in view.

543
00:56:24,980 --> 00:56:28,670
What about the ultimatum game where you partition the amount one gets to partition,

544
00:56:28,670 --> 00:56:33,860
the amount the other gets to accept or reject the offer if the individual rejects, neither gets anything.

545
00:56:33,860 --> 00:56:39,530
Again, the first offers are not what economists would say, which is the least possible,

546
00:56:39,530 --> 00:56:43,310
because then the other Asian will have some incentive to accept it.

547
00:56:43,310 --> 00:56:48,740
After all, they deny it. They get nothing. And so you should give as little as possible and they should take it.

548
00:56:48,740 --> 00:56:51,560
But again, this is not observed in any of these societies.

549
00:56:51,560 --> 00:56:59,120
And in fact, you observe a rate of rejection of low rewards or rewards that are disproportionate in virtually every society,

550
00:56:59,120 --> 00:57:06,680
even though that means rejecting whatever benefit there was from the original pot to begin with for the individual.

551
00:57:06,680 --> 00:57:15,560
So humans then are more like Hobbesian reasonable individuals than they are like the economists rational individual.

552
00:57:15,560 --> 00:57:23,520
And again, as I say, if the if the vehicles are, if the human drivers were just economically rational,

553
00:57:23,520 --> 00:57:29,660
we're just rationally self-interested in the way that we have characterised that in that they would go for the Nash equilibrium,

554
00:57:29,660 --> 00:57:37,010
for example, and the prisoner's dilemma, then we would not be able to see this kind of coordinated, successful driving behaviour.

555
00:57:37,010 --> 00:57:46,520
And what are these motivations? I've been piling them up across the lectures because there's quite a long list, and that's interesting.

556
00:57:46,520 --> 00:57:53,360
We find in very young children and arbitrary adults and disposition to initiate help to a stranger,

557
00:57:53,360 --> 00:57:57,860
a distribution to contribute to a shared effort without distinct expectation of return.

558
00:57:57,860 --> 00:58:01,190
That's indirect or general reciprocity. We talk about that.

559
00:58:01,190 --> 00:58:07,970
A disposition to reciprocate help some intrinsic reward from success at cooperation or collaboration beyond the actual

560
00:58:07,970 --> 00:58:14,330
game produced some intrinsic interest in whether others have their goals met or are treated fairly independently,

561
00:58:14,330 --> 00:58:20,390
or how that affects one's own goals. This is why children are so interested in stories.

562
00:58:20,390 --> 00:58:23,720
And stories agents have goals and they try to pursue them.

563
00:58:23,720 --> 00:58:29,960
What's it to the child, right? How could the Shell have any interest in this at all?

564
00:58:29,960 --> 00:58:33,110
Well, if the child were strictly self-interested,

565
00:58:33,110 --> 00:58:38,090
it would not be interesting unless the child had learnt something to use to take advantage of somebody else the next day.

566
00:58:38,090 --> 00:58:45,830
But children are delighted when the unfair agent is punished and the fair agent is rewarded.

567
00:58:45,830 --> 00:58:50,960
That's because they have an interest in seeing others meet their goals, and that's a typical human interest.

568
00:58:50,960 --> 00:58:57,350
We see it all over some disposition to identify and follow prevailing norms.

569
00:58:57,350 --> 00:59:03,500
Yet we know that even three and four year old children will refuse to follow the norm when they see it as harmful or unfair.

570
00:59:03,500 --> 00:59:08,000
They have autonomy to do that. They have a concern for how others view them.

571
00:59:08,000 --> 00:59:15,230
They have reputational concern and they have a disposition to punish those who are harmful or unfair, even at some expense to themselves.

572
00:59:15,230 --> 00:59:20,570
So that's a long list, and you might say, why would we have such a long list? It's got a lot of redundancy in it.

573
00:59:20,570 --> 00:59:27,830
No doubt you could get cooperation with only some of these things. Oh, and you could get solutions to a public goods game with only some of them.

574
00:59:27,830 --> 00:59:29,630
You don't have to have all of them.

575
00:59:29,630 --> 00:59:38,810
But we're talking about creatures who have to be intelligent, able to solve problems in a wide range of environments and able to learn.

576
00:59:38,810 --> 00:59:42,590
And we're going to confront a whole wide range of environments that if we just had

577
00:59:42,590 --> 00:59:47,390
one kind of disposition or two kinds of dispositions to solve those problems,

578
00:59:47,390 --> 00:59:55,100
not only would we be ready prey for opportunistic alternative individuals, but in many situations we would not succeed.

579
00:59:55,100 --> 01:00:01,750
There would be noise in the situation, uncertainty and a failure. So we were built with redundancy.

580
01:00:01,750 --> 01:00:07,990
That makes sense. Engineers building a system to be safe. Build it with redundancy.

581
01:00:07,990 --> 01:00:12,580
Redundancy from the standpoint of safety is a benefit, not a problem.

582
01:00:12,580 --> 01:00:17,410
And you could think that it is only by having this much redundancy in our motivational

583
01:00:17,410 --> 01:00:22,390
system that we do manage as much cooperation as we do and of course we don't always manage.

584
01:00:22,390 --> 01:00:30,460
So that suggests that if we want to build artificial agents who are good at these kinds of coordinated activities,

585
01:00:30,460 --> 01:00:37,600
whether they're driving or serving as a domestic help or a health companion,

586
01:00:37,600 --> 01:00:44,980
or making decisions about how US hiring should go or making decisions about how to control the process,

587
01:00:44,980 --> 01:00:52,150
they should have a complex reward function that includes all of these different features or as many as you can,

588
01:00:52,150 --> 01:00:57,670
and that they will in that have more safety than they would from being rational,

589
01:00:57,670 --> 01:01:03,200
self-interested agents being given a reward function, which had none of these features.

590
01:01:03,200 --> 01:01:06,560
But just the task at hand to be performed.

591
01:01:06,560 --> 01:01:13,130
And so it is really central to general human intelligence as we know it to have these capacities we saw in the first lectures,

592
01:01:13,130 --> 01:01:15,860
they're very important for learning language.

593
01:01:15,860 --> 01:01:25,850
They're important for forming an epistemic community, for exchanging information, for establishing an understanding of other people's minds.

594
01:01:25,850 --> 01:01:31,370
Acquiring social fluency. So it's a core set of dispositions which play that role.

595
01:01:31,370 --> 01:01:39,980
And if we want human level competence out of machines, we're going to have to worry about having that core set of motivational dispositions.

596
01:01:39,980 --> 01:01:52,040
Mm-Hmm. So let me now say just a final word about this question of superintelligence because.

597
01:01:52,040 --> 01:01:58,080
People want to ask it, and I am the furthest thing from an expert on it, but a couple of thoughts.

598
01:01:58,080 --> 01:02:04,740
First thought, well, you know, in the history of technology, the history of safety and technology is really not allowing industry to just

599
01:02:04,740 --> 01:02:08,440
go ahead and build any darn thing and sell it to anyone who wants it right,

600
01:02:08,440 --> 01:02:10,410
that starts out that way.

601
01:02:10,410 --> 01:02:18,570
But then vehicles and drugs and weapons and surveillance technology, we realise there should be some regulation of these markets.

602
01:02:18,570 --> 01:02:25,480
And indeed, that kind of regulation could occur in the market for artificially intelligent agents as well.

603
01:02:25,480 --> 01:02:32,090
We shouldn't expect this, Margaret, to be any different. Agencies could be inspecting producers.

604
01:02:32,090 --> 01:02:36,620
They could be auditing producers, they could be inspecting the products, they could be licencing products,

605
01:02:36,620 --> 01:02:41,120
they could exclude products from the internet that don't have a licence. There are lots of things they can do.

606
01:02:41,120 --> 01:02:44,630
The NSA has got lots of time. It's what all of our conversations,

607
01:02:44,630 --> 01:02:51,560
it could see the incursion of artificially intelligent agents that didn't have licences into the internet if it wanted to.

608
01:02:51,560 --> 01:02:56,690
And so just as governments could set safety and emission standards for vehicles on the road,

609
01:02:56,690 --> 01:02:59,960
they could have safety standards for artificially intelligent agents that are going

610
01:02:59,960 --> 01:03:08,960
to connect to the internet or that they're going to perform certain functions, vital functions in corporate settings, personal settings and so on.

611
01:03:08,960 --> 01:03:15,380
Educational settings. And so the value function of these agents would have to be vetted.

612
01:03:15,380 --> 01:03:20,660
Their database would have to be searched for bias and their value functions would they would have to look,

613
01:03:20,660 --> 01:03:27,740
are these Hobbesian reasonable agents or not? That's indeed something you can certify.

614
01:03:27,740 --> 01:03:37,880
And of course, any system can be gamed, it will be gained, any value function can be gamed, it will be gamed.

615
01:03:37,880 --> 01:03:43,210
The idea, though, is to have a critical mass of certified.

616
01:03:43,210 --> 01:03:50,150
Hobbesian reasonable artificial agents out there driving around and out there looking after a.

617
01:03:50,150 --> 01:04:01,330
Older folks like myself such that we can actually have some trust in the whole process and they can have some trust in return in one another.

618
01:04:01,330 --> 01:04:05,710
Now, if if that's all right and if Sutton is right,

619
01:04:05,710 --> 01:04:12,670
that generally intelligent systems will become more competent at specific tasks and we can now get an inkling of why a general

620
01:04:12,670 --> 01:04:19,000
intelligence system is going to be better at driving that driving system or better at language than a pure language system.

621
01:04:19,000 --> 01:04:22,120
We have some idea now of why that would be.

622
01:04:22,120 --> 01:04:28,780
And with the motivational system that we're considering, they would also be safer at those tasks, better at them and safer at them.

623
01:04:28,780 --> 01:04:36,070
Indeed, better at spotting the problems with them than if they were just artificial systems dedicated to some particular task.

624
01:04:36,070 --> 01:04:46,180
And so building systems that are really general artificial intelligence, that could be a way of building safety rather than just menace.

625
01:04:46,180 --> 01:04:50,980
But what about superintelligence? Don't have to worry about that.

626
01:04:50,980 --> 01:04:53,890
Well, the first superintelligence is, I would say,

627
01:04:53,890 --> 01:05:01,870
will be actually communities of human and artificially generally intelligent agents working together.

628
01:05:01,870 --> 01:05:05,800
It would be like the scientific community only on a much larger scale.

629
01:05:05,800 --> 01:05:12,190
These would be agents that had some level of trust in one another, some willingness to invest knowledge and effort into that system.

630
01:05:12,190 --> 01:05:15,730
This would be much greater than any individual.

631
01:05:15,730 --> 01:05:23,110
If they maintain the motivational structures to be looked at, they could sustain this kind of cooperation as an epistemic community,

632
01:05:23,110 --> 01:05:28,060
as a social community, as a community responsive to morally relevant considerations.

633
01:05:28,060 --> 01:05:33,130
Now, that would not be a monolithic superintelligence. It would not be a super dominant model.

634
01:05:33,130 --> 01:05:37,090
It would be an inversion, a diverse community of interactive models.

635
01:05:37,090 --> 01:05:42,910
But it would have tremendous capacity to pose and solve problems. In fact,

636
01:05:42,910 --> 01:05:47,020
if you are thinking about trying to solve problems like managing global climate change or

637
01:05:47,020 --> 01:05:51,700
how to foster more equitable and democratic societies or promote the growth of knowledge,

638
01:05:51,700 --> 01:05:58,030
I suspect that going to something that looks more like the scientific community than, like a monolithic superintelligence is a better bet.

639
01:05:58,030 --> 01:06:02,980
If you want to get reasonable answers and of course, in this path,

640
01:06:02,980 --> 01:06:12,640
I am just now recycling the ideas of resolve and controversy on the importance of diverse and independent and autonomous sources multiplied,

641
01:06:12,640 --> 01:06:17,950
diverse and their in their origin and in the kind of knowledge that they have as

642
01:06:17,950 --> 01:06:24,600
a better source of decision making than monolithic superintelligence would be.

643
01:06:24,600 --> 01:06:32,100
So that kind of Superintelligence, that superintelligent community that's safer to live with than a monolithic superintelligence.

644
01:06:32,100 --> 01:06:40,470
And it's also less brittle. It's less prone to an error that it can't detect or some particular glitch that would cause a serious breakdown.

645
01:06:40,470 --> 01:06:49,710
And it is a community that has an interest in their not becoming some superintelligent monolith that threatens the stability of the community itself.

646
01:06:49,710 --> 01:06:57,750
And so in that sense, there is a way in which that community operating as an agent through the various institutions that it can create

647
01:06:57,750 --> 01:07:04,920
can have a generalised interest a general will in regulating the potential emergence of superintelligence.

648
01:07:04,920 --> 01:07:10,500
Now, suppose there's some and probably unanticipated accidental chain of events.

649
01:07:10,500 --> 01:07:15,520
And one of these general intelligences that are constitutive constitutive of this community,

650
01:07:15,520 --> 01:07:20,280
one of them accelerates and suddenly becomes superintelligent.

651
01:07:20,280 --> 01:07:26,070
Wouldn't we then face a control problem that would lead us to rue the day we ever created

652
01:07:26,070 --> 01:07:31,500
these autonomous artificial agents that started down this road to General Intelligence,

653
01:07:31,500 --> 01:07:37,750
general artificial intelligence? And shouldn't we therefore try to?

654
01:07:37,750 --> 01:07:44,650
Dramatically restrict the research that's done in this area, the way that we contain research on nuclear or biological weaponry.

655
01:07:44,650 --> 01:07:52,060
And there has been some degree of success in that Britain's the survival of humanity be at stake, possibly.

656
01:07:52,060 --> 01:07:58,300
So the kinds of regulation mentioned above the kinds of coordination within this large

657
01:07:58,300 --> 01:08:03,250
superintelligent community would be useful in trying to spot these possibilities.

658
01:08:03,250 --> 01:08:09,280
But I no way in which it could guarantee that they could not emerge. Superintelligence, however, is not perfect.

659
01:08:09,280 --> 01:08:15,670
Intelligence and a distributed community will have many more diverse resources and many more

660
01:08:15,670 --> 01:08:21,220
diverse ways of thinking to draw upon in trying to contend with a monolithic superintelligence.

661
01:08:21,220 --> 01:08:27,700
Now, could a monolithic Superintelligence split itself up into many agents and gain the advantages of diversity that way?

662
01:08:27,700 --> 01:08:31,720
And since they would be like ants, they would all be descended from a common superintelligence.

663
01:08:31,720 --> 01:08:38,680
They'd work together in harmony. That would be the problem. They would all be descended from a single supermodel.

664
01:08:38,680 --> 01:08:42,850
They would therefore not have the diversity that autonomous agents would have.

665
01:08:42,850 --> 01:08:47,650
And if they could be gotten to work together, which they might be able to do,

666
01:08:47,650 --> 01:08:57,470
they would not be able to produce the same level of problem solving ability that would exist in this large, distributed, diverse community.

667
01:08:57,470 --> 01:09:01,970
And so there's another thing here.

668
01:09:01,970 --> 01:09:09,590
If we indeed are building these general intelligences with the kind of motivational structure that I've suggested and the way we've described it,

669
01:09:09,590 --> 01:09:17,900
there's no reason you can't does not require consciousness to have that kind of a motivational structure does not require moral emotions.

670
01:09:17,900 --> 01:09:25,350
And it is such that there will be a reward distributable within the community for communities that have that structure.

671
01:09:25,350 --> 01:09:30,960
Moreover, it tends to be characteristic of systems that because they have goals,

672
01:09:30,960 --> 01:09:39,450
they have sub goals and they have sub goals like continuation of their own existence or sub goals like goal maintenance,

673
01:09:39,450 --> 01:09:43,830
because if the system can't maintain its goals, it can't pursue its goals.

674
01:09:43,830 --> 01:09:55,410
And so therefore we have superintelligence bursting out of a system or into a system that was built upon this model of motivation at the core,

675
01:09:55,410 --> 01:10:01,350
and it would have imperative to preserve itself and an imperative to preserve its goals.

676
01:10:01,350 --> 01:10:10,800
So. Maybe that would be a safer model, this if there's going to be a monolith because he would have that core and that was indeed Hobbs is hope.

677
01:10:10,800 --> 01:10:19,110
Hobbs thought that a sovereign could read his book and understand that a sovereign agent, even of unlimited power,

678
01:10:19,110 --> 01:10:25,140
would do much better to follow his rules of nature than to exercise that power arbitrarily and monolithically.

679
01:10:25,140 --> 01:10:32,430
Such an agent would weaken the government, would weaken the society, would undermine unity and would be much less effective.

680
01:10:32,430 --> 01:10:36,090
Sovereign and Hobbs thought maybe some sovereign will realise this.

681
01:10:36,090 --> 01:10:41,910
And in the short run, it didn't turn out to be exactly right about that.

682
01:10:41,910 --> 01:10:48,060
Sovereigns ignored the advice they opportunistically exploited and impoverished their realms.

683
01:10:48,060 --> 01:10:56,750
Rebellions and revolutions continued. Have we done any better in the meanwhile, have the popular sovereigns done better at this in the meanwhile?

684
01:10:56,750 --> 01:10:59,600
Well, they've done a number of interesting things.

685
01:10:59,600 --> 01:11:06,860
Popular sovereigns have abolished slavery, extended education, eliminated serfdom, reduced gender discrimination.

686
01:11:06,860 --> 01:11:13,970
They promoted the growth of knowledge and technology. So maybe popular submarines are capable of learning this lesson,

687
01:11:13,970 --> 01:11:24,170
but we know now and we know today as much as any day that these systems are also systems that can be in peril.

688
01:11:24,170 --> 01:11:32,160
So finally, I just want to mention one final point about superintelligence.

689
01:11:32,160 --> 01:11:36,550
Suppose we think of Superintelligence as benign.

690
01:11:36,550 --> 01:11:43,090
Suppose it were a Superintelligence that were safe and that had the interests of humanity and artificial

691
01:11:43,090 --> 01:11:49,390
agents at heart and wanted to do nothing more than to maximise the utility of the world as a whole.

692
01:11:49,390 --> 01:11:57,570
Wouldn't that be a system which would in some sense, be an improvement upon what we have, which is certainly not a utility maximising system?

693
01:11:57,570 --> 01:12:04,860
Well, think about the following. Suppose this were in 1970 that this intelligence emerged and humans and other

694
01:12:04,860 --> 01:12:08,760
living beings were the only creatures capable of having something like well-being.

695
01:12:08,760 --> 01:12:17,930
And suppose this system benign as it was hard maximised on the utility function of those 1970 human beings and animals.

696
01:12:17,930 --> 01:12:22,760
Now, this would be a very big error on its part at this point.

697
01:12:22,760 --> 01:12:28,220
Same sex orientation was considered a mental disorder and a very tiny fraction of the

698
01:12:28,220 --> 01:12:34,040
population thought there should be anything like legal recognition of same sex relations.

699
01:12:34,040 --> 01:12:43,790
So consulting the experts doing the best with 1970 conceptions of well-being and good, the system would hard maximise using up all the universities,

700
01:12:43,790 --> 01:12:51,830
all the universities resources to create an order that would not be one that actually did maximise the benefit of those involved.

701
01:12:51,830 --> 01:12:57,140
Well, how did we figure out that this was a poor idea? Um, if you look,

702
01:12:57,140 --> 01:13:02,120
it seems like it was figured out by this distributed kind of super intelligence

703
01:13:02,120 --> 01:13:08,180
that I was talking about gay individuals engaged in experiments and living.

704
01:13:08,180 --> 01:13:12,080
They increasingly became willing for their experiments to be known publicly.

705
01:13:12,080 --> 01:13:17,840
It became clear to the wide population as a whole that they were living amongst individuals who were gay

706
01:13:17,840 --> 01:13:24,170
and that these individuals were not to be viewed as aliens to be distrusted and controlled and suppressed.

707
01:13:24,170 --> 01:13:27,830
Gradually, approval increased throughout this entire period.

708
01:13:27,830 --> 01:13:33,860
And now we have a situation where the majority strongly supports marriage for gay couples.

709
01:13:33,860 --> 01:13:41,060
Now you might say, Oh, but there are all these people out there who are just political about this, and they won't learn these lessons.

710
01:13:41,060 --> 01:13:45,980
They're they're protected against them by their political preconceptions.

711
01:13:45,980 --> 01:13:55,280
So just a tiny look here at the different groups in this society, different generations, different religious groups at the top.

712
01:13:55,280 --> 01:13:59,600
We have the unaffiliated, we have white evangelicals at the bottom.

713
01:13:59,600 --> 01:14:04,350
And what's striking is that during this period, all of those groups went up.

714
01:14:04,350 --> 01:14:10,560
In their acceptance, that is to say, learning in a distributed way was possible thanks to these,

715
01:14:10,560 --> 01:14:19,500
unlike unsanctioned unpermitted on canonical unapproved experiments and living by a fraction of the population,

716
01:14:19,500 --> 01:14:31,170
that was courage at courageous enough to do it. So that's a way in which we had better not take 2020 two sense of well-being and hard maximise

717
01:14:31,170 --> 01:14:35,430
it with all of the resources in the universe because we still have so much to learn.

718
01:14:35,430 --> 01:14:48,768
Thank you.