Mappa Psyche

I’m kind of feeling my way, here, trying to work out how to explain a lifetime of treading my own path, and the comments to yesterday’s post have shown me just how far apart we all wander in our conceptual journey through life. It’s difficult even to come to shared definitions of terms, let alone shared concepts. But such metaphors as ‘paths’ and ‘journeys’ are actually quite apt, so I thought I’d talk a little about the most important travel metaphor by far that underlies the work I’m doing: the idea of a map.

This is trivial stuff. It’s obvious. BUT, the art of philosophy is to state the blindingly obvious (or at least, after someone has actually stated it, everyone thinks “well that’s just blindingly obvious; I could have thought of that”), so don’t just assume that because it’s obvious it’s not profound!

So, imagine a map – not a road atlas but a topographical map, with contours. A map is a model of the world. It isn’t a copy of the world, because the contours don’t actually go up and down and the map isn’t made from soil and rock. It’s a representation of the world, and it’s a representation with some crucial and useful correspondences to the world.

To highlight this, think of a metro map instead, for a moment. I think the London Underground map was the first to do this. A metro map is a model of the rail network, but unlike a topographic map it corresponds to that network only in one way – stations that are connected by lines on the map are connected by rails underground. In every other respect the map is a lie. I’m not the only person to have found this out the hard way, by wanting to go from station A to station B and spending an hour travelling the Tube and changing lines, only to discover when I got back to the surface that station B was right across the street from station A! A metro map is an abstract representation of connectivity and serves its purpose very well, but it wouldn’t be much use for navigating above ground.

A topographical map corresponds to space in a much more direct way. If you walk east from where you are, you’ll end up at a point on the map that is to the right of the point representing where you started. Both kinds of map are maps, obviously, but they differ in how the world is mapped onto them. Different kinds of mapping have different uses, but the important point here is that both retain some useful information about how the world works. A map is not just a description of a place, it’s also a description of the laws of geometry (or in the case of metro maps, topology). In the physical world we know that it’s not possible to move from A to B without passing through the points in-between, and this fact is represented in topographical maps, too. Similarly, if a map’s contours suddenly become very close together, we know that in the real world we’ll find a cliff at this point, because the contours are expressing a fact about gradients.

So a map is a model of how the world actually functions, albeit at such a basic level that it might not even occur to you that you once had to learn these truths for yourself, by observation and trial-and-error. It’s not just a static representation of the world as it is, it also encodes vital truths about how one can or can’t get from one place to another.

And of course someone has to make it. Actually moving around on the earth and making observations of what you can see allows you to build a map of your experiences. “I walked around this corner and I saw a hill over there, so I shall record it on my map.” A map is a memory.

Many of the earliest maps we know of have big gaps where knowledge didn’t exist, or vague statements like “here be dragons”. And many of them are badly distorted, partly because people weren’t able to do accurate surveys, and partly because the utility of n:1 mapping hadn’t completely crystallized in people’s minds yet (in much the same way that early medieval drawings tend to show important people as larger than unimportant ones). So maps can be incomplete, inaccurate and misguided, just like memories, but they still have utility and can be further honed over time.

Okay, so a map is a description of the nature of the world. Now imagine a point or a marker on this map, representing where you are currently standing. This point represents a fact about the current state of the world. The geography is relatively fixed, but the point can move across it. Without the map, the point means nothing; without the point, the map is irrelevant. The two are deeply interrelated.

A map enables a point to represent a state. But it also describes how that state may change over time. If the point is just west of a high cliff face, you know you can’t walk east in real life. If you’re currently at the bottom-left of the map you know you aren’t going to suddenly find yourself at the top-right without having passed through a connected series of points in-between. Maps describe possible state transitions, although I’m cagey about using that term, because these are not digital state transitions, so if you’re a computery person, don’t allow your mind to leap straight to abstractions like state tables and Hidden Markov Models!

And now, here’s the blindingly obvious but really, really important fact: If a point can represent the current state of the world, then another point can represent a future state of the world; perhaps a goal state – a destination. The map then contains the information we need in order to get us from where we are to where we want to go.

Alternatively, remembering that we were once at point A and then later found ourselves at point B, enables us to draw the intervening map. If we wander around at random we can draw the map from our experiences, until we no longer have to wander at random; we know how to get from where we are to where we want to go. The map has learned.

Not only do we know how to get from where we are to where we want to go, but we also know something about where we are likely to end up next – the map permits us to make predictions. Furthermore, we can contemplate a future point on the map and consider ways to get there, or look at the direction in which we are heading and decide whether we like the look of where we’re likely to end up. Or we can mark a hazard that we want to avoid – “Uh-oh, there be dragons!”. In each case, we are using points on the map to represent a) our current state, and b) states that could exist but aren’t currently true – in other words, imaginary states. These may be states to seek, to avoid or otherwise pay attention to, or they might just be speculative states, as in “thinking about where to go on vacation”, or “looking for interesting places”, or even simply “dropping a pin in the map, blindfold.” They can also represent temporarily useful past states, such as “where I left my car.” The map then tells us how the world works in relation to our current state, and therefore how this relates functionally to one of these imagined states.

By now I imagine you can see some important correspondences – some mappings – between my metaphor and the nature of intelligence. Before you start thinking “well that’s blindingly obvious, I want my money back”, there’s a lot more to my theories than this, and you shouldn’t take the metaphor too literally. To turn this idea into a functioning brain we have to think about multiple maps; patterns and surfaces rather than points; map-to-map transformations with direct biological significance; much more abstract coordinate spaces; functional and perceptual categorization; non-physical semantics for points, such as symbols; morphs and frame intersections; neural mechanisms by which routes can be found and maps can be assembled and optimized… Turning this metaphor into a real thinking being is harder than it looks – it certainly took me by surprise! But I just wanted to give you a basic analogy for what I’m building, so that you have something to place in your own imagination. By the way, I hesitate to mention this, but analogies are maps too!

I hope this helps. I’ll probably leave it to sink in for a while, at least as far as this blog is concerned, and start to fill in the details later, ready for my backers as promised. I really should be programming!

Introduction to an artificial mind

I don’t want to get technical right now, but I thought I’d write a little introduction to what I’m actually trying to do in my Grandroids project. Or perhaps what I’m not trying to do. For instance, a few people have asked me whether I’ll be using neural networks, and yes, I will be, but very probably not of the kind you’re expecting.

When I wrote Creatures I had to solve some fairly tricky problems that few people had thought much about before. Neural networks have been around for a long time, but they’re generally used in very stylized contexts, to recognize and classify patterns. Trying to create a creature that can interact with the world in real-time and in a natural way is a very different matter. For example, a number of researchers have used what are called randomly recurrent networks to evolve simple creatures that can live in specialized environments, but mine was a rather different problem. I wanted people to care about their norns and have some fun interacting with them. I didn’t expect people to sit around passively watching hundreds of successive generations of norns blundering around the landscape, in the hope that one would finally evolve the ability not to bump into things.

Norns had to learn during their own lifetimes, and they had to do so while they were actively living out their lives, not during a special training session. They also had to learn in a fairly realistic manner in a rich environment. They needed short- and long-term memories for this, and mechanisms to ensure that they didn’t waste neural real-estate on things that later would turn out not to be worth knowing. And they needed instincts to get them started, which was a bit of a problem because this instinct mechanism still had to work, even if the brains of later generations of norns had evolved beyond recognition. All of these were tricky challenges and it required a little ingenuity to make an artificial brain that was up to the task.

So at one level I was reasonably happy with what I’d developed, even though norns are not exactly the brightest sparks on the planet. At least it worked, and I hadn’t spent five years working for nothing. But at another level I was embarrassed and deeply frustrated. Norns learn, they generalize from their past to help them deal with novel situations, and they react intelligently to stimuli. BUT THEY DON’T THINK.

It may not be immediately obvious what the difference is between thinking and reacting, because we’re rarely aware of ourselves when we’re not thinking and yet at the same time we don’t necessarily pay much attention to our thoughts. In fact the idea that animals have thoughts at all (with the notable exception of us, of course, because we all know how special we are) became something of a taboo concept in psychology. Behaviorism started with the fairly defensible observation that we can’t directly study mental states, and so we should focus our attention solely on the inputs and outputs. We should think of the brain as a black box that somehow connects inputs (stimuli) with outputs (actions), and pay no attention to intention, because that was hidden from us. The problem was that this led to a kind of dogma that still exists to some extent today, especially in behavioral psychology. Just because we can’t see animals’ intentions and other mental states, this doesn’t mean they don’t have any, and yet many psychological and neurological models have been designed on this very assumption. Including the vast bulk of neural networks.

But that’s not what it’s like inside my head, and I’m sure you feel the same way about yours. I don’t sit here passively waiting for a stimulus to arrive, and then just react to it automatically, on the basis of a learned reflex. Sometimes I do, but not always by any means. Most of the time I have thoughts going through my mind. I’m watching what’s going on and trying to interpret it in the light of the present context. I’m worrying about things, wondering about things, making plans, exploring possibilities, hoping for things, fearing things, daydreaming, inventing artificial brains…

Thinking is not reacting. A thought is not a learned reflex. But nor is it some kind of algorithm or logical deduction. This is another common misapprehension, both within AI and among the general public. Sometimes, thinking equates to reasoning, but not most of the time. How often do you actually form and test logical propositions in your head? About as often as you perform formal mathematics, probably. And yet artificial intelligence was founded largely on the assumption that thinking is reasoning, and reasoning is the logical application of knowledge. Computers are logical machines, and they were invented by extrapolation from what people (or rather mathematicians, which explains a lot) thought the human mind was like. That’s why we talk about a computer’s memory, instructions, rules, etc. But in truth there is no algorithm for thought.

So a thought is not a simple learned reflex, and it’s not a logical algorithm. But what is it? How do the neurons in the brain actually implement an idea or a hope? What is the physical manifestation of an expectation or a worry? Where does it store dreams? Why do we have dreams? These are some of the questions I’ve been asking myself for the past 15 years or so. And that’s what I want to explore in this project. Not blindly, I should add – it’s not like I’m sitting here today thinking how cool it will be to start coming up with ideas. I already have ideas; quite specific ones. There are gaps yet, but I’m confident enough to stick my neck out and say that I have a fair idea what I’m doing.

Explaining how my theories work and what that means for the design of neural networks that can think, are things that will take some explaining. But for now I just wanted to let you know the key element of this project. My new creatures will certainly be capable of evolving, but evolution is not what makes them intelligent and it’s not the focus of the game. They’ll certainly have neural network brains, but nothing you may have learned about neural networks is likely to help you imagine what they’re going to be like; in fact it may put you at a disadvantage! The central idea I’m exploring is mental imagery in its broadest sense – the ability for a virtual creature to visualize a state of the world that doesn’t actually exist at that moment. I think there are several important reasons why such a mechanism evolved, and this gives us clues about how it might be implemented. Incidentally, consciousness is one of the consequences. I’m not saying my creatures will be conscious in any meaningful way, just that without imagery consciousness is not possible. In fact without imagery a lot of the things that AI has been searching for are not possible.

So, in short, this is a project to implement imagination using virtual neurons. It’s a rather different way of thinking about artificial intelligence, I think, and it’s going to be a struggle to describe it, but from a user perspective I think it makes for creatures that you can genuinely engage with. When they look at you, there will hopefully be someone behind their eyes in a way that wasn’t true for norns.