Grandroids FAQ

I’m putting FAQs for my Kickstarter project here, so that I can add to them without bothering everyone with updates. Oh, damn! I’ve already thought of another one… So if you have a question, check here first! I’ll add a new blog category.

1. Linux: Several people have asked if I’m going to support Linux. I’m committed to using Unity3D as my graphics engine (I chose it very carefully, and I really don’t think I could make this project happen without Unity). At the moment Unity doesn’t support Linux. It does support Windows, Mac, iPhone, Android, X-Box and Wii, so it’s certainly not impossible they’ll support Linux eventually too. In fact the underlying framework is already very Linux-friendly, so it shouldn’t be too difficult if they think there’s a market. A number of Unity developers have asked for it. However, it’s not something I have any control over. If Unity offers Linux support then I’ll definitely port the game to Linux too, but I can’t do anything until/unless that happens.

2. Collaboration: People have offered to help with the project in various ways, which I’m very flattered by. Thank you. The situation is this: As far as the core engine is concerned, I have to work alone. The computational neuroscience and biology involved is very, very complex and unique, and it has an impact on almost every aspect of the code (and even the graphics). There’s no way I could do this stuff in a collaborative environment. I have to keep everything inside my head, because I’m inventing completely new things as I go, and every time one part of it changes, it has knock-on effects throughout the system. So I’m just not in a position to share the core programming with anyone. Sorry.

Having said that, I’m writing an engine, at both the computing and biological levels. It will have an open API and an open genetics, so everyone is free to write new tools, create new objects and scenes, manipulate genes, create new species, etc. and I’d be delighted if you would do that. This is my living, so I need to retain some of the action, but if you had any connection to Creatures you’ll know that I design things in such a way that people can contribute. This project will be more open than Creatures was, because the technology for it has come a long way since then. Some of this may take a while to roll out, but I’ll be publishing updates as time goes on.

3. The AI: Is it for real? Sure it’s for real! But before anyone who’s not familiar with my work gets the wrong idea, I should point out that these creatures are not going to win Jeopardy! The field I work in is biologically-inspired AI, and I make complex, realistic living organisms. Think rabbits and dogs, not Terminator or Data. Most people don’t really question the nature of intelligence much, but I can tell you, winning a game of chess is easy peasy compared to recognizing the difference between a pawn and a bishop, or picking up the chess pieces. Just because we find something easy now, after years of infant practice, it doesn’t mean it IS easy. Most AI is not real intelligence at all. Especially game AI, which is to intelligence what a portrait is to a person – a shallow imitation of the real thing. What I’m interested in is real, learned intelligence and hopefully the first glimmerings of a real mind, with desires and fears and intentions. It’s much more exciting than a pseudo-HAL.

4. Timing, features, etc. I’m banking on this taking about another year. Hopefully I’ll get enough money to go on a little longer than that and do a better job. I don’t know how long until I have alphas, betas, etc. There’s a lot of very new stuff in this project so I don’t have a precedent. I don’t know what I’ll actually be able to achieve either. I’ve found that the key is to get the biology right. Biology is an incredibly powerful toolkit, and very flexible. Get that core right and lots of happy things will fall out of it. So I don’t work in the normal way, with specifications and schedules and milestones. It cramps my style. My job is to be a good biologist and let the creatures emerge. This is all about emergence.

5. Helping out: Some of you have said you don’t have any money but you’ll spread the word. Great! Thank you! I don’t have any money either, so I quite understand. I appreciate all tweets, posts, articles, submissions, reviews… anything. Well, perhaps not holding a knife to someone and stealing their wallet, but most things. I appreciate all kinds of support, even if just good wishes. Oh, and I read every single comment, etc., so I notice and care, even if I don’t get a chance to reply personally.

6. The name: I had to pick a project name for Kickstarter, so I went with Grandroids because I like it (thanks to Andrew Lovelock for coming up with it!). But I see this as a kind of brand name to describe what I “purvey” in general terms. The game will almost certainly be called something else, but I don’t have a clue what, yet. It depends how the creatures turn out and what world they tell me they want to live in.

7. What will the creatures look like? Dunno. In my head the stars of the show are rather like orangutan babies – fairly shy, semi-bipedal, cute, slightly shadowy creatures whose confidence you have to work hard to win, but we’ll see. I’ve also had requests for tails, dragons and cute eyes. The creatures are physics-based, and that is a very demanding thing, especially since computer physics engines have some strange characteristics. The creatures’  limbs have elastic muscles and the weights of different parts of their bodies have an effect on inertia and balance. It’s quite challenging getting one that has a fair chance of learning to walk and doesn’t fall over when it glances sideways! On the upside, real physics allows real intelligence, as well as complex interactions with the world, and their motion can be quite startlingly natural, compared to animation. Animation is cheating.

8. Evolution. Just so’s you know, this is not a game about evolution. The creatures will certainly be able to evolve in a pretty sophisticated way (perhaps even the most sophisticated way ever tried), but in practice it’s not the primary focus of the game. Natural selection is VERY SLOW, and the time it takes is proportional to both the complexity of the creatures that are evolving and their life span. For these creatures to live long enough for you to get to know them and care about them means that they will evolve very slowly – not that many orders of magnitude faster than happens in the real world. Selective breeding will definitely speed this up a lot, so evolutionary changes will doubtless happen. But the most important thing is actually variation – children will inherit characteristics from both parents and so will have their own unique personalities, even if they’re often problematic ones! Evolution is there, but it’s not the point of the game. I just wanted to be sure we’re all clear on that, because most A-life projects are primarily about evolving very simple creatures with very short lifespans.

Blowing my own trumpet

Okay, try not to cringe, but I really need your help. In the interests of full disclosure, that means money. Or if not money then influence. Please nicely.

I’ll just hit you with the funding pitch right off the bat. There’s a fancy widget I’m supposed to be able to embed in my blog but it doesn’t work in this theme, so here instead is a good old-fashioned hyperlink. Click on the image to take you to Kickstarter.

This is the first chance I’ve had to blog about it, because it’s taken off a lot more quickly than I expected and I’ve had a lot of people to thank and queries to field! It’s only the end of Day Two as I write this and the total is already over $11,000, much to my amazement and thanks especially to some extremely generous donors. I think there’s a real chance we can make this happen, with your help. Which is just as well, because I’ve almost completely used up my own resources after all these years of self-funded research and this is the only way I can continue with my work.

If you’ve already pledged then thank you SO MUCH! I really, really appreciate it. If you haven’t and you’d like to then that’s fantastic. My Creatures game inspired quite a lot of people to think differently about life, and even caused a number of them to take up scientific careers. I’m pretty sure this game will do the same, so it’s in a good cause as well as hopefully being fun. If you aren’t in a position to pledge then I quite understand – I’m not either! – but if you can help spread the word by tweeting, blogging, facebooking or pinning notices to telegraph poles then I really appreciate that too. The wider the news spreads, the more chance I have. Thank you.

Oh, and I see 600 people visited my blog today, which is a fair bit higher than usual, so if you came here via Kickstarter then I’m delighted to see you. I hope you’ll come back! 🙂

Incidentally, earlier posts about the design of the artificial brain for this project can be found here, here, here, here, here, here and here. After that I went a bit quiet because I got stuck on a problem that was too complex even to tell you about. But I think I have the answer to that now. After months of banging my head against the wall it just came to me – poof! – while I was driving through the desert thinking about something else. Don’t you just love it when that happens?

[Edit: I fixed the links – whoops.]

So how’s it going?

Just a short post to say that I’m going to tweet my programming journal in real-time, as I work on my new game, so if any of you are fellow Twits, feel free to follow @enchantedloom. I don’t really understand Twitter yet, and 140 characters is just not ‘me’ somehow, but it seems like a good way to keep my nose to the grindstone (or avoid any actual work, possibly) and at the same time let you guys know how things are going. I’d appreciate the company, so see you in Twit-land maybe!

Any Creatures fans (with large houses) out there?

There’s a little bit of history up for sale: Laurence Parry, long-time Creatures fan and illustrious keeper of the Creatures Wiki, just told me that Gameware is selling off two of the three huge models we had made to produce the Creatures 1 backdrop. They’re on eBay (garden; ocean), with a closing date of May 13th. But be warned: they’re HUGE and heavy. You’d need to live near Cambridge and own a van, not to mention a big house!

It looks like Gameware is holding on to the main part of the model, but I hope the other two find good homes somewhere.

Back in those ancient days there were no real-time 3D graphics, and even using 3D to generate 2D sprites and bitmaps was in its infancy. In those days, game graphics were laboriously drawn by hand, and creating such a big scene would have been a major task. I was having a lot of angst about the graphics generally, so one day I sat down and made a little norn burrow out of modeling clay. It was pretty pathetic (I still own it: see below) but it seemed like it might be a feasible approach. So I made a slightly less pathetic one (which I no longer own – I think maybe Gameware has it) and suggested that we make the entire backdrop this way and then digitize it. If I remember correctly, even that would have involved taking photos on old-fashioned film and then digitizing them with a scanner. I remember putting some thought into how to photograph sections and correct for circular distortion. Anyway, we commissioned a company to make the model (I forget who), which they did using modeling foam. They did a great job – way better than my clay! It was a unique approach to creating photoreal graphics, although not long after this, Maya and other 3D packages became available and life moved on.

My first pathetic proof of concept

I would have loved to own the model, but I wasn’t given a chance when Creature Labs went bankrupt – the model and the rights to the Creatures brand went to Gameware before I knew anything about it. But now I simply don’t have room (and I live on the wrong side of the Atlantic), so I hope one of the lovely people with a loyal and longstanding passion for Creatures gets the opportunity to own it. (By the way, please don’t bid unless you’re serious – I’d hate to see the price hiked up unnecessarily).

I still have the airship, which was my favorite object in the game and significant for the back story, but for some reason never made it into C1. That’s enough history for me. I travel light, these days.

It occurred to me recently that my new simulation is likely to hit the streets on or about 20 years from the date when I first started writing Creatures. TWENTY YEARS! Ye gods!

Brainstorm 7: How long is now?

I worry too much. I live too far into the future; always so acutely aware of the potential distant knock-on effects of my actions that I’m sometimes quite paralyzed. On the downside this can be a real handicap, but on the upside it means I’m intelligent, because seeing into the future is what intelligence is for. But how? And how do we differentiate between past, present and future? What do we really mean by “now”?

My main thesis for this project is that the brain is a prediction machine. In other words I think it takes so long for nerve signals to reach the brain and be analyzed by it (you may be surprised to know it takes about a tenth of a second merely for signals to reach the primary visual cortex from the retina, never mind be turned into understanding), that we’d be dead by now if it weren’t for our ability to create a simulation of the world and run it ahead of time, so that we are ready for what’s about to happen instead of always reacting to what has already happened. I’m suggesting that this simulation ability derives, at least in part, from a capacity to make small predictions based on experience, at ALL levels of the nervous system. These little fragments of “if this happens then I suspect that will happen next” are there to counter processing delays and reaction times, and give us the ability to anticipate. But they also (I suggest) provide the building blocks for other, more interesting things: a) our ability to create a contextual understanding of the world – a stable sense of what is happening; b) our ability to form plans, by assembling sequences of little predictions that will get us from a starting state to a goal state; and c) our capacity for imagination, by allowing us to link up sequences of cause and effect in an open-ended way. The capacity for imagination, in turn, is what allows us to be creative and provides the virtual world in which consciousness arises and free thought (unconstrained by external events) can occur.

I rather think some clever tricks are involved, most especially the ability to form analog models of reality, as opposed to simple chains of IF/THEN statements, and the ability to generalize from one set of experiences to similar ones that have never been experienced (even to the extent that we can use analogies and metaphors to help us reason about things we don’t directly understand). But I’d say that the root of the mechanism lies in simple statistical associations between what is happening now and what usually happens next.

So let’s look at a wiring diagram for a very simple predictive machine.

This is the simple touch-sensitive creature I talked about in Brainstorm 6. The blue neurons receive inputs, from touch-sensitive nerve endings, which occurred some milliseconds ago on its skin. The red neuron shows two touch inputs being compared (in this case the cell has become tuned to fire if the right input is present just before the left input). I think we can call the red neuron an abstraction: it takes two concrete “I am being touched” inputs and creates an abstract fact – “I am being stroked leftwards here”. This abstraction then becomes an input for higher-level abstractions and so on.

The green neuron is then a prediction cell. It is saying, “if I’m being stroked leftwards at this point, then I expect to be touched here next.” Other predictions may be more conditional, requiring two or more abstractions, but in this case one abstraction is enough. The strength of the cell’s response is a measure of how likely it is that this will happen. The more often the prediction cell is firing at the moment the leftmost touch sensor is triggered, the stronger the connection will become, and the more often that this fails to happen, the weaker it will become (neurologically I’d hypothesize that this occurs due to LTP and LTD (long-term potentiation and long-term depression) in glutamate receptors, giving it an interesting nonlinear relationship to time).

So what do we DO with this prediction? I’m guessing that one consequence is surprise. If the touch sensor fires when the prediction wasn’t present, or the prediction occurs and nothing touches that sensor, then the creature needs a little jolt of surprise (purple neuron). Surprise should draw the creature’s attention to that spot, and alert it that something unexpected is happening. It may not be terribly surprising that a particular touch sensor fails to fire, but the cumulative effect of many unfulfilled predictions tells the creature that something needs to be worried about, at some level. On the other hand, if everything’s going according to expectations then no action need be taken and the creature can even remain oblivious.

But for the rest of my hypothesis to make sense, the prediction also needs to chain with other predictions. We need this to be possible so that top-down influences (not shown on the diagram) can assemble plans and daydreams, and see far into the future. But I believe there has to be an evolutionary imperative that predates this advanced capacity, and I’d guess that this is the need to see if a trend leads ultimately to pain or pleasure (or other changes in drives). Are we being stroked in such a way that it’s going to hurt when the stimulus reaches a tender spot? Or is the moving stimulus a hint that some food is on its way towards our mouth, which we need to start opening?

Now here comes my problem (or so I thought): In the diagram I’m assuming that the prediction gets mixed with the sensory signal (the green axon leading into the blue cell) so that predictions act like sensations. This way, the organism will react as if the prediction came true, leading to another prediction, and another. Eventually one of these predictions will predict pleasure or pain.

[Technical note: Connectionists wouldn’t think this way. They’d assume that pleasure/pain are back-propagated during learning, such that this first prediction neuron already “knows” how much pleasure or pain is likely to result further down the line, since this fact is stored in its synaptic weight(s). I’m not happy with this. For one thing, thinking is never going to arise in such a system, because it’s entirely reactive. Secondly (and this is perhaps why brains DO think), the reward value for this prediction is likely to be highly conditional upon other active predictions. This isn’t obvious in such a simple model, but in a complete organism the amount of pleasure/pain that ultimately results may depend very heavily on what else is going on. It may depend on the nature of the touch, or have its meaning changed radically by the context the creature is in (is it being threatened or is something having sex with it?). It’s therefore not possible to apportion a fixed estimate of reward by back-propagating it through the network. That sort of thing works up to a point in an abstract pattern-recognition network like a three-layer perceptron, but not in a real creature. In my humble opinion, anyway!]

Oh yes, my problem: So, if a prediction acts as if it were a sensation (and this is the only way it can make use of the subsequent (red) abstraction cells in order to make further predictions) then how does the organism know the difference between what is happening and what it merely suspects will happen??? If all these predictions are chained together, the creature will feel as if everything that might happen next already is happening.

This has bugged me for the past few days. But this morning I came to a somewhat counter-intuitive conclusion, which is that it really doesn’t matter.

What does “now” actually mean? We think of it as the infinitesimal boundary between past and future; between things that are as yet unknown and our memories. But now is not infinitesimal. I realized this in the shower. I was looking at the droplets of water spraying from the shower-head and realized that I can see them. This perhaps won’t surprise you, but it did me, because I’ve become so conditioned now to the view that the world I’m aware of is actually a predictive simulation of reality, not reality itself. This HAS to be true (although now is not the time to discuss it). And yet here I was, looking at actual reality. I wasn’t inventing these water droplets and I couldn’t predict their individual occurrence. Nor was the information merely being used to synchronize my model and keep my predictions in line with how things have actually turned out – I was consciously aware of each individual water droplet.

But I was looking at water that actually came out of my shower-head over a tenth of a second ago; maybe far longer. By the time the signals had caused retinal ganglion cells to fire, zoomed down my optic nerve, chuntered through my optic chiasm and lateral geniculate nucleus, and made their tortuous and mysterious way through my cortex, right up to the level of conscious awareness, those droplets were long gone. So I was aware of the past and only believed I was aware of the present. (In fact, just to make it more complex, I think I was aware of several pasts – the moment at which I “saw” the droplets was different from the moment that I knew that I’d seen the droplets.)

Yet at the same time, I was demonstrably aware of an anticipated present, based upon equally retarded but easier to extrapolate facts. I wasn’t simply responding to things that happened a large fraction of a second ago. If a fish had jumped out of the shower-head I’d certainly have been surprised and it would have taken me a while to get to grips with events, but for the most part I was “on top of the situation” and able to react to things as they were actually happening, even though I wouldn’t find out about them until a moment later. I was even starting bodily actions in anticipation of future events. If the soap had started to slip I’d have begun moving so that I could catch it where it was about to be, not where it was when I saw it fall. But for the most part my anticipations exactly canceled out my processing delays, so that, as far as I knew, I was living in the moment.

So I was simultaneously aware of events that happened a fraction of a second ago, as if they were happening now; events that I believed were happening now, even though I wouldn’t get confirmation of them for another fraction of a second; and events that hadn’t even happened yet (positioning my hands to catch the soap in a place it hadn’t even reached). ALL of these were happening at once, according to my brain; they all seemed like “now”.

Perhaps, therefore, these little predictive circuits really do act as if they are sensations. Perhaps the initial sensation is weak, and the predictions (if they are confident) build up to create a wave of activity whose peak is over a touch neuron that won’t actually get touched until some time in the future. Beyond a certain distance, the innate uncertainty or conditionality of each prediction would prevent the wave from extending indefinitely. Perhaps this blurred “sensation” is what we’re actually aware of. Perhaps for touch there’s an optimum distance and spread. In general, the peak of the wave should lie over the piece of skin that will probably get touched X milliseconds into the future, where X is the time it takes for an actual sensation to reach awareness or trigger an appropriate response. But it means the creature’s sense of “now” is smeared. Some information exists before the event; some reaches awareness at the very moment it is (probably) actually happening; the news that it DID actually happen arrives some time later. All of this is “now.”

Or perhaps not. After all, if I imagine something happening in my mind, it happens more or less in real time, as a narrative. I don’t see the ghosts of past, present and future superimposed. This, though, may be due to the high-level selection process that is piecing together the narrative. Perhaps the building blocks can only see a certain distance into the future. Primitive building blocks, like primary sensations, only predict a few milliseconds. Highly abstract building blocks, like “we’re in a bar; someone is offering me a drink” predict much further into the future, but only in a vague way. To “act out” what actually happens, these abstractions need to assemble chains of more primitive predictions to fill in the details, and so the brain always has to wait and see what happens in its own story, before initiating the next step. I’m not at all sure about this, but I can’t see any other way to assemble a complex, arbitrarily detailed, visual and auditory narrative inside one’s head without utilizing memories of how one thing leads to another at a wide range of abstractions. These memories have to have uses beyond conscious, deliberate thought, and so must be wired into the very process of perception. And in order for them to be chained together, the predicted outcomes need to behave as if they were stimuli.

I’m going to muse on this some more yet. For instance I have a hunch that attention plays a part in how far a chain of predictions can proceed (while prediction in turn drives attention), and I haven’t even begun to think about precisely how these simulations can be taken offline for use as plans or speculations, or precisely how this set-up maps onto motor actions (in which I believe intentions are seen as a kind of prediction). But this general architecture of abstractions and predictions is beginning to look like it might form the basis for my artificial brain. Of course there’s an awful lot of twiddly bits to add, but this seems like it might be a rough starting point from which to start painting in some details, and I have to start somewhere. Preferably soon.

Brainstorm 6: All change

In my last Brainstorming session I was musing on associations and asked myself what is being associated with what, that enables a brain to make a prediction (and hence perform simulations). A present state is clearly being associated with the state that tends to follow it, but what does that mean? It’s obvious for some forms of information but a lot less obvious for others and for the general case. Learning that one ten-million-dimension vector tends to follow another is neither practical nor intelligent – it doesn’t permit generalization, which is essential. Something more compact and meaningful is happening.

If the brain is to be able to imagine things, there must be a comprehensive simulation mechanism, capable of predicting the future state in any arbitrary scenario (as long as it’s sufficiently familiar). If I imagine a coffee cup in my hand and then tilt my imaginary hand, the cup falls. I can even get a fair simulation of how it will break when it hits the floor. If I imagine myself talking to someone, we can have a complete conversation that matches the kinds of thing this person might say in reality – I have a comprehensive simulation of their own mind inside mine. It’s comparatively easy to see how a brain might predict the future position of a moving stimulus on the retina, but a lot less obvious how this more general kind of simulation works. Coffee cups don’t have information about how they fall built into their properties, nor do they fall on a whim. Somehow it’s the entirety of the situation that matters – the interaction of cup and hand – and knowledge of falling objects in general (as well as the physical properties of pottery) somehow gets transferred automatically into the simulation as needed.

Pierre-Simon Laplace once said: “An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed … the future just like the past would be present before its eyes.” In other words, if you know the current state of the universe precisely then you can work out its state at any time in the future. He wasn’t entirely right, as it happens – if Laplace was himself that intellect, then he would also be part of the universe, and so the act of gathering the data would change some of the data he needed to gather. He could never have perfect knowledge. And we know now that the most infinitesimal inaccuracy will magnify very rapidly until the prediction is out of whack with reality. But even so, in practical terms determinism works. If our artificial brain knew everything it was capable of knowing about the state of its region of the universe (in other words, the value of a ten-million-dimensional vector) then it would have enough knowledge to make a fair stab at the value of this vector a short while later. If that weren’t true, intelligence wouldn’t be possible.

But Laplace had a very good point when he mentioned “all forces that set nature in motion.” It’s not just the state of the world that matters, but the rate and direction of change. It’s an interesting philosophical question, how an object can embody a rate of change at an instant in time (discuss!). It has a momentum, but that’s dodging the issue. Nevertheless, change is all-important, and real brains are far more interested in change than they are in static states. In fact they’re more-or-less blind to things that don’t change – quite literally. If you can hold your eyes perfectly still when focusing on a fixed point, you’ll go temporarily blind in a matter of seconds! Try it – it’s not easy but it can be done with practice and it’s quite startling.

Getting preoccupied with recognizing objects, etc. fails to help me with this question of prediction, and vision is misleading because it’s essentially a movement-detection system that has been heavily modified by evolution to make it possible to establish facts about things that aren’t moving. The static world is essentially transformed into a moving one (e.g. through microsaccades) before being analyzed in ways we don’t understand and may never be able to, unless we understand how change and prediction are handled more generally. So how about our tactile sense? Maybe that’s a good model to think about for a while?

Ok, I’ll start with a very simple creature – a straight line, with touch sensors along its surface. If I touch this creature with my finger one of the sensors will be triggered (because its input has changed), but will soon become silent again as the nerve ending habituates. At this point the creature can make a prediction, but not a very useful one: my finger might move left or it might move right. It can’t tell which at first, but if my finger starts to move left, it can immediately predict where it’s going to go next. It’s easy to imagine a neuron connected to a pair of adjacent sensors, which will fire when one sensor is triggered before the other.

Eureka! We have a prediction neuron – it knows that the third sensor in the line is likely to be triggered shortly. In fact we can imagine a whole host of these neurons, tuned to different delays and hence sensitive to speed. Each one can make a prediction about which other sensors are likely to be touched within a given period. We can imagine each neuron feeding some kind of information back down to the sensor that it is predicting will be touched. The neurons have a memory of the past, which they can compare to the present in order to establish future trends. The more abstract this memory, the more we can describe it as forming our present context. Context is all-important. If you’ve ever woken from a general anesthetic, you’ll know that it takes a while to re-establish a context – who you are, where you are, how you got there – and until you have this you can’t figure out what’s likely to happen next.

So far, so good. We have a reciprocal connection of the kind that seems to be universal in the brain. We can imagine a further layer of neurons that listen to these simpler neurons and develop a more general sense of the direction and speed of movement, which is less dependent on the actual location of the stimulus. By the time we get a few layers deep, we have cells that can tell us if the stroking of my finger is deviating from a straight line (well, we could if my simplified creature wasn’t one-dimensional!).

But what’s the point of feeding back this information to the sensory neurons themselves? The first layer of cells is telling specific sensory neurons to expect to be touched in a few milliseconds. Big deal – they’ll soon find out anyway. Nevertheless, two valuable pieces of information come out of this prediction:

Firstly, if a sensory neuron is told to expect a touch and it doesn’t arrive, we want our creature to be surprised. Things that just behave according to expectations can usually be safely ignored, and we only want to be alerted to things that don’t do what we were expecting. Surprise gives us a little shock – it causes a bunch of physiological responses. We may get a little burst of adrenaline, to prepare us in case we need to act, and our other sensory systems get alerted to pay more attention to the source of the unexpected change (this is called an “orienting response”). Neurons higher up in the system are thus primed and able to make decisions about what, if anything, to do about this unexpected turn of events. The shock will ripple up the system until something finally knows what to do about that sort of thing. Most of the time this will be an unconscious response (like when we flick an insect off our arm) but sometimes nothing will know how to deal with this, and consciousness needs to get in on the act.

Secondly, once we have a hunch about where the stimulus is going to show up next, we can start to look further ahead to where it is likely to be heading. The more often our low-level predictions are confirmed, the more confident we can be, and the more time we’ve had in which to make this ripple of predictive activity travel ahead of the stimulus, to figure out what might happen in a few moments’ time. Perhaps my finger is stroking along the creature towards a tender spot that will hurt it; perhaps it’s moving in the other direction, towards the creature’s mouth, where it has a hope of eating my finger. Pain or pleasure get predicted, and behavior results whenever one or the other seems likely.

We have to presume that all of this stuff wires itself up through experience – by association. The first layer of sensory neurons learns when the sensor it is associated with is about to be touched, by understanding statistical relationships between the states of neighboring sensors. These first-level neurons presumably cooperate and compete with each other to ensure that each one develops a unique tuning and all possible circumstances get represented (this is exactly homologous, IMHO, to what happens in primary visual cortex, with edge-orientation/motion-sensitive neurons). The higher layers, which make longer-term predictions, learn to associate certain patterns of movement with pain or pleasure. The most abstract layers are presumably capable of learning that certain responses maximize pleasure or minimize pain.

Leaving aside the question of how these responses get coordinated, we now have a complete behavioral mechanism. And it’s NOT a stimulus-response system. The behavior is being triggered by predictions of what is about to happen, not what has just happened (this is a moot point and you may object that the system is still responding to the past stimuli, but I think an essential threshold has been crossed here and it’s fair to call this an anticipatory mechanism).

It’s clear that somehow the prediction needs to be compared to reality, and surprise should be generated if they don’t match, and it’s clear that predictions need to be able to associate themselves with reward. Somehow predictions also need to take part in servo action – actions are goal-directed, and hence are themselves predictions of a future state. Comparing what your sensors predict is going to happen, to what you intend to happen, is what allows you to make anticipatory changes and bring reality into line with your intentions. I need to think about that a bit, though.

But what about the ability to use this predictive mechanism to imagine possible futures? We presumably now have the facility to imagine a high-level construct, such as “let’s suppose I’m feeling someone stroke my skin” and actually feel the stroke occurring, as these higher-level neurons pass down their predictions to lower levels at which individual touch sensors are told to expect/pretend they’ve been stimulated. Although obviously this time we shouldn’t be surprised when nothing happens! The surprise response needs to be suppressed, and somehow the predictions ought to stand in for the sensations. That has implications for the wiring and all sorts of questions remain unresolved here.

It’s much harder, though, to see how we can assemble an entire context in our heads – the hand and the coffee cup, say. Coffee cups only fall when hands drop them. Dropping something only occurs when a hand is placed at a certain set of angles. A motor action is associated with a visual change, but only in a particular class of contexts, and the actual visual change is also highly context-dependent: If a cup was in your hand, that’s what you’ll see fall. Remarkably, if you imagine holding a little gnome in your hand instead, what you’ll see is a falling gnome, not a falling cup, even if you’ve never actually dropped a minuscule fantasy creature before in your life! In fact your imaginary gnome may even surprise you by leaping to safety! Somehow the properties of objects are able to interact in a highly generalizable way, and these interactions can trigger mental imagery, which eventually trickles down to the actual sensory system as if they’d really occurred (there are several lines of evidence to suggest that when we imagine something we “see” it using the same parts of our visual system that would be active if we’d really seen it).

Somehow the brain encodes cause and effect, at many levels, in a generalizable way. Complex chains of inference occur when we mentally decide to rotate our hand and see what happens to the thing it was holding, and the ability to make these inferences must arise from statistical learning that is designed to predict future states from past ones.

And somehow I have to come up with just such a general scheme, but at a level of abstraction suitable for a game. My creatures are not going to be covered in touch sensors or see the world in terms of moving colored pixels. It’s a shame really, because I understand these things at the low level – it’s the high level that still eludes me…

P.S. This post got auto-linked to a post on the question of why we can’t tickle ourselves (I’m assuming you’re not schizophrenic here, or you won’t know what I’m talking about, because you can!). We can’t tickle ourselves because our brain knows the difference between things we do and things that get done to us (self/non-self determination). If we try to tickle ourselves, we predict there will be a certain sensation and this prediction is used to cancel out the actual sensation. It’s pretty important for an organism to differentiate between things it does to the world and things the world does to it (bumping into something feels the same as being bumped into, but the appropriate responses are different). So here’s another pathway that requires anticipation, and another example of the brain as a simulation engine.

Brainstorm 5: joining up the dots

I promised myself I’d blog about my thoughts, even if I don’t really have any and keep going round in circles. Partly I just want to document the creative process honestly – so this includes the inevitable days when things aren’t coming together – and partly it helps me if I try to explain things to people. So permit me to ramble incoherently for a while.

I’m trying to think about associations. In one sense the stuff I’ve already talked about is associative: a line segment is an association between a certain set of pixels. A cortical map that recognizes faces probably does so by associating facial features and their relative positions. I’m assuming that each of these things is then denoted by a specific point in space on the real estate of the brain – oriented lines in V1 and faces in the FFA. In both these cases there are several features at one level, which are associated and brought together at a higher level. A bunch of dots maketh one line. Two dark blobs and a line in the right arrangement maketh a face. A common assumption (which may not be true) is that neurons do this explicitly: the dendritic field of a visual neuron might synapse onto a particular pattern of LGN fibres carrying retinal pixel data. When this pattern of pixels becomes active, the neuron fires. That specific neuron – that point on the self-organizing map – therefore means “I can see a line at 45 degrees in this part of the visual field.”

But the brain also supports many other kinds of associative link. Seeing a fir tree makes me think of Christmas, for instance. So does smelling cooked turkey. Is there a neuron that represents Christmas, which synapses onto neurons representing fir trees and turkeys? Perhaps, perhaps not. There isn’t an obvious shift in levels of representation here.

Not only do turkeys make me think of Christmas, but Christmas makes me think of turkeys. That implies a bidirectional link. Such a thing may actually be a general feature, despite the unidirectional implication of the “line-detector neuron” hypothesis. If I imagine a line at 45 degrees, this isn’t just an abstract concept or symbol in my mind. I can actually see the line. I can trace it with my finger. If I imagine a fir tree I can see that too. So in all likelihood, the entire abstraction process is bidirectional and thus features can be reconstructed top-down, as well as percepts being constructed/recognized bottom-up.

But even so, loose associations like “red reminds me of danger” don’t sound like the same sort of association as “these dots form a line”. A line has a name – it’s a 45-degree line at position x,y – but what would you call the concept that red reminds me of danger? It’s just an association, not a thing. There’s no higher-level concept for which “red” and “danger” are its characteristic features. It’s just a nameless fact.

How about a melody? I know hundreds of tunes, and the interesting thing is, they’re all made from the same set of notes. The features aren’t what define a melody, it’s the temporal sequence of those features; how they’re associated through time. Certainly we can’t imagine there being a neuron that represents “Auld Lang Syne”, whose dendrites synapse onto our auditory cortex’s representations of the different pitches that are contained in the tune. The melody is a set of associations with a distinct sequence and a set of time intervals. If someone starts playing the tune and then stops in the middle I’ll be troubled, because I’m anticipating the next note and it fails to arrive. Come to that, there’s a piano piece by Rick Wakeman that ends in a glissando, and Wakeman doesn’t quite hit the last note. It drives me nuts, and yet how do I even know there should be another note? I’m inferring it from the structure. Interestingly, someone could play a phrase from the middle of “Auld Lang Syne” and I’d still be able to recognize it. Perhaps the tune is represented by many overlapping short pitch sequences? But if so, then this cluster of representations is collectively associated with its title and acts as a unified whole.

Thinking about anticipating the next note in a tune reminds me of my primary goal: a representation that’s capable of simulating the world by assembling predictions. State A usually leads to state B, so if I imagine state A, state B will come to mind next and I’ll have a sense of personal narrative. I’ll be able to plan, speculate, tell myself stories, relive a past event, relive it as if I’d said something wittier at the time, etc. Predictions are a kind of association too, but between what? A moving 45-degree line at one spot on the retina tends to lead to the sensation of a 45-degree line at another spot, shortly afterwards. That’s a predictive association and it’s easy to imagine how such a thing can become encoded in the brain. But Turkeys don’t lead to Christmas. More general predictions arise out of situations, not objects. If you see a turkey and a butcher, and catch a glint in the butcher’s eye, then you can probably make a prediction, but what are the rules that are encoded here? What kind of representation are we dealing with?

“Going to the dentist hurts” is another kind of association. “I love that woman” is of a similar kind. These are affective associations and all the evidence shows that they’re very important, not only for the formation of memories (which form more quickly and thoroughly when there’s some emotional content), but also for the creation of goal-directed behavior. We tend to seek pleasure and avoid pain (and by the time we’re grown up, most of us can even withstand a little pain in the expectation of a future reward).

A plan is the predictive association of events and situations, leading from a known starting point to a desired goal, taking into account the reward and punishment (as defined by affective associations) along the route. So now we have two kinds of association that interact!

To some extent I can see that the meaning of an associative link is determined by what kind of thing it is linking. The links themselves may not be qualitatively different – it’s just the context. Affective associations link memories (often episodic ones) with the emotional centers of the brain (e.g. the amygdala). Objects can be linked to actions (a hammer is associated with a particular arm movement). Situations predict consequences. Cognitive maps link objects with their locations. Linguistic areas link objects, actions and emotions with nouns, verbs and adjectives/adverbs. But there do seem to be some questions about the nature of these links and to what extent they differ in terms of circuitry.

Then there’s the question of temporary associations. And deliberate associations. Remembering where I left my car keys is not the same as recording the fact that divorce is unpleasant. The latter is a semantic memory and the former is episodic, or at least declarative. Tomorrow I’ll put my car keys down somewhere else, and that will form a new association. The old one may still be there, in some vague sense, and I may one day develop a sense of where I usually leave my keys, but in general these associations are transient (and all too easily forgotten).

Binding is a form of temporary association. That ball is green; there’s a person to my right; the cup is on the table.

And attention is closely connected with the formation or heightening of associations. For instance, in Creatures I had a concept called “IT”. “IT” was the object currently being attended to, so if a norn shifted its attention, “IT” would change, and if the norn decided to “pick IT up”, the verb knew which noun to apply to. In a more sophisticated artificial brain, this idea has to be more comprehensive. We may need two or more ITs, to form the subject and object of an action. We need to remember where IT is, in various coordinate frames, so that we can reach out and grab IT or look towards IT or run away from IT. We need to know how big IT is, what color IT is, who IT belongs to, etc. These are all associations.

Perhaps there are large-scale functional associations, too. In other words, data from one space can be associated with another space temporarily to perform some function. What came to mind that made me think of this is the possibility that we have specialized cortical machinery for rotating images, perhaps developed for a specific purpose, and yet I can choose, any time I like, to rotate an image of a car, or a cat, or my apartment. If I imagine my apartment from above, I’m using some kind of machinery to manipulate a particular set of data points (after all, I’ve never seen my apartment from above, so this isn’t memory). Now I’m imagining my own body from above – I surely can’t have another machine for rotating bodies, so somehow I’m routing information about the layout of my apartment or the shape of my body through to a piece of machinery (which, incidentally, is likely to be cortical and hence will have self-organized using the same rules that created the representation of my apartment and the ability to type these words). Routing signals from one place to another is another kind of association.

Language is interesting (I realize that’s a bit of an understatement!). I don’t believe the Chomskyan idea that grammar is hard-wired into the brain. I think that’s missing the point. I prefer the perspective that the brain is wired to think, and grammar is a reflection of how the brain thinks. [noun][verb][noun] seems to be a fundamental component of thought. “Janet likes John.” “John is a boy.” “John pokes Janet with a stick.” Objects are associated with each other via actions, and both the objects and actions can be modulated (linguistically, adverbs modulate actions; adjectives modify or specify objects). At some level all thought has this structure, and language just reflects that (and allows us to transfer thoughts from one brain to another). But the level at which this happens can be very far removed from that of discrete symbols and simple associations. Many predictions can be couched in linguistic terms: IF [he] [is threatening] [me] AND [I][run away from][him] THEN [I][will be][safe]. IF [I][am approaching][an obstacle]AND NOT ([I][turn]) THEN [I][hurt]. But other predictions are much more fluid and continuous: In my head I’m imagining water flowing over a waterfall, turning a waterwheel, which turns a shaft, which grinds flour between two millstones. I can see this happening – it’s not just a symbolic statement. I can feel the forces; I can hear the sound; I can imagine what will happen if the water flow gets too strong and the shaft snaps. Symbolic representations and simple linear associations won’t cut it to encode such predictive power. I have a real model of the laws of physics in my head, and can apply it to objects I’ve never even seen before, then imagine consequences that are accurate, visual and dynamic. So at one level, grammar is a good model for many kinds of association, including predictive associations, but at another it’s not. Are these the same processes – the same basic mechanism – just operating at different levels of abstraction, or are they different mechanisms?

These predictions are conditional. In the linguistic examples above, there’s always an IF and a set of conditionals. In the more fluid example of the imaginary waterfall, there are mathematical functions being expressed, and since a function has dependent variables, this is a conditional concept too. High-level motor actions are also conditional: walking consists of a sequence of associations between primitive actions, modulated by feedback and linked by conditional constructs such as “do until” or “do while”.

So, associations can be formed and broken, switched on and off, made dependent on other associations, apply specifically or broadly, embody sequence and timing and probability, form categories and hierarchies or link things without implying a unifying concept. They can implement rules and laws as well as facts. They may or may not be commutative. They can be manipulated top-down or formed bottom-up… SOMEHOW all this needs to be incorporated into a coherent scheme. I don’t need to understand how the entire human brain works – I’m just trying to create a highly simplified animal-like brain for a computer game. But brains do some impressive things (nine-tenths of which most AI researchers and philosophers forget about when they’re coming up with new theories). I need to find a representation and a set of mechanisms for defining associations that have many of these properties, so that my creatures can imagine possible futures, plan their day, get from A to B and generalize from past experiences. So far I don’t have any great ideas for a coherent and elegant scheme, but at least I have a list of requirements, now.

I think the next thing to do is think more about the kinds of representation I need – how best to represent and compute things like where the creature is in space, what kind of situation it is in, what the properties of objects are, how actions are performed. Even though I’d like most of this to emerge spontaneously, I should at least second-guess it to see what we might be dealing with. If I lay out a map of the perceptual and motor world, maybe the links between points on this map (representing the various kinds of associations) will start to make sense.

Or I could go for a run. Yes, I like that thought better.