Reverse engineering the brain is a very slow way to make a smart computer

The race is on to build conscious and smart computers and brain replicas. This article explains some of Markam’s approach. http://www.wired.com/wiredscience/2013/05/neurologist-markam-human-brain/all/

It is a nice project, and its aims are to make a working replica of the brain by reverse engineering it. That would work eventually, but it is slow and expensive and it is debatable how valuable it is as a goal.

Imagine if you want to make an aeroplane from scratch.  You could study birds and make extremely detailed reverse engineered mathematical models of the structures of individual feathers, and try to model all the stresses and airflows as the wing beats. Eventually you could make a good model of a wing, and by also looking at the electrics, feedbacks, nerves and muscles, you could eventually make some sort of control system that would essentially replicate a bird wing. Then you could scale it all up, look for other materials, experiment a bit and eventually you might make a big bird replica. Alternatively, you could look briefly at a bird and note the basic aerodynamics of a wing, note the use of lightweight and strong materials, then let it go. You don’t need any more from nature than that. The rest can be done by looking at ways of propelling the surface to create sufficient airflow and lift using the aerofoil, and ways to achieve the strength needed. The bird provides some basic insight, but it simply isn’t necessary to copy all a bird’s proprietary technology to fly.

Back to Markam. If the real goal is to reverse engineer the actual human brain and make a detailed replica or model of it, then fair enough. I wish him and his team, and their distributed helpers and affiliates every success with that. If the project goes well, and we can find insights to help with the hundreds of brain disorders and improve medicine, great. A few billion euros will have been well spent, especially given the waste of more billions of euros elsewhere on futile and counter-productive projects. Lots of people criticise his goal, and some of their arguments are nonsensical. It is a good project and for what it’s worth, I support it.

My only real objection is that a simulation of the brain will not think well and at best will be an extremely inefficient thinking machine. So if a goal is to achieve thought or intelligence, the project as described is barking up the wrong tree. If that isn’t a goal, so what? It still has the other uses.

A simulation can do many things. It can be used to follow through the consequences of an input if the system is sufficiently well modelled. A sufficiently detailed and accurate brain simulation could predict the impacts of a drug or behaviours resulting from certain mental processes. It could follow through the impacts and chain of events resulting from an electrical impulse  this finding out what the eventual result of that will be. It can therefore very inefficiently predict the result of thinking, but by using extremely high speed computation, it could in principle work out the end result of some thoughts. But it needs enormous detail and algorithmic precision to do that. I doubt it is achievable simply because of the volume of calculation needed.  Thinking properly requires consciousness and therefore emulation. A conscious circuit has to be built, not just modelled.

Consciousness is not the same as thinking. A simulation of the brain would not be conscious, even if it can work out the result of thoughts. It is the difference between printed music and played music. One is data, one is an experience. A simulation of all the processes going on inside a head will not generate any consciousness, only data. It could think, but not feel or experience.

Having made that important distinction, I still think that Markam’s approach will prove useful. It will generate many useful insights into the workings of the brain, and many of the processes nature uses to solve certain engineering problems. These insights and techniques can be used as input into other projects. Biomimetics is already proven as a useful tool in solving big problems. Looking at how the brain works will give us hints how to make a truly conscious, properly thinking machine. But just as with birds and airbuses, we can take ideas and inspiration from nature and then do it far better. No bird can carry the weight or fly as high or as fast as an aeroplane. No proper plane uses feathers or flaps its wings.

I wrote recently about how to make a conscious computer:

http://timeguide.wordpress.com/2013/02/15/how-to-make-a-conscious-computer/ and http://timeguide.wordpress.com/2013/02/18/how-smart-could-an-ai-become/

I still think that approach will work well, and it could be a decade faster than going Markam’s route. All the core technology needed to start making a conscious computer already exists today. With funding and some smart minds to set the process in motion, it could be done in a couple of years. The potential conscious and ultra-smart computer, properly harnessed, could do its research far faster than any human on Markam’s team. It could easily beat them to the goal of a replica brain. The converse is not true, Markam’s current approach would yield a conscious computer very slowly.

So while I fully applaud the effort and endorse the goals, changing the approach now could give far more bang for the buck, far faster.

How smart could an AI become?

I got an interesting question in a comment from Jim T on my last blog.

What is your opinion now on how powerful machine intelligence will become?

Funny, but my answer relates to the old question: how many angels can sit on the head of a pin?

The brain is not a digital computer, and don’t think a digital processor will be capable of consciousness (though that doesn’t mean it can’t be very smart and help make huge scientific progress). I believe a conscious AI will be mostly analog in nature, probably based on some fancy combo of adaptive neural nets. as suggested decades ago by Moravec.

Taking that line, and looking at how far miniaturisation can go, then adding all the zeros that arise from the shorter signal transmission paths, faster switching speeds, faster comms, and the greater number of potential pathways using optical WDM than electronic connectivity, I calculated that a spherical pinhead (1mm across) could ultimately house the equivalent of 10,000 human brains. (I don’t know how smart angels are so didn’t quite get to the final step). You could scale that up for as much funding, storage and material and energy you can provide.

However, what that quantifies is how many human equivalent AIs you could support. Very useful to know if you plan to build a future server farm to look after electronically immortal people. You could build a machine with the equivalent intelligence of the entire human race. But it doesn’t answer the question of how smart a single AI could ever be, or how powerful it could be. Quantity isn’t quality. You could argue that 1% of the engineers produce 99% of the value, even with only a fairly small IQ difference. 10 billion people may not be as useful for progress as 10 people with 5 times the IQ. And look at how controversial IQ is. We can’t even agree what intelligence is or how to quantify it.

Just based on loose language, how powerful or smart or intelligent an AI could become depends on the ongoing positive feedback loop. Adding  more AI of the same intelligence level will enable the next incremental improvement, then using those slightly smarter AIs would get you to the next stage, a bit faster, ad infinitum. Eventually, you could make an AI that is really, really, really smart.

How smart is that? I don’t have the terminology to describe it. I can borrow an analogy though. Terry Pratchett’s early book ‘The Dark Side of the Sun’ has a character in it called The Bank. It was a silicon planet, with the silicon making a hugely smart mind. Imagine if a pinhead could house 10,000 human brains, and you have a planet of the stuff, and it’s all one big intellect instead of lots of dumb ones. Yep. Really, really, really smart.

How to make a conscious computer

The latest generation of supercomputers have processing speed that is higher than the human brain on a simple digital comparison, but they can’t think, aren’t conscious. It’s not even really appropriate to compare them because the brain mostly isn’t digital. It has some digital processing in the optics system but mostly uses adaptive analog neurons whereas digital computers use digital chips for processing and storage and only a bit of analog electronics for other circuits. Most digital computers don’t even have anything we would equate to senses.

Analog computers aren’t used much now, but were in fairly widespread use in some industries until the early 1980s. Most IT people have no first hand experience of them and some don’t seem to even be aware of analog computers, what they can do or how. But in the AI space, a lot of the development uses analog approaches.

http://timeguide.wordpress.com/2011/09/18/gel-computing/ discusses some of my previous work on conscious computer design. I won’t reproduce it here.

I firmly believe consciousness, whether externally or internally focused, is the result of internally directed sensing, (sensing can be thought of as the solicitation of feeling) so that you feel your thoughts or sensory inputs in much the same way. The easy bit is figuring out how thinking can work once you have that, how memories can be relived, concepts built, how self-awareness, sentience, intelligence emerge. All those are easy once you have figured out how feeling works. That is the hard problem.

Detection is not the same as feeling. It is easy to build a detector or sensor that flips a switch or moves a dial when something happens or even precisely quantifies something . Feeling it is another layer on that. Your skin detects touch, but your brain feels it, senses it. Taking detection and making it feel and become a sensation, that’s hard. What is it about a particular circuit that adds sensation? That is the missing link, the hard problem, and all the writing available out there just echoes that. Philosophers and scientists have written about this same problem in different ways for ages, and have struggled in vain to get a grip on it, many end up running in circles. So far they don’t know the answer, and neither do I. The best any offer is elucidation of aspects of the problem and at occasionally some hints of things that they think might somehow be connected with the answer. There exists no answer or explanation yet.

There is no magic in the brain. The circuitry involved in feeling something is capable of being described, replicated and even manufactured. It is possible to find out how to make a conscious circuit, even if we still don’t know what consciousness is or how it works, via replication, reverse engineering or evolutionary development. We manage to make conscious children several times every second.

How far can we go? Having studied a lot of what is written, it is clear that even after a lot of smart people thinking a long time about it, there is a great deal of confusion out there, and at least some of it comes basically from trying to use too big words and some comes from trying to analyse too much at once. When it is so obvious that it is a tough problem, simplifying it will undoubtedly help.  So let’s narrow it down a bit.

Feeling needs to be separated out from all the other things going on. What is it that happens that makes something feel? Well, detecting something pre-empts feeling it, and interpreting it or thinking about it comes later. So, ignore the detection and interpretation and thinking bits for now. Even sensation can be modelled as solicitation of feeling, essentially adding qualitative information to it. We ought to be able to make an abstraction model as for any IT system, where feeling is a distinct layer, coming between the physical detection layer and sensation, well below any of the layers associated with thinking or analysis.

Many believe that very simple organisms can detect stimuli and react to them, but can’t feel,  but more sophisticated ones can. Logical deduction tells us either that feeling may require fairly complex neural networks but certainly well below human levels, or alternatively, feeling may not be fundamentally linked to complexity but may emerge from architectural differences that arose in parallel with increasing complexity but aren’t dependent on it. It is also very likely due to evolutionary mechanisms that feeling emerges from similar structures to detection, though not the same. Architectural modifications, feedbacks, or additions to detection circuits might be an excellent point to start looking.

So we don’t know the answer, but we do have some good clues. Better than nothing. Coming at it from a philosophical direction, even the smartest people quickly get tied in knots, but from an engineering direction, I think the problem is soluble.

If feeling is, as I believe, a modified detection system, then we could for example seed an evolutionary design system with detection systems. Mutating, restructuring and rearranging detection systems and adding occasional random components here and there might eventually create some circuits that feel. It did in nature, and would in an evolutionary design system, given time. But how would we know? An evolutionary design system needs some means of selection to distinguish the more successful branches for further development.

Using feedback loops would probably help. A system with built in feedback so that it feels that it is feeling something would be symmetrical, maybe even fractal. Self-reinforcement of a feeling process would also create a little vortex of activity. A simple detection system (with detection of detection) would not exhibit such strong activity peaks due to necessary lack of symmetry in detection of initial and processed stimuli. So all we need do is to introduce feedback loops in each architecture and look for the emergence of activity peaks. Possibly, some non-feeling architectures might also show activity peaks so not all peaks would necessarily show successes, but all successes would show peaks.

So, the evolutionary system would take basic detection circuits as input, modify them, add random components, then connect them in simple symmetrical feedback loops. Most results would do nothing. Some would show self-reinforcement, evidenced by activity peaks. Those are the ones we need.

The output from such an evolutionary design system would be circuits that feel (and some junk). We have our basic components. Now we can start to make a conscious computer.

Let’s go back to the gel computing idea and plug them in. We have some basic detectors, for light, sound, touch etc. Pretty simple stuff, but we connect those to our new feeling circuits, so now those inputs stop being just information and become sensations. We add in some storage, recording the inputs, again with some feeling circuits added into the mix, and just for fun, let’s make those recording circuits replay those inputs over and over, indefinitely. Those sensations will be felt again and again, the memory relived. Our primitive little computer can already remember and experience things it has experienced before. Now add in some processing. When a and b happen, c results. Nothing complicated. Just the sort of primitive summation of inputs we know neurons can do all the time. But now, when that processing happens, our computer brain feels it. It feels that it is doing some thinking. It feels the stimuli occurring, a result occurring. And as it records and replays it, an experience builds. It now has knowledge. It may not be the answer to life the universe and everything just yet, but knowledge it is. It now knows and remembers the experience that when it links these two inputs, it gets that output. These processes and recordings and replays and further processing and storage and replays echo throughout the whole system. The sensory echoes and neural interference patterns result in some areas of reinforcement and some of cancellation. Concepts form. The whole process is sensed by the brain. It is thinking, processing, reliving memories, linking inputs and results into concepts and knowledge, storing concepts, and most importantly, it is feeling itself doing so.

The rest is just design detail. There’s your conscious computer.

When will AI marriage become legal?

Gay marriage is so yesterday. OK, it isn’t quite yet, but everything has been said a million times and I don’t intend to repeat it. A related but much more interesting debate is already gathering volume globally. When will you be able to marry your robot or AI?

The traditional Oxford English definition of marriage:

The formal union of a man and a woman, typically recognized by law, by which they become husband and wife. 

But, as is being asked by some, who says they have to be a man and a woman? Why can’t they be any sex? I don’t want to get into the arguments, because people on both sides argue passionately, often flying in the face of logic, but here is a gender neutral alternative definition:

Marriage is a social union or legal contract between people called spouses that establishes rights and obligations between the spouses, between the spouses and their children, and between the spouses and their in-laws.

Well, I am all for equality for all, but who says they have to be people?

If we are going to fight over definitions, surely we should try to finish with one that might survive more than a decade or two. This one simply won’t.

Artificial intelligence, or AI as it is usually called now, is making good progress. We already have computers with more raw number crunching power than the human brain. Their software, and indeed their requirement to use software, makes them far from equivalent overall, but I don’t think we will be waiting very long now for AI machines that we will agree are conscious, self aware, intelligent, sentient, with emotions, capable of forming human-like relationships. A few cranks will still object maybe, but so what?

These AIs will likely be based on adaptive analog neural networks rather than digital processing so they will not be so different from us really. Different futurists list different dates for AIs with man-machine equivalence, depending mostly on the prejudices and experiences bequeathed by their own backgrounds. I’d say 10 years, some say 15 or 20. Some say we will never get there, but they are just wrong, so wrong. We will soon have artificially intelligent entities comparable to humans in intellect and emotional capability. So how about this definition? :

Marriage is a social union or legal contract between conscious entities called spouses that establishes rights and obligations between the spouses, between the spouses and their derivatives, and those legally connected to them.

An AI might or might not be connected to a robot. An AI may not have any permanent physical form, and robots are really a red herring here. The mind is what is surely important, not the container. An AI can still be an entity that lives for a long enough time to be eligible for a long term relationship. I often watch sci-fi or play computer games, and many have AI characters that take on some sort of avatar – Edi in Mass Effect or Cortana in Halo for example. Sometimes these avatars are made to look very attractive, even super-attractive. It is easy to imaging how someone could fall in love with their AI. It isn’t much harder to imagine that they could fall in love with each other.

It’s a while since I last wrote about machine consciousness so I’ll say how I think it will work again now.

http://timeguide.wordpress.com/2011/09/18/gel-computing/ tells of my ideas on gel computing. A lot of adaptive electronic devices suspended in gel that can set up free space optical links to each other would be an excellent way of making an artificial brain-like processor.

Using this as a base, and with each of the tiny capsules being able to perform calculations, an extremely powerful digital processor could be created. But I don’t believe digital processors can become conscious, however much their processing increases in speed. It is an act of faith I guess, I can’t prove it, but coming from a computer modelling background it seems to me that a digital computer can simulate the processes in consciousness but it can’t emulate them and that difference is crucial.

I firmly believe consciousness is a matter of internal sensing. The same way that you sense sound or images or touch, you can sense the processes based on those same neural functions and their derivatives in your brain. Emotions ditto. We make ideas and concepts out of words and images and sounds and other sensory things and emotions too. We regenerate the same sorts of patterns, and filter them similarly to create new knowledge, thoughts and memories, a sort of vortex of sensory stimuli and echoes. Consciousness might not actually just be internal sensing, we don’t know yet exactly how it works, but even if it isn’t, you could do it that way. Internal sensing can be the basis of a conscious machine, an AI. Here’s a picture. This would work. I am sure of it. There will also be other ways of achieving consciousness, and they might have different flavours. But for the purposes of arguing for AI marriage, we only need one method of achieving consciousness to be feasible.

I think this sort of AI design could work and it would certainly be capable of emotions. In fact, it would be capable of a much wider range of emotions than human experience. I believe it could fall in love, with a human, alien, or another AI. AIs will have a range and variety of gender capabilities and characteristics. People will be able to link to them in new ways, creating new forms of intimacy. The same technology will also enable new genders for people too, as I discussed recently. In the long term view, gay marriage is just another point on a long line.

When we set aside the arguing over gender equality, what we usually agree on is the importance of love. People can fall in love with any other human of any age, race or gender, but they are also capable of loving a sufficiently developed AI. As we rush to legislate for gender equality, it really is time to start opening the debate. AI will come in a very wide range of capability and flavour. Some will be equivalent or even superior to humans in many ways. They will have needs, they will want rights, and they will become powerful enough to demand them. Sooner or later, we will need to consider equality for them too. And I for one will be on their side.