Posts tagged ‘machine learning’

OutputMachine learning and neurology; the perfect match?

Of course there is a bit of a connection already in that modern machine learning draws on approaches which were distantly inspired by the way networks of neurons seemed to do their thing. Now though, it’s argued in this interesting piece that machine learning might help us cope with the vast complexity of brain organisation. This complexity puts brain processes beyond human comprehension, it’s suggested, but machine learning might step in and decode things for us.

It seems a neat idea, and a couple of noteworthy projects are mentioned: the ‘atlas’ which mapped words to particular areas of cortex, and an attempt to reconstruct seen faces from fMRI data alone (actually with rather mixed success, it seems). But there are surely a few problems too, as the piece acknowledges.

First, fMRI isn’t nearly good enough. Existing scanning techniques just don’t provide the neuron-by-neuron data that is probably required, and never will. It’s as though the only camera we had was permanently out of focus. Really good processing can do something with dodgy images, but if your lens was rubbish to start with, there are limits to what you can get. This really matters for neurology where it seems very likely that a lot of the important stuff really is in the detail. No matter how good machine learning is, it can’t do a proper job with impressionistic data.

We also don’t have large libraries of results from many different subjects. A lot of studies really just ‘decode’ activity in one context in one individual on one occasion. Now it can be argued that that’s the best we’ll ever be able to do, because brains do not get wired up in identical ways. One of the interesting results alluded to in the piece is that the word ‘poodle’ in the brain ‘lives’ near the word ‘dog’. But it’s hardly possible that there exists a fixed definite location in the brain reserved for the word ‘poodle’. Some people never encounter that concept, and can hardly have pre-allocated space for it. Did Neanderthals have a designated space for thinking about poodles that presumably was never used throughout the history of the species? Some people might learn of ‘poodle’ first as a hairstyle, before knowing its canine origin; others, brought up to hard work in their parent’s busy grooming parlour from an early age, might have as many words for poodle as the eskimos were supposed to have for snow. Isn’t that going to affect the brain location where the word ends up? Moreover, what does it mean to say that the word ‘lives’ in a given place? We see activity in that location when the word is encountered, but how do we tell whether that is a response to the word, the concept of the word, the concept of poodles, poodles, a particular known poodle, or any other of the family of poodle-related mental entities? Maybe these different items crop up in multiple different places?

Still, we’ll never know what can be done if we don’t try. One piquant aspect of this is that we might end up with machines that can understand brains, but can never fully explain them to us, both because the complexity is beyond us and because machine learning often works in inscrutable ways anyway. Maybe we can have a second level of machine that explains the first level machines to us – or a pair of machines that each explain the brain and can also explain each other, but not themselves?

It all opens the way for a new and much more irritating kind of robot. This one follows you around and explains you to people. For some of us, some of the time, that would be quite helpful. But it would need some careful constraints, and the fact that it was basically always right about you could become very annoying. You don’t want a robot that says “nah, he doesn’t really want that, he’s just being polite”, or “actually, he’s just not that into you”, let alone “ignore him; he thinks he understands hermeneutics, but actually what he’s got in mind is a garbled memory of something else about Derrida he read once in a magazine”.

Happy New Year!

neural-netInteresting piece here reviewing the way some modern machine learning systems are unfathomable. This is because they learn how to do what they do, rather than being set up with a program, so there is no reassuring algorithm – no set of instructions that tells us how they work. In fact they way they make their decisions may be impossible to grasp properly even if we know all the details because it just exceeds in brute complexity what we can ever get our minds around.

This is not really new. Neural nets that learn for themselves have always been a bit inscrutable. One problem with this is brittleness: when the system fails it may not fail in ways that are safe and manageable, but disastrously. This old problem is becoming more important mainly because new approaches to deep machine learning are doing so well; all of a sudden we seem to be getting a rush of new systems that really work effectively at quite complex real world tasks. The problems are no longer academic.

Brittle behaviour may come about when the system learns its task from a limited data set. It does not understand the data and is simply good at picking out correlations, so sometimes it may pick out features of the original data set that work well within that set, and perhaps even work well on quite a lot of new real world data, but don’t really capture what’s important. The program is meant to check whether a station platform is dangerously full of people, for example; in the set of pictures provided it finds that all it needs to do is examine the white platform area and check how dark it is. The more people there are, the darker it looks. This turns out to work quite well in real life, too. Then summer comes and people start wearing light coloured clothes…

There are ways to cope with this. We could build in various safeguards. We could make sure we use big and realistic datasets for training or perhaps allow learning to continue in real world contexts. Or we could just decide never to use a system that doesn’t have an algorithm we can examine; but there would be a price to pay in terms of efficiency for that; it might even be that we would have to give up on certain things that can only be effectively automated with relatively sophisticated deep learning methods. We’re told that the EU contemplates a law embodying a right to explanations of how software works. To philosophers I think this must sound like a marvellous new gravy train, as there will obviously be a need to adjudicate what counts as an adequate explanation, a notoriously problematic issue. (I am available as a witness in any litigation for reasonable hourly fees.)

The article points out that the incomprehensibility of neural network-based systems is in some ways really quite like the incomprehensibility of the good old human brain. Why wouldn’t it be? After all, neural nets were based on the brain. Now it’s true that even in the beginning they were very rough approximations of real neurology and in practical modern systems the neural qualities of neural nets are little more than a polite fiction. Still, perhaps there are properties shared by all learning systems?

One reason deep learning may run into problems is the difficulty AI always has in dealing with relevance.  The ability to spot relevance no doubt helps the human brain check whether it is learning about the right kind of thing, but it has always been difficult to work out quite how our brains do it, and this might mean an essential element is missing from AI approaches.

It is tempting, though, to think that this is in part another manifestation of the fact that AI systems get trained on limited data sets. Maybe the radical answer is to stop feeding them tailored data sets and let  our robots live in the real world; in other words, if we want reliable deep learning perhaps our robots have to roam free and replicate the wider human experience of the world at large? To date the project of creating human-style cognition has been in some sense motivated by mere curiosity (and yes, by the feeling that it would be pretty cool to have a robot pal) ; are we seeing here the outline of an argument that human-style AGI might actually be the answer to genuine engineering problems?

What about those explanations? Instead of retaining philosophers and lawyers to argue the case, could we think about building in a new module to our systems, one that keeps overall track of the AI and can report the broad currents of activity within it? It wouldn’t be perfect but it might give us broad clues as to why the system was making the decisions it was, and even allow us to delicately feed in some guidance. Doesn’t such a module start to sound like, well, consciousness? Could it be that we are beginning to see the outline of the rationales behind some of God’s design choices?