Posts tagged ‘neurology’

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!

waveAn article in the Chronicle of Higher Education (via the always-excellent Mind Hacks) argues cogently that as a new torrent of data about the brain looms, we need to ensure that it is balanced by a corresponding development in theory. That must surely be right: but I wonder whether the torrent of new information is going to bring about another change in paradigm, as the advent of computers in the twentieth century surely did?

We have mentioned before the two giant projects which aim to map and even simulate the neural structure of the brain, one in America, one in Europe. Other projects elsewhere and steady advances in technology seem to indicate that the progress of empirical neuroscience, already impressive, is likely to accelerate massively in coming years.

The paper points out that at present, in spite of enormous advances, we still know relatively little about the varied types of neurons and what they do; and much of what we think we do know is vague, tentative, and possibly misleading. Soon, however, ‘there will be exabytes (billions of gigabytes) of data, detailing what vast numbers of neurons do, in real time’.

The authors rightly suggest that data alone is no good without theoretical insights: they fear that at present there may be structural issues which lead to pure experimental work being funded while theory, in spite of being cheaper, is neglected or has to tag along as best it can. The study of the mind is an exceptionally interdisciplinary business, and they justifiably say research needs to welcome ‘mathematicians, engineers, computer scientists, cognitive psychologists, and anthropologists into the fold’. No philosophers in the list, I notice, although the authors quote Ned Block approvingly. (Certainly no novelists, although if we’re studying consciousness the greatest corpus of exploratory material is arguably in literature rather than science. Perhaps that’s asking a bit too much at this stage: grants are not going to be given to allow neurologists to read Henry as well as William James, amusing though that might be.)

I wonder if we’re about to see a big sea change; a Third Wave? There’s no doubt in my mind that the arrival of practical computers in the twentieth century had a vast intellectual impact. Until then philosophy of mind had not paid all that much attention to consciousness. Free Will, of course, had been debated for centuries, and personal identity was also a regular topic; but consciousness per se and qualia in particular did not seem to be that important until – I think – the seventies or eighties when a wide range of people began to have actual experience of computers. Locke was perhaps the first person to set out a version of the inverted spectrum argument, in which the blue in your mind is the same as the yellow in mine, and vice versa; but far from its being a key issue he mentions it only to dismiss it: we all call the same real world colours by the same names, so it’s a matter of no importance. Qualia? Of no philosophical interest.

I think the thing is that until computers actually appeared it was easy to assume, like Leibniz, that they could only be like mills: turning wheels, moving parts, nothing there that resembles a mind. When people could actually see a computer producing its results, they realised that there was actually the same kind of incomprehensible spookiness about it as there was in the case of human thought; maybe not exactly the same mystery, but a pseudo-magic quality far above the readily-comprehensible functioning of a mill. As a result, human thought no longer looked so unique and we needed something to stand in as the criterion which separated machines from people. Our concept of consciousness got reshaped and promoted to play that role, and a Second Wave of thought about the mind rolled in, making qualia and anything else that seemed uniquely human of special concern.

That wave included another change, though, more subtle but very important. In the past, the answer to questions about the mind had clearly been a matter of philosophy, or psychology; at any rate an academic issue. We were looking for a heavy tome containing a theory. Once computers came along, it turned out that we might be looking for a robot instead. The issues became a matter of technology, not pure theory. The unexpected result was that new issues revealed themselves and came to the fore. The descriptive theories of the past were all very well, but now we realised that if we wanted to make a conscious machine, they didn’t offer much help. A good example appears in Dan Dennett’s paper on cognitive wheels, which sets out a version of the Frame Problem. Dennett describes the problem, and then points out that although it is a problem for robots, it’s just as mysterious for human cognition; actually a deep problem about the human mind which had never been discussed; it’s just that until we tried to build robots we never noticed it. Most philosophical theories still have this quality, I’m afraid, even Dennett’s: OK, so I’m here with my soldering iron or my keyboard: how do I make a machine that adopts the intentional stance? No clue.

For the last sixty years or so I should say that the project of artificial intelligence has set the agenda and provided new illumination in this kind of way. Now it may be that neurology is at last about to inherit the throne.  If so, what new transformations can we expect? First I would think that the old-fashioned computational robots are likely to fall back further and that simulations, probably using neural network approaches, are likely to come to the fore. Grand Union theories, which provide coherent accounts from genetics through neurology to behaviour, are going to become more common, and build a bridgehead for evolutionary theories to make more of an impact on ideas about consciousness.  However, a lot of things we thought we knew about neurons are going to turn out to be wrong, and there will be new things we never spotted that will change the way we think about the brain. I would place a small bet that the idea of the connectome will look dusty and irrelevant within a few years, and that it will turn out that neurons don’t work quite the way we thought.

Above all though, the tide will surely turn for consciousness. Since about 1950 the game has been about showing what, if anything, was different about human beings; why they were not just machines (or why they were), and what was unique about human consciousness. In the coming decade I think it will all be about how consciousness is really the same as many other mental processes. Consciousness may begin to seem less important, or at any rate it may increasingly be seen as on a continuuum with the brain activity of other animals; really just a special case of the perfectly normal faculty of…  Well, I don’t actually know what, but I look forward to finding out.