Machine 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!