Max Little is an applied mathematician and statistician based at Aston University. He is best known for his multi-disciplinary research, such as the Parkinson’s Voice Initiative, which can detect Parkinson’s disease with 99 percent accuracy on a 30-second voice recording. Max vocally encourages collaboration between scientific disciplines and will be speaking atWired Health on 29 April.
Wired.co.uk: Regarding your work studying Parkinson’s disease, is this kind of voice recognition applicable to other neurological illnesses, or is it a unique application?
Max Little: We haven’t yet studied that. But if you look at the literature, you’ll find that a very large number of people have. They’re certainly looking at the voice disorders of Parkinsonism and, say, multiple sclerosis or Huntington’s disease. Even wider, there are people looking at how the voice changes in depression and other mental health issues. And also, this is related to things like biometrics — using the voice as a tool for identifying individuals, or voice as a tool for understanding, in linguistics, when languages have different kinds of inflection and how that is attributed.
There’s a huge number of specialists who are all studying very very similar things. And I suppose the non-expert lay point of view is, “aren’t you all studying the same thing?” And the specialists’ response is usually, “of course not.” But that is a professional habit, not a point of fact. It isn’t helpful to the progress of science or the lay understanding of it. We exist in a world where we have to communicate our science and it’s unfortunate that professional terminological thickets are set up in order to make to progress in [a specific discipline] happen faster and be more convenient. But the net effect of is that it becomes a terminological thicket that people can’t really understand unless you’re steeped in it. Which is very frustrating.
It must mean that for someone such as you, who wants to try and solve problems as an outsider, even approaching the literature can be difficult.
It’s very daunting. We’ve made a habit of trying to make sure we don’t exclude literature on the basis that the terminology doesn’t match. Because that is a vacuous reason, I think, to exclude science, just because it is not explained well. But we see ourselves as having the burden of explaining it properly. The problem is that the current way that science is set up doesn’t really reward that particular way of doing things.
You’ve previously been critical of scientists working in isolation from one another. Can you summarise that position for us?
It is a problem, yes. If you’re a scientist, you’re often trained to be hyper-specialised. And what that means is that you won’t be studying, for example, the processes of life in general, if you’re a biologist. Instead you might be a molecular biologist who studies a particular area, a particular reaction, or a particular molecule of some kind. And if there are lots of other people who study that molecule, you might end up being a specialist on, say, a specific aspect of the reactions of that molecule. You might be a specialist in motor rotation in E. Coli or something like that, but you don’t do other kinds of bacteria.
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