I thought it would be fun to quickly demonstrate how to write a stateless stochastic memoizer in Haskell. The idea behind stochastic memoization is to take some base generative process and transform it into another process which, on each sample, either draws from the original process or draws from the distribution of previously drawn samples. This allows you, for example, to introduce memory or smoothness into your sampling distribution [1, 2]. Continue reading “Stochastic memoization in Haskell”
In today’s New York Times, Huw Price, professor of philosophy at Cambridge, writes about the need for considering the potential dangers associated with a possible “singularity.” The singularity is the idea, I guess, that if people create machines that are smarter than people then those machines would be smart enough to create machines smarter than themselves, etc., and that there would be an exponential explosion in artificial intelligence. Price suggests that whether or not the singularity is likely enough to warrant study in its own right, it is the possible danger associated with it that makes it important.
I’m not remotely worried about this. As someone who has been toiling away for many months at creating an artificial intelligence algorithm that has something evolutionary about it, I feel that my pessimism (or, optimism, as Price calls it) is informed. But rather than try to explain why I’m pessimistic, I thought I would present and react to only one point that Price makes. He writes:
biology got us onto this exalted peak in the landscape, the tricks are all there for our inspection: most of it is done with the glop inside our skulls. Understand that, and you understand how to do it artificially, at least in principle. Sure, it could turn out that there’s then no way to improve things – that biology, despite all the constraints, really has hit some sort of fundamental maximum. Or it could turn out that the task of figuring out how biology did it is just beyond us, at least for the foreseeable future (even the remotely foreseeable future). But again, are you going to bet your grandchildren on that possibility?
One basic aim of cognitive neuroscience is to answer questions like 1) what does a neuron or a group of neurons represent, and 2) how is cognitive computation implemented in neuronal hardware? A common critique is that the field has simply failed to shed light on either of these questions. Our experimental techniques are perhaps too crude: fMRI’s temporal resolution is way too slow, EEG and MEG’s spatial resolution is far too coarse, electrode recordings miss the forest for the trees. But underlying these criticisms is the assumption that there is some implementation-level description of neural activity that is interpretable at the level of cognition: if only we recorded from a sufficient number of neurons and actually knew what the underlying connectivity looked like, then we could finally figure out what neurons are doing, and what they represent — whether it’s features, or population codes, or prediction error, or whatever.
Is this a reasonable thing to hope for? Should neurons be interpretable at all? Clearly, no, Marr Level-1-ophiles will argue. After all, you wouldn’t hope to learn how a computer works by watching its bits flip, right?