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].
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 … Read More
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 … Read More