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Nicholas Shea, professor of philosophy at the Institute of Philosophy, University of London, and an associate member of the Faculty of Philosophy, University of Oxford summarizes, You know the way Google search will sometimes finish your sentences for you? Or, when you’re typing an email, there’s some ghostly predictive text that floats just in front of your cursor?
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| Photo: issaronow |
Well, there’s a new kid on the block that makes these gadgets look like toy tricks out of a Christmas cracker. Give it a sentence of Jane Austen and it will finish the paragraph in the same style. Give it a philosophical conjecture and it will fill the page with near-coherent academic ruminations. GPT-3 is essentially just predicting what words should come next, following on from the prompt it’s been given...
Neuroscientists are recording these patterns with new techniques. But what do the patterns mean? How should they be understood? Neuroscience is increasingly tackling these questions by asking what the activation patterns represent. For example, “representational similarity analysis” (RSA) is used to ask whether the human brain processes images in the same way as the brain of the macaque monkey. Surprisingly, similar techniques can be used to compare the human brain to an AI computer system trained to perform the same task. These AIs are deep neural networks, cousins of the seemingly unfathomable GPT-3 and AlphaFold brains we met at the start. Astoundingly, it turns out that sometimes the deep neural network is processing images in roughly the same way as the human brain. In a general sense, both are performing the same computations en route to working out that they are looking at a picture of two cats on a sofa. In other cases, we see the brain using a hexagonal code to represent physical space—and more abstract conceptual spaces—and to reason about them.
Additional resources
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| Representation in Cognitive Science |
