Quanta MagazineAll the papers in this special issue sit at the intersection between work on predictive processing models in the neurosciences and embodied, enactive perspectives on mind. It is arguably one of the most cutting-edge and fast-moving intersections of research in the contemporary sciences of mind and brain. All contributions deal with questions of whether and how key assumptions of the predictive brain hypothesis can be reconciled with approaches to cognition that take embodiment and enaction as playing a central and constitutive role in our cognitive lives. While there is broad consensus that bodily and worldly aspects matter to cognition, predictive processing is often understood in epistemic, inferential and representational terms. Prima facie this makes is hard to see how it would be possible to square embodied and enactive views, many of which are in direct opposition to inferential and representational accounts of mind, with predictive processing models. The discussions were both lively and productive. Most of the papers presented at this workshop are part of this special issue, with other key contributions added at a later stage.
Predictive brains and embodied, enactive cognition: an introduction to the special issue
Last month, the artificial intelligence company DeepMind introduced new software that can take a single image of a few objects in a virtual room and, without human guidance, infer what the three-dimensional scene looks like from entirely new vantage points. Given just a handful of such pictures, the system, dubbed the Generative Query Network, or GQN, can successfully model the layout of a simple, video game-style maze. There are obvious technological applications for GQN, but it has also caught the eye of neuroscientists, who are particularly interested in the training algorithm it uses to learn how to perform its tasks. From the presented image, GQN generates predictions about what a scene should look like — where objects should be located, how shadows should fall against surfaces, which areas should be visible or hidden based on certain perspectives — and uses the differences between those predictions and its actual observations to improve the accuracy of the predictions it will make in the future. Neuroscientists have long suspected that a similar mechanism drives how the brain works.
Learn more about our integration with Box's information management system and how it can benefit your e-discovery process. Learn more. The sections of our Basics of E-Discovery guide largely map to the phases of the e-discovery process. For the past several years, e-discovery AI has largely been all about predictive coding. Predictive coding software is a form of machine learning that takes data input by people about document relevance and then applies it to much larger document sets. While predictive coding has been the dominant AI technology in e-discovery since , new AI technologies are emerging, and e-discovery could be on the cusp of another technological leap forward. Waze helps us find the quickest route home.