Embodied prediction

Versions of the “predictive brain” hypothesis rank among the most promising and the most conceptually challenging visions ever to emerge from computational and cognitive neuroscience. In this paper, I briefly introduce (section 1) the most radical and comprehensive of these visions —the account of “active inference”, or “action-oriented predictive processing” (Clark 2013a), developed by Karl Friston and colleagues. In section 2, I isolate and discuss four of the framework’s most provocative claims: (i) that the core flow of information is top-down, not bottom-up, with the forward flow of sensory information replaced by the forward flow of prediction error; (ii) that motor control is just more top-down sensory prediction; (iii) that efference copies, and distinct “controllers”, can be replaced by top-down predictions; and (iv) that cost functions can fruitfully be replaced by predictions. Working together, these four claims offer a tantalizing glimpse of a new, integrated framework for understanding perception, action, embodiment, and the nature of human experience. I end (section 3) by sketching what may be the most important aspect of the emerging view: its ability to embed the use of fast and frugal solutions (as highlighted by much work in robotics and embodied cognition) within an over-arching scheme that includes more structured, knowledge-intensive strategies, combining these fluently and continuously as task and context dictate

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Automatic text filtering using limited supervision learning for epidemic intelligence

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