Science & Artificial Intelligence Laboratory, MIT

Department of Brain & Cognitive Sciences, MIT

Our ability to see, think and act all depend on our mind’s ability to process uncertain information and identify probable explanations for inherently ambiguous data. Many computational models of the perception of motion[@weissmotion], motor learning[@kordinbayesian], higher-level cognition[@griffithsoptimal; @blaisdellcausal] and cognitive development[@tenenbaumgrow] are based on Bayesian inference in rich, flexible probabilistic models of the world. Machine intelligence systems, including Watson[@ferruccibuilding], autonomous vehicles[@thrunprobabilisticarticle] and other robots[@thrunprobabilisticbook] and the Kinect[@shottonreal] system for gestural control of video games, also all depend on probabilistic inference to resolve ambiguities in their sensory input. But brains solve these problems with greater speed than modern computers, using information processing units that are orders of magnitude slower and less reliable than the switching elements in the earliest electronic computers. The original UNIVAC I ran at 2.25 MHz[@univac], and RAM from twenty years ago had one bit error per 256 MB per month[@shivakumar2002modeling]. In contrast, the fastest neurons in human brains operate at less than 1 kHz, and synaptic transmission can completely fail up to 50% of the time[@synapticfailure].

This efficiency gap presents a fundamental challenge for computer science. How is it possible to solve problems of 