Learning from Neuroscience: New Prospects for Building Intelligent Machines
Learning from Neuroscience: New Prospects for Building Intelligent Machines
This chapter discusses why further success in the building of intelligent machines will most likely be tied to progress in understanding how the human brain actually works. It argues that the bottom-up view of neuroscience and the top-down view of classic artificial intelligence (AI) do not meet in the middle, where all the interesting behavior—perception, complex movement, and a basic ability to cope with the environment—seems to lie. It describes three current research initiatives that operate in or are moving toward this middle realm. The first (Eric Baum) serves as a transition from classic AI and cognitive science. The second (Jeff Hawkins) plunges us into a theory of the brain as a memory prediction machine. The third (Steve Grand) involves the actual building of a working baby android. The chapter concludes by considering some recent research in self-modeling and communication.
Keywords: artificial intelligence, cognitive science, brain, memory prediction, baby android, self-modeling, communication
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