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9 Open Problems and Directions about Intelligence

ma-lab-berkeley.github.io · 3,805 words · saved by 1 readers

“The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problem now reserved for humans, and improve themselves.”   – Proposal for the Dartmouth AI program, 1956 This manuscript systematically introduces mathematical principles and computational mechanisms for how memory or empirical knowledge can be developed from observed high-dimensional data. The ability to seek parsimony in a seemingly random world is a fundamental characteristic of any intelligence, natural or artificial. We believe the principles and mechanisms presented in this book are unifying and universal, applicable to both animals and machines. We hope this book helps readers fully clarify the mystery surrounding modern practices of artificial deep neural net

9 Open Problems and Directions about Intelligence In this chapter Chapter 9: Open Problems and Directions about Intelligence 9.1: Towards Autonomous Intelligence: Close the Loop? 9.2: Towards Natural Intelligence: Beyond Back Propagation? 9.3: Towards Scientific Intelligence: Beyond the Turing Test? Chapter 9 Open Problems and Directions about Intelligence “ The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find

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