Numenta - Path to Machine Intelligence White Paper.pdf

Numenta - Path to Machine Intelligence White Paper.pdf

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Numenta - Path to Machine Intelligence White Paper

White Paper The Path to Machine Intelligence Path to Machine Intelligence Executive Summary The idea of machines that operate on the principles of the human brain has been around for more than fifty years. However, for most of the history of artificial intelligence, progress has been measured by how well machines solve particular problems, such as playing chess, driving cars, or passing the Turing Test. Relatively few artificial intelligence and machine learning techniques are based on an understanding of how the brain works and how it solves problems. At Numenta we believe that the hallmark of intelligence is extreme flexibility, not the ability to solve any particular problem. The number of things humans can learn to do and the types of problems we can solve is vast. This versatility is a result of how our brains work. To build intelligent machines with equal flexibility, we need to understand the principles brains use to achieve this remarkable ability. Numenta’s approach to machine intelligence is unique; we start with a deep understanding of how the neocortex learns and what makes it so flexible, and then we replicate those principles in software. Intelligent machines designed this way can be applied to a great variety of problems, large and small, problems both that humans can solve as well as those that are beyond human capabilities. The impact of intelligent machines will rival and likely surpass the impact of computers operating under traditional principles, i.e. computers with pre-programmed rules, rather than learning systems. This endeavor will involve many people and many companies around the world. At Numenta, we are completely transparent about how our learning algorithms work, even placing our software in an open source project. There is a growing community around the world that contributes to this open source initiative and is committed to our approach to machine intelligence. A f

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