Machine Learning v. Machine Intelligence v. AI

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techby Angela Guess

Jeff Hawkins and Donna Dubinsky of Numenta recently wrote, “We are frequently asked how we distinguish our technology from others. This task is made difficult by the fact that there is not an agreed vocabulary; everybody uses the above terms (and other associated terms) differently. In addition, the commonly understood meaning of some of these terms has evolved over time. What was meant by AI in 1960 is very different than what is meant today. In our view, there are three major approaches to building smart machines.”

They continue, “Let’s call these approaches Classic AI, Simple Neural Networks, and Biological Neural Networks. The rest of this blog post will describe and differentiate these approaches. At the end, we’ll include an example as to how each approach might address the same problem. This analysis is intended for a business rather than technical audience, so we simplify somewhat and thus beg the indulgence of technical experts who might quibble with the details.”

Hawkins and Dubinsky go on, “The earliest approaches to AI were computer programs designed to solve problems that human brains performed easily, such as understanding text or recognizing objects in an image. Results of this work were disappointing and progress was slow. For many problems, researchers concluded that a computer had to have access to large amounts of knowledge in order to be ‘smart’. Thus they introduced ‘expert systems’, computer programs combined with rules provided by domain experts to solve problems, such as medical diagnoses, by asking a series of questions. If the disease was not properly diagnosed, the expert adds additional questions/rules to narrow the diagnosis.”

Read more here.

photo credit: Flickr/ Keoni Cabral

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