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Monday, July 23, 2018

What Machine Learning Can and Cannot Do

Very thoughtful piece and blog by a former correspondent. Not very technical.

A blog by Irving Wladawsky-Berger
A collection of observations, news and resources on the changing nature of innovation, technology, leadership, and other subjects.

What Machine Learning Can and Cannot Do
Artificial intelligence is rapidly becoming one of the most important technologies of our era.  Every day we can read about the latest AI advances from startups and large companies.  Over the past few years, the necessary ingredients have come together to take AI across the threshold: powerful, inexpensive computer technologies; huge amounts of data; and advanced algorithms, especially machine learning.  Machine learning has enabled AI to get around one of its biggest obstacles, - the so-called Polanyi’s paradox.

Explicit knowledge is formal, codified, and can be readily explained to people and captured in a computer program.  But, tacit knowledge, a concept first introduced in the 1950s by scientist and philosopher Michael Polanyi, is the kind of knowledge we’re often not aware we have, and is therefore difficult to transfer to another person, let alone capture in a computer program.

“We can know more than we can tell,” said Polanyi in what’s become known as Polanyi’s paradox.  This common sense phrase succinctly captures the fact that we tacitly know a lot about the way the world works, yet aren’t able to explicitly describe this knowledge.  Tacit knowledge is best transmitted through personal interactions and practical experiences.  Everyday examples include speaking a language, riding a bike, and easily recognizing many different people, animals and objects.

Machine learning, and related advances like deep learning, have enabled computers to acquire tacit knowledge by being trained with lots and lots of sample inputs, thus learning by analyzing large amounts of data instead of being explicitly programmed.  Machine learning methods are now being applied to vision, speech recognition, language translation, and other capabilities that not long ago seemed impossible but are now approaching or surpassing human levels of performance in a number of domains. .... " 

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