DARPA envisions the future of machine learning | KurzweilAI

DARPA has launched a new programming paradigm for managing uncertain information called “Probabilistic Programming for Advanced Machine Learning”(PPAML).

Machine learning — the ability of computers to understand data, manage results, and infer insights from uncertain information — is the force behind many recent revolutions in computing.

Unfortunately, every new machine-learning application requires a Herculean effort. Even a team of specially trained machine learning experts makes only painfully slow progress, due to the lack of tools to build these systems.

PPAML seeks to greatly increase the number of people who can successfully build machine learning applications and make machine learning experts radically more effective. It also seeks to create more economical, robust and powerful applications that need less data to produce more accurate results — features inconceivable with today’s technology.

“We want to do for machine learning what the advent of high-level program languages 50 years ago did for the software development community as a whole,” said Kathleen Fisher, DARPA program manager.

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Preparing Watson for the Jeopardy! stage posed a unique challenge to the team: how to represent a system of 90 servers and hundreds of custom algorithms for the viewing public. IBM, in collaboration with a team of partners, created a representation of this computing system for the viewing audience — from its stage presence to its voice.

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Education is on the cusp of a transformation because of recent scientific findings in neuroscience, psychology, and machine learning that are converging to create foundations for a new science of learning.

Physorg.com