Wednesday, April 17, 2013


I got this following DARPA call from Mike Draugelis, our man in Lockheed Martin:

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. Email spam filters, smartphone personal assistants and self-driving vehicles are all based on research advances in machine learning. Unfortunately, even as the demand for these capabilities is accelerating, every new 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.
The Probabilistic Programming for Advanced Machine Learning (PPAML) program was launched to address this challenge. Probabilistic programming is a new programming paradigm for managing uncertain information. By incorporating it into machine learning, PPAML seeks to greatly increase the number of people who can successfully build machine learning applications and make machine learning experts radically more effective. Moreover, the program seeks to create more economical, robust and powerful applications that need less data to produce more accurate results – features inconceivable with today’s technology.

And here is the call abstract:
The goal of the PPAML program is to advance machine learning by using probabilistic programming to 1) dramatically increase the number of people who can successfully build machine learning applications, 2) make machine learning experts radically more effective, and 3) enable new applications that are impossible to conceive of using today’s technology. In support of this overarching goal, PPAML has a number of sub-goals. Specifically, the sub-goals are 1) to make machine learning model code shorter, 2) to reduce development time, 3) to facilitate the construction of richer models, 4) to require lower levels of expertise in building machine learning applications, and 5) to support the construction of integrated models.

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