DARPA envisions the future of machine learning

Mar 20, 2013

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.

"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.

"Our goal is that future machine learning projects won't require people to know everything about both the domain of interest and machine learning to build useful machine learning applications. Through new probabilistic specifically tailored to probabilistic inference, we hope to decisively reduce the current barriers to machine learning and foster a boom in innovation, productivity and effectiveness."

To familiarize potential participants with the technical objectives of PPAML, DARPA will host a Proposers' Day on Wednesday, April 10, 2013. For details, visit: www.solers.com/BAAinfo-reg/ppaml. Registration closes on Friday, April 5, 2013 at 5 p.m. ET.

The PPAML program is scheduled to run 46 months, with three phases of activity from 2013 to 2017. Fisher believes a successful solution will involve contributions from many areas, including statistics and probabilistic modeling, approximation algorithms, , programming languages, program analysis, compilers, high-performance software, and parallel and distributed computing.

The DARPA Special Notice document describing the specific capabilities sought is available at go.usa.gov/2PhW.

Explore further: A new kind of data-driven predictive methodology

add to favorites email to friend print save as pdf

Related Stories

Machines that learn better

May 18, 2010

(PhysOrg.com) -- In the last 20 years or so, many of the key advances in artificial-intelligence research have come courtesy of machine learning, in which computers learn how to make predictions by looking ...

Turing award goes to 'machine learning' expert

Mar 09, 2011

A Harvard University professor has been awarded a top technology prize for research that has paved the way for computers that more closely mimic how humans think, including the one that won a "Jeopardy!" tournament.

How to engineer intelligence

Mar 20, 2012

"Do we actually want machines to interact with humans in an emotional way? Will it be possible for them to interact with us?"

Recommended for you

New frontier in error-correcting codes

14 hours ago

Error-correcting codes are one of the glories of the information age: They're what guarantee the flawless transmission of digital information over the airwaves or through copper wire, even in the presence of the corrupting ...

Five ways the superintelligence revolution might happen

Sep 26, 2014

Biological brains are unlikely to be the final stage of intelligence. Machines already have superhuman strength, speed and stamina – and one day they will have superhuman intelligence. This is of course ...

User comments : 1

Adjust slider to filter visible comments by rank

Display comments: newest first

antialias_physorg
1 / 5 (1) Mar 20, 2013
Email spam filters...

An intelligent algorithm to act as spam filter. I wonder how long it will take for someone to us the same algorithm to circumvent the spam filter.

May the best (anti-)spambot win.