ppaml

So It Begins: Darpa Sets Out to Make Computers That Can Teach Themselves

The Pentagon’s blue-sky research agency is readying a nearly four-year project to boost artificial intelligence systems by building machines that can teach themselves — while making it easier for ordinary schlubs like us to build them, too.

When Darpa talks about artificial intelligence, it’s not talking about modeling computers after the human brain. That path fell out of favor among computer scientists years ago as a means of creating artificial intelligence; we’d have to understand our own brains first before building a working artificial version of one. But the agency thinks we can build machines that learn and evolve, using algorithms — “probabilistic programming” — to parse through vast amounts of data and select the best of it. After that, the machine learns to repeat the process and do it better.

But building such machines remains really, really hard: The agency calls it “Herculean.” There are scarce development tools, which means “even a team of specially-trained machine learning experts makes only painfully slow progress.” So on April 10, Darpa is inviting scientists to a Virginia conference to brainstorm. What will follow are 46 months of development, along with annual “Summer Schools,” bringing in the scientists together with “potential customers” from the private sector and the government.

Called “Probabilistic Programming for Advanced Machine Learning,” or PPAML, scientists will be asked to figure out how to “enable new applications that are impossible to conceive of using today’s technology,” while making experts in the field “radically more effective,” according to a recent agency announcement. At the same time, Darpa wants to make the machines simpler and easier for non-experts to build machine-learning applications too.

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.