DARPA's latest idea could put today's Turing-era computers at risk

PCWorld

The U.S. Defense Advanced Research Projects Agency (DARPA) has come up with some crazy ideas in the past, and its latest idea is to create computers that are always learning and adapting, much like humans. Mobile devices, computers, and gadgets already have artificial intelligence features, with notable examples being Apple's Siri, Microsoft's Cortana, and Amazon's Alexa. But these devices can only learn and draw conclusions within the scope of information pre-programmed into systems. Existing machine-learning techniques don't allow computers to think outside the box, so to speak, or think dynamically based on the situations and circumstances. The goal of a new DARPA project is to create computers that think like biological entities and are continually learning.


A computing visionary looks beyond today's AI ZDNet

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For decades, Hava Siegelmann has explored the outer reaches of computing with great curiosity and great conviction. The conviction shows up in a belief that there are forms of computing that go beyond the one that has dominated for seventy years, the so-called von Neumann machine, based on the principles laid down by Alan Turing in the 1930s. She has long championed the notion of "Super-Turing" computers with novel capabilities. And curiosity shows up in various forms, including her most recent work, on "neuromorphic computing," a form of computing that may more closely approximate the way that the brain functions. Siegelmann, who holds two appointments, one with the University of Massachusetts at Amherst as professor of computer science, and one as a program manager at the Defense Advanced Research Projects Agency, DARPA, sat down with ZDNet to discuss where neuromorphic computing goes next, and the insights it can bring about artificial intelligence, especially why AI succeeds and fails.


DARPA Approaches Massive New AI, Machine Learning 'Breakthrough' - Warrior Maven

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The Defense Advanced Research Projects Agency is pursuing an unprecedented machine-learning "breakthrough" technology -- and pioneering a new cybersecurity method intended to thwart multiple attacks at one time and stop newer attacks less recognizeable to existing defenses. A DARPA-led "Lifelong Learning Machines" (L2M) program, intended to massively improve real-time AI and machine learning, rests upon the fundamental premise that certain machine-learning-capable systems might struggle to identify, integrate and organize some kinds of new or complicated yet-to-be-seen information. "If something new is different enough, the system may fail. This is why I wanted to have some kind of machine learning that learns during experiences. Systems do not know what to do in some situations," Hava Siegelmann, DARPA program manager at the Information Innovation Office and Professor of Computer Science at the University of Massachusetts.


DARPA Wants Artificial Intelligence That Doesn't Forget Everything It Knows

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Biological organisms are pretty good at navigating life's unpredictability, but computers are embarrassingly bad at it. That's the crux of a new military research program that aims to model artificially intelligent systems after the brains of living creatures. When an organism encounters a new environment or situation, it relies on past experience to help it make a decision. Current artificial intelligence technology, on the other hand, relies on extensive training on various data sets, and if it hasn't encountered a specific situation, it can't select a next step. The Defense Advanced Research Projects Activity is searching for technology that constantly updates its decision-making framework to incorporate past experience and new "lessons learned" to situations it encounters.


DARPA Wants Artificial Intelligence That Doesn't Forget Everything It Knows

#artificialintelligence

Biological organisms are pretty good at navigating life's unpredictability, but computers are embarrassingly bad at it. That's the crux of a new military research program that aims to model artificially intelligent systems after the brains of living creatures. When an organism encounters a new environment or situation, it relies on past experience to help it make a decision. Current artificial intelligence technology, on the other hand, relies on extensive training on various data sets, and if it hasn't encountered a specific situation, it can't select a next step. The Defense Advanced Research Projects Activity is searching for technology that constantly updates its decision-making framework to incorporate past experience and new "lessons learned" to situations it encounters.