Over its 60-year history, DARPA has played a leading role in the creation and advancement of artificial intelligence (AI) technologies that have produced game-changing capabilities for the Department of Defense. Starting in the 1960s, DARPA research shaped the first wave of AI technologies, which focused on handcrafted knowledge, or rule-based systems capable of narrowly defined tasks. While a critical step forward for the field, these systems were fragile and limited. Starting in the 1990s, DARPA helped usher in a second wave of AI machine learning technologies that created statistical pattern recognizers from large amounts of data. The agency’s funding of natural language understanding, problem solving, navigation and perception technologies has led to the creation of self-driving cars, personal assistants, and near-natural prosthetics, in addition to a myriad of critical and valuable military and commercial applications. However, these second wave AI technologies are dependent on large amounts of high quality training data, do not adapt to changing conditions, offer limited performance guarantees, and are unable to provide users with explanations of their results.
To address the limitations of these first and second wave AI technologies, DARPA seeks to explore new theories and applications that could make it possible for machines to adapt to changing situations. DARPA sees this next generation of AI as a third wave of technological advance, one of contextual adaptation. To better define a path forward, DARPA is announcing today a multi-year investment of more than $2 billion in new and existing programs called the “AI Next” campaign. Agency director, Dr. Steven Walker, officially unveiled the large-scale effort during closing remarks today at DARPA’s D60 Symposium taking place Wednesday through Friday at the Gaylord Resort and Convention Center in National Harbor, Maryland.
“With AI Next, we are making multiple research investments aimed at transforming computers from specialized tools to partners in problem-solving,” said Dr. Walker. “Today, machines lack contextual reasoning capabilities, and their training must cover every eventuality, which is not only costly, but ultimately impossible. We want to explore how machines can acquire human-like communication and reasoning capabilities, with the ability to recognize new situations and environments and adapt to them.”
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