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The history of AI has included several "waves" of ideas. The first wave, fromthe mid-1950s to the 1980s, focused on logic and symbolic hand-encodedrepresentations of knowledge, the foundations of so-called "expert systems".The second wave, starting in the 1990s, focused on statistics and machinelearning, in which, instead of hand-programming rules for behavior, programmersconstructed "statistical learning algorithms" that could be trained on largedatasets. In the most recent wave research in AI has largely focused on deep(i.e., many-layered) neural networks, which are loosely inspired by the brainand trained by "deep learning" methods. However, while deep neural networkshave led to many successes and new capabilities in computer vision, speechrecognition, language processing, game-playing, and robotics, their potentialfor broad application remains limited by several factors.  A concerning limitation is that even the most successful of today's AIsystems suffer from brittleness-they can fail in unexpected ways when facedwith situations that differ sufficiently from ones they have been trained on.This lack of robustness also appears in the vulnerability of AI systems toadversarial attacks, in which an adversary can subtly manipulate data in a wayto guarantee a specific wrong answer or action from an AI system. AI systemsalso can absorb biases-based on gender, race, or other factors-from theirtraining data and further magnify these biases in their subsequentdecision-making. Taken together, these various limitations have prevented AIsystems such as automatic medical diagnosis or autonomous vehicles from beingsufficiently trustworthy for wide deployment. The massive proliferation of AIacross society will require radically new ideas to yield technology that willnot sacrifice our productivity, our quality of life, or our values.

Next Wave Artificial Intelligence: Robust, Explainable, Adaptable, Ethical, and Accountable