  The history of AI has included several "waves" of ideas. The first wave, from
the mid-1950s to the 1980s, focused on logic and symbolic hand-encoded
representations of knowledge, the foundations of so-called "expert systems".
The second wave, starting in the 1990s, focused on statistics and machine
learning, in which, instead of hand-programming rules for behavior, programmers
constructed "statistical learning algorithms" that could be trained on large
datasets. 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 brain
and trained by "deep learning" methods. However, while deep neural networks
have led to many successes and new capabilities in computer vision, speech
recognition, language processing, game-playing, and robotics, their potential
for broad application remains limited by several factors.
  A concerning limitation is that even the most successful of today's AI
systems suffer from brittleness-they can fail in unexpected ways when faced
with situations that differ sufficiently from ones they have been trained on.
This lack of robustness also appears in the vulnerability of AI systems to
adversarial attacks, in which an adversary can subtly manipulate data in a way
to guarantee a specific wrong answer or action from an AI system. AI systems
also can absorb biases-based on gender, race, or other factors-from their
training data and further magnify these biases in their subsequent
decision-making. Taken together, these various limitations have prevented AI
systems such as automatic medical diagnosis or autonomous vehicles from being
sufficiently trustworthy for wide deployment. The massive proliferation of AI
across society will require radically new ideas to yield technology that will
not sacrifice our productivity, our quality of life, or our values.
