Daniel Lopresti

Odest Chadwicke Jenkins, Daniel Lopresti, Melanie Mitchell

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.

Elisa Bertino, Finale Doshi-Velez, Maria Gini et al.

The rise of Artificial Intelligence (AI) will bring with it an ever-increasing willingness to cede decision-making to machines. But rather than just giving machines the power to make decisions that affect us, we need ways to work cooperatively with AI systems. There is a vital need for research in "AI and Cooperation" that seeks to understand the ways in which systems of AIs and systems of AIs with people can engender cooperative behavior. Trust in AI is also key: trust that is intrinsic and trust that can only be earned over time. Here we use the term "AI" in its broadest sense, as employed by the recent 20-Year Community Roadmap for AI Research (Gil and Selman, 2019), including but certainly not limited to, recent advances in deep learning. With success, cooperation between humans and AIs can build society just as human-human cooperation has. Whether coming from an intrinsic willingness to be helpful, or driven through self-interest, human societies have grown strong and the human species has found success through cooperation. We cooperate "in the small" -- as family units, with neighbors, with co-workers, with strangers -- and "in the large" as a global community that seeks cooperative outcomes around questions of commerce, climate change, and disarmament. Cooperation has evolved in nature also, in cells and among animals. While many cases involving cooperation between humans and AIs will be asymmetric, with the human ultimately in control, AI systems are growing so complex that, even today, it is impossible for the human to fully comprehend their reasoning, recommendations, and actions when functioning simply as passive observers.

Kevin Fu, Tadayoshi Kohno, Daniel Lopresti et al.

The Internet of Things (IoT) is already transforming industries, cities, and homes. The economic value of this transformation across all industries is estimated to be trillions of dollars and the societal impact on energy efficiency, health, and productivity are enormous. Alongside potential benefits of interconnected smart devices comes increased risk and potential for abuse when embedding sensing and intelligence into every device. One of the core problems with the increasing number of IoT devices is the increased complexity that is required to operate them safely and securely. This increased complexity creates new safety, security, privacy, and usability challenges far beyond the difficult challenges individuals face just securing a single device. We highlight some of the negative trends that smart devices and collections of devices cause and we argue that issues related to security, physical safety, privacy, and usability are tightly interconnected and solutions that address all four simultaneously are needed. Tight safety and security standards for individual devices based on existing technology are needed. Likewise research that determines the best way for individuals to confidently manage collections of devices must guide the future deployments of such systems.