CS Department Colloquium Series

Algorithmic and artificial intelligence techniques show immense potential to deepen our understanding of socioeconomic inequality and inform interventions designed to improve access to opportunity.

Recently, there has been a huge interest in Internet of Things (IoT) systems, which bring the digital world into the physical world around us. However, barriers still remain to realizing the dream applications of IoT.

Much of our success in artificial intelligence stems from the adoption of a simple paradigm: specify an objective or goal, and then use optimization algorithms to identify a behavior (or predictor) that optimally achieves this goal.

How can we build autonomous robots that operate in unstructured and dynamic environments such as homes or hospitals? This problem has been investigated under several disciplines, including planning (motion planning, task planning, etc.), and reinforcement learning.

Humans can learn to solve an endless range of problems: building, drawing, designing, coding, and cooking, to name a few, and need relatively modest amounts of experience to acquire any one new individual skill.

Reconfigurable analog devices are a powerful new computing substrate especially appropriate for executing dynamical systems in an energy efficient manner. These devices leverage the physical behavior of transistors to directly implement computation.

As an increasingly important workload, machine learning (ML) applications require different performance optimization techniques from traditional runtimes and compilers.

Project Debater is the first AI system that can meaningfully debate a human opponent. The system, an IBM Grand Challenge, is designed to build coherent, convincing speeches on its own, as well as provide rebuttals to the opponent's main arguments.

Our world faces increasingly complex challenges: we destabilized the climate, haven’t beaten all diseases, and haven’t spread the values of democracy and freedom to large parts of the globe, where violence and riots reign supreme. The world must be fixed in our generation - everyone would agree.

Recently, learned deep models have surpassed human performance on a number of question answering benchmarks such as SQuAD. However, these models resort to simple word matching and answer typing heuristics, and they are easily fooled.