Like the Roman god Janus, cognitive computing projects can have two faces, their science face and their engineering face. The science face fleshes out the global workspace theory of consciousness into a full cognitive model of how minds work. The engineering face of cognitive computing explores architectural designs for software information agents and cognitive robots that promise more flexible, more human-like intelligence within their domains. This fleshed out global workspace theory is yielding hopefully testable hypotheses about human cognition. The architectures and mechanisms that underlie intelligence and consciousness in humans can be expected to yield information agents, and cognitive robots that learn continualy, adapt readily to dynamic environments, and behave flexibly and intelligently when faced with novel and unexpected situations.

In 10 years, we'll see big changes in how people live their lives and how companies operate, thanks to the innovation that's now being kindled around the world. So says Alec Ross, former innovation adviser to Hillary Clinton during her term as U.S. Secretary of State, who lays out the thesis in his new book, The Industries of the Future. In Part 1 of our interview, Ross explains how the technology underlying the cryptocurrency Bitcoin, blockchain technology, will greatly reduce the friction in financial and other transactions. Here he discusses the impact of cognitive robots and the outlook for U.S. companies trying to compete on a global scale. You talked about robotics a lot in your book and you focused for the most part on physical robots.

Under what conditions should a cognitive robot act? How do we define "opportunities" for robot action? How can we characterize their properties? In this po- sition paper, we offer an initial apparatus to formalize opportunities and to frame this discussion.

Robots may encounter undesirable outcomes due to failures during the execution of their plans in the physical world. Failures should be detected, and the underlying reasons should be found by the robot in order to handle these failure situations efficiently. Sometimes, there may be more than one cause of a failure, and they are not necessarily related to the action in execution. In this paper, we propose a temporal and Hierarchical Hidden Markov Model (HHMM) based failure isolation method. These HHMMs run in parallel to determine causes of unexpected deviations. Experiments on our Pioneer 3-AT robot show that our method successfully isolates failures suggesting possible causes.

A cognitive robot should construct a plan to attain its goals. While it executes the actions in its plan, it may face several failures due to both internal and external issues. We present a taxonomy to classify these failures that may be encountered during the execution of cognitive tasks. The taxonomy presents a wide range of failure types. To recover from most of these failures presented in this taxonomy, we propose a Robust Planning Framework for cognitive robots. Our framework combines planning, reasoning and learning procedures into each other for robust execution of cognitive tasks. Failures can be detected and handled by reasoning and replanning, respectively. The framework also facilitates learning new hypotheses incrementally based on experience. It can successfully detect and recover from temporary failures on a selected set of actions executed by a Pioneer3DX robot. It has been shown that our preliminary results for hypothesis learning in failure scenarios are promising.