Bart Selman is quoted in an article in Business Insider investigating whether mimicking human intelligence is the path to artificial intelligence. Selman uses speech recognition to say "no": "We don't quite know how the brain does it," said Selman, but it's "probably more complicated than the way [AI is] doing it right now": "The main progress right now and in the near future will be getting to a performance at a human-level without getting the details of the human brain all figured out."

Every two years, the current AAAI president gives the opening address at the AAAI Conference on Artificial Intelligence. This year it was the turn of Bart Selman. In his talk he reviewed the current state of AI and presented examples of three different applications of AI to aid scientific discovery. Bart began his talk by considering the deep-learning "revolution", highlighting some of the areas that it has transformed, namely computer vision, natural language processing, machine translation, game play, and reinforcement learning. He noted that the field is undergoing a rapid acceleration at the moment, with an incredible rate of progress.

From Uber's advanced computer vision system to Netflix's innovative recommendation algorithm, machine learning technologies are nearly omnipresent in our society. They filter our emails, personalize our newsfeeds, update our GPS systems, and drive our personal assistants. However, despite the fact that such technologies are leading a revolution in artificial intelligence, some would contend that these machine learning systems aren't truly intelligent. The argument, in its most basic sense, centers on the fact that machine learning evolved from theories of pattern recognition and, as such, the capabilities of such systems generally extend to just one task and are centered on making predictions from existing data sets. AI researchers like Rodney Brooks, a former professor of Robotics at MIT, argue that true reasoning, and true intelligence, is several steps beyond these kinds of learning systems.

Hoy traemos a este espacio este panel de Future of Life Institute sobre la Inteligencia artificial de 8 reconocidos pensadores gurús tecnológicos actuales: Elon Musk, Stuart Russell, Ray Kurzweil, Demis Hassabis, Sam Harris, Nick Bostrom, David Chalmers, Bart Selman, and Jaan Tallinn discuss with Max Tegmark (moderator) what likely outcomes might be if we succeed in building human-level AGI, and also what we would like to happen. The Beneficial AI 2017 Conference: In our sequel to the 2015 Puerto Rico AI conference, we brought together an amazing group of AI researchers from academia and industry, and thought leaders in economics, law, ethics, and philosophy for five days dedicated to beneficial AI. We hosted a two-day workshop for our grant recipients and followed that with a 2.5-day conference, in which people from various AI-related fields hashed out opportunities and challenges related to the future of AI and steps we can take to ensure that the technology is beneficial. You can find an audio balanced version of this panel here: https://www.youtube.com/watch?v OFBwz...

When scholars explore new ideas, it seems instinctive to share their thinking. Last year computer science professors Joseph Halpern and Bart Selman became co-principal investigators for a nationwide project to ensure that robots and artificial intelligences (AIs) will act in ways beneficial to humans. "We thought we should get undergrads involved in thinking about these ideas," Selman said. Thus was born a new course, CS 4732, Ethical and Social Issues in AI, about how robots and artificial intelligences may change our world, and what we ought to be doing about it. "These undergrads may be directly involved in developing the software behind these systems," Selman added, noting that many may go on to jobs with Google, Tesla and other major players.

We introduce a new local search algorithm for satisfiability problems. Usual approaches focus uniformly on unsatisfied clauses. The new method works by picking uniformly random variables in unsatisfied clauses. A Variable-based Focused Metropolis Search (V-FMS) is then applied to random 3-SAT. We show that it is quite comparable in performance to the clause-based FMS. Consequences for algorithmic design are discussed.