researcher


Google's AI chief says forget Elon Musk's killer robots, and worry about bias in AI systems instead

#artificialintelligence

Google's AI chief isn't fretting about super-intelligent killer robots. Instead, John Giannandrea is concerned about the danger that may be lurking inside the machine-learning algorithms used to make millions of decisions every minute. "The real safety question, if you want to call it that, is that if we give these systems biased data, they will be biased," Giannandrea said before a recent Google conference on the relationship between humans and AI systems. The problem of bias in machine learning is likely to become more significant as the technology spreads to critical areas like medicine and law, and as more people without a deep technical understanding are tasked with deploying it. Some experts warn that algorithmic bias is already pervasive in many industries, and that almost no one is making an effort to identify or correct it (see "Biased Algorithms Are Everywhere, and No One Seems to Care").


How an A.I. 'Cat-and-Mouse Game' Generates Believable Fake Photos

#artificialintelligence

The woman in the photo seems familiar. She looks like Jennifer Aniston, the "Friends" actress, or Selena Gomez, the child star turned pop singer. She appears to be a celebrity, one of the beautiful people photographed outside a movie premiere or an awards show. That's because she's not real. She was created by a machine.


AI and Deep Learning in 2017 – A Year in Review

#artificialintelligence

The year is coming to an end. I did not write nearly as much as I had planned to. But I'm hoping to change that next year, with more tutorials around Reinforcement Learning, Evolution, and Bayesian Methods coming to WildML! And what better way to start than with a summary of all the amazing things that happened in 2017? Looking back through my Twitter history and the WildML newsletter, the following topics repeatedly came up.


Building AI systems that work is still hard

#artificialintelligence

Martin Welker is the chief executive of Axonic. Even with the support of AI frameworks like TensorFlow or OpenAI, artificial intelligence still requires deep knowledge and understanding compared to a mainstream web developer. If you have built a working prototype, you are probably the smartest guy in the room. Congratulations, you are a member of a very exclusive club. With Kaggle, you can even earn decent money by solving real-world projects.


Report on the First International Conference on Knowledge Capture (K-CAP)

AI Magazine

This new conference series promotes multidisciplinary research on tools and methodologies for efficiently capturing knowledge from a variety of sources and creating representations that can be (or eventually can be) useful for reasoning. The conference attracted researchers from diverse areas of AI, including knowledge representation, knowledge acquisition, intelligent user interfaces, problem solving and reasoning, planning, agents, text extraction, and machine learning. Knowledge acquisition has been a challenging area of research in AI, with its roots in early work to develop expert systems. Driven by the modern internet culture and knowledge-based industries, the study of knowledge capture has a renewed importance. Although there has been considerable work over the years in the area, activities have been distributed across several distinct research communities.


The AI Program at the National Aeronautics & Space Administration

AI Magazine

Thsi article is a slightly modified version of an invited address that was given at the Eighth IEEE Conference on Artificial Intelligence for Applications in Monterey, California, on 2 March 1992. It describes the lessons learned in developing and implementing the Artificial Intelligence Research and Development Program at the National Aeronautics and Space Administration (NASA). In so doing, the article provides a historical perspective of the program in terms of the stages it went through as it matured. These stages are similar to the "ages of artificial intelligence" that Pat Winston described a year before the NASA program was initiated. The final section of the article attempts to generalize some of the lessons learned during the first seven years of the NASA AI program into AI program management heuristics.


Column

AI Magazine

The Jobs of the Future Are a Thing of the Past. "You may have read about the outsourcing issue, the great X-factor in American politics today, in cover articles in Time, Wired, Business Week.... In New Hampshire, John Kerry was asked about the problem. His answer: 'We have to create the next wave of those kinds of jobs that come from the fact that we're highly educated and deeply committed to science and technology education.' He mentioned artificial intelligence--and drew a laugh from a computer science professor who noted that artificial intelligence, the gleaming dream of the 1990s, has hardly created a single job in the world."


The Yale Artificial Intelligence Project: A Brief Historv

AI Magazine

This overview of the Yale Artificial Intelligence Project serves as an introduction to Scientific Datalink's microfiche publication of Yale AI Technical Reports Researchers develop new ideas and plant them in programs. The programs are cultivated, hybridized, nurtured. The weaker ideas die out. The stronger ideas are grafted onto new stock and serve as the basis of hearty new strains. At Yale, there has been a traditional summer seminar series at which graduate students present their unprepossessing theories to the vocal and critical review of their colleagues.


The Workshop on Logic-Based Artificial Intelligence

AI Magazine

The workshop was organized by Jack Minker and John McCarthy. The Program Committee members were Krzysztof Apt, John Horty, Sarit Kraus, Vladimir Lifschitz, John McCarthy, Jack Minker, Don Perlis, and Ray Reiter. The purpose of the workshop was to bring together researchers who use logic as a fundamental tool in AI to permit them to review accomplishments, assess future directions, and share their research in LBAI. This article is a summary of the workshop. The areas selected for discussion at the workshop were abductive and inductive reasoning, applications of theorem proving, commonsense reasoning, computational logic, constraints, logic and high-level robotics, logic and language, logic and planning, logic for agents and actions, logic of causation and action, logic, probability and decision theory, nonmonotonic reasoning, theories of belief, and knowledge representation.


The Seventh International Workshop on Qualitative Reasoning about Physical Systems

AI Magazine

The Seventh International Workshop on Qualitative Reasoning about Physical Systems was held on 16-19 May 1993. The bulk of the 50 attendees work in the AI area, but several engineers and cognitive psychologists also attended. The two topics attracting special attention were automated modeling and the design task. This article briefly describes some of the presentations and discussions held during the workshop. To promote deep and focused discussion, participation was limited to 50 researchers; the bulk of attendees work in the area of AI, but several engineers and cognitive psychologists enriched the atmosphere.