Inflexibility of these expert systems in applying rules, and the tunnel vision implied in their limited knowledge, that can result in poor conclusions. Expert systems couldn't reverse their logical conclusions if later given contradictory facts. For example, an expert system would conclude that Bill Smith has ten toes because Bill Smith is a person and all people have ten toes. However, it couldn't then deal with the fact that Bill Smith lost three toes in an industrial accident. A human, using "non-monotonic" reasoning, has no problem concluding Bill Smith has only seven toes.

Machine learning is, regrettably, not one of the day-to-day chores assigned to most programmers. However, with data volumes exploding, and high-profile successes such as IBM's Jeopardy-beating Watson and the recommendation engines of Amazon and Netflix, the odds are increasing that ML's opportunity might knock on your door one day. From the 1960s to the 1980s, the emphasis of artificial intelligence was in "top-down" approaches in which expertise from domain experts was somehow transcribed into a fixed set of rules and their relations. Often, these would be a series of small "if-then" rules, and the "magic sauce" of expert systems was that they could draw conclusions by automatically chaining together the execution of those rules whose "if" parameters were known. The technology for inferencing worked well enough, but it turned out that very large rulebases were hard to debug and maintain, while not very large rulebases didn't produce many compelling applications (for instance, my expert system for identifying seabirds failed to make me a billionaire).

Like a lot of information-age talk, the phrase is both schizophrenic and appropriate. For if this World Wide Web we grow to depend on is all-connected and all-connecting, an enfolding organism spinning out and out and out, it is also oceanic. And when it comes to using such a vast, dynamic resource, most of us are like the skinny kid in the half-zipped wetsuit, paddling just beyond the first break. On a good day, we can handle a three-foot curl. But we never stray too far from shore. And we can easily wind up a long way down the beach, with no idea how we got there. The images illustrating this article were drawn from a computer animation created by Steve Coast, a computer artist and student of physics at University College London.

There are several seemingly reasonable problems that are exciting and challenging, and yet are currently unsolvable. Solutions to these problems will require major new insights and fundamental advances in computer science and artificial intelligence. Such problems include: World Champion Chess machine, Translating Telephone, Discovery of a major mathematical result by a computer, and so on. Here I will present two such grand challenges which if successful can be expected have major impact on society: Self-Organizing Systems that learn from examples and observations and Self-Replicating Systems that can make copies of themselves.

Human brains seem to accomplish tasks in ways that would require absurd amounts of computer power if they were to be duplicated by machines. The first expert-systems experiments were not focused exclusively on machine capabilities nor on human capabilities, but on the border between the two types of symbol processors. How could a machine be used to transfer expertise from one human to another? The emerging differences between machine capabilities and human cognitive talents were brought into sharper focus when it was demonstrated by systems like MYCIN that this kind of software was capable of measurably augmenting the power of human judgment. Doctors who used MYCIN to aid their diagnostic decision-making ended up making accurate diagnoses more often than they did before they used the program to assist them.

Report on the 1984 Distributed Artificial Intelligence Workshop. Reprinted in Readings in Artificial Intelligence and Databases, J. Mylopoulos and M. L. Brodie, editors, Morgan Kaufmann Publishers, Inc., 1988. Report on the 1984 Distributed Artificial Intelligence Workshop. Reprinted in Readings in Artificial Intelligence and Databases, J. Mylopoulos and M. L. Brodie, editors, Morgan Kaufmann Publishers, Inc., 1988.

At least 160 federal agencies churn out rules and regulations -- more than 4,000 a year -- from specifying the height of steps on buses for the disabled to the method of calculating food's fiber content. Before finalizing a rule, government agencies are required to solicit and consider public comment, which, until recently meant publishing a notice in the Federal Register, accessible mostly to lobbyists. Now, all notices and requests for comment are to go through the Web site http://regulations.gov. Although that site communicates about as clearly as the instructions that come with income tax forms, it sometimes produces more public participation than regulators would prefer. To help the agencies deal with rule-making in the Internet age and make the process more accessible to the public, Cornell scientists and legal experts have created the Cornell e-Rulemaking Initiative (CeRI), funded by a $750,000, three-year grant from the National Science Foundation.

The essence of the Turing Test revolves around whether a computer can successfully impersonate a human. The test is to be put into practice under a set of detailed conditions which rely on human judges being connected with test subjects (a computer and a person) solely via an instant messaging system or its equivalent. That is, the only information which will pass between the parties is text. To pass the test, a computer would have to be capable of communicating via this medium at least as competently as a person. There is no restriction on the subject matter; anything within the scope of human experience in reality or imagination is fair game.

Debrief tool used in the experiment displays a video replay of the operator console (similar to this map display), and a timeline of events suggested by AEMASE for discussion during debrief. The tool also includes visualizations of entity movement over time. Navy pilots and other flight specialists soon will have a new "smart machine" installed in training simulators that learns from expert instructors to more efficiently train their students. Sandia National Laboratories' Automated Expert Modeling & Student Evaluation (AEMASE, pronounced "amaze") is being provided to the Navy as a component of flight simulators. Components are now being used to train Navy personnel to fly H-60 helicopters and a complete system will soon be delivered for training on the E-2C Hawkeye aircraft, said Robert G. Abbott, a Sandia computer scientist and AEMASE's inventor.