When Dutch entrepreneur Teun van den Dries started GeoPhy, an artificial intelligence (AI)-driven service that performs real estate valuations, there was nothing like it on the market. When he went to buy a house, Bloomberg reported, the appraiser he used simply asked him how much he thought it should cost. That led him to realize the potential market for his company, which he started in 2014. The round was led by Index Ventures, which has backed companies like Deliveroo and Slack. Hearst Ventures and Inkef Capital also participated in the round.

THE UNIVERSITY OF MARYLAND'S Computer Science Department conducts a broad research program in both theoretical and applied artificial intelligence. Nine faculty and more than fifty research associates and graduate students are involved in AI research. Projects are funded by a large number of government agencies, as well as by several major corporations. The computing environment will improve dramatically over the next several years, due in large part to a Coordinated Experimental Research Equipment Grant awarded to the Computer Science Department by the National Science Foundation in 1982. In addition to the research program in AI, the Department offers a large number of courses at both the graduate and undergraduate levels on all facets of AI.

Default and auto-epistemic reasoning were also well represented, with a number of papers discussing aspects, applications, and implementations of default reasoning systerns. Several papers emphasized nonmonotonic facets of computational vision, natural language understanding, and conimo1i-sense reasoning. Thursday evening, a panel discussion was held, with John McCarthy, Dana Scott, and Richmond Thomason as panelists. Compare it with a merely COMMON LISP (Golden Common Lisp@ Version 1.OO): Golden Common Lisp is a registered trademark of Gold Hill Computers. Our low-key, dignified approach to matchingquality candidates with quality companies will offer you the opportunity to examine your alternatives in a confidential, systematic fashion Openingsarenationwide.

I developed what I thought was an extremely clever method for detecting "bad" training instances. Each instance was scored, and those with the lowest scores could be removed before running C4.5 to build a decision tree with the remainder. I ran an experiment in which I removed the bottom 10 percent of the instances in a University of California, Irvine (UCI) data set. The resulting tree was smaller and more accurate (as measured by 10-fold CV) than the tree built on the full data set. Then I removed the bottom 20 percent of the instances and got a tree that was smaller than the last one and just as accurate.

Humans learn from their mistakes. When things go badly, we notice that something is amiss, figure out what went wrong and why, and attempt to repair the problem. Artificial systems depend on their human designers to program in responses to every eventuality and therefore typically don't even notice when things go wrong, following their programming over the proverbial, and in some cases literal, cliff. This article describes our past and current work on the metacognitive loop, a domain-general approach to giving artificial systems the ability to notice, assess, and repair problems. The goal is to make artificial systems more robust and less dependent on their human designers.

Reasoning does not work well when done in isolation from its significance, both to the needs and interests of an agent and with respect to the wider world. Moreover, those issues may best be handled with a new sort of data structure that goes beyond the knowledge base and incorporates aspects of perceptual knowledge and even more, in which a kind of anticipatory action may be key.

How can a robot tell when it — rather than another agent — is making an utterance or performing an action? This is rather tricky and also very important for human-robot (or even robot-robot) interaction. Here we outline our beginning attempt to deal with this issue.

I will present a perspective on human-level commonsense behavior (HLCSB) that differs from commonsense reasoning (CSR) as the latter is often characterized in AI. I will argue that HLCSB is not far beyond the reach of current technology, and that it also provides solutions to some of the problems that plague CSR, most notably the brittleness problem. A key is the judicious use of metacognitive monitoring and control, especially in the area of automated learning.

Humans learn from their mistakes. When things go badly, we notice that something is amiss, figure out what went wrong and why, and attempt to repair the problem. Artificial systems depend on their human designers to program in responses to every eventuality and therefore typically don’t even notice when things go wrong, following their programming over the proverbial, and in some cases literal, cliff. This article describes our past and current work on the Meta-Cognitive Loop, a domain-general approach to giving artificial systems the ability to notice, assess, and repair problems. The goal is to make artificial systems more robust and less dependent on their human designers.

The University of Maryland's Computer Science Department conducts a broad research program in both theoretical and applied artificial intelligence. Nine faculty and more than fifty research associates and graduate students are involved in AI research. Projects are funded by a large number of government agencies, as well as by several major corporations. The computing environment will improve dramatically over the next several years, due in large part to Coordinated Experimental Research Department by the National Science Foundation in 1982. In addition to the research program in AI, the Department offers a large number of courses at both the graduate and undergraduate levels on all facets of AI. The principal AI laboratories also sponsor numerous colloquia by visiting scientists and permanent laboratory personnel. The principal research areas are computer vision, search and decision making, parallel problems solving, and database research.