As the volume of publicly available data continues to grow, researchers face the challenge of limited diversity in benchmarking machine learning tasks. Although thousands of datasets are available in public repositories, the sheer abundance often complicates the search for suitable data, leaving many valuable datasets underexplored. This situation is further amplified by the fact that, despite longstanding advocacy for improving data curation quality, current solutions remain prohibitively time-consuming and resource-intensive. In this paper, we propose a novel approach that combines intelligent agents with retrieval augmented generation to automate data analysis, dataset curation and indexing at scale. Our system leverages multiple agents to analyze raw, unstructured data across public repositories, generating dataset reports and interactive visual indexes that can be easily explored. We demonstrate that our approach results in more detailed dataset descriptions, higher hit rates and greater diversity in dataset retrieval tasks. Additionally, we show that the dataset reports generated by our method can be leveraged by other machine learning models to improve the performance on specific tasks, such as improving the accuracy and realism of synthetic data generation. By streamlining the process of transforming raw data into machine-learning-ready datasets, our approach enables researchers to better utilize existing data resources.

ChatGPT knows how to use the word "tickle" in a sentence but it cannot feel the sensation. Can it then be said to understand the meaning of the word tickle the same way we humans do? In an ongoing debate, AI researchers are teasing apart whether large language models (LLMs) like ChatGPT and Google's PaLM understand language in any humanlike sense. The relationship between embodiment and understanding is one question, along with the nature of intelligence and understanding. Should concepts of meaning, understanding, and intelligence be revisited to create a distinction between how humans and machines understand the world?

When IBM's Deep Blue first defeated Garry Kasparov in 1997, the world chess champion accused the company of cheating. There was no way, he thought, that the computer could have beaten him without direct assistance from a skilled human player. But now the situation has flipped entirely. When grandmasters find themselves at the receiving end of a few mind-blowingly brilliant moves today, they accuse their opponent of using a computer. The only worthwhile competition for top chess engines is one another. The programs have become too powerful; humankind has lost.

"It was good to see the number of student attendees up," noted American Association for Artificial Intelligence (AAAI) President Tom Mitchell, "and that our attendance was so high despite the economic downturn. I think the meeting was even more stimulating because of the co-location of AAAI with so many other conferences in Edmonton at the same time." This article provides a few snapshots of the vast and varied content of the 2002 conferences. Proceedings of AAAI-02 and IAAI-02 are available from AAAI Press (www.aaaipress.org). AAAI is grateful for the outstanding work of the conference committee members as well the support of the following organizations for this year's conference: Association of Computing Machinery SIGART, Alberta Informatics Circle of Research Excellence (iCORE), Defense Advanced Research Projects Agency (DARPA), NASA Ames Research Center, the National Science Foundation's Directorate for Computer and Information Science and Engineering (CISE), and the Naval Research Laboratory.

Department of Computer Science Rutgers Universaty New Brunswick, New Jersey 08903 Abstract In this article we discuss a method for learning useful conditions on the application of operators during heuristic search Since learning is not attempted until a complete solution path has been found for a problem, credit for correct moves and blame for incorrect moves is easily assigned We review four learning systems that have incorporated similar techniques to learn in the domains of algebra, symbolic integration, and puzzle-solving We conclude that the basic approach of learning from solution paths can be applied t,o any situation in which problems can be solved by sequential search Finally, we examine some potential difficulties that may arise in more complex domains, and suggest some possible extensions for dealing with them. PEOPLE LEARN FROM EXPERIENCE, and for the past 25 years, Artificial Intelligence researchers have been attempting to replicate this process. In t,his article we focus on learning in domains where search is involved. Furthermore, we will restrict our attention t,o cases in which the legal operators for a task are known, and the learning task is to determine the conditions under which those operators can be usefully applied. Once such a set of heuristically useful conditions has been discovered, search will be directed down profitable We would like to thank Jaime Carbonell and Hans Berliner for helpful comments on an earlier version of this article.

Haussler's paper was therefore important in linking the new PAC learning theory work with the ongoing work on machine learning within AI. Twenty years later that link is firmly established, and the two research communities have largely merged into one. In fact, much of the dramatic progress in machine learning over the past two decades has come from a fruitful marriage between research on learning theory and design of practical learning algorithms for particular problem classes. Mitchell and Levesque provide commentary on the two AAAI Classic Paper awards, given at the AAAI-05 conference in Pittsburgh, Pennsylvania. The two winning papers were "Quantifying the Inductive Bias in Concept Learning," by David Haussler, and "Default Reasoning, Nonmonotonic Logics, and the Frame Problem," by Steve Hanks and Drew Mc-Dermott.

An Artist at RPI Who Draws on the Future--Graduate student in electronic arts uses provocative acts to make people think about issues. "Last February, Boryana Rossa and her colleagues sent a decree of robot rights by email to the Pope's people at the Vatican. It should be considered a sin, the decree said, to kill an artificially created, sentient being (that is, a robot). Robots have the right to chose their own religion, it continued. An entity or creature created by humans must be considered equal to humans.

Machine learning draws on multiple disciplines. Mitchell provides the necessary background in both statistics and computational learning theory (a chapter on each) so that results from these fields can be understood and applied. He does not go overboard and overwhelm students in these areas. Instead, Mitchell takes the practical point of view. Students are provided with enough information to understand and use results from these ancillary fields.

In 1940, a 20-year-old science fiction fan from Brooklyn found that he was growing tired of stories that endlessly repeated the myths of Frankenstein and Faust: Robots were created and destroyed their creator; robots were created and destroyed their creator; robots were created and destroyed their creator-ad nauseum. So he began writing robot stories of his own. "[They were] robot stories of a new variety," he recalls. "Never, never was one of my robots to turn stupidly on his creator for no purpose but to demonstrate, for one more weary time, the crime and punishment of Faust. My robots were machines designed by engineers, not pseudo-men created by blasphemers. My robots reacted along the rational lines that existed in their'brains' from the moment of construction. " In particular, he imagined that each robot's artificial brain would be imprinted with three engineering safeguards, three Laws of Robotics: 1. A robot may not injure a human being or, through inaction, allow a human being to come to harm. 2. A robot must obey the orders given it by human beings except where such orders would conflict with the first law. The young writer's name, of course, was Isaac Asimov (1964), and the robot stories he began writing that year have become classics of science fiction, the standards by which others are judged. Indeed, because of Asimov one almost never reads about robots turning mindlessly on their masters anymore. But the legends of Frankenstein and Faust are subtle ones, and as the world knows too well, engineering rationality is not always the same thing as wisdom. M Mitchell Waldrop is a reporter for Science Magazine, 1333 H Street N.W., Washington D C. 2COO5. His work covers the areas of physics, astronomy, space, and computers This article is an excerpt from Mitch Waldrop's book entitled "Mm-Made Minsk The Promise of Artifkial Intelligence," to be published in March 1987, by Walker and Company, New York

See Toward the Principled Enganeering of Knowledge. Expert Systems: Where are we? And where do we go from here? Feigenbaum, Edward A, See Signal-to-symbol transformation: HASP/SIAP case study. Research in Progress Vol IV, No. 4, p. 58, Winter, 1983 THE AI MAGAZINE Spring 1984 83 K Minsky, Marvin Why People Think Computers Can't.