The significance of Kasparov versus Deep Blue and the future of computer chess

Journal of the International Computer Chess Association (ICCA), March 1998
The significance of Kasparov versus Deep Blue and the future of computer chess
Dennis DeCoste

In this paper we argue that the recent Garry Kasparov vs. Deep Blue matches are significant for the field of artificial intelligence in several ways, including providing an example of valuable baseline benchmarks for more complex alternatives to contrast and justify themselves.

We will also briefly summarize some of the latest developments on computer chess research and highlight how our own work on a program called Chester tries to build on those developments to provide such justifications.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.