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Jonathan P. E. Hodgson
St. Joseph's University
Philadelphia, Pennsylvania
 

Dr. Jonathan Hodgson is a Professor of Mathematics and Computer Science at Saint Joseph's University in Philadelphia, PA, where he teaches a wide variety of courses at both the undergraduate and master's level. Previously, he was on the faculty at Adelphi University and, prior to that, at the University of Pennsylvania. Dr. Hodgson started his career as a mathematician working in the field of topology. He holds a Ph.D. in Mathematics from the University of Cambridge. Dr. Hodgson became involved with computers, originally with the idea of drawing pictures of knots on a Tektronix machine using Plot-90. In the 1980s, he developed an interest in artificial intelligence, particularly problem solving and logic programming, both of which are his areas of major research interest.

Dr. Hodgson has published papers on differential topology, problem solving, and the use of Hypertext Markup Language (HTML) tags for flagging semantic content in Web pages. He is currently the convenor of WG17, the ISO/IEC JTC1 working group on Prolog standardization.


     

Virtual machine consolidation using constraint-based multi-objective optimization
Terra-Neves M., Lynce I., Manquinho V.  Journal of Heuristics 25(3): 339-375, 2019. Type: Article

As cloud computing grows, its demands on resources play an ever-increasing role in the economics of machine maintenance. One potential way of addressing this issue is by improving usage. For example, by judiciously placing the tasks requested of i...

 

The challenge of crafting intelligible intelligence
Weld D., Bansal G.  Communications of the ACM 62(6): 70-79, 2019. Type: Article, Reviews: (1 of 2)

This thoughtful review of “intelligible intelligence” work is at the same time a manifesto on the desirability of intelligent systems that have the ability to explain their reasoning, at least to some extent....

 

Automatic language identification in texts: a survey
Jauhiainen T., Lui M., Zampieri M., Baldwin T., Lindén K.  Journal of Artificial Intelligence Research 65(1): 675-782, 2019. Type: Article

One might think that automatic language identification (LI) is straightforward--surely, distinguishing English from Polish is easy. This review shows that the problem is much harder than one might expect. For example, distinguishing Modern St...

 

The seven tools of causal inference, with reflections on machine learning
Pearl J.  Communications of the ACM 62(3): 54-60, 2019. Type: Article, Reviews: (3 of 3)

There are three obstacles to meeting the increasing expectations for artificial intelligence (AI), according to this article: the lack of adaptability or robustness; the lack of explainability; and “the lack of understanding of cause-effect ...

 

 Fairness-aware machine learning: practical challenges and lessons learned
Bird S., Hutchinson B., Kenthapadi K., K c man E., Mitchell M.  WWW 2019 (Companion Proceedings of The 2019 World Wide Web Conference, San Francisco, CA,  May 13-17, 2019) 1297-1298, 2019. Type: Proceedings

This is a timely paper in light of recent stories about bias in artificial intelligence (AI) systems, such as the COMPAS system used in Florida to predict recidivism. The tutorial’s aim is to describe what the authors call a “fairness-...

 
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