Dr. Dobb's Journal - October 2007 - (Page 19)

Science and Engineering So what about the engineering side of AI, what about real working systems that solve real problems? There the news seems good. In terms of real engineering and applied science accomplishments, “[t]he most active and productive strand of AI research today is the application of machine learning techniques to a wide variety of problems,” Winograd says, “from web search to finance to understanding the molecular basis of living systems.” Work like this, and advances in other areas such as robotics, are taking us in the direction of more intelligent artifacts, “and will lead to a world with many ‘somewhat intelligent’ systems, which will not converge to humanlike intelligence.” Rodney Brooks sees great progress being made in practical systems involving language, vision, search, learning, and navigation, systems that are becoming part of our daily lives. Nils Nilsson took time out from writing a book on the history of AI to share some thoughts on its state today, citing practical results of AI work in adjacent fields like genomics, control engineering, data analysis, medicine and surgery, computer games, and animation. In a forthcoming book, Hamid Ekbia examines the unique tension between the engineering and science goals of AI: Artificial Intelligence seeks to do three things at the same time: 1. as an engineering practice, AI seeks to build precise working systems; 2. as a scientific practice, it seeks to explain the human mind and human behavior; 3. as a discursive practice, it seeks to use psychological terms (derived from its scientific practice) to describe what its artifacts (built through the engineering practice) do. This third practice, which acts like a bridge between the other two, is more subjective than the other two. And that, he argues, is why the field has such dramatic ups and downs and is so often burdened with over-promising and grandiosity. The gap between AI engineering and AI as a model of intelligence is so large that trying to bridge it almost inevitably leads to assertions that later prove embarrassing. McCarthy said AI was “the science and engineering of making intelligent machines.” If that is its hope, maybe it can’t escape hype. Winners and Losers Right now, the balance in AI work seems to be tipped toward applied over theoretical, and toward the connectionist over the symbolist. But if history is a guide, things could shift back. Another tilt noticeable in the AI work presented at AAAI this summer is modesty over hype. It’s something that’s been going on since the AI Winter of the ‘90s that followed the disappointment over the overpromising of the ‘80s. AI advances are not trumpeted as artificial intelligence so much these days, but are often seen as advances in some other field. “AI has become more important as it has become less conspicuous,” Winston says. “These days, it is hard to find a big system that does not work, in part, because of ideas developed or matured in the AI world.” And that note of modesty may be a good thing both for the work and for AI. DDJ October 2007 l www.ddj.com l Dr. Dobb’s Journal 19 http://www.mcobject.com/enterprise http://www.mcobject.com http://www.ddj.com

Table of Contents Feed for the Digital Edition of Dr. Dobb's Journal - October 2007

Cover Contents Hmmmm Alia Vox Developer Diaries Developer’s Notebook AI: It’s OK Again! Conversations Visual Cryptography and Bit-Plane Complexity Segmentation Inside the Windows Vista Disk Encryption Algorithm Memory-Aware Components Software and the Core Description Process Logging In C++ Effective Concurrency The Agile Edge Swaine’s Flames

Dr. Dobb's Journal - October 2007

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