Machine Learning for Intelligent Systems
نویسنده
چکیده
Recent research in machine learning has focused on supervised induction for simple classification and reinforcement learning for simple reactive behaviors. In the process, the field has become disconnected from AI’s original goal of creating complete intelligent agents. In this paper, I review recent work on machine learning for planning, language, vision, and other topics that runs counter to this trend and thus holds interest for the broader AI research community. I also suggest some steps to encourage further research along these lines.
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