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This one-semester course is focused on the history, applications, and innovations of artificial intelligence. Students will learn about intelligence agents, problem solving using search algorithms, knowledge representation, and reasoning in artificial intelligence. Students will also learn about the basic concepts of machine learning and natural language processing (NLP). Students will also learn about expert systems, computer vision and robotics. This 12-lesson course also covers ethics and safety related to artificial intelligence. Online discussions and course activities require students to develop and apply critical thinking skills, while the included games appeal to a variety of learning styles and keep students engaged.
In the period 1966-1968, my doctoral student, Carl Rischer, and I were studying wheel-running by old-field mice, Peromyscus polionotus, when we made an almost unbelievable discovery, published with the above title in Science (1968;161:1256-1259). We were studying the influences of both artificial dusks and natural dawns (astronomical dawn = sun 18° below the horizon) on activity in one of our indoor enclosures. One side of the enclosure was open to a window pane with an eastern exposure to capture the natural dawn.
In the above example where the clutch torque was increased gradually, a clear-cut answer from the five possibilities was obtained. This was typical of my experimental results in general. I rarely, or never, asked a question of my animals to which they did not give a clear-cut answer yes, no, or otherwise (true, also, in my studies of birds; see Chap. 10). If the answer was not clear-cut, I felt that there was something wrong with the question, and rethought and reformulated it. The animals never failed me or, rather, nature never failed me, since the animals were merely a product of organic evolution, molded by natural selection. 2b1af7f3a8