The tremendous advancements in artificial intelligence remain focused in narrow applications. As described in a recent article authored by Ben Dickson, these narrow systems have been designed to perform specific tasks instead of having general problem-solving abilities. The quest for general problem-solving ability has long been pursued, with many focused-on replicating aspects of human intelligence like vision, language, reasoning, and motor skills. Now, a new paper submitted to the peer-reviewed Artificial Intelligence journal describes an argument put forward by scientists at U.K.-based AI lab DeepMind. They argue that intelligence and its associated abilities likely emerge by rewarding maximization versus formulating and solving complicated problems.
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The Next Generation Of Artificial Intelligence
Two recent articles caught my eye this week. One article focused on the Fourth Generation of artificial intelligence, calling it artificial intuition. The other article explores the shift from artificial narrow intelligence to Artificial General Intelligence. In the case of artificial intuition, author Mark Gazit describes how helpful AI has become, and its ongoing limitations. Machine learning is still fully dependent on historic data. New and unknown scenarios leave data scientists helpless. Mr. Gazit suggests that in order to have true artificial intelligence, we need machines that can think on their own.
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Reimagining the Future 