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Sunday, August 13, 2017

Addressing the Analogy Gap

Reminiscent of using humans as a peripheral, here determining high level relationships, then having the deep learning sort out the lower level patterns.  Points to Mechanical Turk, which we used this way.  At what point are the results general?  The comment about scale is key.  Also the mapping involved, very useful for generalizing and testing.

Crowdsourcing may have just helped close the "analogy gap" for computers    It's vexed computer scientists for decades, but a huge roadblock for true AI is falling    By Greg Nichols for Robotics

Researchers at Carnegie Mellon University (CMU) and the Hebrew University of Jerusalem in Israel have used crowdsourcing to teach computers to generate analogies so they can mine datasets to address new challenges by repurposing old concepts. "After decades of attempts, this is the first time that anyone has gained traction computationally on the analogy problem at scale," says CMU professor Aniket Kittur. The researchers hired participants via Amazon Mechanical Turk, tasking them to look through products on an innovation website and find analogous products from the same source. The participants noted which words caused them to link disparate products, mapping each pathway. Computers with deep-learning algorithms used these insights to analyze additional product descriptions and find new analogies. The researchers say this strategy can be used to customize computer programs to identify analogies between patent applications and literature on global problems.  ... " 

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