{"id":775,"date":"2014-09-13T07:31:00","date_gmt":"2014-09-13T12:31:00","guid":{"rendered":"http:\/\/haleyai.com\/wordpress\/?p=775"},"modified":"2019-04-05T06:29:24","modified_gmt":"2019-04-05T11:29:24","slug":"deep-learning-big-data-and-common-sense","status":"publish","type":"post","link":"http:\/\/haleyai.com\/wordpress\/2014\/09\/13\/deep-learning-big-data-and-common-sense\/","title":{"rendered":"Deep Learning, Big Data and Common Sense"},"content":{"rendered":"

Thanks to John Sowa<\/a>‘s comment on LinkedIn for this link <\/a>which, although slightly dated, contains the following:<\/p>\n

In August, I had the chance to speak with Peter Norvig<\/a>, Director of Google Research, and asked him if he thought that techniques like deep learning could ever solve complicated tasks that are more characteristic of human intelligence, like understanding stories, which is something Norvig used to work on in the nineteen-eighties. Back then, Norvig had written a brilliant review<\/a> of the previous work on getting machines to understand stories, and fully endorsed an approach that built on classical \u201csymbol-manipulation\u201d techniques. Norvig\u2019s group is now working within Hinton, and Norvig is clearly very interested in seeing what Hinton could come up with. But even Norvig didn\u2019t see how you could build a machine that could understand stories using deep learning alone.<\/p>\n

Other quotes along the same lines come from Oren Etzioni<\/a> in Looking to the Future of Data Science<\/a>:<\/p>\n

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  1. But in his keynote speech on Monday, Oren Etzioni, a prominent computer scientist and chief executive of the recently created Allen Institute for Artificial Intelligence<\/a>, delivered a call to arms to the assembled data mavens. Don\u2019t be overly influenced, Mr. Etzioni warned, by the \u201cbig data tidal wave,\u201d with its emphasis on mining large data sets for correlations, inferences and predictions. The big data approach, he said during his talk and in an interview later, is brimming with short-term commercial opportunity, but he said scientists should set their sights further. \u201cIt might be fine if you want to target ads and generate product recommendations,\u201d he said, \u201cbut it\u2019s not common sense knowledge.\u201d<\/li>\n
  2. The \u201cbig\u201d in big data tends to get all the attention, Mr. Etzioni said, but thorny problems often reside in a seemingly simple sentence or two. He showed the sentence: \u201cThe large ball crashed right through the table because it was made of Styrofoam.\u201d He asked, What was made of Styrofoam? The large ball? Or the table? The table, humans will invariably answer. But the question is a conundrum for a software program, Mr. Etzioni explained<\/li>\n
  3. Instead, at the Allen Institute, financed by Microsoft co-founder Paul Allen, Mr. Etzioni is leading a growing team of 30 researchers that is working on systems that move from data to knowledge to theories, and then can reason. The test, he said, is: \u201cDoes it combine things it knows to draw conclusions?\u201d This is the step from correlation, probabilities and prediction to a computer system that can understand<\/li>\n<\/ol>\n

    This is a significant statement from one of the best people in fact extraction on the planet!<\/p>\n

    As you know from elsewhere on this blog, I’ve been involved with the precursor to the AIAI (Vulcan’s Project Halo<\/a>) and am a fan of Watson<\/a>.\u00a0 But Watson is the best example of what Big Data, Deep Learning, fact extraction, and textual entailment aren’t even close to:<\/p>\n