AI sifts enormous amounts of accumulated data. But successful military strategy often depends on creating a new approach to a problem, one that lies outside the historical data available to the opposing forces. Muhammad Ali and Hannibal were famous for using such strategies.
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If Alphabet’s Deep Mind or Microsoft had successfully data mined the 29,000 papers and found useful coronavirus information, that would be pretty impressive. But they appear to be giving others a chance to try instead, raising issues once again about the value of data mining in medicine.
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To remain competitive, the U.S. military must respond and adapt to new warfare technology including weapons using AI, sometimes called killer robots. This includes autonomous AI that acts on its own. Chillingly, unlike atomic weapons, the tools to construct lethal AI weapons are cheap and readily available to all.
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Exponential growth is often the beginning of a sigmoid or s-shaped curve where growth that appears to be exponential but eventually slows and reaches a saturation point. We see this in nature, for example, in bacteria.
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Virtual reality got its start in the US Air Force’s investigation of heads-up display for pilots. Just as you see a candy wrapper on your dashboard reflected in the windshield while you drive, pilots could have nearly a instantaneous view of instrumentation simply by focusing their eyes from far objects to near.
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Also, McDonald’s doubles down on self-ordering kiosks, acquires automation firm.
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When we get to the end of the list, we will see that it is like beads connected by a string—revealing the most dangerous threat.
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In some Smart City master plans, our privacy will be seriously compromised.
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Twenty years or so ago, when I worked in Seattle, Microsoft was famous for the testing coding skills of their applicants and asking Mensa-like questions. Degrees were secondary.
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Gregory Coppola’s revelations about Google’s politically biased search engine shone a spotlight on how algorithms are written.
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The police union’s 1996 objection to fingering specific officers as violence risks without a detailed explanation pinpoints a weakness of neural networks even to this day. The neural network is basically a black box.
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Measurement creates a temptation to achieve a measurable goal by less than totally honest means. As in physics, the simple act of measuring invariably disturbs what you are trying to measure.
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A good rule of thumb is that unexpected outcomes increase exponentially as a function of AI complexity.
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Because computers lack common sense, they cannot interpret statements that assume a background of general knowledge, as the Winograd Schema challenges show.
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Programs for AI and evolution share the limitation that nothing creative happens without the guidance of a programmer. And a thriving economy based on creative entrepreneurship is one of the things that cannot be automated.
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