The neat thing about machine learning is that the algorithm can extract general principles from the dataset that can then be applied to new problems. It is like the story that Newton observed an apple fall and then derived from it the general law of gravity that applies to the entire universe.
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George Montañez, Assistant Professor of Computer Science at Harvey Mudd College, took issue with Kurzweil’s claim that AlphaGoZero needed no instructions to beat humans at the game of Go: “For a system like this to work, a human must define the incentive structure, also encoding the assumptions.” The sheer power of a computing system does not cause it to do anything at all.
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Mundie, former Microsoft Chief Research & Strategy Officer, formerly told his audience that Big Data will enable each person to be “completely understood” by machines that can produce a computer facsimile of each detail. It would be far too complex for human physicians to make sense of, he said.
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We want the machine learning algorithms to learn general principles from the data, and not merely little tricks and trivia that that score high but ignore problems.
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In 2002, he received the National Medal of Technology for a number of “pioneering contributions to microelectronics,” which underlies cell phones and computer neural networks.
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Natural Language Interfaces (the technical term for a chatbot) are becoming more and more popular. Many dial-in phone services have switched from numeric interfaces (“Dial 1 for sales, 2 for service, etc.”) to natural language interfaces (“Please say what you are calling about”). Where they have taken off though is with chatbots. Many online help systems at least start with chatbots, which collect basic information about a problem or situation and point to existing solutions before passing the contact off to a human expert. Additionally, the rise of the Generation Text, as well as the proliferation of chat-based groupware such as Slack, means that text-based natural language interfaces are one of the best ways of interacting with young people. Is…
Building from scratch is different. Knowing when to use a tool and why and knowing the limitations of each tool separates the craftsperson from the novice.
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We’re stuck, working for free, training the Web giants’ ML systems to reap benefits for them while enduring (assuming we notice) the downsides.
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Optimism is not driving the recent collaboration and corporate consolidation in the self-driving car industry. Rather, their retrenchment is protection against an uncertain future.
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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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An endless variety of word games derives from the fact that only certain combinations and orderings of words can be correct. Machine learning applies vast resources to cracking the code.
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Contrary to the dogma of hypothesis testing, it is possible to do after-the-fact pattern analysis while limiting the probability of false positives.
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Humans intrinsically understand causation and, therefore know which pieces of data likely have some correlation. Therefore, when we select data for computers to analyze, we are drastically reducing the size of the problem for computers.
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AI tools help us do things better, faster, or more efficiently. But they lack the mind needed to know when “I’m loving’ it” is the winning slogan—and stop there.
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This human-plus-machine combination is proving more potent than the machine-only hype/promise.
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In the model of generalization set out in the paper, imperfect models can get better scores but they are discounted according to the amount of error they have.
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Machine learning is not a new technique, but is simply a modern extension of a tool that we have had in our toolbox since the days of the Babylonians. It continues to serve us well to help us extrapolate our data to estimate the value of unknown results and to help find the signal in noisy data.
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