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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Some of the recent conflicts around algorithms and ethnicity are flubs that social media entrepreneurs will regret. Others may endanger life.
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One technique to avoid data snooping is based on the intersection of information theory and probability: An object’s probability is related to its information content. The greater an object’s information content, the lower its probability. We measure a model’s information content as the logarithmic difference between the probability that the data occurred by chance and the number of bits required to store the model. The negative exponential of the difference is the model’s probability of occurring by chance. If the data cannot be compressed, then these two values are equal. Then the model has zero information and we cannot know if the data was generated by chance or not. For a dataset that is incompressible and uninformative, swirl some tea Read More ›
The problem with machine learning-based AI in police work is not so much its inherent bias (none of us is bias-free) but the delegation to a machine of what should be a human decision.
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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 Read More ›
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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