Machine learning is not at all like human learning. For example, machine learning frequently requires millions of examples. Humans learn from a few examples.
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One new issue that the cloud creates is that programmers are more often required to be “full stack” developers,” Jonathan Bartlett explains. “Unfortunately, most programmers coming out of college have little to no system administration experience. That’s why this book is based on the ‘full stack’ concept, showing how system administration and programming relate to each other.”
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Turning AI loose on some of these vexing problems should give literary scholars more to write about rather than less. The AI verdict may not always be right but it is bound to be food for thought.
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As long as we can establish that our theories, hypotheses, and/or models are independent of the data, then we can trust that their predictive power will generalize beyond the data we have observed.
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The question was not whether the infants understood the exact numbers (they didn’t) but whether they understood that the researchers were, in fact, counting things.
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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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Hyperreal numbers are a new type of number that was developed to simplify and rethink the way that we deal with very large and very small numbers.
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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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One of the problems with modern secondary mathematics education is that it teaches lots of details about how to solve problems but provides very little insight into how to understand problems. You may have learned to solve a quadratic equation but you may not have learned what life situations generate a quadratic equation.
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We are not truly likely to be ruled by AI overlords (as opposed to powerful people using AI. But even doubtful predictions may be self-fulfilling if enough impressionable people come to believe them. Children, for example. We adults are aware of the limitations of AI. But if we talk about AI devices as if they were people, children—who often imbue even stuffed toys with complex personalities—may be easily confused. Sue Shellenbarger, Work & Family columnist at The Wall Street Journal, warns that already, “Many children think robots are smarter than humans or imbue them with magical powers.” While she admits that the “long-term consequences” are still unclear, “an expanding body of research” suggests we need to train children to draw Read More ›
What makes you an expert today is not your clarity of thought but rather your ability to conform your thoughts entirely to the constraints of your profession’s vocabulary.
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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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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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At first sight, the number of options might seem bewildering. The key question is: Will you ignore the coming job disruption, fear it, or treat it as an opportunity?
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