What, Exactly, Do Economists Do?
Economist make the world a better placeThanks to economists, during the economic crisis that began in 2007, the President, Congress, and Federal Reserve did not repeat the errors of the 1930s.
Read More ›Thanks to economists, during the economic crisis that began in 2007, the President, Congress, and Federal Reserve did not repeat the errors of the 1930s.
Read More ›Recently, a Harvard prof chose to launch an attack on homeschoolers, portraying them as driven by narrow religious concerns. Given how many parents COVID-19 has forced to homeschool, the attack was, at best, poorly timed. But it usefully focused attention on the ways education needs to change in an online world.
Jonathan Bartlett tells us, “The review was mixed, but most importantly the reviewer didn’t disagree with the results, only their potential usefulness."
Read More ›Every human being, whether office worker or high school student, bucks against digital harnesses.
Read More ›It turns out that hyperreal numbers (i.e., infinities that obey algebraic rules) resolve many of the paradoxes that previously plagued conceptions of divergent series. It is now possible to assign specific values to divergent series.
Read More ›Machine learning is not at all like human learning. For example, machine learning frequently requires millions of examples. Humans learn from a few examples.
Read More ›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.”
Read More ›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.
Read More ›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.
Read More ›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.
Read More ›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.
Read More ›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.
Read More ›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.
Read More ›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.
Read More ›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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