The research suggests a radically different view, in which learning of a child’s first language does not rely on an innate grammar module. Instead the new research shows that young children use various types of thinking that may not be specific to language at all—such as the ability to classify the world into categories (people or objects, for instance) and to understand the relations among things.
good point: constructed languages should be so constructed that they are easily processed by humans (i.e. respect the info processing "habits" of our brains). So moderate redundancy and irregularity might be both principles of good conlang design.
Support vector machines is one of the most popular methods of classification in machine learning although they can be used as a black box, understanding what’s happening behind scenes can be very useful not to mention interesting.
This is an edited excerpt from “How to Teach Computational Thinking,” first published by Stephen Wolfram on Sept. 7, 2016.
Pick any field “X,” from archaeology to zoology. There either is now a “computational X”, or there soon will be. Doctors, lawyers, teachers, farmers, whatever—the future of all these professions will be full of computational thinking. Whether it’s sensor-based medicine, computational contracts, education analytics or agriculture—success is going to rely on being able to do computational thinking well.
Computational thinking is going to be a defining feature of the future, and it’s an incredibly important thing to be teaching to kids today. But where does it fit into the standard educational curriculum? The answer, I think, is simple: everywhere!
Ever wondered how your mind deals with complex sums and multiplications?
A new study has imaged how the brain's activity levels change while taking on serious maths problems, and reveals for the first time that there are four distinct neural stages involved in coming up with a solution.
This is a syntax highlighter for English. It uses natural language processing to determine the part of speech for each word and highlights accordingly. It works with tricky sentences too, e.g. The man we saw saw a saw.
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