What is Machine Learning?


Machine learning refers to computers that are able to act and react without being explicitly programmed to do so. Practical speech recognition, semantic applications, and even self-driving cars all leverage machine learning via data systems that not only intake, retrieve, and interpret data, but also learn from it. To do this, the machine must make a generalisation, using algorithms to respond to new inputs after being “trained” on a different learning data set — much like a human learns from experiences and uses that knowledge to respond appropriately in a different encounter. In this sense, machine learning is widely considered by many researchers and thought leaders as a step towards human-like artificial intelligence. Recent incarnations of machine learning include a university-developed telescope that can automatically detect significant changes pointing to supernova occurrences. The software Xapagy improvises dialogue and plot moves in stories fed to it by users. The potential of machine learning for education is still some years away, but the potential of learning systems that can adapt and learn on their own is driving research around the world.

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(1) How might this technology be relevant to the educational sector you know best?

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(2) What themes are missing from the above description that you think are important?

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(3) What do you see as the potential impact of this technology on teaching, learning, or creative inquiry?

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(4) Do you have or know of a project working in this area?

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