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MIT researchers can now estimate how much information data are likely to contain, in a more accurate and scalable way than previous methods.

Not all data are created equal. But how much information is any piece of data likely to contain? This question is central to medical testing, designing scientific experiments, and even to everyday human learning and thinking. MIT researchers have dev
Claude Shannon Feras Saad Vikash Mansinghka Department Of Brain Cognitive Sciences Mathematical Theory

Estimating the informativeness of data

MIT researchers discovered a new way to estimate the amount of information contained in a piece of data using probabilistic programming and probabilistic inference. The breakthrough entropy estimators open up new applications in medicine, scientific discovery, cognitive science, and artificial intelligence.
Claude Shannon Feras Saad Vikash Mansinghka Department Of Brain Cognitive Sciences Mathematical Theory
Source: mit.edu

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