The Big Deal about Big Data and Deep Learning

Recently, you may have been hearing a lot about “Big Data”, this refers to our growing ability to find patterns and process very  large amounts of data with or without structure. There are many reasons contributing to this but a recent set of algorithmic advances known as “Deep Learning” have led to a surge in accuracy in voice recognition and vision. This is relevant for scientists because Deep Learning is a particularly effective type of  ‘unsupervised learning’ which does not require you to specify a bias up front to speed up learning. Thus it only learns a pattern from the data if there is a statistical basis for it.

This recent article in Nature is causing some waves and explains the algorithms quite well, the discussion in Andrew Ng’s Google+ post is also good to add a bit more context about the wider progress in Artificial Intelligence research.

Talking Robots and Psychedelic Drugs

Baby robot learns first words from human teacher – tech – 15 June 2012 – New Scientist

I’m always glad to see more methods from research in Artificial Intelligence/Machine Learning getting coverage in the media and being explained with some level of detail. Take a look at these two articles on applications of Artificial Intellgience methods in the study of the learning language in infants and in the effects of psychedelic drugs diagnosis. They give a nice high level overview of two powerful approaches that are not quite standard in AI and Machine Learning. The language learning robot is doing supervised learning with reinforcement learning approach where the agent randomly explores a landscape and weights good experiences to improve it’s model. The drugs study is applying a classifier to text descriptions about psychedelic trips and trying to predict the drug that causes it.

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