Introduction to biological Neural Networks
So i came across this on google research's blogs:
Traditionally machine learning requires previously labeled data as a training set in order to then work on new input data. Many researchers these days are exploring methods to work with data that has not been labeled before hand, essentially allowing the system to learn from 'unknown' data and be able to associate items that were previously not known to be connected. Artificial neural networks are a conceptualization/mimicry of a mammalian brain's learning process.
Google's neural network for the research is made from a distributed infrastructure consisting of 100 machines with total of 16000 cpu cores, fed data into the system to produce models with more than 1 billion connections. The input data was derived by showing the system data from 10million stills (200x200px) taken from youtube videos
Google say that that the neural network learned to respond to pictures of human faces, human bodies and cat faces - cat faces being the thing that journalists are picking out from the research because lets face it - it's funny - and yep, cats rule the internet - as we kind of showed in some research we did with a Social robot with a persona geared around cat fanciers: Socialbots
Source: @AeroFade's Blog
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