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Transcript of Neural Networks
Google X Labs
Self driving vehicles
Augmented reality glasses
Space elevators How can a machine learn?
System of interconnected artificial neurons
Adapt to and seek patterns within input data AKA Google Brain What's with the name? There's a reason it's a network
Traditionally refers to the mammalian brain's framework
Biological neurons connected by synapses
Activated by recognition of known stimuli
Specific groups of neurons perform the task-specific functions they have adapted to
Frequency of activation strengthens neural connections - recognition
Google's artificial neural network functions similarly
Operates by recognizing stimuli, creating unique categories, and strengthening recognition
Many current recognition systems rely on
Objects defined only by low level features SOME NUMBERS Google What if we could scale it up to the magnitude of a brain? What's the big deal? Deep Learning Surface Learning Artificial Neural Network One of Google's Neuron's represents a completely unique high level feature Image Map - Set of Pixels representing an image
Compare to and categorize unlabeled data
Capable of Recognition - Activation Values The Problem amounts of unlabeled data exist Huge It is useless Harnessing the potential and applying it Brain 3 Million Neurons 1 Billion Connections 100 Billion Neurons 100 Trillion Synapses neural Cat Neuron Face Neuron Human Body Neuron ? Neuron Google Brain: 1000 machines
16000 cores What is Recognition? Network can recognize invariance in an input image
Categorize similar data into unique but unlabeled groups The machine only knows that different entities exist - but not what they are What makes that exciting? A human child learns in much the same way - with some guidance Google's neural network does not have the advantage of nature on its side And it doesn't require any amount of instruction That means Google Brain is capable of conceptualization the idea of a from only raw data Create cat cat face body No instruction other than an algorithm and
unlabeled, meaningless data Causes inaccuracy in current
recognition systems Google's artificial neurons respond to extremely specific high level features What does a neuron really do? SPARSE CODING ImageNet Dataset
22k Categories, 14m Images
15.8 9.3 "Cat Detector" Computes extremely complex function of the input
Function creates useful representation of the input Apply It Data Labeling 10m YouTube Images 200x200 3 Days - Neurons Become Selective Financial Potential REALLY GOOD Google X Labs ---> Search & Services Division supervised pretraining The Programmer Chooses POSITRONIC BRAIN Character Recognition Stock Prediction Applications (Better than a Human) PREDICTIONS Sociology Speech Recognition