Bringing the Deep Learning Revolution into the EnterpriseΒΆ

Speaker: Michael Gschwind

  • Collecting data is useful but you must make sense out of it, especially in real-time
  • Deep Learning is at the intersection of AI + machine learning + big data
  • What is deep learning?
    • Things you do without thinking about it – like recognizing a bicycle
    • Bicycles come in many forms and it’s difficult to teach a computer how to recognize them
    • Humans can deal with complexity/variance easily, but traditional recognition technology cannot
    • Deep learning involves building a model based on patterns, and then allow you to take an action on that (and predict outcomes)
  • Applications
    • Automotive/transportation: driverless cars
    • Security/Safety: facial/object recognition
    • Consumer web/mobile/retail: natural language processing, recommendations
    • Medicine/biology: diagnostic assistance and drug discovery
    • Broadcast/media: Captioning, recommendations
  • Computing capacity has been a big limiter in deep learning, but accelerators are helping
  • Based on artificial neural networks (ANNs)
    • Inspired by biological neural networks in the brain
    • A neuron is a basic cognitive unit and you need a lot of them to begin making decisions
    • Expressed in computers as multiple layers, nodes, weights and activation functions (what to do when a threshold is crossed)
    • Recognizing something takes multiple steps in animals and in computers
  • Training a neural network
    • Called stochastic gradient descent (SGD)
    • Shows how well a trained network is
    • Involves taking a lot of derivatives to find “valleys”
  • Middleware exists to help with the mathematics and training of a neural network, such as Caffe, TensorFlow, CNTK, and torch
  • Two-tiered workload
    • Training: extract a model from historical data
    • Inference: classify new objects based on the model
  • Computers will return results with a probability attached
  • More training improves the probability of being right
  • Pre-packaged for POWER systems: http://ibm.biz/power-mldl