Deep Neural Nets, Deep Belief Nets, Deep Learning, DeepMind, DeepFace, DeepSpeech, DeepImage… Deep is all the rage! In my next few blogs I will try to address some of the questions and issues surrounding all of these “deep” thoughts including:

• What is Deep Learning and why has it gotten so popular as of late
• Is Sensory just jumping on the bandwagon with Deep Nets for voice and vision?
• How does Big Data and Privacy fit into the whole Deep Learning arena?
• How can a tiny player like Sensory compete in this “deep” technology with giants like Microsoft, Google, Facebook, Baidu and others investing so heavily?

Part 1: What is Deep Learning and is Sensory Just Jumping on the Bandwagon?

Artificial Neural Network approaches have been around for a long time, and have gone in and out of favor. Neural Nets are an approach within the field of Machine Learning and today they are all the rage. Sensory has been working with Neural Net technology since our founding more than 20 years ago, so the approach is certainly not new for us. We are not just jumping on the bandwagon… we are one of the leading carts! ;-)

Neural Networks are very loosely modeled after how our brains work – nonlinear, parallel processing, and learning from exposure to data rather than being programmed. Unlike common computer architectures that separate memory from processing, our brains have billions of neurons that communicate and process all in parallel and with huge quantities of connections. This architecture based on how our brains work turns out to be much better than traditional computer programs at dealing with ambiguous and “sensory” information like vision and speech – a little Trivia: that’s how we came up with the name Sensory!

In the early days of Sensory, we were often asked by engineers, “What kind of neural networks are you running?” They were looking for a simple answer, something like a “Kohonen Net.”  I once asked my brother, Mike Mozer, a pioneer in the field of neural nets, a Sensory co-founder, and a professor of computer science at U. Colorado Boulder, for a few one liners to satisfy curious engineers without giving anything away. We had two lines: the first being, “a feed forward multi-layer net” which satisfied 90% of those asking, and the other response for those that asked for more was, “it’s actually a nonlinear and multivariate function.” That quieted pretty much everyone down.

In the last five years Neural Networks have proven to be the best-known approaches for various recognition and ambiguous data challenges like vision and speech. The breakthrough and improvement in performance came from these various terms that use the word “deep.” The “deep” approaches entailed more complex architectures that receive more data. The architecture relates to the ways that information is shared and processed (like all those connections in our brain), and the increased data allows the system to adapt and improve through continuous learning, hence the terms, “Deep Learning” and “Deep Learning Net.” Performance has improved dramatically in the past five years and Deep Learning approaches have far exceeded traditional “expert-based” techniques for programming complex feature extraction and analysis.