Forbes published an interesting interview with Jeff Dean of Google, along with an introduction to the concept of Deep Learning. Dean’s work using the Deep Learning paradigm has led to a fundamental change in the way Google’s speech recognition systems work. The previous model was an acoustic model, whereas the new approach uses deeply layered neural networks to sequence and categorize phonemes.
Using this new process, Google saw dramatic improvements in speech recognition.
The three keys areas contributing to these advancement are:
- The Deep Learning architecture
- The ability to feed systems vast amounts of data for training
- The availability of extremely powerful computer processing
One of the advantages of the Deep Learning technology is that software can train itself to recognition patterns. This unsupervised learning enables machines to improve their performance without relying on humans to painstakingly point out every identifying trait of the object or sound the machine needs to learn to recognize. Instead of describing to the program every individual feature of a cat, for example, the Deep Learning network is fed with millions of examples of cats. The network teaches itself to recognize the features that typically identify a cat. This unsupervised learning approach turns out to be much more effective in reality. It’s impossible for a human to code every possible feature of every possible cat in every possible position. A Deep Learning network that has trained itself to pick out a cat by looking at millions of examples is less likely to be fooled.
So what does the Deep Learning technology have to do with intelligent virtual agents and web self-service? Though many virtual agents are still text based, the tide is slowly shifting towards voice activated apps. Customers want their questions to be understood. High performing speech recognition technologies may very well be the backbone of customer-facing and enterprise virtual agents of the future.