The most recent IEEE Signal Processing Society Newsletter has an interesting article by David Suendermann, "Speech scientists are dead. Interaction designers are dead. Who is next?".
His argument is that "Commercial spoken dialog systems can process millions of calls per week", and therefore "one can implement a variety of changes at different points in the application and randomly choose one competitor every time the point is hit in the course of a call", using techniques like reinforcement learning to adaptively optimize the design. As a result, "the contender approach can change the life of interaction designers and speech scientists in that best practices and experience-based decisions can be replaced by straight-forward implementation of every alternative one can think of".
I yield to no one in my appreciation for Big Data, eScience (or in this case, I guess, eEngineering…), the Fourth Paradigm, and all that. And reinforcement learning is a fine technique, with interesting roots in the extraordinary Rescorla-Wagner model of classical conditioning (though there are other ideas around). Everyone should know about this stuff, and apply it where it works, in spoken dialog system optimization as elsewhere.
But I think that David goes (or implies going) way too far: IMHO, the massive data accumulation in the digital networking of the whole world is going to increase, not decrease, the demand for scientists and engineers. I don't have time to say anything more about it, for now, so feel free to discuss the question among yourselves.
- Even with all that data — and I agree it's growing fast as voice interaction on smart phones becomes ubiquitous — randomized search will be swamped by the combinatorial possibilities of interface design.
- The only way to manage the combinatorics is to impose intelligent biases on the search process. That is, we need engineers and designers who understand and know how to apply the relevant computer science and statistics.
- Automated tools do not achieve good designs by themselves, because we do not know how to quantify good design as a mathematical objective function, even if the combinatorial problem could be tamed. We need human designers to steer the tools, evaluate the results, and recognize potential disasters. Their training may be different, but they are not 'dead'.