Will engineers use AI to replace themselves?

A piece in IEEE Spectrum asked this question recently: Are engineers designing their own robotic replacements? There's no question that AI is transforming many engineering disciplines. Control engineering and manufacturing optimization have seen a number of new tools come out that are likely to change industrial practice significantly. And I frequently describe our own Reality AI as an artificial intelligence substitute or supplement for a signal processing engineer working on sensors and signals. (For a free trial, click here.)

AI stimulates demand, not suppresses it

But I think most practicing engineers have very little to fear. For starters, these tools are far more likely to stimulate demand for engineering skills than to replace engineering jobs. In our area of signal processing and working with sensors, the sheer increase in the number of connected devices and economic activity associated with deploying them will keep engineering teams quite busy for a long time to come, even with all the AI assistance we can give them. Gartner predicts more than 21 billion connected devices by 2020, and McKinsey says that will create economic opportunity in the trillions. In order to make that happen, AI will have to enable more engineering productivity -- there won't be enough of them to do the job without it.

Computers aren't brains, and vice versa

Plus, there's another important thing to remember. Computers aren't brains, and brains aren't computers. There's lots of things people can do that machines can't: creativity and social interaction among them. Oh, some AI systems can do a pretty good impersonation of these things. But there's something deeper here -- computers are information processors, but people are experiencers. We do things differently, and do different things well -- and always will.

Take for example the case of a baseball player catching a fly ball quoted in a recent essay "Your Brain does not Process Information and it is not a Computer": "The information processing perspective requires the player to formulate an estimate of various initial conditions of the ball’s flight – the force of the impact, the angle of the trajectory, that kind of thing – then to create and analyze an internal model of the path along which the ball will likely move, then to use that model to guide and adjust motor movements continuously in time in order to intercept the ball. That is all well and good if we functioned as computers do, but McBeath and his colleagues gave a simpler account: to catch the ball, the player simply needs to keep moving in a way that keeps the ball in a constant visual relationship with respect to home plate and the surrounding scenery... This might sound complicated, but it is actually incredibly simple, and completely free of computations, representations and algorithms."

Breakthroughs come from experiences, not from algorithms

Ultimately, engineering jobs will follow the path that many other white collar jobs have done as computer technology has encroached on their turf. Those jobs will become less rote, and there will be fewer of them required per unit of work produced. But the very act of creating the new technology that pushes this ratio down (also known as increasing productivity), will create the demand for more engineering.

#sensors #signalprocessing #artificialintelligence