|Jack Ganssle's Blog
This is Jack's outlet for thoughts about designing and programming embedded systems. It's a complement to my bi-weekly newsletter The Embedded Muse. Contact me at firstname.lastname@example.org. I'm an old-timer engineer who still finds the field endlessly fascinating (bio).
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Can AI Replace Firmware?
May 1, 2019
Scott Rosenthal and I go back about a thousand years; we've worked together, helped midwife the embedded field into being, had some amazing sailing adventures, and recently took a jaunt to the Azores just for the heck of it. Our sons are both big data people; their physics PhDs were perfect entrees into that field, and both now work in the field of artificial intelligence.
At lunch recently we were talking about embedded systems and AI, and Scott posed a thought that has been rattling around in my head since. Could AI replace firmware?
Firmware is a huge problem for our industry. It's hideously expensive. Only highly-skilled people can create it, and there are too few of us.
What if an AI engine of some sort could be dumped into a microcontroller and the "software" then created by training that AI? If that were possible - and that's a big "if" - then it might be possible to achieve what was hoped for when COBOL was invented: programmers would no longer be needed as domain experts could do the work. That didn't pan out for COBOL; the industry learned that accountants couldn't code. Though the language was much more friendly than the assembly it replaced, it still required serious development skills.
But with AI, could a domain expert train an inference engine?
Consider a robot: a "home economics" major could create scenarios of stacking dishes from a dishwasher. Maybe these would be in the form of videos, which were then fed to the AI engine as it tuned the weighting coefficients to achieve what the home ec expert deems worthy goals.
My first objection to this idea was that these sorts of systems have physical constraints. With firmware I'd write code to sample limit switches so the motors would turn off if at an end-of-motion extreme. During training an AI-based system would try and drive the motors into all kinds of crazy positions, banging destructively into stops. But think how a child learns: a parent encourages experimentation but prevents the youngster from self-harm. Maybe that's the role of the future developer training an AI. Or perhaps the training will be done on a simulator of some sort where nothing can go horribly wrong.
Taking this further, a domain expert could define the desired inputs and outputs, and then a poorly-paid person do the actual training. CEOs will love that. With that model a strange parallel emerges to computation a century ago: before the computer age "computers" were people doing simple math to create tables of logs, trig, ballistics, etc. A room full all labored at a problem. They weren't particularly skilled, didn't make much, but did the rote work under the direction of one master. Maybe AI trainers will be somewhat like that.
Like we outsource clothing manufacturing to Bangladesh, I could see training, basically grunt work, being sent overseas as well.
I'm not wild about this idea as it means we'd have an IoT of idiots: billions of AI-powered machines where no one really knows how they work. They've been well-trained but what happens when there's a corner case?
And most of the AI literature I read suggests that inference successes of 97% or so are the norm. That might be fine for classifying faces, but a 3% failure rate of a safety-critical system is a disaster. And the same rate for less-critical systems like factory controllers would also be completely unacceptable.
But the idea is intriguing.
Feel free to email me with comments.
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