Like “cloud” and “big data” a few years ago, “AI” has been pretty hype-y for the past couple of years. It’s always important to look beyond the hype, to try to understand if there’s a real technical advance underlying the trend, and what it is. In the case of AI, there sort of is actually, as this figure shows:For complete article https://connectivity.substack.com/p/ai-omnipotent-or-impotent?

Basically, between 2011 and 2015, computers got to be about as good at recognizing objects in images as people are. They also got a lot better at understanding language; the two problems are similar. I worked in a speech lab for a summer in 2006, and it’s hard to overemphasize how much different this curve is than the progress back then. At that time, entire Ph.D. theses were written about low-single-digit improvements on standard tasks like this.

That’s cool! And that’s what all the hype around RNNs and so forth has been about. For the IoT world, this means that products which involve imaging in some way have gotten a lot better. For instance PIR sensors were state of the art a few years ago for occupancy sensing; now you can buy a VergeSense or PointGrab that does a great job not just of occupancy detection, but people counting or lots of other features you can extract from an image. Verkada will upgrade your CCTV system with structured data, extracted from the live feed. Nest will identify children, dogs, and fire alarms. There are plenty more floating around.

So, are we close to the omniscient AI overlord? In a word, no.

Data apps

When I think about “AI” applications, it’s useful to divide them into a few types. The first, and easiest to deploy, are “sensor enriching” applications — things like Nest, VergeSense, and Moen Flo; these use AI to enrich an existing sensor stream with structured events — frequently but not always from images.

 Flo is actually my favorite residential IoT app so far, and extracts water-fixture-level data from the main water meter on your house, and shuts off the water when it detects a leak. Stuff like this has appeared in the literature for decades (for instance in 2011), but now you see it reaching the mass market. I would also place techniques like certain types of fault detection into this bucket (some of which involve more “AI” than others).

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