Most of what we call “AI” today also uses prelearned weights for their neural networks and in many use cases these weights are not touched after deployment.
I don’t see why a neural network encoded in glas should not be an AI while the same neural network on a computer is one — either you have to call both AI or neither.
But you’re right, I think many “AIs” shouldn’t really be named that either!
It is like using AI to design, say, the most aerodynamic plane. Only here they used an AI to design something that performs a task that we traditionally use as a benchmark for AI models. But this piece of glass, just like the plane mentionned above, is not learning anything and it's not an AI.
If I understand correctly, it’s the design process, not glass, that used learning. Along the same analogy, I guess the sculpture in London (glass, here), which was designed using random walk (neural nets), would be the same: the sculpture in itself isn’t “random walk”, but the design process was.
(I couldn’t recall what was the name of the sculpture. Here’s the wiki link: https://en.m.wikipedia.org/wiki/Quantum_Cloud )
Edit: I read the other comments and it’s getting more confusing! AI, from my school courses, would be implementation of algorithms like Hill climbing where a system is online: it takes some input, and tries its best to find a solution. Now if I take the output itself for use in, say, signal processing — that “output” would be a “device” to do something and won’t be an “AI device”. Does this make any sense at all? I’d love to get some pointers on this to read.
That's exactly as self-assessing and self-modifying as a neural network implemented using any other kind of computation substrate.
I suppose the question is, if an AI has learned and you export that final learned state to use in a now-hardcoded classifier, is that classifier still AI (or part of the overall AI) or is it simply the output? I can imagine arguments on both sides. If you accept that as AI, then sure, this fits the bill!
Most of what we call AI are hardcoded solutions once in production. There may be ongoing offline improvements being made, but once the improvements are established the production AI is replaced with a point-in-time snapshot of the AI undergoing offline training. Self-learning in production causes all kinds of problems, but most significantly it's a security issue since it gives an attacker the ability to manipulate behavior by curating examples.
FWIW having worked with some serious ML heads at Google and Facebook in the past, none of them referred to ML as AI.
And you're a peace of matter that has been designed by a lot of trial and error to perform one specific task: to propagate your species.
The parent is not saying the glass is useless, just that it isn't intelligent
and silicon is?
Just seems a bit like being told you can buy a flying car, only to find out that it's a short flight time, not flying very high and depends upon approaching a ramp at speed.
> Training the AI involved presenting the glass with different images of handwritten numbers. If the light did not pass through the glass to focus on the correct spot, the team adjusted the size and locations of impurities slightly. After thousands of iterations, the glass “learned” to bend the light to the right place. (...)
This sounds pretty much like they're training a neural network.
This single piece of glass can do as many visual recognitions as physics allows, using no energy (light aside).
It's definitely AI - it's still recognizing a handwritten digit. But not all AI applications require further training. Sometimes you just need the end result.
- Thinking Humanly - the ability to problem solve, introspection, and learning (Cognitive Science)
- Thinking Rationally - the ability to perceive, reason, and act (Logic)
- Acting Humanly - simply do things that (at the moment) humans do better (Turing Machines)
- Acting Rationally - the design of intelligent agents (the focus of the textbook, Betty's Brain[2])
Yes, they all vaguely sound the same, but the point is, if you took the glass and had to select which definition it marks off, which would it be? My point is what about this glass makes it "intelligent"? The end of the article starts to talk about a combination of AI-glasses could form some sort of efficient image recognition process. Since we still don't have a clear definition of what intelligence is, is it simply a combination of tiny little perceptrons (from neural networks) that have specific differentiation tasks?
https://www.e-flux.com/journal/101/273221/three-thousand-yea...
Because this system can’t adapt, I agree it is probably not meaningfully “intelligent”.
source, me: Stanford CS grad.