This paragraph scares me. I'm not convinced that technology getting good enough to where it doesn't need to be understood to be used is a good thing in most cases. Especially when it's a black box.
Banking computer closed your account. Why? We can't tell you why.
Google or Apple rejected your app from their app store. Why, won't tell you why.
Me trying to get my prescription refilled at Walgreens. Walgreens computer shows they never got it from the doctor. Doctor sent it 5 times. Turned out the computer was marking it invalid each time. Pharmacist isn't allowed to tell neither me or my doctor why.
And it doesn't take a deep CNN to become a black box - any sufficient complex algorithm will do. I doubt anybody in the world understands all the details of how the latest CPU actually works, for example.
You would not believe a human is intelligent if he just tells you what to do without being able to explain why, would you?
Will it matter whether the AI is "really" intelligent when companies that implement the black box to spec thrive, while the companies that insist on having a human understand everything fall behind and fail?
Or think of the “uncanny valley” in 3D graphics -- most people can tell when a rendered image looks slightly off somehow, but most people can’t pinpoint the exact problems. “Something about the eyes just isn’t quite right”, etc.
But experience people can. I've worked at a 3D animation studio and we had this 'old guy' who started his career as a classically trained artist and sculptor. He could look at a character model that we all agreed felt a bit 'off' and point out that the problem is how you'd modeled the muscles connecting the shoulders to the arm. Hell sometimes he'd look at a model that we all felt was fine and suggest tweaking the bridge of the nose and that made it much better.
That sounds like a hellish dystopia.
On the one hand, opaque bureaucratic processes already exist today. It's not like the bank that refuses you a loan will tell you "oh, but we would totally have granted it if you were 20% richer and also not a woman, so come back after you've achieved that and we'll give you the loan"!
On the other hand, even opaque models can be studied and systematized. You can't run a simulation of a judge on 100 sample cases, then on the same 100 sample cases with names changed to sound like immigrants, and numerically measure the judge's bias. You can do that with a ML model, and compensate in various ways.
That's because they don't want to tell you, not because they don't know. The bank knows exactly why they refused to give you a loan and exactly which series of policy changes led to that outcome. They also know exactly how to change the policy to achieve a different outcome.
None of those things are true with AIs.
I don't see how machine learning, at least in its current non-AGI state, will be any different. It's just that your average end-user will have no idea how to "open up the machine", but that's also true for compiler technology today.
And you are onto something when you say that "empirical records" can be learned by ML models, and a huge amount of grunt work is required for that. Every tom, harry, and dick can overfit to MNIST today, it used to be hard someday. And note the amount of grunt work it took to build MNIST- thousands of human hours. Building Datasets does not pay you back instantly, but it has benefits for all. I know some people who are paying money out of their own pockets and creating niche Datasets and making them available under MIT Lisence.
Hope that more and more companies and people do that for really niche fields.
And I don't completely agree with not understanding things that DL models do and relying on them. We have started to understand much of it. ML interpretation is a field with mot much success but I am hopeful. We did not know jack about how very deep CNNs work, but that changed with the Zeiler-Fergus paper[0]. Later with things like Grad-CAM[1]. Now we are trying to understand latent representation fully. We (even I) can create GAN generated pictures with different haircolor, different kinds of glasses, etc. from scratch.
I read not more than two days ago that Microsoft and Peking Uni researchers found a way to identify "knowledge neurons" in unsupervized pretrained embeddings in NLP and they can edit facts with that [2].
So, I am optimistic about our "interpretation" future.
[0]: https://cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf
What happens when this kind of tool will calculate your credit score, social score, or the proceeds of your savings ?
Computers are good at recording structured knowledge - who owns that, this merch goes where, things that were recorded on stone tablets since times immemorial. But human experts also have a different type of knowledge, empiric, accumulated through years of experience. Artisan crafters "know" how good some material is by sense of touch, smell, sight, without always being able to say WHY or HOW this wood is better than that wood for this table. This is why apprenticeship with a master was a key part of developing an artisan - you'd transfer SOME of this empirical knowledge from a master that took a lifetime to develop it.
The humanity took a huge step forward by moving a lot of empirical knowledge to structured knowledge through the use of models (mathematical?) and books. Instead of an apprentinceship with a mason, a builder now goes to school and learns how to structurally design a building based on construction codes. This allows huge scaling of knowledge, at the expense of missing subtle details which are not modelled (spherical cows in a void, right?).
With this article it just occured to me that Deep Learning may be the tool to record this type of empirical knowledge. And it's going to scale out - unlike the human version never did - because digital copying is cheap, and the knowledge doesn't die with the artisan that developed it - it only gets more accurate. The models we build about reality will get more and more accurate - the spherical cows will grow legs in the air, not in a void.
Humanity will start doing more and more things without understanding why, but because "the computer said so", and things work out when the computer says they will. Black boxes will explode in usage, and God forgive those that may be on the side where The computer says No.