This AI Boom Will Also Bust

What? I'm sorry but this runs counter to everything in my experience, both professionally, and just casual very day experience.

More data, helps to a point, but then there's diminishing returns, and it certainly doesn't eliminate the ambiguity. On the contrary, you discover diversity, and you still have a misclassification and perhaps even a harder data cleaning problem, because now you're seeing cases that aren't actually clear cut. Even if you're only talking about adding more features, well again, that works up to a point, but then you hit sparsity issues.

Off the top of my head the closest analogy would be number theory, especially the study of prime numbers. Before information technology in general, number theory was esoteric and considered useless by many pure and applied mathematicians (I'm simplifying a bit for argument's sake) but all of that accumulated research proved massively useful once we started to communicate electronically. WWI and II cryptographers didn't become or absorb number theorists as a group, they just adapted the knowledge to their field under the umbrella of electronic warfare. I think this is happening with data scientists, who are starting to experiment with ML but it's still just another tool in their toolbox. The media hype train focuses on the flashy AI contests and muddles the terminology but the real work [1] is happening behind the scenes in data science.

[1] By "real work" I mean work that directly translates into value on a company balance sheet. The theoretical work is important in and of itself and has been happening for decades.

Arguing for more linear regression to solve a firms problems, is equivalent to arguing for machine learning. Now, if instead he wanted to argue that the vast majority of a businesses prediction problems can be solved by simple algorithms, that is most likely true. but economic impact of this is still a part of the economic impact of machine learning.

Yes, DL has proven itself to perform (most?) gradient-based tasks better than any other algorithm. It maximizes the value in large data, minimizing error brilliantly. But ask it to address a single feature not present in the zillion images in ImageNet, and it's lost. (E.g. Where is the person in the image looking? To the left? The right? No DN using labels from ImageNet could say.) This is classic AI brittleness.

With all the hoolpa surrounding DL's successes at single task challenges (mostly on images), we've failed to notice that nothing has really changed in AI. The info available from raw data remains as thin as ever. I think soon we'll all see that even ginormous quantities of thinly labeled supervised data can take your AI agent only so far -- a truly useful AI agent will need info that isn't present in all the labeled images on the planet. In the end the agent still needs a rich internal model of the world that it can further enrich with curated data (teaching) to master each new task or transfer the skill to a related domain. And to do that, it needs the ability to infer cause and effect, and explore possible worlds. Without that, any big-data-trained AI will always remain a one trick pony.

Alas, Deep Learning (alone) can't fill that void. The relevant information and inferential capability needed to apply it to solve new problems and variations on them -- these skills just aren't present in the nets or the big data available to train them to high levels of broad competence. To create a mind capable of performing multiple diverse tasks, like the kinds a robot needs in order to repair a broken toaster, I think we'll all soon realize that DL has not replaced GOFAI at all. A truly useful intelligent agent still must learn hierarchies of concepts and use logic, if it's to do more than play board games.

I'm pretty sure "grownups playing with very expensive toys" accurately characterizes >100,000 software employees in the US right now.