If anything, machine learning is applied to real world problems these days more than it ever was.
For better or worse, AGI is a hard problem, that's going to take a long time to solve. And we're not going to solve it without exploring what works and what doesn't.
And there are definitely researchers, top ones no less, who play along with the hype. Very likely to secure more funding, and more attention for themselves and the field. Which has turned out to be quite an effective strategy, if you think about it.
The other upside of this hype is that it ends up attracting a lot of really smart people to work on this field, because of the money involved. So each hype cycle leads to greater progress. The crash afterwards might slow things down a bit, particularly in the private sector. But the quantum of government funding available changes much more slowly, and could well last until the next hype cycle starts.
The hype certainly attracts people who are "smart" in the sense that they know how to profit from it, but that doesn't mean they can actually do useful research. The result is, like the other poster says, a huge number of papers that claim to have solved really hard problems, which of course remain far from solved; in other words, so much useless noise.
It's what you can expect when you see everyone and their little sister jumping on a bandwagon when the money starts pouring in. Greed is great for making money, but not so much for making progress.
Could the answer be holding these papers to a stricter standard during peer review?
Unfortunately, while machine learning is a very active research field that has contributed much technology, certainly to the industry but also occasionally to the sciences, it has been a long time since anyone has successfully accused it of science. There is not so much a deficit of scientific rigour, as a complete and utter disregard for it.
Machine learning isn't science. It's a bunch of grown-up scientists banging their toy blocks together and gloating for having made the tallest tower.
(there, I said it)
As to traditional publications in the field, these have often been criticised for their preference for work reporting high performance. In fact that's pretty much a requirement for publication in the most prestigious machine learning conferences and journals, to show improved performance against some previous work. This strongly motivates researchers to focus on one-off solutions to narrow problems, so that they can typeset one of those classic comparison tables with the best results highlighted, and claim a new record in some benchmark.
This has now become the norm and it's difficult to see how it is going to change any time soon. Most probably the field will need to go through a serious crisis (an AI winter or something of that magnitude) before things seriously change.
In a vast error landscape of non-working models, a working model is extremely rare and provides valuable information about that local optima.
The only way publishing non-working models would be useful would be to require the authors to do a rigorous analysis of why exactly the model did not work (which is extremely hard with our current state of knowledge, although some people are starting to attempt this).
And yet the field seems to accept that a research team might train a bunch of competing models on a given dataset, compare them to their favourite model and "show" that theirs performs better - even if there's no way to know whether they simply didn't tune the other models as carefully as theirs.