One major problem is not enough diversity in ML/DL research. I don't mean this from a social sense only. Most of the major development is lead by FAANG or research labs doing FAANG'ish work but real ML/DL work doesn't have trillion token datasets or fuss free gpu budgets. Industrial AI for egs is a field which is underperforming compared to advances in certain sciences and general b2C areas like NLP or recsys.

Even computer vision long thought to be solved still struggles in many applications to provide great solutions for example in segmentation of artifacts in.used catalysts .

We need more folks from industrial/ real life areas working with ML on small data/ extreme sparse phenomena or complex natural science systems in an interdisciplinary sense.

On a completely different but related note . If you look at ML for climate change, it's so far from what's required to actually make a change in climate. Stuff like using NLP for catalysts or conv lstm for weather like metnet from Google makes for great PR, but it's useless in the greater scheme of things. None of those ideas get us to developing and shipping climate tech related solutions in the short term. Perhaps if we had more multidisciplinary teams both in research as well as in management, because decision is made by non tech folks in general, we might have much better outcomes.

Narrow AI still has tremendous potential to change our world for the better. We are in the early stages of Cambrian explosion era of narrow AI is what I feel