[underdeserver]

Seriously? A narrow category of problems?

ML has been used and is being used to significantly advance image processing, video processing, image classification, speech-to-text, natural language understanding, medical imaging interpretation, medical notes and differential diagnosis, warehouse management, shipping and delivery, transportation, networking, agriculture, biomedical research, insurance, law practice (document scanning), journalism, politics (through better polling, targeting, gerrymandering, whatever), probably other things I'm missing.

[ssivark]

That list is flag planting of the first order — like a dog claiming territory as a kingdom after a few stray golden showers here and there.

Yes, ML has been applied to all those topics, but to narrow/superficial applications & with limited success (in most of those areas, any how). The applications have also been explored in relatively ad-hoc ways, with little improvement in systematic understanding/knowledge of any of those fields.

[whatever1]

Please. The vast majority of the above are fields where ML failed spectacularly.

If you had any idea about medical diagnosis, biomedical research, supply chain optimization, politics and journalism you would know that machine learning is a laughing stock in these fields.

ML had 2 big wins: (image & data) Classification & NLP. It is stupid to not use ML for these problems, but it equally stupid to try to fit ML in fields that it cannot work.

[emteycz]

Let's not claim something has failed when it has just begun... Given today's hardware and given that it's a very new topic of research, IMHO the accomplishments are incredible. It's not yet production ready, but that doesn't mean another 10 years of progress won't get it there.

[whatever1]

We need to invest in long term R&D to potentially achieve an ML breakthrough in one of the above fields instead of allocating enormous capital to ML unicorn businesses.

But to do so, we need to first openly admit the truth. ML is not working for the wide range the problems it is currently pitched for.

[wardnath]

Seconded. Today’s “narrow”applications are quite wide compared to the expert systems of decades ago. I wouldn’t say we are in a second AI winter when cool new applications of DNNs pop up frequently on HN.