[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.