The recent high level of funding (Stargate, HUMAIN, ...), seemingly prompted mostly by LLMs, could plausibly be an emperor's new clothes fear of missing out among investors - will have to wait and see how it pans out.
But for 2010s-era machine learning this article is talking about, I feel it largely already has been validated - from shunned and unfunded at the start of the decade to being the almost universal go-to for any NLP or computer vision task by the end. The article itself lists a few use-cases (protein folding, weather forecasting, drug discovery), and I think it's unlikely you've gone through the day without encountering at least a few more (maybe search engines query-understanding, language translation, generated video captions, OCR, or using a product that was scanned for defects).
Not that every ML method will work out first try when applied to a new problem, but it's far from the case that we're waiting 15 years hoping for someone to maybe find a use-case for the field.
But for 2010s-era machine learning this article is talking about, I feel it largely already has been validated - from shunned and unfunded at the start of the decade to being the almost universal go-to for any NLP or computer vision task by the end. The article itself lists a few use-cases (protein folding, weather forecasting, drug discovery), and I think it's unlikely you've gone through the day without encountering at least a few more (maybe search engines query-understanding, language translation, generated video captions, OCR, or using a product that was scanned for defects).
Not that every ML method will work out first try when applied to a new problem, but it's far from the case that we're waiting 15 years hoping for someone to maybe find a use-case for the field.