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Comment by Retric | Hacker News Reader

AI was also having huge success before the last AI winter. It’s mostly a question of buzzword turnover not underlying technology.

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You need to understand "AI winter" as referring to Academic and (to a lesser extent) private funding of AI research, specifically academic AI research. It goes through these booms of optimism "We'll have self-driving cars in 5 years! Here's a hundred million" and then pessimism "It's been 15 years and all we have is driver assist features, we're going to fund grants in more practical areas now" - the pessimism is followed by a drying up of research funds for AI, even though there is not a drying up of research in general. This winter is very real if you are trying to get a job in AI research, even thought it's impact is quite limited as most CS research is not and has never been AI focused. I would say that CS in general is a very fad-driven field so this phenomenom is to be expected.

Not quite as all funding is cyclical. My point was the bust cycle had little to do with marketable products last time, and the same is true this time around. Consider the ideal outcome, once you have actual self driving cars, funding for self driving car research dries up. Success or failure doesn’t actually matter here, the funds are going away.

The post Facebook boom and bust cycle around social networks wasn’t such a big deal for those involved because skills transfer. The issue is getting a PHD in AI related topics is far less so. Time it just right and a 500k/year job is on the table, but get the cycle wrong and it’s a waste of time and money.

I wasn't around back then, I'm curious what were the business use-cases of AI/ML back then?

Spam filtering and route-planning are big successes from previous generations of techniques labelled as "AI".

Optical sorting of fruit and various other things is a great example where early AI techniques made significant real world progress. It’s not sexy by today’s standards, but is it’s based on a lot of early image classification work.

We are talking about money-making applications here. Not progress.

By progress I mean actual money making products. If your widget is doing shape recognition based on training sets and your competitors are hard coding color recognition then you end up making more money.

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