I agree machine learning isn't going anywhere - and yes the big companies are going to solve increasingly complex problems using increasingly complex methods using their data and resources.
The trend I'm seeing is medium sized companies (think 10's of millions to low billions market cap) not solving simple, highly impactful problems using potentially simple machine learning techniques. A basic regression model that was state of the art 75 years ago can often add millions of dollars in immediate value if applied to the right problems. Instead, these companies often prefer to do what they know best, which is hire people to perform repeatable-but-not-easily-scriptable tasks.
The moment machine learning-illiterate executives start getting burned (one CEO to the other: "oh yeah, we spent X dollars on <insert hyped machine learning platform here> and it was useless"), is the moment that the hype can start destroying value.
> The moment machine learning-illiterate executives start getting burned (one CEO to the other: "oh yeah, we spent X dollars on <insert hyped machine learning platform here> and it was useless"), is the moment that the hype can start destroying value.
This is a perfect description of how AI winters come to be. However, I think the upcoming winter will be more like an AI Fall. Some technologies over hyped and over sold will fall out of favor and people will be skeptical. However, companies like Google, Facebook, Microsoft, Apple who are using AI internally and can quantify improvements will continue to do so. The hype cycle is definitely going toward the slowly rising plateau rather than another crash. There is critical mass. Maybe this is what people though before in AI hype cycles (we are definitely in one), but in previous cycles we didn't have near human image recognition essentially solved.
The trend I'm seeing is medium sized companies (think 10's of millions to low billions market cap) not solving simple, highly impactful problems using potentially simple machine learning techniques. A basic regression model that was state of the art 75 years ago can often add millions of dollars in immediate value if applied to the right problems. Instead, these companies often prefer to do what they know best, which is hire people to perform repeatable-but-not-easily-scriptable tasks.
The moment machine learning-illiterate executives start getting burned (one CEO to the other: "oh yeah, we spent X dollars on <insert hyped machine learning platform here> and it was useless"), is the moment that the hype can start destroying value.