On the contrary, I’d say a reduction in “applied” work and a re-focus toward research would be quite consistent with an “AI winter” scenario. There’s always open-ended research somewhere; a big part of an AI “boom” narrative is trying to apply the method of the day to all sorts of areas (and the method of the day mostly failing to make economically meaningful headway in most areas).
To put it differently: the AI boom/bust narrative usually revolves around faddish ML algorithms (expert systems, SVMs, neural networks...). If people are cutting back on trying to apply the most recent faddish algorithms, and instead researching new algorithms, that sounds a lot like the typical AI winter story. On the other hand, if people are continuing to apply e.g. neural networks in new areas, and continuing to find that they work well enough to bring to market, then that would not sound like the AI winter story.
On the contrary, I’d say a reduction in “applied” work and a re-focus toward research would be quite consistent with an “AI winter” scenario. There’s always open-ended research somewhere; a big part of an AI “boom” narrative is trying to apply the method of the day to all sorts of areas (and the method of the day mostly failing to make economically meaningful headway in most areas).
To put it differently: the AI boom/bust narrative usually revolves around faddish ML algorithms (expert systems, SVMs, neural networks...). If people are cutting back on trying to apply the most recent faddish algorithms, and instead researching new algorithms, that sounds a lot like the typical AI winter story. On the other hand, if people are continuing to apply e.g. neural networks in new areas, and continuing to find that they work well enough to bring to market, then that would not sound like the AI winter story.