I can imagine DM deciding that some very applied department is going to be discontinued, like healthcare, or something else kinda flashy.
With Mustafa Suleyman, the cofounder most focused on applied (and leading DeepMind Applied) leaving for google, this seems like quite a plausible prediction. So a refocusing on being a primarily research company with fewer applied staff (an area that can soak up a lot of staff) resulting in a 20% reduction of staff probably wouldn’t provide a lot of evidence (and is probably not what Robin had in mind). A reduction of research staff, on the other hand, would be very interesting.
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.
With Mustafa Suleyman, the cofounder most focused on applied (and leading DeepMind Applied) leaving for google, this seems like quite a plausible prediction. So a refocusing on being a primarily research company with fewer applied staff (an area that can soak up a lot of staff) resulting in a 20% reduction of staff probably wouldn’t provide a lot of evidence (and is probably not what Robin had in mind). A reduction of research staff, on the other hand, would be very interesting.
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.