I've seen internal work at large companies like Amazon and my sense was that the financial impact was very meaningful. Deep Learning is in many ways a new economy of scale and a 5% reduction in say warehouse space or reduced fraud at Amazon scale is a LOT of money in comparison to the researchers and compute that went into building that model.
Not to mention the impact of completely new technologies like voice assistants, automated high-quality image modification, automated computer vision systems (ones that have produced real value like automated industrial maintenance notification systems).
> In many cases they are delivering 1-10% gains over prior techniques.
As a general statement, that is false. At least among the companies that are good at DL.
As for alphastar, I somewhat agree - it's very early and that tech hasn't left the lab yet AFAICT. But if you could build an agent that was human average at some common human task (e.g. driving) that is easily worth hundreds of millions of dollars if not billions.
It takes time to go from the lab to commercial usage. Older breakthroughs like CV and transcription/TTS has made that transition successfully. More recent breakthroughs like like RL agents, NLP, style transfer are still making that transition (at varying paces). And there continue to be new research breakthroughs like protein folding that are still many years from making their way into industry, but the continuous nature of these breakthroughs bode very well for the future of DL.
Not to mention the impact of completely new technologies like voice assistants, automated high-quality image modification, automated computer vision systems (ones that have produced real value like automated industrial maintenance notification systems).
> In many cases they are delivering 1-10% gains over prior techniques.
As a general statement, that is false. At least among the companies that are good at DL.
As for alphastar, I somewhat agree - it's very early and that tech hasn't left the lab yet AFAICT. But if you could build an agent that was human average at some common human task (e.g. driving) that is easily worth hundreds of millions of dollars if not billions.
It takes time to go from the lab to commercial usage. Older breakthroughs like CV and transcription/TTS has made that transition successfully. More recent breakthroughs like like RL agents, NLP, style transfer are still making that transition (at varying paces). And there continue to be new research breakthroughs like protein folding that are still many years from making their way into industry, but the continuous nature of these breakthroughs bode very well for the future of DL.