It’s not as clear cut on the commercial impact front. While reading the literature can sometimes give the impression that NNs have delivered game changing performance. In many cases they are delivering 1-10% gains over prior techniques.
Alternatively in fields like speech to text the most impressive neural results are commercially impractical due to the cost of inference.
It cost roughly 30 million dollars to train alpha star which rated in the top .3% of Starcraft players. Presuming that the technology reasonably transferred to other fields ( lack of prepplayed pro games would be a problem ) there are few reasons to believe one could gain 30 million dollars of value from the result.
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.
Alternatively in fields like speech to text the most impressive neural results are commercially impractical due to the cost of inference.
It cost roughly 30 million dollars to train alpha star which rated in the top .3% of Starcraft players. Presuming that the technology reasonably transferred to other fields ( lack of prepplayed pro games would be a problem ) there are few reasons to believe one could gain 30 million dollars of value from the result.