1. Games are great for studying intelligent agent behavior and intelligence itself. An intelligent agent will find hidden abstractions and patterns in a large d

1. Games are great for studying intelligent agent behavior and intelligence itself. An intelligent agent will find hidden abstractions and patterns in a large dimensionality. Language understanding, vision, decision science, solving long-temporal credit assignment etc. all are present. Russell & Norvig (2003) considered goal-directed behavior as the essence of intelligence. Also see possibly the first papers on AI and their focus on parlor games: https://en.wikipedia.org/wiki/Theory_of_Games_and_Economic_Behavior https://en.wikipedia.org/wiki/Theory_of_Games_and_Economic_B... & http://www.loebner.net/Prizef/TuringArticle.html http://www.loebner.net/Prizef/TuringArticle.html 2. This platform will provide a test bed for AI algo's and help "democratize" AI. One does not need to set up their own platform. One can compare approaches. One can learn from other implementations running on a common ground. Being resource constrained forces one to be more creative and this paves the way for more energy-friendly methods. Sure, a high school student will not dominate the power houses like DeepMind et al. But the high school student can get up and running in a week or few days. 3. https://arxiv.org/abs/1608.08225 https://arxiv.org/abs/1608.08225 Physics and Deep Learning are well entwined. Deep Learning certainly is a big idea, up there with VC theory and boosting. It exists for decades now, I agree the more recent incarnation was made possible with more computing power and better bigger datasets, and relies less on new tricks. Yet, still tricks are being invented in the recent years that have majorly contributed to better generalization. Dropout being one. DL, and especially the relevant Deep Reinforcement Learning, is not just GPU's, but a lot of new (and budding) theory. One can run Random Forests (and other approaches) on CUDA too. The Neural Turing Machine heralded a whole new, intellectually deep and stimulating, field in Deep Learning, and we haven't seen the best of it yet. There are also fields, like vision, where the other approaches are significantly underperforming relative to DL. Try to train a SVM or RF on ImageNet. Also, one is not required to use DL for your agent. Experiment with the classic approaches and see which is better (https://arxiv.org/abs/1603.04119 https://arxiv.org/abs/1603.04119). I actually shared some of your concern, not for AI research, but for game development. I thought it was very hard to actually get a good job in that field. Then the mobile game market started booming, and indy developers could make a living. AI has got the backing of all the major players in industry. Instead of pipe dreams and philosophical meanderings, we have actually business-value adding working models now. It is not going anywhere soon. If all else fails, you remain a good coder or data analyst with a lot of automation skills.