Timeline for What type of reinforcement learning can I do restricted to ~200MB on an average smartphone?
Current License: CC BY-SA 3.0
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| Sep 16, 2018 at 22:48 | comment | added | DukeZhou | @DuttaA thanks for the poke. When I originally asked it, I wasn't even sure how valid of a question it was. (Luckily, there's been some recent clarification of the approach in terms of the project, where learning algorithms will be the final part, and are still some ways off;) | |
| Sep 13, 2018 at 8:55 | comment | added | user9947 | Edited to poke the question. This is a question which can have lot of implications in portable ML algos. | |
| Sep 13, 2018 at 8:54 | history | edited | user9947 |
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| Oct 27, 2017 at 17:30 | comment | added | DukeZhou | @user12889 That's a great point and you're not the only one to indicate this NN capability. Part of this initiative will be to see if we can do "mini-NNs" on the local mobile devices, where the local automata get "tuned" by playing against their human, with the intent of leveraging evolutionary game theory (the idea being that each discrete automata is unique do to the "personalization".) But if we find we need to produce strong AI for an expert-player version of the product, the NN capability you mention will surely be extremely effective! | |
| Oct 27, 2017 at 2:32 | comment | added | user12889 | I just want to point out that training and executing don't have to be done on the same device. E.g. (as far as I know) Tensorflow allows you to train a NN on a powerful machine (e.g. PC with GPUs) and then deploy the trained NN on a much less powerful machine, e.g. an Android phone. | |
| Aug 23, 2017 at 13:38 | answer | added | A.Rashad | timeline score: 2 | |
| Aug 7, 2017 at 19:28 | history | asked | DukeZhou | CC BY-SA 3.0 |