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> ML/AI has no conception of Time: False. Time can be included as an input. Previous system state can also be communicated forward in various ways (ex the LSTM
by Reelin 6y ago
> ML/AI has no conception of
Time: False. Time can be included as an input. Previous system state can also be communicated forward in various ways (ex the LSTM architecture).
Conceptual context: I'll agree, since that seems like it would require general abstract reasoning ability (ie strong AI). Similarly, robust treatment in the general case of physical continuity and cause and effect both seem like special cases of conceptual context to me.
That being said, bear in mind that we already have examples of not so robust treatment for special cases of physical continuity as well as cause and effect. (Example: https://news.developer.nvidia.com/transforming-standard-video-into-slow-motion-with-ai/ https://news.developer.nvidia.com/transforming-standard-vide...) Who knows how close (or far) we might be from a general solution?
- novia 6y ago> Time: False. Time can be included as an input. I mean that they don't understand time as something that's unidirectional. (To the best of my knowledge! Would be happy to be proven wrong.)
- Reelin 6y agoWell I guess without abstract reasoning ability (and thus no conceptual context in the human sense) "understanding" in the way the word is typically used is presumably impossible. But I don't see any reason you couldn't train a model to incorporate the underlying assumption that the flow of time is unidirectional. I expect you would just need inputs (ie training data) reflecting that fact coupled with an appropriate loss function. (Aren't networks that predict future physical states, such as motion, more or less doing this?)