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But it’s not memory. Currently you just show your neural net a million examples of a thing and it derives a function which, given an example input, minimizes th
by throwaway84742 8y ago
But it’s not memory. Currently you just show your neural net a million examples of a thing and it derives a function which, given an example input, minimizes the output error. That’s it. It’s not like “last time I caught a ball in a similar situation I moved like this, so let me start with that and correct based on visuals, audio, proprioception, and cognition”, all within 20 milliseconds, before you even fully understand you’re about to catch a ball. That’s not to mention that you also maintain the illusion of a continuous and static visual field, without even noticing, in stereo.
- Erlich_Bachman 8y ago> last time I caught a ball in a similar situation I moved like this, so let me start with that and correct based on visuals, audio, proprioception, and cognition There are types of neural networks (and other algorithms) that work literally like that. Just because a simple deep perceptron does not work like that does not mean no network does. This one is just off the top of my head, it's quite recent but the memory part is based on old prior work. https://medium.com/applied-data-science/how-to-build-your-own-world-model-using-python-and-keras-64fb388ba459 https://medium.com/applied-data-science/how-to-build-your-ow... UPD: without even digging deep in the different types of networks, even AlphaGo(/Zero) works like that.