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Re:High-Throughput Generative Inference of Large Language Mo... by noduerme on Tuesday March 14, 2023 @02:56AM (#35148192)
I think this can all be boiled down to "memory", and I think the only thing restraining it is that these models don't retrain and store full reweighted snapshots after each move... so basically a storage, not a processing bottleneck. I'd predict the next major innovation in the space will be some type of NN quick diff that lets you restore a memory and personality right into the weights, from a relatively small file without having to go through a whole training process and dialog every time you boot it. Right now it's like getting back to level 8 of Contra... or level 100 of Flappy Bird... but it's going to be a few simple hacks to jump it to the point you want it. Then there's memory, de-falsification, and what we'd describe as "personality".
Re:High-Throughput Generative Inference of Large Language Mo... by imtringued on Tuesday March 14, 2023 @03:29AM (#35148360)
In Forward Forward Networks, inference and training are the same operation.
Re:High-Throughput Generative Inference of Large Language Mo... by pksebben on Tuesday March 14, 2023 @04:05AM (#35148558)
can you recommend any papers on this?
Re:High-Throughput Generative Inference of Large Language Mo... by noduerme on Tuesday March 14, 2023 @09:07AM (#35150831)
I think when the spec is hammered out, we'll know about 12 hours later, because everyone's phone will ring at the same time and the missiles will launch.