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farmingvillein t1_ixgd88a wrote

Yeah, understood, but that wasn't really what was going on here (unless you take a really expansive definition).

They were basically doing a ton of hand-calibration of a very large # of models, to achieve the desired end-goal performance--if you read the supplementary materials, you'll see that they did a lot of very fiddly work to select model output thresholds, build training data, etc.

On the one hand, I don't want to sound overly critical of a pretty cool end-product.

On the other, it really looks a lot more like a "product", in the same way that any gaming AI would be, than a singular (or close to it) AI system which is learning to play the game.

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graphicteadatasci t1_ixh2mk4 wrote

But they specifically created a model for playing Diplomacy - not a process for building board game playing models. With the right architecture and processes then they could probably do away with most of that hand-calibration stuff but the goal here was to create a model that does one thing.

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farmingvillein t1_ixictvr wrote

Hmm. Did you read the full paper?

They didn't create a model that does one thing.

They built a whole host of models, with high levels of hand calibration, each configured for a separate task.

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