Jeff Beck: preference

Transcript context

…do like that it's a computational that ended up working out. So And just quickly riff on the benefits of having models that preference causal relationships? So the nice thing about causal, so when you have a causal relationship it reduces the number of variables you have to worry about and track. That's the beauty of having a causal. It's like a Markov. It's same argument with momentum and Markov models. We chose to have that hidden variable because the thing that made the model simpler. Right? It made the calculations easy. Now we can just like go forward in time, just make predictions in a in a in a totally like iterative fashion. That's what makes causal models great. The other thing that makes causal models great is if you do ever intend to sort of, you know, act or behave, right, then you still need to be, you know, you need to be able to, predict the consequences of your action. The more tightly linked your actions or your affordances are to the things that causally impact the world, the more effective those actions are with respect to your model, but hopefully also with respect to reality. And so we prefer causal models, you know, in part because they are relative, you know, relatively speaking simpler to execute, right, in in in a simulation form, but also because they they point directly to, well, where should I intervene? You know, where should I go in? And And you know and how should I choose my like series of actions that will give me the desire, that will lead me to the desired conclusion or goal. What's the difference between micro causation and macro causation?…

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