Machine Learning Street Talk / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
12 published records
01 / belief
“I don't believe, you know, I I believe you can have a model of agency and not have an agent.”
02 / belief
“My take on it is is that an energy based model and a Bayesian model have a lot in common.”
03 / preference
“Right? So for example, when we think of how the difference between, like, us and, like like, really, like, amoebas, we we often cite things like planning, counterfactual reasoning, goal oriented behavior.”
04 / evaluation
“Geometric deep learning is a big part of like is a big part of the stack if for no other reason than when we talk about, like, modeling the physical world. That means, like, incorporating the symmetries that exist in the physical world.”
05 / evaluation
“Because you know what's going on, you could say, oh, well, it's it's clearly like executing, you know, this is it's doing planning and counterfactual reasoning.”
06 / preference
“I think my intuition is if it feels to me that a function, a simple input output mapping can't be an agent.”
07 / evaluation
“The ARC was actually really amazing because it's the only intelligence benchmark that has survived for 5 years before being defeated.”
08 / preference
“In fact, time someone hands me a new neural data set, the first thing I do I'm not ashamed to admit, I run PCA on and pass it through a VAE, and then sort of take a look, right?”
09 / evaluation
“I think the counterfactual thing is an important feature here because we could take something which was conscious or something which had agency, and we could just take a trace of the actual path which was found.”
10 / preference
“I don't like talking about these dystopian futures because honestly, I think people are too clever, and I think people are too motivated, and people are too interested in how the world really works, and then people are too interested in actually understanding things that they will never stop.”
11 / preference
“And the best 1 is so are you familiar with maximum entropy inverse reinforcement learning? I like to call it active inference because it's really similar.”
12 / evaluation
“I like thinking about the distinction between an energy based model and a and a traditional sort of feed forward neural network has to do with where your cost function is applied, right?”