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Jeff Beck

Published podcast speaker

Claims
26
Episodes
2
Shows
1
Named items
0

Claim ledger

What Jeff said.

7 transcript-backed records

01 / 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.

“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.”
Speaker
Jeff Beck
Publisher
Machine Learning Street Talk

02 / 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?

“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?”
Speaker
Jeff Beck
Publisher
Machine Learning Street Talk

03 / 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.

“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.”
Speaker
Jeff Beck
Publisher
Machine Learning Street Talk

05 / preference

There's the work, you know, I mean, I've been using I've been using like natural gradient methods for a very long time, which allow you to like massively speed up gradient inference and in some situations completely eliminate the need to do gradient inference and instead like, you know, do coordinate descent and allowing you to take massive jumps in parameter space and not actually lose the the ability to do learning of sophisticated model in in a sophisticated modeling scenario.

“There's the work, you know, I mean, I've been using I've been using like natural gradient methods for a very long time, which allow you to like massively speed up gradient inference and in some situations completely eliminate the need to do gradient inference and instead like, you know, do coordinate descent and allowing you to take massive jumps in parameter space and not actually lose the the ability to do learning of sophisticated model in in a sophisticated modeling scenario.”
Speaker
Jeff Beck
Publisher
Machine Learning Street Talk

07 / preference

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?

“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?”
Speaker
Jeff Beck
Publisher
Machine Learning Street Talk
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