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The Mathematical Foundations of Intelligence [Professor Yi Ma]
13 Dec 2025 10 published claims 2 attributable people
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“So we want to have understanding which carves the world up by the joints, which represents the important invariances in the world. And the thesis is, I think, that compression might be necessary for understanding.”
- Publisher
- Machine Learning Street Talk
“Has low dimensional structures that allow us to predict, to rule out variabilities, to predict world tomorrows, or predict world better, in essence. So in a sense that that is the ability we believe is really what intelligence is all about, at least the common intelligence we're talking about, right?”
- Publisher
- Machine Learning Street Talk
“I think we're sort of managed to do that, at least for the structure we've discovered so far, provide rather unified explanation to what they have done.”
- Publisher
- Machine Learning Street Talk
“Because there is still compression, then you get a convolution, naturally, as the structure for the compression operator.”
- Publisher
- Machine Learning Street Talk
“I think we have reached the point, right, we'll be able to address what's next for understanding even more advanced forms of intelligence.”
- Publisher
- Machine Learning Street Talk
“Though, from our lesson, we realized, indeed, actually, that actually those option function has very benign landscapes.”
- Publisher
- Machine Learning Street Talk
“We we just find that structure. And that's why when I watched your presentation, I was very intrigued when you said that denoising, iterative denoising is is a form of of compression.”
- Publisher
- Machine Learning Street Talk
“If you think about the whole diffusion denoising model, right, people are very popular right now to do why do we add noise to data, right, and to the whole world? Because we don't know where the distribution is, right?”
- Publisher
- Machine Learning Street Talk
“The role of epsilon actually plays different roles, right? And I think definitely in the past many years, our understanding about the subject, how do we compress, how do we pursue the low dimensional structure from finite samples, it's quite our understanding about this problem has truly advanced dramatically.”
- Publisher
- Machine Learning Street Talk
“You never over so compression, by nature, if the operator are performing compression or denoising, which means this process will no longer overfit anything, right, if you conduct it right, if you converge, the solution will converge on the structure”
- Publisher
- Machine Learning Street Talk