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Public evidence record

Akarsh Kumar

Published podcast speaker

Claims
6
Episodes
2
Shows
1
Named items
0

Claim ledger

What Akarsh said.

4 transcript-backed records

01 / belief

I think Ken would agree with the statement that if we really were to understand it, then we should have, then there should be exist like an algorithm that we can scale up right now that can recreate all the glamour of evolution.

“I think Ken would agree with the statement that if we really were to understand it, then we should have, then there should be exist like an algorithm that we can scale up right now that can recreate all the glamour of evolution.”
Speaker
Akarsh Kumar
Publisher
Machine Learning Street Talk

02 / belief

I mean, like, creating an open end algorithm which solves this issue, I think, Jeff Kloon on our paper, he calls it, like, the trillion dollar question or the trillion dollar algorithm because that's basically, like, how you, if you think that the UFR, like, the unified factor representations are akin to, like, a human, then that's basically, like, creating, like, a human representation.

“I mean, like, creating an open end algorithm which solves this issue, I think, Jeff Kloon on our paper, he calls it, like, the trillion dollar question or the trillion dollar algorithm because that's basically, like, how you, if you think that the UFR, like, the unified factor representations are akin to, like, a human, then that's basically, like, creating, like, a human representation.”
Speaker
Akarsh Kumar
Publisher
Machine Learning Street Talk

03 / belief

What we care about is the all the downstream stuff that we're gonna use it for later, which is much harder to quantify, much harder to formalize than just a training loss.

“What we care about is the all the downstream stuff that we're gonna use it for later, which is much harder to quantify, much harder to formalize than just a training loss.”
Speaker
Akarsh Kumar
Publisher
Machine Learning Street Talk
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