Jack Morris
person on 2 shows · 13 statements across 1 episodes · said 11 times in 3 episodes since 2025
On the record as a speaker too: Jack Morris across every show, appearances and statements → this page counts the times other people say the name.
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Latent Space 8
TBPN 3
2026 10 mentions in 2 episodes 5 per episode
2025 1 mention in 1 episode
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13 statements about Jack Morris, every show
Jack Morris: Most AI research was previously open, but is now closed
“Most stuff was open. Now most stuff is not open.”
Jack Morris: Fundamental AI science shifted to companies due to academic compute limits
“That's when I think things really started to change in terms of the types of questions you wanted to ask can't always be answered with academic resources. So a lot of the like fundamental kind of like boundary pushing and AI science moved into companies.”
Morris: Re-implementing new paradigm shifts quickly is the best AI grad strategy
“Honestly, if I were to give advice to a younger grad student, I think the way to do it would be literally just like sit and wait until the next kind of paradigm shift and then just immediately start working as fast as you can to like re-implement it. Like, I d…”
Morris: Information in AI should be measured under computational constraints
“There's this theoretical framework proposed in this paper, which is a theory of usable information under computational constraints from 20, 20. It really doesn't have that much press. They're not aren't as many citations as you would think, but I think it's a …”
Morris: Machine learning lacks a fundamental unit of deep learning information
“I don't think we know what a bit is yet in terms of like deep learning models.”
Morris: New embedding inversion model exactly recovers 90% of source text
“Like we ended up building a system that can do this quite well, like taking an embedding and I think our highlight number is like at a certain length, like a long sentence length, we can get 90% of the text back exactly.”
Morris: Embedding inversion requires access to and repeated queries of the encoder
“Like none of the vector to text stuff works unless you have this assumption of like knowing the encoder and also being able to make a lot of queries to it.”
Morris: Language models hit a hard memorization plateau regardless of dataset scaling
“Like, no matter how you scale the training size, you hit this like perfect, perfect ish plateau in auto memorization, which we call the model capacity.”
Morris: No Evidence We Can Build Pure Reasoning Models Without World Knowledge
“I don't think we have a lot of evidence that we can build a system like this that like is really, really good at reasoning, but really dumb about the world. Like, I don't know if we have the tools.”
Morris: 32-bit transformer models store only 3.6 to 3.9 bits per parameter
“Transformers that are trained in 32 bit precision, we approximate can store about 3.6 bits of information to maybe 3.9 bits somewhere in there per parameter.”
Morris: Weight deltas can reconstruct a competitor's proprietary fine-tuning dataset
“There's some tricks to it, but it's basically just like gradient based selection based on this weight difference. And it seems to be okay. Like it can get us pretty good training data. So I guess if you actually wanted to use this, it would be like your compet…”
Morris: AI paradigm shifts are driven by novel datasets, not architectures
“I think, like, all of the things that I would consider paradigm shifts in the Kuhnian sense came from a new technique, but trained on new data, and I think the new data is super, super important”