The Ledger, every show
Every statement that passed quotation and attribution checks, across all 44 shows. Pick shows below, then mix any filter with any other.
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every show 44 of 44
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: 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: 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: Top AI graduate programs do not teach multi-node distributed training
“Oh, to be clear, they don't teach you anything, like anything, like if you see a paper coming out from even, you know, Stanford, they're probably the best school in AI if you had to choose. And it's not like they're learning how to do like multi-node distribut…”
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: CycleGAN mapping aligns disparate model embeddings without paired data
“We took it and we applied it to model embeddings where instead of zebras and horses, we have like BERT embeddings and GPT embeddings, or like two completely different models with different architectures. So I think these are GTR, which is a T five based retrie…”