How Large Are Lions? Inducing Distributions over Quantitative Attributes · HackerPapers discussions

How Large Are Lions? Inducing Distributions over Quantitative Attributes — mentioned by bravura

Fri, 14 Mar 2025 22:40:54 GMT

Okay I think I qualify. I'll bite. LeCun's argument is this: 1) You can't learn an accurate world model just from text. 2) Multimodal learning (vision, language, etc) and interaction with the environment is crucial for true learning. He and people like Hinton and Bengio have been saying for a while that there are tasks that mice can understand that an AI can't. And that even have mouse-level intelligence will be a breakthrough, but we cannot achieve that through language learning alone. A simple example from "How Large Are Lions? Inducing Distributions over Quantitative Attributes"…

Paper and discussion history. A mention is not an endorsement.

How Large Are Lions? Inducing Distributions over Quantitative Attributes — mentioned by bravura

Mon, 27 Mar 2023 01:22:23 GMT

Yes. In Elazar et al. (2019) "How Large Are Lions? Inducing Distributions over Quantitative Attributes" (https://arxiv.org/pdf/1906.01327.pdf), it requires 100M English triples to roughly induce the answer to this question. How many images do you think a model needs to see in order to answer this question? Of course, there are fact books that contain the size of the lion. But fact books are non-exhaustive and don't contain much of the information that can quickly be gleaned through other forms of perception. Additionally, multimodal learning simply learns faster. What could be a long slog…

Paper and discussion history. A mention is not an endorsement.