Andrew Ellis
@awellis.bsky.social
Reposted by Andrew Ellis
Devastating for scientists and their research, and everyone who benefits from that research.
I’m not sure if everyone outside academia is aware that a delay or “pause” in grant funding often means the researchers themselves are lost from the field, along with their expertise.
I’m not sure if everyone outside academia is aware that a delay or “pause” in grant funding often means the researchers themselves are lost from the field, along with their expertise.
NIH appears to have canceled/postponed all of its study sections—the independent review panels that approve federal grants for health research.
Such grants fund the work/salaries of 300k people at more than 2,500 institutions
Such grants fund the work/salaries of 300k people at more than 2,500 institutions
All NIH study sections canceled indefinitely. This will halt science and devastate research budgets in universities.
January 22, 2025 at 11:24 PM
Devastating for scientists and their research, and everyone who benefits from that research.
I’m not sure if everyone outside academia is aware that a delay or “pause” in grant funding often means the researchers themselves are lost from the field, along with their expertise.
I’m not sure if everyone outside academia is aware that a delay or “pause” in grant funding often means the researchers themselves are lost from the field, along with their expertise.
Reposted by Andrew Ellis
This is a very cool integration of LLMs + Bayesian methods.
LLMs serve as *likelihoods*: how likely would the human be to have issued this (English) command, given a particular (symbolic) plan? No generation, just scoring :)
A Bayesian agent can then resolve ambiguity in really sensible ways
LLMs serve as *likelihoods*: how likely would the human be to have issued this (English) command, given a particular (symbolic) plan? No generation, just scoring :)
A Bayesian agent can then resolve ambiguity in really sensible ways
November 19, 2024 at 7:19 PM
This is a very cool integration of LLMs + Bayesian methods.
LLMs serve as *likelihoods*: how likely would the human be to have issued this (English) command, given a particular (symbolic) plan? No generation, just scoring :)
A Bayesian agent can then resolve ambiguity in really sensible ways
LLMs serve as *likelihoods*: how likely would the human be to have issued this (English) command, given a particular (symbolic) plan? No generation, just scoring :)
A Bayesian agent can then resolve ambiguity in really sensible ways