Amir Rahnama

11 Followers
30 Following
65 Posts
Ph.D. Student in Machine Learning and Interpretability,
KTH Royal Institute of Technology
Chris Chambers (@[email protected])

Attached: 1 image Following Elsevier's decision to raise the article processing charge for NeuroImage to $3,450, all editors (inc. chief editors) from NeuroImage and NeuroImage:Reports have resigned, effective immediately. I am joining this action and have also resigned. Full announcement: https://imaging-neuroscience.org/Announcement.pdf

A science community for science communication.
We did this.
Three key facts about the new year. Enjoy!
2024 = 8 x 11 x 23.
2024 = 2³+3³+4³+5³+6³+7³+8³+9³
2024 is a tetrahedron (pile of 2,024 oranges).
The guys peddling this software don't know the difference between spam, "content," and actual creative work. It's all spam to them already, or they would like it to be because they can't do it. They undervalue creative work because they don't value anything. They just want to be handed a pile of money for turning in shit.
"The use case for LLMs is spam." Spam email, spam websites, spam news stories, spam books. Nothing anybody wants to read. Software that costs a million dollars a day to produce shit.

Add ChatGpT to your product, label ChatGPT as "myAI" and your product looks and works exactly same as 100,000 other things.

It makes my head spin to watch that lemming race to the bottom. Everyone is doing the same thing and no one thinks about what can be done against this insane downlevelling. No one thinks at all. Who suggested that suicidal tactic to you? ChatGPT? Microsoft?

CausalPy, a Python package focussing on causal inference in quasi-experimental settings. The package allows for sophisticated Bayesian model fitting methods to be used in addition to traditional OLS. #python https://github.com/pymc-labs/CausalPy
GitHub - pymc-labs/CausalPy: A Python package for causal inference in quasi-experimental settings

A Python package for causal inference in quasi-experimental settings - GitHub - pymc-labs/CausalPy: A Python package for causal inference in quasi-experimental settings

GitHub

In the last few months I’ve received 4 speaking invites:
•One to speak on the Black American experience—I said no & referred a Black scholar
•One to speak on Iranian women—I said no & referred several Iranian women
•One to speak on Arab American students—I said no & referred an Arab woman & activist
•One to speak on my experience in Turkey providing earthquake relief, on a panel with a Turkish scholar—I said yes

Lesson: You don’t need to be a voice for the voiceless—just learn to pass the mic.