Tootfinder

Holy shit Eero was a Harkonnen 🤣
From: @…
https://sfba.social/@garybarker/112061237497368336

Gary Barker (@garybarker@sfba.social)
Attached: 4 images “Brutalism Friday” “North Christian Church” Eero Saarinen, 1959, Columbia, Indiana. Saarinen was inspired by the steep steps at Angkor Wat and Borobudur and traditions that make a visitor interact with the architecture and gives a sense of magnanimity while entering the church. The odyssey from the exterior to interior is organically augmented to give worshippers a spiritual experience. The use of earthy materials, grey slate floors, dark mahogany pews to the spir…

Aaron Sorkin confirmed during a live recording of “The Town” podcast that
he is currently working on some kind of sequel to “The Social Network,”
David Fincher’s acclaimed 2010 drama about the creation of Facebook that won Sorkin the Oscar for best adapted screenplay.
“Look, yeah, I’ll be writing about this,” Sorkin said about the social media company’s recent years.
“I blame Facebook for January 6.”
Sorkin would not answer why he blamed Facebook for Trump suppor…

Aaron Sorkin Writing 'The Social Network 2,' Teases Jan. 6-Inspired Sequel
Aaron Sorkin is writing a sequel to "The Social Network" and blames Facebook for the Jan. 6 riot at the U.S. Capitol.

Aaron Sorkin confirmed during a live recording of “The Town” podcast that
he is currently working on some kind of sequel to “The Social Network,”
David Fincher’s acclaimed 2010 drama about the creation of Facebook that won Sorkin the Oscar for best adapted screenplay.
“Look, yeah, I’ll be writing about this,” Sorkin said about the social media company’s recent years.
“I blame Facebook for January 6.”
Sorkin would not answer why he blamed Facebook for Trump suppor…

Aaron Sorkin Writing 'The Social Network 2,' Teases Jan. 6-Inspired Sequel
Aaron Sorkin is writing a sequel to "The Social Network" and blames Facebook for the Jan. 6 riot at the U.S. Capitol.

Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Samuel Jia Cong Chua, Matthieu Geist, Olivier Pietquin, Ankur Mehta
https://arxiv.org/abs/2403.03552

Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Mean Field Games (MFGs) have the ability to handle large-scale multi-agent systems, but learning Nash equilibria in MFGs remains a challenging task. In this paper, we propose a deep reinforcement learning (DRL) algorithm that achieves population-dependent Nash equilibrium without the need for averaging or sampling from history, inspired by Munchausen RL and Online Mirror Descent. Through the design of an additional inner-loop replay buffer, the agents can effectively learn to achieve Nash equil…

Ivie: Lightweight Anchored Explanations of Just-Generated Code
Litao Yan, Alyssa Hwang, Zhiyuan Wu, Andrew Head
https://arxiv.org/abs/2403.02491 https://…

Ivie: Lightweight Anchored Explanations of Just-Generated Code
Programming assistants have reshaped the experience of programming into one where programmers spend less time writing and more time critically examining code. In this paper, we explore how programming assistants can be extended to accelerate the inspection of generated code. We introduce an extension to the programming assistant called Ivie, or instantly visible in-situ explanations. When using Ivie, a programmer's generated code is instantly accompanied by explanations positioned just adjacent…

Explainable Automatic Grading with Neural Additive Models
Aubrey Condor, Zachary Pardos
https://arxiv.org/abs/2405.00489 https://arxi…

Explainable Automatic Grading with Neural Additive Models
The use of automatic short answer grading (ASAG) models may help alleviate the time burden of grading while encouraging educators to frequently incorporate open-ended items in their curriculum. However, current state-of-the-art ASAG models are large neural networks (NN) often described as "black box", providing no explanation for which characteristics of an input are important for the produced output. This inexplicable nature can be frustrating to teachers and students when trying to interpret,…

Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Samuel Jia Cong Chua, Matthieu Geist, Olivier Pietquin, Ankur Mehta
https://arxiv.org/abs/2403.03552

Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Mean Field Games (MFGs) have the ability to handle large-scale multi-agent systems, but learning Nash equilibria in MFGs remains a challenging task. In this paper, we propose a deep reinforcement learning (DRL) algorithm that achieves population-dependent Nash equilibrium without the need for averaging or sampling from history, inspired by Munchausen RL and Online Mirror Descent. Through the design of an additional inner-loop replay buffer, the agents can effectively learn to achieve Nash equil…

Trump himself doesn’t appear to be even remotely confident
—at least in private.
The New York Times reports that behind the scenes, Trump has been angrily venting about one of his lawyers,
Todd Blanche, ever since the trial began:
He has griped that Mr. Blanche, has not been following his instructions closely,
and has been insufficiently aggressive.
Mr. Trump wants him to attack witnesses, attack what the former president sees as a hostile jury pool, and …

Trump Is Now Raging at His Own Lawyer—and Wrecking a Big MAGA Fantasy
Over on Fox News, even Trump’s naps are a power move. The reality is different—and no one knows it more than Trump himself.

Explainable Automatic Grading with Neural Additive Models
Aubrey Condor, Zachary Pardos
https://arxiv.org/abs/2405.00489 https://arxi…

Explainable Automatic Grading with Neural Additive Models
The use of automatic short answer grading (ASAG) models may help alleviate the time burden of grading while encouraging educators to frequently incorporate open-ended items in their curriculum. However, current state-of-the-art ASAG models are large neural networks (NN) often described as "black box", providing no explanation for which characteristics of an input are important for the produced output. This inexplicable nature can be frustrating to teachers and students when trying to interpret,…