https://www.youtube.com/watch?app=desktop&v=21qPOReu4FI
"Five Stages of Accepting Constructive Mathematics - Andrej Bauer"
hegelpilled ancientgreececore math with non-duality embedded on deeper level
juiciest part: 44:44 - 50:25
"Five Stages of Accepting Constructive Mathematics - Andrej Bauer"
hegelpilled ancientgreececore math with non-duality embedded on deeper level
juiciest part: 44:44 - 50:25
YouTube
Five Stages of Accepting Constructive Mathematics - Andrej Bauer
Andrej Bauer
University of Ljubljana, Slovenia; Member, School of Mathematics
March 18, 2013
Discussions about constructive mathematics are usually derailed by philosophical opinions and meta-mathematics. But how does it actually feel to do constructive mathematics?…
University of Ljubljana, Slovenia; Member, School of Mathematics
March 18, 2013
Discussions about constructive mathematics are usually derailed by philosophical opinions and meta-mathematics. But how does it actually feel to do constructive mathematics?…
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Forwarded from Just links
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model https://arxiv.org/abs/2306.03341
via @seeallochnaya
Just find a "truth" direction in latent space and move there
via @seeallochnaya
Just find a "truth" direction in latent space and move there
Forwarded from Цуберок 🇺🇦 #УкрТґ
Apple Researchers Introduce ByteFormer: An AI Model That Consumes Only Bytes And Does Not Explicitly Model The Input Modality - MarkTechPost
https://www.marktechpost.com/2023/06/09/apple-researchers-introduce-byteformer-an-ai-model-that-consumes-only-bytes-and-does-not-explicitly-model-the-input-modality/
https://www.marktechpost.com/2023/06/09/apple-researchers-introduce-byteformer-an-ai-model-that-consumes-only-bytes-and-does-not-explicitly-model-the-input-modality/
MarkTechPost
Apple Researchers Introduce ByteFormer: An AI Model That Consumes Only Bytes And Does Not Explicitly Model The Input Modality
The explicit modeling of the input modality is typically required for deep learning inference. For instance, by encoding picture patches into vectors, Vision Transformers (ViTs) directly model the 2D spatial organization of images. Similarly, calculating…
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Forwarded from нс (egor)
baker_2022_the_ultimate_think_tank_the_rise_of_the_santa_fe_institute.pdf
621.9 KB
Forwarded from Known unknown unknowns
Lectures on high-dimensional probability https://www.math.uci.edu/~rvershyn/teaching/hdp/hdp.html
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https://news.uchicago.edu/story/tempest-teacup-uchicago-physicists-make-breakthrough-creating-turbulence pondering
YouTube
V0008 - Turbulence through sustained vortex ring collisions
"Turbulence through sustained vortex ring collisions
Takumi Matsuzawa, The University of Chicago
Noah Mitchell, The University of Chicago
Stéphane Perrard, The University of Chicago
William Irvine, The University of Chicago
DOI: https://doi.org/10.1103…
Takumi Matsuzawa, The University of Chicago
Noah Mitchell, The University of Chicago
Stéphane Perrard, The University of Chicago
William Irvine, The University of Chicago
DOI: https://doi.org/10.1103…
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https://neurosciencenews.com/soybean-oil-genetics-asd-15505/
tldr: causes reduction in oxytocin
tldr: causes reduction in oxytocin
Neuroscience News
America’s most widely consumed cooking oil causes genetic changes in the brain
Soybean oil, the most consumed cooking oil in the US, has been linked to neurological and metabolic alterations in mice. Soybean oil-fed mice showed decreased levels of oxytocin in the hypothalamus.
Learning how network structure shapes decision-making for bio-inspired computing
Abstract:
To better understand how network structure shapes intelligent behavior, we developed a learning algorithm that we used to build personalized brain network models for 650 Human Connectome Project participants. We found that participants with higher intelligence scores took more time to solve difficult problems, and that slower solvers had higher average functional connectivity. With simulations we identified a mechanistic link between functional connectivity, intelligence, processing speed and brain synchrony for trading accuracy with speed in dependence of excitation-inhibition balance. Reduced synchrony led decision-making circuits to quickly jump to conclusions, while higher synchrony allowed for better integration of evidence and more robust working memory. Strict tests were applied to ensure reproducibility and generality of the obtained results. Here, we identify links between brain structure and function that enable to learn connectome topology from noninvasive recordings and map it to inter-individual differences in behavior, suggesting broad utility for research and clinical applications.
https://www.nature.com/articles/s41467-023-38626-y
Abstract:
To better understand how network structure shapes intelligent behavior, we developed a learning algorithm that we used to build personalized brain network models for 650 Human Connectome Project participants. We found that participants with higher intelligence scores took more time to solve difficult problems, and that slower solvers had higher average functional connectivity. With simulations we identified a mechanistic link between functional connectivity, intelligence, processing speed and brain synchrony for trading accuracy with speed in dependence of excitation-inhibition balance. Reduced synchrony led decision-making circuits to quickly jump to conclusions, while higher synchrony allowed for better integration of evidence and more robust working memory. Strict tests were applied to ensure reproducibility and generality of the obtained results. Here, we identify links between brain structure and function that enable to learn connectome topology from noninvasive recordings and map it to inter-individual differences in behavior, suggesting broad utility for research and clinical applications.
https://www.nature.com/articles/s41467-023-38626-y
Nature
Learning how network structure shapes decision-making for bio-inspired computing
Nature Communications - Better understanding of a trade-off between the speed and accuracy of decision-making is relevant for mapping biological intelligence to machines. The authors introduce a...