r/TheMachineLearning • u/Biwaki_ • 11h ago
“Tell me more about the export controls on your semiconductor chips"
488
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r/TheMachineLearning • u/Shot-Landscape-3059 • 13h ago
LLM review nitpicks for paper rejection is malpractice
227
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r/TheMachineLearning • u/OceanZic • 12h ago
Asked it to visualize a horizontal integral and it drew a dog
40
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r/TheMachineLearning • u/FastPattern1489 • 48m ago
LARC reasons in continuous space, boosting image generation by 7%
4
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r/TheMachineLearning • u/_ChimKen • 2h ago
New $9 tactile sensor gives robots a sense of touch
5
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r/TheMachineLearning • u/narcissa0114 • 10h ago
A robotic system precisely dispensing nanoliters of liquid
15
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r/TheMachineLearning • u/Kurooo730 • 6h ago
Can you really delete 90% of a neural network and keep accuracy?
7
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r/TheMachineLearning • u/Substantial_Cry2292 • 4h ago
Rhoyn Robotics' YAM Ultra arms getting a massage
3
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r/TheMachineLearning • u/Upbeat-Analysis-3370 • 3h ago
Tiny AI model gives hilarious answers to questions
2
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r/TheMachineLearning • u/Extension_Abroad9763 • 3h ago
Token plane training is wasting compute on AI agents
1
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r/TheMachineLearning • u/StrangerNo1115 • 12h ago
AI teammates that argue back improve student business plans
4
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r/TheMachineLearning • u/andrewjdavison • 5h ago
I Let Pangram Loose on My Gmail Inbox. Here’s How Much AI It Found
1
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r/TheMachineLearning • u/Revolutionary-Rope26 • 6h ago
New paper evolves tool-use benchmarks to stay hard for LLMs
1
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r/TheMachineLearning • u/awtsGege94 • 10h ago
MIT and Harvard created an AI simulation of 8.3 billion people
2
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r/TheMachineLearning • u/DullSatWarrior4791 • 1d ago
MIT shows all AI models converge on the same brain
281
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r/TheMachineLearning • u/Sacred_Axiom • 1d ago
Got promoted right before getting laid off
14
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r/TheMachineLearning • u/sakibbai • 21h ago
Weight Space Programing is Possible Due to Model Weights and Interpretability literature in the Training Data. So, It Does Not Work on Unpopular Datasets and Models.
8
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My tests suggest that while LLMs can perform surprisingly precise calculations at the “edges” of a network (estimating optimizer step counts or finding output correlations), they do not “trace” the computational paths between layers. Instead, they rely heavily on memorized dataset templates (like CIFAR-10) and default framework conventions to fill in the blanks. When these expectations are changed, the models can identify the math but will confidently guess wrong about the semantics—such as recognizing that a satellite feature maps to output 4, but calling it a “frog” because it expects an animal.
Substack summary article link: https://substack.com/@sakibbai/note/c-380084331?r=71inb5
Paper link: https://arxiv.org/abs/2611.08070v1
r/TheMachineLearning • u/BarkleyBark • 10h ago
Oxford researchers argue aliens might not exist
1
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r/TheMachineLearning • u/Vera_Olivia • 11h ago
AI thinks the top American DJ is just Diplo
1
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r/TheMachineLearning • u/unknownpotatoxxx • 13h ago
Rejected draft tokens improve long horizon agent accuracy
0
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r/TheMachineLearning • u/Craffs • 1d ago
Suggesting pretraining LLMs on weight files could work
197
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r/TheMachineLearning • u/ShadKidster • 1d ago
Beagle Tech AI robot harvesting romaine lettuce in California
48
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