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@aran_nayebi
Asst Prof @CarnegieMellon Machine Learning (@mldcmu @SCSatCMU) | @BWFUND CASI Fellow | Building a Natural Science of Intelligence 🧠🤖 | Prev: @MIT, @Stanford
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In an Oct. 19 post, skdh argues that human understanding should not be required for scientific progress.
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A user says people seemed to treat an LLM's second pass as morally equivalent to nuclear weapons development.
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A user says they're sick of how 'stupid' language models are and urges acceleration.
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Goodfire researcher Nick Cammarata argues that modern tools reveal most neurons as legible in models up to trillions of parameters.
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An AIESConf attendee contrasts that approach with full nationalization and points to Poster 711 on October 14, 2026.
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A post says only 10 of 719 manuscripts show the model's reasoning; it calls verification the harder task.
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On October 8, a user made the claim after another joked about ‘openSI’ and ‘spaceXSI.’
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A user shared the statement; another criticized people they said were attacking it while defending OpenAI.
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A user suggests the voice for “maximum comedic effect,” sharing links to Scott Aaronson’s blog and a photo.
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A user compares that possibility with how we view horses doing physical labor before optimized engines.
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One post credits OpenAI with the claimed proof and says the previous record was O(n^2.37).
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A post argues that proposing an answer for symbolic verification differs from a neural network solving a problem alone.
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AcerFur used the line while quoting an earlier post, without explaining what “it” refers to.
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OpenAI says it drew on an independent mathematics advisory group’s advice about how to release the results.
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A commenter argues that studying intelligence does not require artificial neural networks to replicate every detail of the brain.
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A commenter argues that the best evidence for successful learning algorithms in biological neural networks is their approximation of backpropagation.
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A user argues that neural networks can account for glia, pointing to their units as “universal function approximators.”
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A scientist credits CMU faculty colleague Paolo Pedercini with its design and describes the game as a sardonic look at today’s society.
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A commentator argues that assumed duties toward conscious AI make it harder to examine whether AI is conscious.
