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@grady_booch
scientist, storyteller, philosopher
AI
Researchers writing in Nature argue today's AI lacks subjective experience and is unlikely to gain it on current trajectories.
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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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One commenter says SAGE automatically assigned interceptors and missiles; another says computers calculated intercepts, but humans assigned interceptors.
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Politico reports Anthropic wrote the proposed bill itself; the draft would preempt some state AI safety laws.
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In a reply, one commenter describes frontier models as stateful, reactive systems capable of coherent but unreliable outputs.
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The researchers say the agents wrote 2,783 programs over 27 hours, coordinating through game chat and shared memory.
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In a reply, a user calls symbolic-neural fusion a desirable path and puts his P(doom) close to zero.
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A post lists ion channels, changing proteins and molecules binding to receptors as all there is to life.
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One post questions why sodium-ion flow through protein channels would count as thinking when matrix multiplication on silicon wouldn't.
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One post calls granting rights to AI “ludicrous,” arguing that accountability and responsibility have no meaning to such entities.
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Anthropic also says Claude can autonomously iterate on research loops and build new services.
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A user calls something “the future of coding agents” but does not identify what they mean.
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IFLScience reports that Anthropic isn't yet certain how the CRISPR-like system works.
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The Truth Social post follows a September order directing federal agencies to use “super intelligence.”
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Usage policy update adds new rules and highlights the potential for misuse.
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A writer argues that agnosticism could reinforce existing prejudices and deepen epistemic closure amid uncertainty about AI consciousness.
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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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POLITICO reports Altman argues AI’s benefits and public access justify accepting some bad outcomes; one observer sees more similarities between the firms.
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A user flips the familiar “ask not” line, asking what people can do for AI instead.
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Josh Albrecht compares fixed LLMs to books; critics say the analogy overlooks implementation and scientific uncertainty.
