@srchvrs
Machine learning scientist and engineer @Oracle speaking πtorch & C++. Past @awscloud, @LTIatCMU. Opinions sampled from MY OWN 100T param LM.
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Bhuwan Dhingra says the tool handles math, but AI-polished writing can also be flagged.
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A user asks how models could learn to do this and points to a thread.
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The essay’s author shares personal reflections on a growing disconnect between scientific rigor, professional recognition and organizational rewards.
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A member of the paper’s team says a little fine-tuning takes the models further.
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A commentator disputes AI 2027's human-response forecasts, arguing labs accelerate when perceived risk falls and act when it rises.
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One poster calls Anthropic early on reinforcement learning and tool use; a reply points to a LaMDA paper.
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A user frames the choice around how an AI’s intelligence compares with their own.
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The author says AutoRubric's 25-recipe cookbook includes runnable Python examples on judge calibration and cost control.
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The paper's authors say the Lean-verified formal proof does not correspond to OpenAI's announced natural-language Navier-Stokes argument.
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Peter Norvig argues that probabilistic language models can capture usage patterns that simpler grammatical rules miss.
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A user says models wrote a couple hundred lines of Blender Python per scene, arranging cameras, lighting and materials.
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The poster says they’re open to thinking differently and tagged people they thought would add to the comments.
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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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Its creator says they translated MBPP and MBPP+ into PL/SQL using rule-based mappings of unit tests to test models and agents.
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A user thinks too few people have seriously considered why these conferences don't accept every good paper.
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Leo Boytsov supports cleaning up the literature, while Susan Zhang questions whether AI can produce more elegant arguments.
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A commenter says the paper leaves “single-shot accuracy” unclear and omits key evaluation settings.
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A user likes coding agents but says using them as an AI band-aid can add technical debt instead of fixing systemic problems.
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Meta says researchers used Muse Spark 1.1 and 1.2 through its standard chat interface, with human review.
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The participant said they were honored to be in the cohort and looked forward to continuing work on multi-agent communication.