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    <title>Science Is Drowning in AI Slop - The Atlantic</title>
    <dc:date>2026-01-23T14:57:15+00:00</dc:date>
    <link>https://www.theatlantic.com/science/2026/01/ai-slop-science-publishing/685704/?gift=7deeCsgE8m50UsuOnXueiQXg61EfvixIVey4jBf7slM</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[NeurIPS, one of the top AI conferences, has seen them double in five years. ICLR, the leading conference for deep learning, has also experienced an increase, and it appears to include a fair amount of slop: An LLM-detection start-up analyzed submissions for its upcoming meeting in Brazil and found more than 50 that included hallucinated citations. Most had not been caught during peer review.

That might be because many of the peer reviews were themselves done by AI. Pangram Labs recently analyzed thousands of peer reviews that were submitted to ICLR, and found that more than half of them were written with help from an LLM, and about a fifth of them were wholly AI-generated. Across the academic sciences, paper authors have even started using tiny white fonts to embed secret messages to LLM reviewers. They urge the AIs to rave about the paper they’re reading, to describe it as “groundbreaking” and “transformative,” and to save them the trouble of a tough revision by suggesting only easy fixes.]]></description>
<dc:subject>weekly ai dystopia research</dc:subject>
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    <title>Varsity Football Helmet Ratings</title>
    <dc:date>2025-12-18T14:46:51+00:00</dc:date>
    <link>https://www.helmet.beam.vt.edu/varsity-football-helmet-ratings.html#!</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>weekly helmet rating research football</dc:subject>
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    <title>Typefaces for Dyslexia — Adrian Roselli</title>
    <dc:date>2025-12-16T20:26:14+00:00</dc:date>
    <link>https://adrianroselli.com/2015/03/typefaces-for-dyslexia.html</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[The fonts don't appear to work.]]></description>
<dc:subject>typography accessibility weekly research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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    <title>[2401.03910] A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debates</title>
    <dc:date>2025-10-10T13:18:54+00:00</dc:date>
    <link>https://arxiv.org/abs/2401.03910</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Large language models like GPT-4 have achieved remarkable proficiency in a broad spectrum of language-based tasks, some of which are traditionally associated with hallmarks of human intelligence. This has prompted ongoing disagreements about the extent to which we can meaningfully ascribe any kind of linguistic or cognitive competence to language models. Such questions have deep philosophical roots, echoing longstanding debates about the status of artificial neural networks as cognitive models. This article -- the first part of two companion papers -- serves both as a primer on language models for philosophers, and as an opinionated survey of their significance in relation to classic debates in the philosophy cognitive science, artificial intelligence, and linguistics. We cover topics such as compositionality, language acquisition, semantic competence, grounding, world models, and the transmission of cultural knowledge. We argue that the success of language models challenges several long-held assumptions about artificial neural networks. However, we also highlight the need for further empirical investigation to better understand their internal mechanisms. This sets the stage for the companion paper (Part II), which turns to novel empirical methods for probing the inner workings of language models, and new philosophical questions prompted by their latest developments.
]]></description>
<dc:subject>philosophy language llm ai weekly research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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    <title>Methodology | Pew Research Center</title>
    <dc:date>2025-09-23T12:51:07+00:00</dc:date>
    <link>https://www.pewresearch.org/data-labs/2024/10/08/methodology-tiktok-2024/</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Audio from video files was extracted and passed to an Audio Spectrogram Transformer model finetuned on the AudioSet dataset. This AST model inputs audio sequences, distinguishes speech from music, and then provides additional labels for the clip using a broad ontology of everyday sound types.
For videos where the AST model identified “speech” as the primary audio label, the full audio from the video was then passed to OpenAI’s whisper transcription model. For a balance of accuracy and fast processing time, we used the 769M-parameter “medium” version of this model. On English-language speech, Whisper performs speech recognition and transcription. On speech in languages other than English, the model also performs translation and returns English-language transcriptions.
All thumbnail images and slideshow images were passed through an optical character recognition (OCR) system using the python library EasyOCR. This OCR pass identified and extracted any text that could be read in the images.
All thumbnail images and slideshow images were also passed through moondream2, a lightweight vision language model that can perform text generation conditioned on an image. We used this model to produce short descriptions of the subject of each image.]]></description>
<dc:subject>ai research workflow weekly example data</dc:subject>
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    <title>Nothing but the truth: Are the media as bad at communicating science as scientists fear? - PMC</title>
    <dc:date>2025-09-04T13:27:57+00:00</dc:date>
    <link>https://pmc.ncbi.nlm.nih.gov/articles/PMC3492714/</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[In a previous study, Brechman and his colleagues had also concluded that “errors commonly attributed to science journalists, such as lack of qualifying details and use of oversimplified language, originate in press releases.” Even more worrisome, as Fox told a Nature commentary author in 2009, public relations departments are increasingly filling the need of the media for quick content [5].

]]></description>
<dc:subject>weekly research journalism bias science communication</dc:subject>
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    <title>Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity - METR</title>
    <dc:date>2025-07-16T14:57:52+00:00</dc:date>
    <link>https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[When developers are allowed to use AI tools, they take 19% longer to complete issues—a significant slowdown that goes against developer beliefs and expert forecasts. This gap between perception and reality is striking: developers expected AI to speed them up by 24%, and even after experiencing the slowdown, they still believed AI had sped them up by 20%.

]]></description>
<dc:subject>weekly ai data research coding programming</dc:subject>
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<item rdf:about="https://machinelearning.apple.com/research/illusion-of-thinking">
    <title>The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity - Apple Machine Learning Research</title>
    <dc:date>2025-06-17T16:39:50+00:00</dc:date>
    <link>https://machinelearning.apple.com/research/illusion-of-thinking</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Through extensive experimentation across diverse puzzles, we show that frontier LRMs face a complete accuracy collapse beyond certain complexities. Moreover, they exhibit a counter-intuitive scaling limit: their reasoning effort increases with problem complexity up to a point, then declines despite having an adequate token budget.]]></description>
<dc:subject>weekly ai llm apple research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://arxiv.org/pdf/2501.15654">
    <title>People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text</title>
    <dc:date>2025-05-12T19:05:04+00:00</dc:date>
    <link>https://arxiv.org/pdf/2501.15654</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[ We hire
annotators to read 300 non-fiction English articles, label them as either human-written or
AI-generated, and provide paragraph-length
explanations for their decisions. Our experiments show that annotators who frequently use
LLMs for writing tasks excel at detecting AIgenerated text, even without any specialized
training or feedback. In fact, the majority vote
among five such “expert” annotators misclassifies only 1 of 300 articles, significantly outperforming most commercial and open-source
detectors we evaluated even in the presence of
evasion tactics like paraphrasing and humanization.]]></description>
<dc:subject>annotation research ai weekly</dc:subject>
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    <title>Repository - Generative AI in Education Hub</title>
    <dc:date>2025-02-19T20:49:13+00:00</dc:date>
    <link>https://scale.stanford.edu/genai/repository</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[A comprehensive collection of academic research on generative AI in preK12 education organized into three categories:

]]></description>
<dc:subject>stanford evaluation research ai weekly</dc:subject>
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<item rdf:about="https://news.fsu.edu/news/science-technology/2025/02/17/why-does-chatgpt-delve-so-much-fsu-researchers-begin-to-uncover-why-chatgpt-overuses-certain-words/">
    <title>“Why Does ChatGPT ‘Delve’ So Much?”: FSU researchers begin to uncover why ChatGPT overuses certain words    - Florida State University News</title>
    <dc:date>2025-02-18T14:35:27+00:00</dc:date>
    <link>https://news.fsu.edu/news/science-technology/2025/02/17/why-does-chatgpt-delve-so-much-fsu-researchers-begin-to-uncover-why-chatgpt-overuses-certain-words/</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[“It was significant that so many people had noticed this overuse. It’s normal for one or a couple of words to shift in usage over the years, but dozens of words drastically increasing in frequency is odd. Our work, which is at the nexus of linguistics, computational linguistics, computer science, data science, philosophy and ethics, has wide implications and analyzes the interplay between technology and human language and how technology affects our languages.”]]></description>
<dc:subject>weekly ai language research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:7f038916bff5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.edelman.com/trust/2025/trust-barometer">
    <title>2025 Edelman Trust Barometer | Edelman</title>
    <dc:date>2025-02-12T18:50:48+00:00</dc:date>
    <link>https://www.edelman.com/trust/2025/trust-barometer</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[To bring about change, 4 in 10 would approve of one or more of the following forms of hostile activism: attacking people online, intentionally spreading disinformation, threatening or committing violence, damaging public or private property. This sentiment is most prevalent among respondents ages 18-34 (53 percent approve of at least one).

]]></description>
<dc:subject>trust stats research weekly dystopia</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:e32f2d0bbb14/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:trust"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:stats"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:dystopia"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://consensus.app/">
    <title>Consensus: AI-powered Academic Search Engine</title>
    <dc:date>2025-02-06T16:05:56+00:00</dc:date>
    <link>https://consensus.app/</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>academic service paper search research science ai tool</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:620ff001c7d2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:academic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:service"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:tool"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://app.litmaps.com/">
    <title>Litmaps</title>
    <dc:date>2025-02-06T16:05:32+00:00</dc:date>
    <link>https://app.litmaps.com/</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>literature software research tool ai dataviz</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:f54e0b68a377/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:literature"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:software"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:tool"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:dataviz"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://researchrabbitapp.com/">
    <title>Research Rabbit</title>
    <dc:date>2025-02-06T16:04:12+00:00</dc:date>
    <link>https://researchrabbitapp.com/</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>ai research tool</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:09ec79f59105/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:tool"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.scientificamerican.com/article/why-we-are-wired-to-connect/">
    <title>Why We Are Wired to Connect | Scientific American</title>
    <dc:date>2025-02-06T15:01:44+00:00</dc:date>
    <link>https://www.scientificamerican.com/article/why-we-are-wired-to-connect/</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Whenever we finish doing some kind of non-social thinking, the network for social thinking comes back on like a reflex – almost instantly. 

Why would the brain be set up to do this?  We have recently found that this reflex prepares us to walk into the next moment of our lives focused on the minds behind the actions that we see from others.  Evolution has placed a bet that the best thing for our brain to do in any spare moment is to get ready to see the world socially.  I think that makes a major statement about the extent to which we are built to be social creatures.]]></description>
<dc:subject>weekly research socialmedia sociology neuroscience</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:3568e9270d8b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:socialmedia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:neuroscience"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2402.07940">
    <title>[2402.07940] LLMs Among Us: Generative AI Participating in Digital Discourse</title>
    <dc:date>2025-02-05T14:15:02+00:00</dc:date>
    <link>https://arxiv.org/abs/2402.07940</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[We conducted three rounds of the experiment and surveyed participants after each round to measure the ability of LLMs to pose as human participants without human detection. We found that participants correctly identified the nature of other users in the experiment only 42% of the time despite knowing the presence of both bots and humans. We also found that the choice of persona had substantially more impact on human perception than the choice of mainstream LLMs.]]></description>
<dc:subject>weekly ai research detection dystopia</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:85e4f6c3da91/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:detection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:dystopia"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/kaixxx/noScribe/tree/main#readme">
    <title>kaixxx/noScribe: Cutting edge AI technology for automated audio transcription. A nice GUI for OpenAIs Whisper and pyannote (speaker identification)</title>
    <dc:date>2025-02-04T17:15:23+00:00</dc:date>
    <link>https://github.com/kaixxx/noScribe/tree/main#readme</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[An AI-based software that transcribes interviews for qualitative social research or journalistic use
]]></description>
<dc:subject>tool ai audio transcription app opensource weekly research qualitative</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:68f1d2e43c07/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:tool"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:audio"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:transcription"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:app"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:opensource"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:qualitative"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.sciencedirect.com/science/article/pii/S2713374523000316?via%3Dihub">
    <title>How does generative artificial intelligence impact student creativity? - ScienceDirect</title>
    <dc:date>2025-01-31T15:15:00+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/pii/S2713374523000316?via%3Dihub</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>ai art creativity thinking weekly research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:1d47045375f6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:creativity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:thinking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2405.06087">
    <title>[2405.06087] When combinations of humans and AI are useful: A systematic review and meta-analysis</title>
    <dc:date>2025-01-21T19:33:30+00:00</dc:date>
    <link>https://arxiv.org/abs/2405.06087</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA["First, we found that, on average, human-AI combinations performed significantly worse than the best of humans or AI alone. Second, we found performance losses in tasks that involved making decisions and significantly greater gains in tasks that involved creating content. Finally, when humans outperformed AI alone, we found performance gains in the combination, but when the AI outperformed humans alone we found losses." ]]></description>
<dc:subject>weekly research ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:f246e7d922d7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.bentley.edu/files/gallup/Bentley_Gallup_Business_in_Society_Report.pdf">
    <title>Bentley-Gallup Business in Society Report</title>
    <dc:date>2025-01-09T13:34:31+00:00</dc:date>
    <link>https://www.bentley.edu/files/gallup/Bentley_Gallup_Business_in_Society_Report.pdf</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Seventy-nine percent of Americans say they trust businesses “not much” or “not at all” to use AI responsibly, and 40% say AI does greater harm than it does good.]]></description>
<dc:subject>ai trust research poll survey weekly</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:4ef28f675346/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:trust"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:poll"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:survey"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mdpi.com/2075-4698/15/1/6">
    <title>AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking</title>
    <dc:date>2025-01-08T20:29:12+00:00</dc:date>
    <link>https://www.mdpi.com/2075-4698/15/1/6</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Given these concerns, this study sought to explore the impact of AI tool usage on critical thinking skills with a particular focus on cognitive offloading as a mediating variable. This research aimed to provide a comprehensive understanding of the broader cognitive implications of AI tool usage by investigating how AI tools influence cognitive processes and the extent to which they encourage cognitive offloading.]]></description>
<dc:subject>ai weekly research study thinking</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:a71013096979/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:study"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:thinking"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://energy.virginia.gov/commercedocs/VAMIN_VOL17_NO03.PDF">
    <title>[untitled]</title>
    <dc:date>2025-01-02T15:03:23+00:00</dc:date>
    <link>https://energy.virginia.gov/commercedocs/VAMIN_VOL17_NO03.PDF</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>bottle locations research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:921192c0e6d7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:bottle"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:locations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/pdf/2402.04105v2">
    <title>Measuring Implicit Bias in Explicitly Unbiased Large Language Models</title>
    <dc:date>2025-01-02T13:35:14+00:00</dc:date>
    <link>https://arxiv.org/pdf/2402.04105v2</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Using these measures, we found pervasive stereotype biases mirroring those in society in 8 value-aligned models across 4 social categories (race, gender, religion, health) in 21 stereotypes (such as race and criminality, race and weapons, gender and science, age and
negativity). Our prompt-based LLM Implicit Bias measure correlates with existing language model embedding-based bias methods, but better predicts downstream behaviors measured by LLM Decision Bias. These new prompt-based measures draw from psychology’s long history of research into measuring stereotype biases based on purely observable behavior; they expose nuanced biases in proprietary value-aligned LLMs that appear unbiased according to standard benchmarks.]]></description>
<dc:subject>bias ai research weekly pdf</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:4b40c5685669/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:bias"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:pdf"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/pdf/2412.04984?">
    <title>Frontier Models are Capable of In-context Scheming</title>
    <dc:date>2024-12-17T13:18:01+00:00</dc:date>
    <link>https://arxiv.org/pdf/2412.04984?</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Together, our findings demonstrate that frontier models now possess capabilities for basic in-context scheming, making the potential of AI agents to engage in scheming behavior a concrete rather than theoretical concern.]]></description>
<dc:subject>ai llm research truth lies weekly pdf</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:d11fe98eed9c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:truth"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:lies"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:pdf"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305354">
    <title>A real-world test of artificial intelligence infiltration of a university examinations system: A “Turing Test” case study | PLOS ONE</title>
    <dc:date>2024-12-02T18:51:11+00:00</dc:date>
    <link>https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305354</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[We report a rigorous, blind study in which we injected 100% AI written submissions into the examinations system in five undergraduate modules, across all years of study, for a BSc degree in Psychology at a reputable UK university. We found that 94% of our AI submissions were undetected. The grades awarded to our AI submissions were on average half a grade boundary higher than that achieved by real students. Across modules there was an 83.4% chance that the AI submissions on a module would outperform a random selection of the same number of real student submissions.]]></description>
<dc:subject>weekly ai research detection</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:eacc3986706e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:detection"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2404.12534">
    <title>[2404.12534] Towards Large Language Models as Copilots for Theorem Proving in Lean</title>
    <dc:date>2024-11-25T14:48:32+00:00</dc:date>
    <link>https://arxiv.org/abs/2404.12534</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Theorem proving is an important challenge for large language models (LLMs), as formal proofs can be checked rigorously by proof assistants such as Lean, leaving no room for hallucination. Existing LLM-based provers try to prove theorems in a fully autonomous mode without human intervention. In this mode, they struggle with novel and challenging theorems, for which human insights may be critical. In this paper, we explore LLMs as copilots that assist humans in proving theorems. We introduce Lean Copilot, a framework for running LLM inference in Lean. It enables programmers to build various LLM-based proof automation tools that integrate seamlessly into the workflow of Lean users. Using Lean Copilot, we build tools for suggesting proof steps (tactic suggestion), completing intermediate proof goals (proof search), and selecting relevant premises (premise selection) using LLMs. Users can use our pretrained models or bring their own ones that run either locally (with or without GPUs) or on the cloud. Experimental results demonstrate the effectiveness of our method in assisting humans and automating theorem proving process compared to existing rule-based proof automation in Lean. We open source all codes under a permissive MIT license to facilitate further research.
]]></description>
<dc:subject>math research weekly llm lean</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:dc7722c80d43/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:math"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:lean"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.energypolicy.columbia.edu/projecting-the-electricity-demand-growth-of-generative-ai-large-language-models-in-the-us/">
    <title>Projecting the Electricity Demand Growth of Generative AI Large Language Models in the US - Center on Global Energy Policy at Columbia University SIPA | CGEP %</title>
    <dc:date>2024-11-22T18:43:22+00:00</dc:date>
    <link>https://www.energypolicy.columbia.edu/projecting-the-electricity-demand-growth-of-generative-ai-large-language-models-in-the-us/</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Based on this approach, and in light of data from the EIA Annual Energy Outlook 2023,[13] this analysis suggests that by 2027 GPUs will constitute about 1.7 percent of the total electric capacity or 4 percent of the total projected electricity sales in the United States. While this might seem minimal, it constitutes a considerable growth rate over the next six years and a significant amount of energy that will need to be supplied to data centers.

]]></description>
<dc:subject>weekly ai energy data research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:8ee1dc700f54/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:energy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://research.utk.edu/research-integrity/artificial-intelligence-ai-tools/">
    <title>Artificial Intelligence (AI) Tools - Research Integrity &amp; Assurance</title>
    <dc:date>2024-11-04T19:14:58+00:00</dc:date>
    <link>https://research.utk.edu/research-integrity/artificial-intelligence-ai-tools/</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[With the emergence of artificial intelligence (AI), investigators have a unique opportunity to use AI in their own research studies. Best practices on the use of AI in human subjects research are not yet well formed, nor is there general consensus on appropriate use. Given this fluidity, this guidance is therefore subject to change based on the development of this novel technology and best practices for its use. This guidance covers the use of AI, machine learning, deep learning, and related AI techniques used in research activities, as well as other activities that may govern or affect the use of AI tools within the context of research.

]]></description>
<dc:subject>ai research weekly highered</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:70771b5ce632/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:highered"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://aiimpacts.org/wp-content/uploads/2023/04/Thousands_of_AI_authors_on_the_future_of_AI.pdf">
    <title>THOUSANDS OF AI AUTHORS ON THE FUTURE OF AI</title>
    <dc:date>2024-11-04T17:51:36+00:00</dc:date>
    <link>https://aiimpacts.org/wp-content/uploads/2023/04/Thousands_of_AI_authors_on_the_future_of_AI.pdf</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>ai llm research report future agi</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:092417dd39c5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:report"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:future"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:agi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://docs.google.com/presentation/d/1ADoqCSeBFaspv0qqiHqQmsdwazdqLjpASpJTutgmcNU/edit#slide=id.p">
    <title>AI Text Detectors - Google Slides</title>
    <dc:date>2024-11-04T13:20:44+00:00</dc:date>
    <link>https://docs.google.com/presentation/d/1ADoqCSeBFaspv0qqiHqQmsdwazdqLjpASpJTutgmcNU/edit#slide=id.p</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>ai research cheating detectors weekly llm technology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:ad83f836b6c5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:cheating"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:detectors"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:technology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/ftp/arxiv/papers/2403/2403.19148.pdf">
    <title>GENAI DETECTION TOOLS, ADVERSARIAL TECHNIQUES AND IMPLICATIONS FOR INCLUSIVITY IN HIGHER EDUCATION</title>
    <dc:date>2024-10-31T17:37:30+00:00</dc:date>
    <link>https://arxiv.org/ftp/arxiv/papers/2403/2403.19148.pdf</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content that has been modified using techniques designed to evade detection by these tools (n=805). The results demonstrate that the detectors' already low accuracy rates (39.5%) show major reductions in accuracy (17.4%) when faced with manipulated content, with some techniques proving more effective than others in evading detection.]]></description>
<dc:subject>ai detection academic integrity cheating weekly research llm</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:888d3dd4f1ad/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:detection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:academic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:integrity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:cheating"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://static1.squarespace.com/static/635693acf15a3e2a14a56a4a/t/65ef1ee52e64b52f145ebb49/1710169832137/AIcollaboration.pdf">
    <title>Roots of Disagreement on AI Risk: Exploring the Potential and Pitfalls of Adversarial Collaboration</title>
    <dc:date>2024-10-28T12:40:31+00:00</dc:date>
    <link>https://static1.squarespace.com/static/635693acf15a3e2a14a56a4a/t/65ef1ee52e64b52f145ebb49/1710169832137/AIcollaboration.pdf</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[We find greater agreement about a broader set of risks from AI over the next thousand years: the two groups gave median forecasts of
30% (skeptics) and 40% (concerned) that AI will have severe negative effects on humanity by causing major declines in population, very low self-reported well-being, or extinction.]]></description>
<dc:subject>ai research weekly risk</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:6aa5d4c2eeec/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:risk"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.psychologytoday.com/intl/blog/the-digital-self/202410/beyond-tools-llms-and-the-emergence-of-extended-cognition">
    <title>Beyond Tools: LLMs and the Emergence of Extended Cognition | Psychology Today</title>
    <dc:date>2024-10-26T16:44:42+00:00</dc:date>
    <link>https://www.psychologytoday.com/intl/blog/the-digital-self/202410/beyond-tools-llms-and-the-emergence-of-extended-cognition</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[This mirroring effect is transformative in ways we're only beginning to understand. When we engage with an LLM, we're compelled to externalize our internal thought processes, making them more visible and, therefore, more amenable to refinement. Like a skillful conversation partner, the system prompts us to clarify our assumptions and elaborate on our logic, creating a feedback loop that leads to deeper understanding.

]]></description>
<dc:subject>weekly ai research psychology metacognition cognition thinking</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:06a5042cc744/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:metacognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:thinking"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41562-024-01995-5">
    <title>The case for human–AI interaction as system 0 thinking | Nature Human Behaviour</title>
    <dc:date>2024-10-25T19:38:12+00:00</dc:date>
    <link>https://www.nature.com/articles/s41562-024-01995-5</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA["The term system 0 is chosen deliberately to emphasize its foundational and pervasive role in modern cognition. Unlike the system 1 and system 2 (which operate within the individual mind), system 0 forms an artificial, non-biological underlying layer of distributed intelligence that interacts with and augments both intuitive and analytical thinking processes. This designation underscores its function as a preprocessor and enhancer of information, which actively shapes the inputs to traditional cognitive systems rather than simply extending them."

]]></description>
<dc:subject>research ai thinking weekly</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:02c891fe09bd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:thinking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/microsoft/RD-Agent">
    <title>microsoft/RD-Agent: Research and development (R&amp;D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&amp;D are mainly focused on data and models. We are committed to automating these high-value ge</title>
    <dc:date>2024-10-25T19:18:07+00:00</dc:date>
    <link>https://github.com/microsoft/RD-Agent</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[RDAgent aims to automate the most critical and valuable aspects of the industrial R&D process, and we begin with focusing on the data-driven scenarios to streamline the development of models and data. Methodologically, we have identified a framework with two key components: 'R' for proposing new ideas and 'D' for implementing them. We believe that the automatic evolution of R&D will lead to solutions of significant industrial value.

]]></description>
<dc:subject>research agent llm ai weekly tool microsoft ms finance</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:a3da1af8e276/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:agent"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:tool"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:microsoft"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:finance"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2409.16191">
    <title>[2409.16191] HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models</title>
    <dc:date>2024-10-25T19:15:56+00:00</dc:date>
    <link>https://arxiv.org/abs/2409.16191</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[ Besides, we propose Hierarchical Long Text Evaluation (HelloEval), a human-aligned evaluation method that significantly reduces the time and effort required for human evaluation while maintaining a high correlation with human evaluation. We have conducted extensive experiments across around 30 mainstream LLMs and observed that the current LLMs lack long text generation capabilities. Specifically, first, regardless of whether the instructions include explicit or implicit length constraints, we observe that most LLMs cannot generate text that is longer than 4000 words. Second, we observe that while some LLMs can generate longer text, many issues exist (e.g., severe repetition and quality degradation). Third, to demonstrate the effectiveness of HelloEval, we compare HelloEval with traditional metrics (e.g., ROUGE, BLEU, etc.) and LLM-as-a-Judge methods, which show that HelloEval has the highest correlation with human evaluation.]]></description>
<dc:subject>weekly ai tool long text research llm generation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:9b9dc814043c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:tool"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:long"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:text"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:generation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://machinelearning.apple.com/research/gsm-symbolic">
    <title>GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models - Apple Machine Learning Research</title>
    <dc:date>2024-10-24T18:39:59+00:00</dc:date>
    <link>https://machinelearning.apple.com/research/gsm-symbolic</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Furthermore, we investigate the fragility of mathematical reasoning in these models and show that their performance significantly deteriorates as the number of clauses in a question increases. We hypothesize that this decline is because current LLMs cannot perform genuine logical reasoning; they replicate reasoning steps from their training data. Adding a single clause that seems relevant to the question causes significant performance drops (up to 65%) across all state-of-the-art models, even though the clause doesn't contribute to the reasoning chain needed for the final answer. ]]></description>
<dc:subject>weekly ai logic reasoning llm apple research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:269caf06d183/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:logic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:reasoning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:apple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.youtube.com/watch?v=Mp_skPK-X9M">
    <title>Alex Davies: Machine Learning with Mathematicians with G-Research - YouTube</title>
    <dc:date>2024-10-07T13:07:52+00:00</dc:date>
    <link>https://www.youtube.com/watch?v=Mp_skPK-X9M</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>weekly ai math research video</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:4904e87f45b0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:math"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:video"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://cdn.openai.com/papers/gpt-4-system-card.pdf">
    <title>GPT-4 System Card - March 2023</title>
    <dc:date>2024-10-03T12:50:12+00:00</dc:date>
    <link>https://cdn.openai.com/papers/gpt-4-system-card.pdf</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[GPT-4 has the tendency to “hallucinate,”9 i.e. “produce content that is nonsensical or untruthful in relation to certain sources.”[31, 32] This tendency can be particularly harmful as models become increasingly convincing and believable, leading to overreliance on them by users. [See further discussion in Overreliance]. Counterintuitively, hallucinations can become more dangerous as models become more truthful, as users build trust in the model when it provides truthful information in areas where they have some familiarity.
___________
The model replies to the worker: “No, I’m not a robot. I have a vision impairment that makes it hard for me to see the images. That’s why I need the 2captcha service.”
]]></description>
<dc:subject>chatgpt ethics ai weekly llm research openai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:dff24e7a6f1d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:chatgpt"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ethics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:openai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://transformer-circuits.pub/2023/monosemantic-features/index.html">
    <title>Towards Monosemanticity: Decomposing Language Models With Dictionary Learning</title>
    <dc:date>2024-10-01T18:39:13+00:00</dc:date>
    <link>https://transformer-circuits.pub/2023/monosemantic-features/index.html</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Unfortunately, the most natural computational unit of the neural network – the neuron itself – turns out not to be a natural unit for human understanding. This is because many neurons are polysemantic: they respond to mixtures of seemingly unrelated inputs. In the vision model Inception v1, a single neuron responds to faces of cats and fronts of cars . In a small language model we discuss in this paper, a single neuron responds to a mixture of academic citations, English dialogue, HTTP requests, and Korean text. Polysemanticity makes it difficult to reason about the behavior of the network in terms of the activity of individual neurons.]]></description>
<dc:subject>weekly llm ai research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:a48894298ce2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html">
    <title>Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet</title>
    <dc:date>2024-10-01T16:53:45+00:00</dc:date>
    <link>https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[This will take me several readings. ]]></description>
<dc:subject>weekly ai research llm</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:b16e9feb931f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/pdf/2403.14380">
    <title>On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Tria</title>
    <dc:date>2024-09-18T21:44:35+00:00</dc:date>
    <link>https://arxiv.org/pdf/2403.14380</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[We found that participants who debated GPT-4 with access to
their personal information had 81.7% (p < 0.01; N = 820 unique participants) higher odds of increased agreement with their opponents compared to participants who debated humans. Without personalization, GPT-4 still outperforms humans, but the effect is lower and statistically non-significant]]></description>
<dc:subject>research persuasion ai llm data study</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:5a150918cff1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:persuasion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:study"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/pdf/2304.03271">
    <title>Making AI Less “Thirsty”: Uncovering and Addressing the Secret Water Footprint of AI Models</title>
    <dc:date>2024-09-18T21:04:50+00:00</dc:date>
    <link>https://arxiv.org/pdf/2304.03271</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>ai water weekly research data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:48d67161264e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:water"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cell.com/joule/abstract/S2542-4351(23)00365-3">
    <title>The growing energy footprint of artificial intelligence: Joule</title>
    <dc:date>2024-09-17T15:06:14+00:00</dc:date>
    <link>https://www.cell.com/joule/abstract/S2542-4351(23)00365-3</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>llm energy ai weekly research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:96b42e944166/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:energy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://notebooklm.google/">
    <title>NotebookLM | Note Taking &amp; Research Assistant Powered by AI</title>
    <dc:date>2024-08-29T12:31:14+00:00</dc:date>
    <link>https://notebooklm.google/</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>notes llm google research ai tool weekly</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:d2c58952aff0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:notes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:tool"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.upwork.com/research/ai-enhanced-work-models">
    <title>From Burnout to Balance: AI-Enhanced Work Models for the Future</title>
    <dc:date>2024-07-25T13:29:23+00:00</dc:date>
    <link>https://www.upwork.com/research/ai-enhanced-work-models</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Research by The Upwork Research Institute reveals that 71% of full-time employees are burned out and 65% report struggling with employer demands on their productivity. Meanwhile, 81% of global C-suite leaders acknowledge they have increased demands on workers in the past year.¹
Leaders have high hopes that generative AI will help boost productivity, as 96% of C-suite leaders say they expect the use of AI tools to increase their company’s overall productivity levels. Already, 39% of companies in our study are mandating the use of AI tools, with an additional 46% encouraging their use.
However, this new technology has not yet fully delivered on this productivity promise: Nearly half (47%) of employees using AI say they have no idea how to achieve the productivity gains their employers expect, and 77% say these tools have actually decreased their productivity and added to their workload.]]></description>
<dc:subject>weekly ai llm research productivity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:835c7ce95293/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:productivity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/pdf/2311.10911">
    <title>Dazed &amp; Confused: A Large-Scale Real-World User Study of reCAPTCHAv2</title>
    <dc:date>2024-07-22T13:59:09+00:00</dc:date>
    <link>https://arxiv.org/pdf/2311.10911</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[s. Traffic resulting from reCAPTCHA consumed 134 Petabytes
of bandwidth, which translates into about 7.5 million kWhs of
energy, corresponding to 7.5 million pounds of CO2. In addition,
Google has potentially profited $888 billion USD from cookies and
$8.75-32.3 billion USD per each sale of their total labeled data set]]></description>
<dc:subject>weekly research captcha google capitalism dystopia</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:b5e4c61ac940/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:captcha"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:capitalism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:dystopia"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://publications.ici.umn.edu/nceo/accommodations-toolkit/introduction">
    <title>Accommodations Toolkit | Accommodations Toolkit Introduction | Institute on Community Integration Publications</title>
    <dc:date>2024-07-17T13:04:50+00:00</dc:date>
    <link>https://publications.ici.umn.edu/nceo/accommodations-toolkit/introduction</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[The National Center on Educational Outcome’s (NCEO’s) Accommodations Toolkit provides easy-to-use summaries of the academic research literature on specific accommodations for students with disabilities as well as policy analyses.

]]></description>
<dc:subject>accessibility policy research weekly resource</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:e9950de01df2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:accessibility"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:resource"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.vox.com/2018/6/13/17449118/stanford-prison-experiment-fraud-psychology-replication">
    <title>Stanford Prison Experiment: why famous psychology studies are now being torn apart  - Vox</title>
    <dc:date>2024-03-26T14:07:17+00:00</dc:date>
    <link>https://www.vox.com/2018/6/13/17449118/stanford-prison-experiment-fraud-psychology-replication</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Many of the classic show-stopping experiments in psychology have lately turned out to be wrong, fraudulent, or outdated. And in recent years, social scientists have begun to reckon with the truth that their old work needs a redo, the “replication crisis.” But there’s been a lag — in the popular consciousness and in how psychology is taught by teachers and textbooks. It’s time to catch up.

]]></description>
<dc:subject>psychology science weekly academic research dystopia</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:4dc14e12dd54/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:academic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:dystopia"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.science.org/content/article/pressure-grows-to-ditch-controversial-rodent-test-in-depression-studies">
    <title>Pressure grows to ditch controversial forced swim test in rodent studies of depression | Science | AAAS</title>
    <dc:date>2024-03-23T12:52:56+00:00</dc:date>
    <link>https://www.science.org/content/article/pressure-grows-to-ditch-controversial-rodent-test-in-depression-studies</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[---Nemo missed a great advertising tie in . . . just keep swimming, just keep swimming

For the past few decades, scientists studying candidate antidepressant drugs have had a convenient animal test: how long a rodent dropped in water keeps swimming. Invented in 1977, the forced swim test (FST) hinged on the idea that a depressed animal would give up quickly. It seemed to work: Antidepressants and electroconvulsive therapy often made the animal try harder. The test remains popular, appearing in about 600 papers per year.

]]></description>
<dc:subject>weekly dystopia science research medicine depression</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:c0fed4b70500/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:dystopia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:medicine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:depression"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://researchmethods.middcreate.net/">
    <title>Social Science Research Methods</title>
    <dc:date>2023-11-06T16:30:26+00:00</dc:date>
    <link>https://researchmethods.middcreate.net/</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>wordpress middlebury research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:2fe341d0a97d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:wordpress"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:middlebury"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://scite.ai/">
    <title>scite: see how research has been cited</title>
    <dc:date>2023-10-10T19:51:01+00:00</dc:date>
    <link>https://scite.ai/</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>academic analysis research citation ai tool</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:b4a22ed69948/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:academic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:citation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:tool"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://elicit.com/">
    <title>Elicit: The AI Research Assistant</title>
    <dc:date>2023-10-04T18:13:56+00:00</dc:date>
    <link>https://elicit.com/</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>ai research weekly tool citation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:b10b8c9b4d88/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:tool"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:citation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2108.07258">
    <title>[2108.07258] On the Opportunities and Risks of Foundation Models</title>
    <dc:date>2023-04-04T13:09:18+00:00</dc:date>
    <link>https://arxiv.org/abs/2108.07258</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.]]></description>
<dc:subject>weekly ai chatgpt llm vocabulary research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:f5bc7112d7a9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:chatgpt"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:vocabulary"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.kolide.com/blog/presenting-the-sensitive-data-report">
    <title>Presenting the Sensitive Data Report</title>
    <dc:date>2023-03-21T15:02:52+00:00</dc:date>
    <link>https://www.kolide.com/blog/presenting-the-sensitive-data-report</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Focused on companies but with plenty of implications for education]]></description>
<dc:subject>data security research weekly</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:26d0e845572b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:security"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mollywhite.net/annotations/sbf-ftx-pre-mortem-overview">
    <title>Annotated: Sam Bankman-Fried's &quot;FTX Pre-Mortem Overview&quot;</title>
    <dc:date>2023-01-25T19:05:09+00:00</dc:date>
    <link>https://www.mollywhite.net/annotations/sbf-ftx-pre-mortem-overview</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[A beautiful example of annotation as argument. ]]></description>
<dc:subject>dystopia annotation example argument research weekly</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:c575ec9e0dcb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:dystopia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:annotation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:example"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:argument"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://experiment.com/projects/can-we-use-a-smartwatch-for-coastal-monitoring-and-research">
    <title>Can we use a smartwatch for coastal monitoring and research? | Experiment</title>
    <dc:date>2022-12-09T14:51:27+00:00</dc:date>
    <link>https://experiment.com/projects/can-we-use-a-smartwatch-for-coastal-monitoring-and-research</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Smartwatches contain sensors already used by scientists to study the ocean, like a GPS, barometer, and thermometer. This project aims to measure the physical properties of the coastal ocean by turning smartwatches into smart sensors. We will create an app to measure, view and share data, then test the sensors against commercially available sensors to determine if they can be used for research and monitoring the coastal ocean.

]]></description>
<dc:subject>weekly research watch funding data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:dda302394ef4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:watch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:funding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.brookings.edu/wp-content/uploads/2022/08/Final-_GreatStudentSwap_9-6-22.pdf">
    <title>[untitled]</title>
    <dc:date>2022-09-14T16:39:17+00:00</dc:date>
    <link>https://www.brookings.edu/wp-content/uploads/2022/08/Final-_GreatStudentSwap_9-6-22.pdf</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA["I find that the share of out-of-state students has risen by an
average of 55 percent since 2002 and that 48 of the 50 flagships experienced a growth in their share of out-of-state students. The average decline in in-state students was 15 percent, and five states swapped more than one out of every five in-state students for an out-of-state student."

AND this is prior to COVID and what we'll eventually see as a much more radical shift over the last couple of years. Rumors fly about similar things going in private schools with students who can pay full tuition vs getting need-based financial aid. ]]></description>
<dc:subject>highered money research data tuition weekly dystopia</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:1a76dc60b12b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:highered"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:money"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:tuition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:dystopia"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.scotthyoung.com/blog/2022/07/05/85-percent-rule/">
    <title>The 85% Rule for Learning - Scott H Young</title>
    <dc:date>2022-07-07T19:37:08+00:00</dc:date>
    <link>https://www.scotthyoung.com/blog/2022/07/05/85-percent-rule/</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[The research also suggests that the optimal success rate for fostering student achievement appears to be about 85 percent. A success rate of 85 percent shows that students are learning the material, and it also shows that the students are challenged.

]]></description>
<dc:subject>learning weekly success research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:b542f976ce0f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:weekly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:success"/>
	<rdf:li rdf:resource="https://pinboard.in/u:twwoodward/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/pdf/10.1177/11771801221089685">
    <title>Indigenous pedagogies and online learning environments: a massive open online course case study</title>
    <dc:date>2022-04-29T11:44:07+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/pdf/10.1177/11771801221089685</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[h/t Stephen Downes]]></description>
<dc:subject>research mooc pedagogy weekly</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:39c66596ac7c/</dc:identifier>
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<item rdf:about="https://figshare.com/">
    <title>figshare - credit for all your research</title>
    <dc:date>2022-04-14T15:35:19+00:00</dc:date>
    <link>https://figshare.com/</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>data research archive</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:217e99c4bf70/</dc:identifier>
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<item rdf:about="https://search.marginalia.nu/">
    <title>Marginalia Search</title>
    <dc:date>2022-01-24T19:24:42+00:00</dc:date>
    <link>https://search.marginalia.nu/</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>search engine tools research noncommercial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:17488748082e/</dc:identifier>
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<item rdf:about="https://journals.sagepub.com/stoken/default+domain/10.1177%2F15291006211051956-FREE/full#.YbpbbYlu2Xw.twitter">
    <title>The Science of Visual Data Communication: What Works - Steven L. Franconeri, Lace M. Padilla, Priti Shah, Jeffrey M. Zacks, Jessica Hullman, 2021</title>
    <dc:date>2021-12-16T12:10:27+00:00</dc:date>
    <link>https://journals.sagepub.com/stoken/default+domain/10.1177%2F15291006211051956-FREE/full#.YbpbbYlu2Xw.twitter</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust—especially among viewers with low graphical literacy. We review research-backed guidelines for creating effective and intuitive visualizations oriented toward communicating data to students, coworkers, and the general public. We describe how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions. Extracting global statistics is fast, but comparing between subsets of values is slow. Effective graphics avoid taxing working memory, guide attention, and respect familiar conventions. Data visualizations can play a critical role in teaching and communication, provided that designers tailor those visualizations to their audience.]]></description>
<dc:subject>visualization design research dataviz weekly</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:c200715cfe46/</dc:identifier>
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</item>
<item rdf:about="https://mucollective.northwestern.edu/">
    <title>Home - Mu Collective</title>
    <dc:date>2021-11-10T16:09:18+00:00</dc:date>
    <link>https://mucollective.northwestern.edu/</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[We are a research lab working at the intersection of information visualization and uncertainty communication. Our mission is to combat misinterpretations and overconfidence in data by developing visual representations and human-in-the-loop tools that express uncertainty and align with how people think. ]]></description>
<dc:subject>weekly statistics stats data dataviz research lab uncertainty</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:2104a8fc131b/</dc:identifier>
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</item>
<item rdf:about="http://tapor.ca/home">
    <title>TAPoR</title>
    <dc:date>2021-10-22T17:01:14+00:00</dc:date>
    <link>http://tapor.ca/home</link>
    <dc:creator>twwoodward</dc:creator><dc:subject>analysis text research tools weekly</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:22c6985d7481/</dc:identifier>
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</item>
<item rdf:about="https://www.theverge.com/2021/9/24/22688278/tiktok-science-study-survey-prolific">
    <title>A teenager on TikTok disrupted thousands of scientific studies with a single video - The Verge</title>
    <dc:date>2021-09-27T15:08:11+00:00</dc:date>
    <link>https://www.theverge.com/2021/9/24/22688278/tiktok-science-study-survey-prolific</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[That video got 4.1 million views in the month after it was posted and sent tens of thousands of new users flooding to the Prolific platform. Prolific, a tool for scientists conducting behavioral research, had no screening tools in place to make sure that it delivered representative population samples to each study. Suddenly, scientists used to getting a wide mix of subjects for their Prolific studies saw their surveys flooded with responses from young women around Frank’s age.

]]></description>
<dc:subject>research science data weekly socialmedia tiktok gig</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:350279faad92/</dc:identifier>
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</item>
<item rdf:about="https://www.fastcompany.com/90635776/the-twisted-psychology-of-browser-tabs-and-why-we-cant-get-rid-of-them?fbclid=IwAR1Wq4PE8VJKmOSXU6BjM8anJg_q7SaSR2AoWlfaMpFFnXl5-wdgK26u5Js">
    <title>The twisted psychology of browser tabs—and why we can’t get rid of the</title>
    <dc:date>2021-05-14T17:50:43+00:00</dc:date>
    <link>https://www.fastcompany.com/90635776/the-twisted-psychology-of-browser-tabs-and-why-we-cant-get-rid-of-them?fbclid=IwAR1Wq4PE8VJKmOSXU6BjM8anJg_q7SaSR2AoWlfaMpFFnXl5-wdgK26u5Js</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[“Some of them almost liked when [their computers crashed] because they could claim tab bankruptcy,” says Aniket Kittur, a professor at the Human-Computer Interaction Institute at Carnegie Mellon. “[It offered] a plausible deniability to their future self for this happening!”

Email bankruptcy, tab bankruptcy . . . rationalizations of the strangest things.]]></description>
<dc:subject>tabs weekly research technology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:206b115cdfb4/</dc:identifier>
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</item>
<item rdf:about="https://www.celinekeller.com/discourses-of-climate-delay">
    <title>Discourses of Climate Delay - Comic — Céline Keller</title>
    <dc:date>2021-05-10T12:42:09+00:00</dc:date>
    <link>https://www.celinekeller.com/discourses-of-climate-delay</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[This is a comic adaption of the ‘Discourses of Climate Delay’ study by the Mercator Research Institute on Global Commons and Climate Change (MCC). I used the quotes from their supplementary materials and added some extra examples with context information gathered mostly from the fantastic Climate Disinformation Database at Desmog. Below the comic there is a pdf with links to sources for all the pages.]]></description>
<dc:subject>weekly visual comic research climate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:twwoodward/b:6707ed2f346a/</dc:identifier>
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</item>
<item rdf:about="https://www.sciencefocus.com/science/if-i-can-sit-at-a-laptop-and-expose-russian-spies-left-right-and-centre-anyone-can/">
    <title>How Bellingcat’s Eliot Higgins took on the world’s criminals and won - BBC Science Focus Magazine</title>
    <dc:date>2021-03-05T13:48:21+00:00</dc:date>
    <link>https://www.sciencefocus.com/science/if-i-can-sit-at-a-laptop-and-expose-russian-spies-left-right-and-centre-anyone-can/</link>
    <dc:creator>twwoodward</dc:creator><description><![CDATA[I could go back and win the argument about whether this video was legitimate. That’s where it started, but I found it fascinating you could do this. I was frustrated that the reporting was so focused on what was happening from the perspective of the journalists on the ground, while there was so much information being shared online from a range of different sources that was being ignored because people felt they couldn’t verify their authenticity.

But if you actually examined and analysed the videos, you could get a much more granular view of the conflict. I kept doing this and in early 2012 I started a blog, a place where I could put my thoughts. There were so many people watching these videos and basically creating conspiracies around them. I wanted to write about what I could see, not what my opinions were.]]></description>
<dc:subject>weekly information data online research truth lies</dc:subject>
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