Knowledge collapse

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In a world where AI increasingly mediates access to knowledge, future generations might lose connection with vast bodies of experience, insight and wisdom. AI developers might argue that this is simply a data problem, solvable by incorporating more diverse sources into training datasets. While that might be technically possible, the challenges of data sourcing, prioritization and representation are far more complex than such a solution implies. - Deepak Varuvel Dennisonis

AI Definitions: Liquid Foundation Models

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Liquid Foundation Models (LFM) – This type of AI has a smaller memory footprint but packs greater computational power than the transformer models found in most GenAI systems. Using fewer parameters and neurons than transformers, LFMs are designed to handle a variety of sequential data (such as text, video, and audio) with significant accuracy. LFMs do not rely on existing frameworks as transformers do. They are built from the ground up (that is, built on “first principles”).

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17 Recent Articles about AI Fakes

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18 Articles about AI & the Creative Arts

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AI Definitions: Reinforcement Learning

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Reinforcement Learning - Rather than being given specific goals, the AI is deployed into an environment where it can train with minimal feedback. This trial-and-error approach involves adjusting weights until high-reward outcomes are achieved. Desirable behaviors are rewarded, and undesirable behaviors are punished. It is similar to a person learning how to work through levels of a video game, searching for an effective strategy. This type of machine learning sits somewhere in between supervised (by humans) and unsupervised learning. Reinforcement learning is used in video game development and has helped robots adapt to new environments.  

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Writing your Own Eulogy

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A visualization technique that asks people to write their own eulogy. It’s a technique that Daniel Harkavy, co-author of Living Forward, has been teaching executives for over 20 years.

Harkavy’s tip is to write your eulogy first as if your funeral was today and everything you’ve accomplished so far was all you ever would. “Picture your memorial service as if it were being held right now. Your casket is sitting center stage, and as you look down the center aisle you see the first three rows, usually reserved for those with whom we were closest. Who’s sitting there for you?” he asks. “Most likely your family and dearest friends. Now keep looking down the aisle, and now you’re looking at rows 10 through 20. Who’s sitting there? Probably acquaintances, clients, customers. What did you give to the people in these rows?”

Harkavy says when he walks clients through this exercise during his speaking engagements, they usually all say the same thing: “We gave them our best!” He then asks them what they gave to the people sitting in rows one through three–and their answers usually amount to “We gave them our leftovers.” In other words, their work-life balance is out of whack.

“When you go to write your eulogy, you need to be brutally honest. Don’t pull any punches. You want to really feel this,” Harkavy says. “What would those closest to you say about who you were, how you lived, and what you had to give them, and why would they say that?”

Michael Grothaus writing in Fast Company

LLMs Reflect Western Cultural Value

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It should not come as a surprise that a growing body of studies shows how LLMs predominantly reflect Western cultural values and epistemologies. They overrepresent certain dominant groups in their outputs, reinforce and amplify the biases held by these groups, and are more factually accurate on topics associated with North America and Europe. - Deepak Varuvel Dennisonis

19 Articles about AI & Coding

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AI Definitions: Vibe Coding

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Vibe Coding – An LLM generates code that meets the specifications stated in the user's prompt. This is not the same as software development, where the user reviews the AI coding and can explain it. This type of coding uses natural language to communicate desired outcomes. Vibe coding platforms would include Claude Artifacts, Creator Hunter, and Cursor. While the goal is a finished product, in practice, this approach entails risks, such as hidden bugs and subtle security issues. Some degree of human oversight and refinement is still needed for most LLM-generated code outcomes to become production-ready.

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25 Articles about AI & Academic Scholarship

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Do you use generative AI to help identify literature you missed? If so, how? – Dynamic Ecology

Two-thirds of universities report AI use among doctoral students – Times Higher Ed 

After the PDF: A new unit of knowledge for the AI era – research Information

AI in Scholarly Publishing — SSP Pulse Check Report – Scholarly Kitchen

AI Shift: Agentic AI is coming for quantitative research – Financial Times

Peer review needs a revolution. AI is already driving it – Scholarly Futures

In Memoriam: The Academic Journal (death by LLM) – Arxiv

Guidelines needed for the use of AI in the preparation or review of IRB, IBC, and IACUC applications – Tandfonline 

What to expect in scholarly communications in 2026 (?Or what AI believes could occur...)  – Scholarly Futures 

A Cross-Disciplinary Analysis of AI Policies in Academic Peer Review – Wiley  

Fine-Grained Detection of AI-Generated Writing in the Biomedical Literature – Bioxiv

Funders ‘should support shared AI tools for translational research’ – Research Professional News  

AI-generated commentaries and letters to the editor of peer-reviewed publications: editors and authors beware! - Tandfonline 

A bibliography of genAI-fueled research fraud from 2025 - Sharonkabel

Meet the author who has published more than 500 letters to the editor in a year – Retraction Watch  

Evaluating the Use of Large Language Models as Synthetic Social Agents in Social Science Research – Sciopen 

Publisher under fire after ‘fake’ citations found in AI ethics guide – The Times

The H-Index of Suspicion: How Culture, Incentives, and AI Challenge Scientific Integrity – NEJM

Researchers who use generative AI to write papers are publishing more – C&EN 

Deep Research, Shallow Agency: What Academic Deep Research Can and Can't Do – Aaron Tay

Will AI stop new curation-led publishing models thriving before they’ve even had a chance to grow? – Scholarly Futures

AI-assisted cheating could impact universities' global standings – Korean Times

AI Slop Is Spurring Record Requests for Imaginary Journals – Scientific American

Publisher under fire after ‘fake’ citations found in AI ethics guide – the Times   

Hack reveals reviewer identities for huge AI conference – Science.org

Three types of Perfectionists

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A study measured three types of perfectionism: self-oriented, or a desire to be perfect; socially prescribed, or a desire to live up to others’ expectations; and other-oriented, or holding others to unrealistic standards. A person living with an other-oriented perfectionist might feel criticized by the perfectionist spouse for not doing household chores exactly the “right” way. Socially prescribed perfectionism is “My self-esteem is contingent on what other people think.”

Perfectionists tend to devalue their accomplishments, so that every time a goal is achieved, the high lasts only a short time, like “a gas tank with a hole in it.” 

There are also different ways perfectionism manifests. Some perfectionists are the sleeping-bag-toting self-flagellants, always pushing themselves forward. But others actually fall behind on work, unable to complete assignments unless they’re, well, perfect. Or they might self-sabotage, handicapping their performance ahead of time. They’re the ones partying until 2 a.m. the night before the final, so that when the C rolls in, there’s a ready excuse. Anything to avoid facing your own imperfections.

Olga Khazan writing in The Atlantic

AI Definitions: Opaque AI

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Opaque AI – This is when an AI algorithm operates as a black box that we can’t understand. This can lead to AI systems inadvertently perpetuating and amplifying biases. AI transparency, on the other hand, allows for the examination and understanding of how these biases occur, leading to more ethical and fair AI systems. The level of AI opacity varies depending on the industry. For example, in highly regulated industries, transparency is paramount for legal and regulatory compliance.

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