CETI is a nonprofit organization applying advanced machine learning and state-of-the-art robotics to listen to and translate the communication of sperm whales.

The chatbot interface barely scratches the surface of what AI can do. It is also being used to hide genuinely harmful and even criminal activities.

Cal Newport joins us on Intentional AI to talk about automating tedious work, protecting judgment, and keeping room for deep thinking. We also discuss digital minimalism and why a robot’s convincing answer can still be wrong. Episode Links

h/t Mike Caulfield and Anna Mills

Here are four things your students don’t understand about AI—and what you can do about it.

After almost two years, our paper "The shrinking landscape of linguistic diversity in the age of large language models" is out in Nature Human Behaviour, and it made the cover! Three studies, seven datasets, 880,000+ texts. The short version: when an LLM polishes your writing, the content survives but you don't. What we found: - After ChatGPT's release, the variance in writing complexity dropped across Reddit, arXiv, and Patch News. Writing converged toward a common style. - LLM rewriting cuts writing-complexity variance by 21–50% across datasets and models. - Classifiers that recover an author's age, gender, personality, empathy, moral values, and political affiliation from their original text stop working after a rewrite. The signal doesn't just weaken. It shifts toward dominant categories. - For psychology and the behavioral sciences: a century of work treats language as a window into the person. That window is closing. Text-based measurement (clinical screening, personality assessment, corpus studies of culture and morality) assumes the writing carries individual signal. As LLM-assisted text fills our datasets, that assumption needs checking, not inheriting.- - For AI: homogenization isn't a style complaint. Models that flatten their inputs degrade the personalization built on top of them, feed narrower text back into the next round of training, and quietly push writers toward dominant categories they didn't choose. Preserving variation should be a design target, not a side effect. Paper: https://lnkd.in/gDGU55vZ Research Briefing: https://lnkd.in/ge2Ka4ka Our earlier theoretical piece in Trends in Cognitive Sciences: https://lnkd.in/gQtPFtf4 Congratulations to Zhivar Sourati, who led this, and to Farzan Karimi-Malekabadi, Meltem Ozcan, Colin McDaniel, Alireza Ziabari, Jackson Trager, Ala N. Tak, Meng Chen, and Fred Morstatter. | 109 comments on LinkedIn

Large language models (LLMs) used as writing assistants homogenize writing style. Studies across 880,000 texts show that, following the release of ChatGPT, writing became less varied as artificial intelligence (AI) use spread. The LLMs preserved meaning but compressed the variety of language and biased author traits inferred from the words.

Sourati et al. find that variance in writing complexity dropped on social media, news and scientific writing after ChatGPT’s release, a shift from individuality towards uniformity. LLM polishing strips cues to gender, age, ideology and moral values.

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Jonathan Haidt sounded the alarm on phones in schools. He thinks what’s coming will be even worse.

AI can now build a decent presentation in less time than it takes to refill your coffee. Give it a report, meeting notes, and an old deck you like. A few minutes later, you may have a polished set of slides with charts, colors, layouts, and formatting that look surprisingly good. That is impressive. ItContinue reading "When AI Can Make Almost Anything, Judgment Becomes the Job"

Advanced models can help researchers sort through mountains of biological data.

Anthropic is moving your Cowork tasks to the cloud, and it’s a bigger deal than it looks. Here’s what changes and what to do about it

One of a number of ideas I'm thinking about ahead of 2026. The personal context passport lets people carry a portable, user-owned profile they can share on their terms - from low-stakes convenience (travel, fitness) and evolving into granular, time-limited permissions for high-stakes public services

h/t temit g.?

In my first note on OrgLens I asked whether an AI agent could diagnose an organisation using Stafford Beer’s Viable System Model (VSM). The answer was yes, more…

And that’s important for nonobvious reasons

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Large Language Models (LLMs) are becoming increasingly used to support higher education assessment, yet evidence of their capability on reproducing authentic institutional grade-band labels remains...

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I wouldn’t grant a stranger access to my email. So why would I grant a robot?

The use of artificial intelligence by hospitals and health insurers is escalating their longstanding feud over paying for medical care.

New York and Los Angeles have switched it off for around a million children. We have run this experiment before.

In summer 2026, Sarah Elaine Eaton taught a redesigned online course focused on academic integrity amidst AI challenges. She emphasized real-time engagement through hybrid learning and group tasks,…

An update on the fast-developing story

Convocation address at University of Maine - Augusta (9/18/26)

At our annual kickoff, I told my team that they needed to take AI seriously or they might not be a fit here anymore. This left some people feeling overwhelmed and wondering whether they still belonged (where most of our kickoffs leave them feeling energized). I had been convinced that we should be an AI-first company for two years.  I even restructured things so that I could step away from the day-to-day and immerse myself in AI. But for most of my team, the morning of the kickoff was the first time they heard about any of that. I came in excited and laid out the vision, but it didn’t land the way I expected. Later in our anonymous Q&A, employees had questions about AI’s environmental impact, whether using AI more would hurt output, whether their hard-earned skill sets would still be needed, and more. All of these were questions about values, belonging, and mastery… But I was coming at it from a strategy perspective, which is why it didn’t resonate with them. Even when your vision is right as a leader, you have to give your team time to absorb it.  You may have been thinking, dreaming, and creating this vision for years. You can’t expect your team to see it the way you do within an hour. And I talk more about how to close this gap in my Forbes C200 piece that just went live. Link below. #LeadershipCommunication #ChangeCommunication

h/t Lance Eaton