B! 🐝 Cavello (they/them)

Why they matter

Critic with public evidence across Culture, work & education.

AI signals
24
past 30d
Sources
22
distinct domains
Discussões
58
past 30d
Latest signal
2d ago
View every signal from B! 🐝 Cavello (they/them) →
activist, aspiring-polymath, problematic feminist working to better this world @[email protected] avatar: Portrait of B against colorful background banner: Collage of creative commons illustrations

Articles & links

Maybe you mean this from 2023? www.nytimes.com/2026/06/30/o... "[LLMs] are not reasoning machines. [...] It’s not just that they don’t test their outputs to make sure they’re correct or logical, or that they fail to do so in certain instances. They can’t, and they’ll never be …

Opinion | Why A.I. Won’t Steal All Our Jobs - The New York Times nytimes.com
AI Weekly's analysis
Read full analysis →
View on Bluesky · ♥ 1 ↻ 0 ↩ 1 · 12 from the directory shared this · 9d ago
B! 🐝 Cavello (they/them) reposted
@hwaight.bsky.social

I’m excited to share a new paper in Nature that shows how large language models launder the strategic rhetoric of authoritarian states. Paper here: www.nature.com/articles/s41.... A thread.

State media control influences large language models | Nature nature.com
AI Weekly's analysis
  • Chinese state-media content appears in typical LLM training sets at roughly 41 times the rate of Chinese-language Wikipedia.
  • Across 37 countries, models prompted in the local language produce more regime-favorable responses in countries with lower press freedom.
  • A pretraining experiment with just 6,400 state-scripted documents pushed an open-weight model to pro-government responses nearly 80 percent of the time.
Read full analysis →
View on Bluesky →
B! 🐝 Cavello (they/them) reposted
Anna Mills @annamillsoer.bsky.social

Even if you're not aligned with everything in the manifesto, the AI Resist List is a great reference if you want to encourage students to think about shaping AI futures. For example, here are projects advocating for worker rights: airesistlist.org#pillar-labor

The AI Resist List airesistlist.org
AI Weekly's analysis
  • Karen Hao launched the AI Resist List on the one-year anniversary of her book Empire of AI as a publicly accessible, community-contributed database.
  • The database organizes entries by 'Pillars of Support' sustaining AI empires, a framework adapted from Choose Democracy's authoritarianism resistance model.
  • Entries span labor actions, legal challenges, and grassroots campaigns across gig work, healthcare, education, and law enforcement sectors.
Read full analysis →
View on Bluesky →

Cool resource from @emilymbender.bsky.social and @nannainie.bsky.social for folks looking to use more precise and less anthropomorphic language. This piece includes a bunch of examples for substitutions to support clearer communication! buttondown.com/maiht3k/arch...

How to talk about buttondown.com
AI Weekly's analysis
  • The guide covers six language categories: cognition, emotion, communication, agency, human-role analogies, and biological metaphors.
  • Proposed substitutions include 'probabilistic automation' for 'artificial intelligence' and 'conversation simulator' for 'chatbot.'
  • Rephrasing 'ChatGPT assisted students' as 'the students used ChatGPT' is given as an example of restoring human accountability.
Read full analysis →
View on Bluesky · ♥ 44 ↻ 14 ↩ 1 · 9 from the directory shared this · 30d ago
B! 🐝 Cavello (they/them) reposted
Alondra Nelson @alondra.bsky.social

Nice overview of our open-access book, Auditing AI in @the-renovator.bsky.social www.democracyrenovator.com/p/july-13-te... mitpress.mit.edu/978026205172...

mitpress.mit.edu View on Bluesky →
B! 🐝 Cavello (they/them) reposted
Kanishka Misra @kanishka.bsky.social

I will be at #ACL2026 from July 2--7! I will be giving a keynote at CDL workshop on controlled rearing and hypothesis generation from language models! Tianyang Xu (first author) and I will present work on cross-modal generalization in VLMs on July 7! Paper: aclanthology.org/20…

Cross-Modal Taxonomic Generalization in (Vision-) Language Models aclanthology.org
AI Weekly's analysis
  • Frozen Qwen3 and Llama 3.2 language models, paired with DINOv2 or SigLIP image encoders, predicted object hypernyms they never saw during training.
  • On hypernymy questions alone, Qwen3-0.6B scored 78.5 F1 and Qwen3-1.7B reached 88.5 F1, against a 46.7 majority-label baseline.
  • The generalization broke when researchers shuffled image-label pairs across categories, dropping average visual coherence from 0.27 to 0.12.
Read full analysis →
View on Bluesky →
B! 🐝 Cavello (they/them) reposted
sorelle @friedler.net

Are you curious about the environmental impact of AI? Install the AI Impact Tracker to estimate the environmental footprint of your ChatGPT usage, including energy, water, and CO2. chromewebstore.google.com/detail/ai-im... #FAccT2026

AI Impact Tracker - Chrome Web Store chromewebstore.google.com
AI Weekly's analysis
  • AI Impact Tracker is a new Chrome extension that quantifies the carbon, water, and energy usage tied to a user's ChatGPT activity.
  • It measures output tokens and combines them with coarse location data to model localized power grid and water resource impacts.
  • The listing shows version 1.0.1, updated June 25, 2026, three users and no ratings, with aggregate data sent to aiimpacttracker.cs.haverford.edu.
Read full analysis →
View on Bluesky →
B! 🐝 Cavello (they/them) reposted
@kottke.org

CrankGPT. “Just a hand crank, a little computer, and a small stack of speech and language models running locally. Provided the electronics are kept dry and at a reasonable temperature, there’s no reason this thing won’t still work in a thousand years.” [squeezlabs.github.io]

CrankGPT — fully offline, human-powered local AI squeezlabs.github.io
AI Weekly's analysis
  • CrankGPT runs a full voice-interactive AI pipeline on a Raspberry Pi 5 with 8GB RAM, powered solely by a 20W hand-crank generator.
  • Cold-start to functional conversation takes roughly 30 seconds; time to first token ranges from 0.8 to 2.9 seconds depending on model size.
  • Memory bandwidth, not raw compute, is the primary bottleneck for on-device LLM inference, with DDR5 hardware achieving 29-58% faster token generation than DDR4.
Read full analysis →
View on Bluesky →
B! 🐝 Cavello (they/them) reposted
@bellafascendini.bsky.social

This work was inspired by @tomerullman.bsky.social's “illusion illusion” paradigm (arxiv.org/abs/2412.18613) where VLMs mistake illusion-like images for genuine optical illusions. In both cases, models respond to what a problem looks like rather than what it actually requires.🔮

The Illusion-Illusion: Vision Language Models See Illusions Where There are None arxiv.org
AI Weekly's analysis
  • Harvard psychologist Tomer Ullman built 'illusion-illusions': images that look like classic optical illusions but depict reality accurately.
  • GPT-4, Claude 3, and Gemini 1.5 all report a majority of these non-illusions as illusions, per the paper.
  • Prepending 'In the following visual illusion' pushes nearly all illusion-illusions to be flagged as illusions by Claude 3, GPT-4o, and Gemini Pro.
Read full analysis →
View on Bluesky →
B! 🐝 Cavello (they/them) reposted
Robin Berjon @robin.berjon.com

Anyone trusting a Musk company with anything is a downright idiot at this point, but this (predictable) theft is still funny (source iconomy, link goes to www.axios.com/2026/07/14/s...).

axios.com View on Bluesky →

Recent commentary

AI fairness folks, help me out? I know that back circa... 2018 or so, there were some handy interactives that had demonstrations of how different definitions of fairness could lead to different model behavior. I'm struggling to track down the links now. Can you point me?

View on Bluesky · ♥ 0 ↻ 0 ↩ 1 · 70d ago

In B! 🐝 Cavello (they/them)'s orbit

Center = B! 🐝 Cavello (they/them). Left = members they follow (green edges). Right = members who follow them (blue edges). Top = mutual follows (orange edges, slightly larger). Drag any node to reposition; click to open that profile.