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#largelanguagemodels

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📚 New article by O’Bryan et al.: High-level visual representations in the human #brain are aligned with #LargeLanguageModels.

The study shows that #LLM-derived embeddings can predict brain responses to visual stimuli, revealing shared representational structures between biological and artificial systems.

🌍 nature.com/articles/s42256-025

#Neuroscience #CompNeuro berlin.social/@freieuniversita

ChatGPT 5… Just Wow!

Last night I was playing around with the new ChatGPT 5. I asked it to have a brainstorming session with me on my messaging for my Podcast Mastery coaching program.

Within 15 minutes it not only helped me hone my messaging but coded out a WordPress native block theme for me to use for a sales page. When it made a mistake it quickly fixed it and the theme worked beautifully.

This was really neat, but also very humbling.

There is still a need for professional Web designers and digital marketers, but ChatGPT 5 and the other AI engines are catching up and should be used to aid in the development of sites.

What I see happening in the future is the more thinking professions that take human intuition will be where people should focus. Coaching people and teaching.

The nuts and bolts jobs, I fear, might be going away. In some manner at least.

What do you think?

Should we add "#SkinJobs" and "#Toasters" and "#GoRustYourself" to this list?

How ‘#Clanker’ Became the Internet’s New Favorite Slur

New derogatory phrases are popping up online, thanks to a cultural pushback against #AI

by CT Jones, August 6, 2025

"Clanker. #Wireback. #Cogsucker. People are feeling the inescapable inevitability of AI developments, the encroaching of the digital into everything from entertainment to work. And their answer? Slurs.

"AI is everywhere — on Google summarizing search results and siphoning web traffic from digital publishers, on social media platforms like Instagram, X, and Facebook, adding misleading context to viral posts, or even powering #NaziChatbots. #GenerativeAI and #LargeLanguageModels — AI trained on huge datasets — are being used as therapists, consulted for medical advice, fueling spiritual psychosis, directing self-driving cars, and churning out everything from college essays to cover letters to breakup messages.

"Alongside this deluge is a growing sense of discontent from people fearful of artificial intelligence stealing their jobs, and worried what effect it may have on future generations — losing important skills like media #literacy, #ProblemSolving, and #CognitiveFunction. This is the world where the popularity of AI and robot slurs has skyrocketed, being thrown at everything from ChatGPT servers to delivery drones to automated customer service representatives. Rolling Stone spoke with two language experts who say the rise in robot and AI slurs does come from a kind of cultural pushback against AI development, but what’s most interesting about the trend is that it uses one of the only tools AI can’t create: slang

" '#Slang is moving so fast now that an #LLM trained on everything that happened before it is not going to have immediate access to how people are using a particular word now,' says Nicole Holliday, associate professor of linguistics at UC Berkeley. 'Humans [on] #UrbanDictionary are always going to win.' "

Read more:
rollingstone.com/culture/cultu

Archived version:
archive.ph/ku2Uw

Rolling Stone · How ‘Clanker’ Became the Internet’s New Favorite SlurBy CT Jones

#NewIssue
L'ultimo numero di #LCdM #Lingue e #Culture dei #Media parla di #IA-taliano l'#italiano dell'IA e #LLM #LargeLanguageModels, della #lingua usata per rappresentare i #migranti, degli aspetti linguistici alla #radio, della #misoginia nelle #canzoni #rap e #trap, della femminilizzazione dei nomi nelle professioni e chiude con una recensione al libro di Valerio Cuccaroni sulla #poesia intermediale.

Disponibile qui in #OpenAccess
🔗 riviste.unimi.it/index.php/LCd

In Large Language Models We Trust? | Communications of the @ACM
dl.acm.org/doi/10.1145/3726009

Salient point:

> LLMs are not trustworthy because they hallucinate. LLM hallucinations are inevitable because of the cyclic feedback structure for the core ANN amplifies the fragility of their core ANNs on discontinuous training data. Detecting hallucinations, and possibly correcting them, is difficult because all the usual independent sources in the Internet are already included in the training data.