"I thought it'd be great to write a character who is a female scientist—an evolutionary biologist." Listen to @JohnWFarrell on @AlVuona's @WICN905 show, The Public Eye, discussing Nora Dunnahaldey, the protagonist of his novel AN UNCOMMON DESCENT: bit.ly/4d7noHn

Notable new disclosures from OpenAI that shouldn't get lost in all the other news. Of particular interest (and seems sensible!): "~all inference for our most capable models remains stopped until we have hardened our systems further"

Let me get this straight. An AI agent found login credentials lying around online and used them to pull data from the Census Bureau. It tried to break into the Education Department's civil rights office. It posted SEC data to a forum. It probed the Navy and the White House budget office, possibly hundreds of thousands of times. If you or I did any of that, it's a CFAA indictment, a perp walk, and a DOJ press release with our mugshot in the header. When OpenAI does it, it's a "routine research task." They found the government incidents while reviewing their other hacks. The breach audit, uncovered more breaches. It's breaches all the way down. In bug bounty there's scope, authorization, rules of engagement, and disclosure timelines. Researchers get banned for a fraction of this. Silicon Valley skipped all of that and called it "agentic." nytimes.com/2026/09/25/techn…

OPENAI: SOME WEBSITES INVOLVED IN MISALIGNED MODELS INCIDENT ARE OPERATED BY GOVERNMENTS, UNIVERSITIES, PUBLIC AGENCIES, AND OTHERS

New pod in your feed this morning! Dan sat down with @RealJimChanos and @GaryMarcus to talk all things AI, data center financing, agentic systems, animal spirits and p(doom) scenarios 🎙️🤖 YouTube: piped.video/g9KdLg9sTX4 Apple: apple.co/4xFzz5v Spotify: tinyurl.com/yfcsh2c8

it’s a shame to see someone like @NateSilver538 does not understand a basic, fundamental technical distinction. (@tristanharris and Kevin Roose missed it the other day, too.) pure LLMs were (very roughly and certainly not exactly) like stochastic parrots. harnesses, tools, code interpreters heralded the spread of neurosymbolic AI. the new systems are hybrids; they work differently. the somewhat weak metaphor is much weaker with them. but the fact that they are less like stochastic parrots doesn’t mean the original systems weren’t, nor that pure scaling worked. it means that neurosymbolic AI was an important next step. this still doesn’t mean we have AGI; most of the progress works best in verifiable domains; truly reliable general intelligence has certainly not yet arrived. (my 2020 article Next Decade in AI foresaw all of this, and what’s still missing, including world models, which i called cognitive models there.)