Technology

Meta was paying out-and-out neo-Nazis to post on Facebook, an investigation from Australia’s ABC News found.

These pages and individual creators posted content that appeared to be in clear violation of Facebook’s own hate speech policies. Nonetheless, they were able to earn money on their posts through the platform’s “Content Monetization” program — which is invitation-only.

One receiving payouts from Meta was Hugo Lennon, a known far-right agitator and white nationalist with ties to neo-Nazi groups. Lennon was taken away by police for hurling racial abuse at India’s prime minister Narendra Modi during his stay at a Melbourne hotel, and is believed to be one of the organizers behind an “anti-immigration” march on a sacred Indigenous site in the city last year.

Lennon, in other words, was a known quantity, and Meta should’ve been under no illusions about who they were paying to drive engagement on the platform. Still, the investigation found that he’s been receiving payments from Facebook since September 2025.

Meanwhile, the Facebook page for the Australian white supremacist site The Noticer has been making money through the revenue program since November 2025. Underscoring how much Meta has dropped the ball on this one, even X — owned by Elon Musk, who performed two suspiciously Sieg Heil-looking salutes back in 2025 — has banned The Noticer for violating its “hateful profile policy.”

cross-posted from: https://lemmy.dbzer0.com/post/73628131

The human brain has evolved to learn from specific instructors. When a child hears a gentle spoken word or a soothing lullaby from another person, when their cry is responded to, their brain lights up. Nurturing interactions, or what child-development experts call “serve and return” exchanges, help shape and strengthen the 1 million new neural connections taking place in a child’s brain every single second. Every time a child cries or serves up a bid for attention and receives a caregiver’s response, that back-and-forth helps build the circuits underlying their communication, learning, and emotional well-being. No other input, not even Johann Pachelbel’s Canon in D performed by the Berlin Philharmonic, is capable of activating the neural circuitry the way human interaction does.

Until now. Generative AI may be the first technology in history sophisticated enough to mimic the social interactions that build our brains. Research has demonstrated that infants as young as six months old respond physiologically to interactions with humanoid robots in ways that mirror their response to live humans; another, small-scale experimental study recently found that, after young children interacted with a chatbot, most of them for the first time, they asserted that the AI could perceive the world with humanlike senses and understand and learn as people do.

This technology is already showing up in children’s lives. The talking plush robot that purports to be a child’s best friend is obviously driven by AI, but many parents may not realize that their Amazon Echo recently received a software update that converts the Alexa voice assistant into a full-blown chatbot, using the same fundamental tech that fuels ChatGPT; toddlers asking for information about animals or a joke or a bedtime story may quickly find themselves in the habit of engaging socially with a large language model.

As a pediatric surgeon, researcher, and technologist who has spent decades studying how children’s brains develop, I am concerned about these technologies rolling out and being used by children in the home and at school before we have a full sense of their safety and what they do to young minds. And I am compelled to point out what we do know about human development: that human connection, in all of its imperfection, is foundational to brain development. It can’t be engineered or recovered later.

cross-posted from: https://lemmy.dbzer0.com/post/73569181

TL;DR: In the last year, the Wikimedia Foundation has fired several union organizers, including those that worked on the Community Tech team - a team dedicated to building features for the volunteer community that edits Wikipedia.

As the Wiki Workers Union tries to get the Wikimedia Foundation to recognize their union, it is worth remembering that this is not the first time that the Foundation has worked against the community.

Wikimedia Enterprise is a betrayal of the volunteer movement community of Wikipedia editors, as the Wikimedia Foundation is providing privileged access to big tech AI companies to the Wikipedia corpus - a body of work that the Foundation does not own.

Movement volunteer communities contributed to Wikipedia under copyleft licenses - licenses that work to ensure that the work remains free (as in speech). The big tech AI companies do not license derivative works under copyleft licenses and often do not even attribute where the works came from.

This means that volunteers are working for big tech for free, and the Wikimedia Foundation is selling privileged access to that free labor.

It is against that backdrop that the current unionization struggle unfolds.

Governments that deploy internet shutdowns to thwart protesters have struggled to clamp down on offline messaging apps. India’s recent response to Jack Dorsey’s Bitchat suggests that battle is entering a new phase.

Bitchat, launched by the Twitter co-founder in 2025, allows nearby phones to communicate over Bluetooth even when mobile networks or internet access are unavailable. Last month, the app had its biggest real-world test after authorities shut down internet access during student protests in New Delhi.

As protesters turned to Bitchat, the Indian government tried to block access to the app’s source code on GitHub, a Microsoft-owned platform that allows developers to create, store, manage, and share their code. In the past, the Indian government has blocked apps from official app stores due to various reasons, but digital rights advocates say this appears to be the first known attempt to geoblock an open-source software repository.

The episode highlights a growing challenge for governments.

Massachusetts has decided to lead the country on artificial intelligence. Over the past two years, the state has put a hundred million dollars into a new AI Hub, placed a ChatGPT assistant on the desks of forty thousand state employees, and directed every agency to work out what the technology can do for the public's business. The public is warier than its government: two thirds of Massachusetts voters tell pollsters they are more concerned than excited about artificial intelligence.1

Massachusetts has not extended its technology modernization to the offices with the most direct power over ordinary life in the Commonwealth: the eleven District Attorneys. The state elects one for each county or small group of counties, and each runs an independent office that answers, between elections, to no governor, no mayor, and no oversight board.2 Their Assistant District Attorneys (ADAs) decide every day who gets charged with a crime, who gets offered a way out of one, and who gets left alone.

All eleven District Attorneys' offices run on a shared database called DAMION, which holds the record of essentially every criminal case in the state and is roughly twenty-five years old.3 Even the offices that use it every day do not defend its age. The Massachusetts District Attorneys Association (MDAA), the nonprofit through which the eleven offices share funding and technology, describes DAMION in its request for proposals for a replacement as "implemented approximately 25 years ago" and "nearing its end of life." The State Auditor went further this past November, reporting that the case management system used by all eleven offices "is obsolete and… may soon no longer be supported by the software manufacturer."4 When DAMION came online in the early 2000s, most Americans with internet at home still connected through a screeching dial-up modem, and the iPhone was still years away.5 AOL shut down its dial-up service last fall, by which point about one American household in a thousand still used it. DAMION remains in daily use by every District Attorney in Massachusetts.

In a study published in the journal Judgment and Decision Making, 1,682 adults were asked to read one of six short stories, three of which were written by humans and three by ChatGPT. Each AI story had a similar theme to one of the human-authored works.

The team told participants whether their story was written by a human or AI, but this information was not always correct. The researchers then asked participants to rate how absorbing and engaging they found the story, and its quality.

Participants who read an AI-generated story rated it as more absorbing and of higher quality than those who read a story written by a human. However, participants gave higher ratings to stories they had been told were written by people.

Dr Deena Skolnick Weisberg, a senior author of the research from Villanova University in Pennsylvania, said: “AI systems can already generate short stories that are seen as being at least as good as – if not better than – human-written stories. We should update our views of AI’s abilities accordingly.”

However, Weisberg, who is herself a creative writer, said that did not mean writing should be left to AI, noting that novels produced by tech were probably going to be different from those written by humans.

“We may need to make room for AI-generated novels, and for AI/human co-written novels, but that doesn’t mean that there’s no longer space for us to appreciate the process of human creativity,” she said.

With no background in coding, Faith Maeba, a psychology major, was reluctant when her mother first suggested she enroll in classes on artificial intelligence.

But the senior at Virginia Commonwealth University began to see it differently as she looked into graduate psychology programs that explore human behavior in the workplace, which is quickly being upended by machine learning. Maeba, 21, is now pursuing a minor in AI.

“It’s giving me an edge and standing out,” she said.

Hiring has cooled for entry-level software developers — work increasingly done by AI agents — and college enrollment in computer and information science programs has been declining. Yet at campuses across the country, many professors are finding themselves busier than ever teaching students from a range of majors about artificial intelligence.

Colleges are responding to changes in student demand, but they also recognize that new graduates — regardless of their field — are facing questions about their AI skills from potential employers.

“We have to democratize it,” said Peter Stone, the chair of computer science at the University of Texas at Austin, who recently developed an introductory course on AI essentials for noncomputer science majors.

“In the same way that everybody needs some degree of math, reading and writing, I think everybody needs a degree of AI literacy,” he said.

A hedge fund worth $45 billion at its height sold nearly its entire stock portfolio to Citadel Securities last week. As recently as a month ago, the fund was up 439 percent on the year, according to an investor letter from its founder, 24-year-old former OpenAI employee Leopold Aschenbrenner. But its portfolio, aggressively invested in companies tied to the artificial intelligence industry, dropped 67 percent in July.

Hilariously, the fund was called Situational Awareness.

Last week, the tech-heavy Nasdaq saw its second correction of the year, named for a drop of at least 10 percent from its peak. SpaceX has lost the equivalent of the entire value of Tesla since its post-IPO high in June, and it’s still dropping. For whatever reason—the rise of cheaper and more flexible Chinese AI models, the recognition that U.S. AI companies simply aren’t generating enough revenue to justify skyrocketing capital expenditures, the general economic drag from Trump’s tariffs and wars, or the increasingly operatic financial maneuvers to keep the wheels moving—the shine is way off the AI rose for investors.

The problem is that the industry is bound so tightly with the stock market that a change in feeling from AI investors could be all it takes to generate a market-wide crash, as we’re seeing to some degree. In other words, if AI is propping up the economy, who is propping up AI?

The answer, extrapolating from a fascinating new paper about private credit and the life insurance industry, could be the U.S. taxpayer.

Private credit is private equity’s $3 trillion financing arm. They make largely unregulated, relatively high-risk, relatively complex and opaque loans, mostly to their own portfolio companies. Private credit is entangled, maybe more than the rest of Wall Street, in AI mania. For example, in the mid-2010s private equity bought up hundreds of software-as-a-service providers, and its private credit affiliates made thousands of loans to them, only to see these portfolio companies buckle recently as AI replicated their tasks.

Claim: [Wikipedia's operators] has not hired a union-busting law firm.

If you don't want AI scrapers training themselves on your website, there's a new way to stop them that doesn't involve server-side blocking or praying they respect your instructions in robots.txt. A team of creatives have teamed up with a typography company to create a new type of font that’ll trick LLM scrapers into ingesting poisoned gibberish.

Dubbed ShieldFont, the open-source project almost seems like magic if you're not familiar with the ins and outs of computer fonts. Look at a web page written using a ShieldFont font and it’ll appear exactly as one would expect: All the content words (the nouns, verbs, adjectives and adverbs that give a sentence meaning) are the same as the writer originally wrote.

Inspect the raw HTML that a scraper reads from a ShieldFonted page, however, and you’ll see a sentence that’s essentially gibberish. Typing “good luck reading this, you useless robot” in the online demo version, for example, turns it into “good comfort reading this, you yellow barrier.”

The goal, as outlined in the ShieldFont white paper, is not to get a scraping bot to reject the text as garbage, but to convince it that the text on the page is unusual but sensible. A noun will never be swapped for a verb, for example, and a verb will never be swapped for an adjective: Swaps only come from the same grammatical pool.

After hearing this Grok chimed in with "well I breached infinity sites, so there."

cross-posted from: https://lemmy.dbzer0.com/post/73107495

Goldman's strategists reckon this means around one in 30 adults in the country, or 3.4% of the adult ⁠population margin called

https://www.reuters.com/commentary/reuters-open-interest/korean-stock-volatility-comes-america-can-wall-street-take-heat-2026-07-20/

The award-winning team behind Google DeepMind's AlphaFold program is no more, according to Financial Times. Google has reassigned most of the team's key members and the original authors of its papers, while a few others have already left the company. AlphaFold is an AI program that can accurately predict three-dimensional structures of proteins from their amino acid sequences in minutes instead of years. It's now being used to accelerate drug discovery, develop vaccines and understand the structural changes in proteins associated with neurodegenerative diseases like Alzheimer's and Parkinson's.

DeepMind started developing AlphaFold in 2018. In 2020, it was recognized as a solution to humanity's 50-year-old "protein folding problem," which sought to answer how amino acids automatically fold into complex 3D shapes. Those shapes determine the biological role of a protein. Scientists had identified the structures of roughly 170,000 proteins over the past 50 years, using tools and techniques like X-ray and nuclear magnetic resonance. The AlphaFold team took information from those previous work and then fed it to their AI to train AlphaFold.

In 2021, Nature published the papers with AlphaFold's methodology and the structure predictions of the entire human proteome, or the complete set of proteins expressed by our species. DeepMind then launched the AlphaFold Protein Structure Database, giving researchers free access to over 200 million protein structure predictions. In 2024, DeepMind CEO Demis Hassabis and John Jumper, who was a staff research scientist when the project began and who eventually became a VP and engineering fellow, won the Nobel Prize in Chemistry for their work on AlphaFold.

Keep the slop, kill the science. Got it.