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OpenAI has temporarily slowed training of its most advanced AI models as it strengthens safeguards around increasingly capable systems.

The company paused reinforcement learning training for two weeks, and its largest planned frontier training run is still on hold.

The move came after a security incident involving OpenAI and Hugging Face, and signs that its upcoming Astra model may have highly advanced cyber capabilities.

OpenAI is now adding stronger monitoring, tighter internet access, safer research environments, and more alignment testing.

Some Astra training and testing remain paused until they meet the new security standards.

Researchers at Shenzhen University have developed an AI model that may predict depression risk up to four years in advance by analysing how teenagers’ brains respond to facial expressions.

The system used data from European adolescent studies involving MRI scans, blood tests, and questionnaires.

Researchers found that 19-year-olds whose brains had difficulty distinguishing emotional expressions, particularly those showing stronger negative interpretations, were more likely to develop depression by age 23.

The model was also tested on more than 400 adolescents with different mental health conditions, with abnormal readings mainly appearing in people diagnosed with depressive disorder.

The judge spotted it by eye, and the plaintiff has now lost the right to file anything electronically.

Matthew Elliott, representing himself in Connecticut, hid instructions in tiny white text telling any AI model reviewing his July 24 motion to agree with it. Text extraction pulls every character out of a PDF regardless of color, so invisible lines carry full weight.

The court does not use AI to review records, so nothing read it. The August 6 decision requires all his future filings on paper.

Geoffrey Hinton, the Nobel Prize-winning "Godfather of AI," says billionaires like Elon Musk aren't exaggerating: mass unemployment caused by AI is a real possibility, and Big Tech's trillion-dollar data centre bets partly depend on it. He argues one of the main ways tech companies plan to recoup those investments is by selling AI that does workers' jobs for far less.

He does expect AI to create new jobs too, just not nearly as many as it eliminates. His caveat: predictions about anything past a year or two are like driving in fog — clear up close, invisible further out.

Bernie Sanders has already put a number on it: up to 100 million U.S. jobs at risk of displacement, with accounting, software development, and even nursing roles potentially seeing cuts, not just manual and customer-facing work.

Bernie Sanders urged OpenAI’s Sam Altman, Anthropic’s Dario Amodei, and Meta’s Mark Zuckerberg to immediately pause AI development to protect humanity. He warned about uncontrollable AI, biological threats, job losses, and dangers to democracy.

Apple has reportedly trained its own large language model specifically for China with support from Alibaba.

The move marks a shift from Apple’s earlier strategy of relying mainly on third-party Chinese AI models.

Apple Intelligence is expected to launch in China in the coming months after clearing a key regulatory hurdle.

Alibaba’s Qwen model will still be integrated into the service, though the role of Apple’s own model remains unclear.

Bengaluru-based startup Dognosis is developing an early cancer detection system that combines trained dogs with AI.

Patients provide breath samples through masks, which dogs then sniff for chemical signals associated with cancer.

Sensors monitor the dogs’ behavior, breathing, movements, and other responses, while AI analyzes those signals.

A Phase 2 study reported about 90% sensitivity for early-stage cancers across seven broad cancer groups covering more than 20 cancer types.

The company has begun a Phase 3 trial involving around 10,000 people across 10 Indian hospitals to further validate the technology.

OpenAI is now on track to generate more than $40 billion in annualized revenue based on its current performance.

That would roughly double its revenue run rate from the end of 2025.

The company’s growth has accelerated in recent months, driven by demand for its AI coding products, ChatGPT subscriptions, and its growing advertising business.

NVIDIA CEO Jensen Huang once pointed to radiology as one of the first professions he expected AI to replace.

The logic seemed obvious. Computer vision was already becoming better than humans at recognizing certain patterns in images, and radiology is built around interpreting scans like X-rays, CTs, and MRIs.

But that prediction didn’t play out quite as expected.

AI has become increasingly capable at detecting abnormalities, prioritizing urgent cases, and helping doctors analyze medical images. Yet instead of making radiologists obsolete, it has mostly become another tool in their workflow.

The job is changing, not disappearing.

And radiology may be an early example of a much bigger pattern: AI doesn’t necessarily replace an entire profession at once. It automates parts of the job, changes what humans spend their time on, and raises the value of skills AI still struggles with.

Harvard and MIT built 8.3 billion AI personas to simulate the world.

These personas can be dropped into a live product and told to act: take a survey, argue with a chatbot, shop a site, tap through an app.

The project is called MatrAIx, published on arXiv on August 4 by researchers at Harvard and MIT.

Its foundation is Persona 8B, 8.3 billion records built to match the world population, each a plausible person described across 1,290 attributes, from age and career to risk tolerance.

A persona becomes an agent when a language model plays it. The team built 4 testing environments and ran 18,189 trials using Claude Opus 4.8, GPT 5.5, and Haiku 4.5.

Product research used to mean weeks of recruiting panels. Here a cohort runs in hours and can be rerun after a design change. Results depended heavily on the model doing the acting.

On one price increase, 98.3% of GPT 5.5 personas hesitated, against 27% of Opus 4.8 personas on the same cohort.

Choosing which model plays your customer now shapes the result as much as choosing which customer to ask.

A San Francisco billboard advertising the leading chat interface powered by AI hides one detail in the fine print.

The AI stands for Average Individual.

ChatTJB is a performance art project by Tucker Bryant, a conceptual artist and former Google employee. He rented the SoMa billboard for $6,000 a month. There is no language model behind ChatTJB.org. Bryant reads the messages himself and types back answers, sometimes for 10 hours a day, and draws requested images by hand.

After the billboard went up on July 27, traffic peaked at 5,000 prompts per hour, and the project logged over 30,000 queries by August 6. Bryant says the goal is to make people pause before accepting chatbot answers at face value.

It worked because the billboard looked like every other AI ad in San Francisco.

Mark Zuckerberg just posted a video announcing Muse Glimmer, while at the same time publishing a massive 6,500-word essay explaining his views on the future of AI.

A big part of his argument is that powerful AI should be widely accessible instead of being controlled by a small number of companies or governments.

The timing makes the launch much more interesting. Glimmer feels like one of the first clear examples of Meta actually building around the ideas Zuckerberg laid out in the essay.

Nvidia brought together 37 founding partners across cloud, cybersecurity, enterprise software, research, and open-source infrastructure for the new alliance.

Nvidia is contributing models, weights, data, and its NOOA framework, built to make agent behaviour easier to test and audit.

Members are also adding tools for agent identity, safe model storage, signed patches, vulnerability scanning, and secure coding workflows.

The alliance believes shared defenses can move faster than isolated companies, though open systems still need strong safeguards against misuse.