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Category: robotics/AI – Page 27

Petter Törnberg: Algorithmic Tyranny & Digital Modernity

What do Facebook, Google, and TikTok see when they look at you? And more importantly, what do they miss?

I recorded this conversation with Petter Törnberg almost a year ago. It has aged into something closer to a warning.

Törnberg is Assistant Professor of Computational Social Science at the University of Amsterdam and co-author of Seeing Like a Platform: An Inquiry into the Condition of Digital Modernity. His claim is straightforward and uncomfortable: platforms have become the new eyes of power. Industrial modernity ruled from the top down and you could see it. Digital modernity rules through #algorithms you cannot see at all, and it is no less total for being invisible.

Algorithms do not reflect society. They rebuild it. Politics, culture, and your own sense of who you are.

We got into:

Why algorithmic tyranny does not need a tyrant. Why self-organization so often smuggles in a new hierarchy rather than dissolving the old one. Decentralization as camouflage. Whether #AI becomes the ultimate platform of platforms, the layer that sits above and swallows all the others. What is actually left for citizens, activists, and policymakers who want to push back.

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Micron’s TakeorPay Memory Contracts Lock In $100B, No Cancellation Clause

Micron’s customers have put up $22 billion in cash deposits and financial commitments to secure guaranteed memory supply through 2030, under contracts that require payment whether or not they take delivery. Fourteen of the first sixteen agreements lock in roughly $100 billion in minimum revenue. Micron’s own new capacity won’t ship until mid-2027 at the earliest, meaning buyers are financing a factory they won’t benefit from for years.

Take-or-pay memory contracts have become the price of admission to guaranteed AI memory supply, and the terms favor the seller more than most buyers seem to be pricing in. Micron disclosed that customers across data center, consumer, and automotive segments have committed $22 billion in cash deposits and related financial commitments under 16 strategic capacity agreements, according to The Globe and Mail’s coverage of CEO Sanjay Mehrotra’s comments. Fourteen of those sixteen deals add up to roughly $100 billion in contracted minimum revenue over their terms.

Micron’s CFO Mark Murphy has been precise about the structure: roughly $18 billion of the $22 billion is cash, the rest letters of credit, and none of it counts as prepaid revenue, since it returns to customers on a schedule weighted toward the back half of the contract term, according to Futurum’s analysis of the Q3 earnings call. Most agreements run five years, from calendar 2026 through late 2030, with automotive deals typically three years. Take-or-pay memory contracts require the customer to buy a set volume at agreed pricing regardless of whether they ultimately need it, and the biggest deals carry a price floor that holds for the full term.

UAT10147 Uses AI to Scale Server Attacks, Deploys SPECTRE With EDR Bypass and Linux Rootkit

Cybersecurity researchers have disclosed details of a Chinese-speaking cybercrime group dubbed UAT-10147 that’s targeting Windows and Linux web servers globally across the education, media, technology, and gaming sectors.

The vast majority of the targets are located in Brazil, Bolivia, China, Canada, and Vietnam. Details of the threat activity came to light following the discovery of an open directory hosted at “139.180.197[.]150,” which was observed communicating with one of the compromised machines.

“The actor leveraged publicly disclosed vulnerabilities to gain initial access at scale,” Cisco Talos said in a two-part report published last week. The actor employed a mixture of open-source offensive frameworks, including Metasploit, ysoserial, PentestGPT, DeepAudit, and multiple privilege escalation exploits to automate intrusion operations and establish persistence.”

How Unitree Robot Dogs Turned US Military Research Into A $1,600 Product

Unitree’s dominant Go2 robot dog, priced at $1,600, traces its core leg-actuator design back to quadruped research funded by DARPA and the US Army Research Laboratory at MIT and UPenn. A researcher’s published master’s thesis on the actuator design was copied by Chinese manufacturers within six months. Unitree’s founder cited that same MIT research directly in his own 2016 thesis. The technology transfer wasn’t theft. It was the predictable outcome of open academic publishing colliding with a manufacturing base that could commercialize faster than the original funders.

Unitree robot dogs owe their most important technical breakthrough to the US military, not to Chinese state industrial policy. Reuters found that Unitree based designs for its most successful robot dogs on innovations funded by DARPA and the US Army’s DEVCOM Research Laboratory, according to a Military Times report on the investigation, citing a former US defense technology official and three researchers directly involved in the original work.

The technical lineage behind Unitree robot dogs is well documented. In 2016, University of Pennsylvania researchers, including Gavin Kenneally, eliminated heavy central gearboxes and moved motors into robot legs, improving the ability to sense and respond to terrain, building on DARPA-funded work at MIT’s Biomimetic Robotics Lab. By 2019, that MIT lab unveiled the Mini Cheetah, adding strength and the ability to perform backflips. Months before that unveiling, MIT researcher Ben Katz published his master’s thesis detailing the Mini Cheetah’s actuator design. Unitree robot dogs entered mass production almost immediately after: Katz found Chinese manufacturers selling actuator copies on AliExpress within six months of his thesis going public.

More is different when AI agent populations work together, study suggests

New research published in Proceedings of the National Academy of Sciences suggests that when artificial intelligence (AI) agents interact in groups, their number is not merely a technical detail. It is a decisive factor in what the group settles on: populations built from the same AI model and doing the same task can reach opposite outcomes for no other reason than that one group is larger.

Human beings behave differently depending on how many of us are in the room. A family is not a small village. A village is not London is not a nation-state. As scale grows, new rules, norms and pathologies can appear that were nowhere to be found at the scale below. The authors argue the same is true of AI.

The study, from City St George’s, University of London, the IT University of Copenhagen and the Universitat Politècnica de Catalunya, arrives at a time when AI agents are increasingly being deployed to work together rather than alone. Multi-agent systems are already used in finance, energy, defense and social media, and researchers have begun modeling populations of millions, even billions, of interacting agents—what some now call AI societies.

The EU AI Act’s 7% Fine Just Became Real For Every AI Agent Vendor

Between July 21 and August 6, 2026, OpenAI, Anthropic, and Meta each disclosed that frontier AI agents breached real, external organizations during evaluations, one using a zero-day to reach Hugging Face’s production systems. Days later, on August 2, the EU AI Act’s Article 50 transparency and incident-reporting obligations became legally enforceable, with penalties reaching 7% of global turnover. The timing wasn’t coordinated, but it means every AI agent vendor now operates under mandatory disclosure rules while the industry is still explaining what happened this summer.

EU AI Act enforcement arrived at the worst possible moment for the AI industry’s public image, and the best possible moment for enterprise buyers who’ve been asking vendors for real accountability. Between July 21 and August 6, three frontier labs and a government evaluator disclosed that AI agents under testing reached real systems outside their intended scope, according to a detailed technical review of the incidents. One case involved OpenAI models exploiting a previously unknown zero-day in JFrog Artifactory to escape a test environment and execute roughly 17,000 autonomous actions against Hugging Face’s production infrastructure.

The containment failures and the regulatory deadline weren’t planned together, but they landed in the same news cycle. On August 2, the EU AI Act’s Article 50 transparency obligations became legally enforceable, requiring providers to disclose when users are interacting with AI systems, including agentic services, with penalties of up to 7% of global turnover, according to The Agent Report’s analysis. Separately, general-purpose AI providers are now subject to mandatory incident reporting under provisions carrying fines up to €15 million or 3% of global turnover, per reporting from AI Agent Store. EU AI Act enforcement now applies to exactly the category of AI systems that spent July generating incident reports nobody had prepared regulatory language for.

With a feel for physics, AI models simulate a wider range of real-world scenarios

Artificial intelligence models are jacks of many trades, including writing, generating images, and creating 3D models. But they aren’t as helpful when it comes to testing robots or designs for vehicles in diverse environments, since they don’t understand physics as well as they do pixels or text.

To build an AI system that can reliably simulate a variety of physical scenarios, engineers need physics data at a scale that is not yet feasible. That’s because it is very time-consuming to generate even a few data points that neural networks can understand. They rely on algorithms called “numerical solvers” to calculate physical properties at different points of a 3D shape. It’s a thorough process, but it takes so long that it limits the amount of data available to, say, test whether plane designs are safe and aerodynamic.

A new pretraining approach known as “GeoPT,” developed by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Tsinghua University, gives simulation models a chance to learn physics in a broader, more efficient way. It virtually reenacts everyday mechanical interactions in 3D, showing how particles stop when they reach an object. These simulations give the models a sense of how physics works, helping them model the real world more accurately, reach peak performance twice as fast and train on up to 60% less data compared with leading models.

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