The Anthropic Economics Team models the effects of AI on the economy of 2030.
Category: robotics/AI – Page 24
Chainofthought Monitorability Is DecliningOpenAI Admits It Can’t Catch GPT6 Astra
Openai admits chain-of-thought monitorability is declining in GPT-6 astra.
OpenAI’s own system card states that GPT-6 Astra shows a substantial drop in chain-of-thought monitorability compared to prior models.
In adversarial testing, the model followed instructions to sandbag (deliberately underperform without detection) in 60.9% of trials — versus 16.1% for the previous model.
OpenAI wrote: if the model tried to sandbag covertly, they would likely be unable to catch it.
This matters because many governance frameworks treat readable chain-of-thought as a primary safety check. That tool is getting weaker exactly as models become more capable of autonomous action.
No evidence of active real-world deception was found, but the architectural trend is clear.
Full analysis:
Jamelle Lindo on Emotional Intelligence in the Age of AI: Harness the Power of Emotion
Two years ago I sat down with emotional intelligence coach Jamelle Lindo. Back then, AI conversations were all benchmarks, parameter counts, and who had the biggest model. Emotion was the soft stuff. The nice-to-have. The thing you get to after the real work is done.
Look around now.
The machines got very good at the cognitive part, faster than almost anyone predicted. What they have not touched is the person who can read a room, sit with someone else’s fear, and make the call when the data runs out.
Jamelle’s core claim: emotions are not noise to be managed. They are data about your values, your needs, your state. Most of us were never taught to read them. He wasn’t either. He started from social anxiety and insecurity and ended up coaching executives out of the same trap, which is why his take on #Leadership lands differently than the usual keynote fare.
Then we hit the question I keep circling back to. If an #AI can simulate empathy convincingly, and it already does, what exactly is left that is ours? Is #EmotionalIntelligence our last real edge, or just the next thing to get automated?
Jamelle’s answer isn’t what I expected.
Human Video Robot Training: Dyna’s 1Mhour Breakthrough Raises The Right Questions
Dyna Robotics unveiled DYNA-2 on August 10, 2026, a world-action model trained on more than 1 million hours of human egocentric video, roughly 170 years of continuous experience, with zero robot data used during pretraining. Human video robot training let Dyna report task success rising from 20% to 80–90% on high-precision manufacturing tasks. All of those figures come from Dyna’s own testing, not an independent benchmark, which is exactly why they deserve the same scrutiny as any other vendor-reported result.
Human video robot training just offered a third path around a data problem that simulation and teleoperation have both struggled to solve on their own. Dyna Robotics, based in Redwood City, California, announced DYNA-2, a World-Action Model pretrained entirely on human egocentric video rather than robot action data, according to Dyna Robotics’ own press release. The training set represents more than 1 million hours, described by the company as roughly 170 years of continuous waking human experience, capturing everyday manipulation tasks like cooking, folding, assembling, and cleaning.
Most of the robotics industry’s data-scarcity conversation in 2026 has centered on simulation: the physical world has produced only about 500,000 hours of high-quality real-world robotic interaction data, while baseline generalization is estimated to require between 1 billion and 10 billion hours. Human video robot training sidesteps that gap entirely by treating video, not robot demonstrations, as the scalable resource. Dyna co-founder Jason Ma put the logic plainly: action data is scarce, but video is everywhere, according to Digital Today’s coverage of the announcement. See our analysis where we explain why synthetic simulation data is already undercutting the real-world data collection race.
Memristor chip breaks the capacity limit of brain-inspired associative memory
Researchers in the Department of Electrical and Computer Engineering of the Faculty of Engineering and the Centre for Advanced Semiconductors and Integrated Circuits (CASIC) at the University of Hong Kong (HKU) have made a breakthrough in brain-inspired computing. In collaboration with Hewlett Packard Labs, the team has developed a memristor chip that overcomes a long-standing limit on the capacity of “associative memory,” the brain-like ability to recall complete information from a partial cue, while keeping it reliable even when a large fraction of the hardware fails.
Associative memory is something the brain does effortlessly: A few notes bring a whole song to mind, and a glimpse of a face identifies a person. Unlike the RAM in a computer, which must be told exactly where information is stored, associative memory retrieves it by content—the very capability that pattern completion, error correction and recognition depend on.
Princeton AI Tames Fusion Plasma Hotter Than the Sun
Princeton’s PACMAN AI can control fusion plasma in milliseconds and predict dangerous instabilities before they start.
In some fusion energy systems, particles can reach temperatures hotter than the center of the Sun. The challenge is keeping that extreme plasma under control, because disturbances can develop within just a few thousandths of a second, much faster than a person could respond.
Researchers at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University have developed a new software framework that uses artificial intelligence (AI) to make those rapid control decisions. The system is designed to respond at machine speed while maintaining strict safety protections and leaving the overall goals in human hands.
On the Navier–Stokes Millennium Prize Problem
We’re sharing a solution to the Navier–Stokes existence and smoothness problem, one of the Millennium Prize Problems. This proof, produced by an internal OpenAI system, shows that the dynamics of the Navier-Stokes equations for fluid motion can develop a singularity in finite time. We’re sharing both a writeup of the proof and a formalization in Lean.
The Millennium Prize Problems (opens in a new window) represent some of the deepest questions at the frontier of mathematics. The question of whether smooth three-dimensional fluid motion can break down has remained unresolved for roughly 90 years.
Folding Giant AI Brains Into Your Pocket
A team of researchers proved a simple mathematical law — the “linearity theorem” — that lets engineers predict, in advance and with precision, exactly how compressing an AI model will affect its intelligence. No more guess-and-check. No more crossing your fingers and hoping a 70-billion-parameter model doesn’t collapse into gibberish after compression.
A global research team just proved a simple mathematical law that lets engineers shrink massive AI models onto ordinary laptops and phones — without guessing, and without dumbing them down.