T-cell receptor, or TCR, therapy is a cancer treatment that genetically reprograms immune cells, called T cells, to hunt down cancer with precision. It’s similar to another treatment, CAR T-cell therapy, but with one key difference: CAR T-cell therapy can only spot proteins that naturally appear outside a cancer cell. TCR therapy, however, can also catch small protein fragments from inside the cell that are carried to the surface and displayed like little name tags.
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.
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.
In dark skies at night, distant starlight twinkles and speaks to vast cosmic histories almost as old as time itself. New data from instruments such as NASA’s James Webb Space Telescope are helping astrophysicists probe deep cosmic mysteries, including the evolution of black holes and galaxies.
Using supercomputers to help make sense of the data, researchers from multiple institutions worked together to complete the largest cosmological hydrodynamic simulation, called ASTRID—a mind-boggling computational run that traces the evolution of the universe from its earliest times to the present.
Persistent pain often has no clear cause. However, studies show measurable differences in the structure and activity of the brains of those affected. This might explain why they experience chronic pain. “Many of our study participants were grateful that someone was taking their symptoms seriously and conducting research into them,” says neuroscientist Salome Häuselmann.
Two studies find that fundamental quantum constraints rule out a previously proposed neutrino-laser scheme.
One of the most striking demonstrations of collective quantum behavior is superradiance. When photons emitted by many particles carry no information about which particle produced them, the different emission pathways interfere constructively, and the ensemble radiates far more intensely than independent emitters would. Last year, scientists proposed that this principle could extend from photons to neutrinos, potentially enabling the first neutrino laser (see Viewpoint: Envisioning a Neutrino Laser) [1]. The idea was especially appealing because neutrinos are otherwise extremely difficult to control and detect, owing to their weak interactions with matter. Now Wolfgang Ketterle and his colleagues at MIT have demonstrated that this vision of neutrino superradiance runs up against fundamental constraints—ones imposed not by engineering challenges but by quantum mechanics itself [2, 3].
Superradiance is a collective enhancement of spontaneous emission [4]. An isolated atom emits at its natural rate γ so N independent atoms radiate at a total rate N γ But when all the atoms radiate into the same mode, constructive interference of the different emission pathways can cause the maximum emission rate to scale as N2 γ This superradiant regime can occur when the atoms occupy a region much smaller than the radiation’s wavelength or when an optical cavity forces them to couple to a common mode [5–7]. Such collective emission can also arise in extended atomic systems, where it becomes directional and is shaped by propagation effects. This extended-ensemble superradiance has been observed in free space [8] and in waveguides [9].
How did a photon survive a journey of more than 2 billion light-years when, according to known physics, it should have been absorbed long before reaching Earth? This is the question at the heart of a new study by Giorgio Galanti (INAF) and Marco Roncadelli (INFN), accepted for publication in Physical Review Letters.
The research addresses one of the most fascinating puzzles in contemporary astrophysics. It proposes a possible explanation for the highest-energy photon ever observed from a gamma-ray burst, suggesting that under extreme conditions spacetime may behave differently from what Einstein’s theory of relativity predicts.
At the center of the story is the brightest gamma-ray burst ever observed, GRB 221009A, nicknamed BOAT by astronomers—the Brightest Of All Time. The explosion, which occurred about 2 billion light-years from Earth and was observed on Oct. 9, 2022, reached our planet as an enormous shower of photons, the elementary particles of light.
For decades, physicists have worked to prove the strange predictions of quantum mechanics with real experiments. As quantum computers have grown more powerful, researchers have devised increasingly sophisticated ways to test whether these machines are truly harnessing quantum effects—but every method so far has run into limits.
In new research published in Nature Communications, a team led by computer scientists Marcello Benedetti and Harry Buhrman at Quantinuum in the U.K. has proposed a new kind of test built around a simple game with a mathematically guaranteed outcome.