Mise nese název Artemis II a v jejím rámci se čtyři astronauti vydají na desetidenní cestu kolem odvrácené strany Měsíce a zpět. To podle agentury připraví půdu pro budoucí přistání na Měsíci. Mluvčí NASA ale uvedla, že posádka na povrchu Měsíce nepřistane, protože nemá potřebné vybavení jako například lunární přistávací modul, napsal web CNN. Mise by měla překonat rekord nejvzdálenější cesty do vesmíru, který doposud drží mise Apollo 13.

Moon to Mars | NASA's Artemis Program - NASA
https://www.nasa.gov/humans-in-space/artemis/


Artemis II mission is about to fly humans to the Moon — here’s the science they’ll do
https://www.nature.com/articles/d41586-026-00964-4

Here is an excellent paper that clearly explains the philosophy that guides Yann LeCun's research in AI and his new company, AMI Labs. It also perfectly expresses my complaints about the trope of artificial general intelligence -- AGI, or BS for short.

LeCun et al reject the idée fixe that obsesses the Promethean dreams of too many of the AI boys: that they have the power, nearly there, to surpass human intelligence in every way: thus, it is general. The paper argues instead that human intelligence itself is not general: Each of us is good at some things, incompetent at others.

To set the goal for AI development in anthropomorphic and ultimately hubristic terms is a mistake. Instead, how much better it will be to build systems that are specialized (as humans are) to concentrate scarce resources on efficiently advancing toward one skill or another, not all. "Given finite energy, an approach that directs available energy towards learning a finite set of tasks will reasonably outperform an approach that distributed the finite energy over an infinite amount of tasks." Or in its pithy conceit quoted here: "The AI that folds our proteins should not be the AI that folds our clothes!"

LeCun also believes that embracing specialization will enable a system's creators to limit its function, thus its power, and ensure its safety. The other AI boys think they will create the God machine whose fury even they cannot contain. LeCun has the more mature view that machines, even intelligent ones, are still machines with plugs to pull.

The paper indirectly illuminates LeCun's devotion to world models over large-language-models' text prediction. Or as the company's homepage puts it: "We share one belief: real intelligence does not start in language. It starts in the world." LeCun himself pioneered thinking that helped lead to LLMs, but he believes text can take the technology only so far. He aims to build systems that can adapt to reality because they are trained on reality, not on text as tokens or pixels next to pixels, but as machines able to train themselves to understand the laws of nature that toddlers and cats discern, without language.

Here's the paper, written by LeCun, Judah Goldfeder, Philippe Wyder, and Ravid Shwartz-Ziv :

https://arxiv.org/pdf/2602.23643


src: https://www.facebook.com/share/1AmNPF2MNz/

TADEAS:

The Sweet Lesson of Neuroscience—Asterisk
https://asteriskmag.com/issues/13/the-sweet-lesson-of-neuroscience

I believe the brain may have something more to teach us about AI — and that, in the process, AI may have quite a bit to teach us about the brain. Modern AI research centers on three key ingredients: architectures, learning rules, and training signals. The first two — how to build up complex patterns of information from simple ones and how to learn from errors to produce useful patterns — have been substantially mastered by modern AI. But the third factor — what training signals (typically called “loss” functions, “cost” functions, or “reward”) should drive learning — remains deeply underexplored. And that, I think, is where neuroscience still has surprises left to deliver.

I’ve been fascinated by this question since 2016, when advances in artificial deep learning led me to propose that the brain probably has many highly specific cost functions built by evolution that might train different parts of the cerebral cortex to help an animal learn exactly what it needs to in its ecological niche.

More recently, Steve Byrnes, a physicist turned AI safety researcher, has shed new light on the question of how the brain trains itself. In a remarkable synthesis of the neuroscience literature, Byrnes recasts the entire brain as two interacting systems: a learning subsystem and a steering subsystem. The first learns from experience during the animal’s lifetime — a bit like one of AI’s neural networks that starts with randomly initialized “weights,” or “parameters,” inside the network, which are adjusted by training. The second is mostly hardwired and sets the goals, priorities, and reward signals that shape that learning. A learning machine — like a neural network — can learn almost anything; the steering subsystem determines what it is being asked to learn.

Byrnes’ work suggests that some of the most relevant insights in AI alignment will come from neuroscientific frameworks about how the steering system teaches and aligns the learner from within. I agree. This perspective is the seed of what we might call the “sweet lesson” of neuroscience.

Guided by Plant Voices - Nautilus
https://nautil.us/guided-by-plant-voices-237798/

Researcher and ecologist Monica Gagliano says her experiences with indigenous people, such as the Huichol in Mexico (pictured), informed her view that plants have a range of feelings. “I don’t know if they would use those words to describe joy or sadness, but they are feeling bodies,” she says.

Her experiments suggest plants have the capacity to learn, remember, and make choices.

She's also embraced the use of psychedelics in her research. Galgiano considers these explorations in non-Western ways of seeing the world to be part of her scientific work.

While a visiting scholar at Dartmouth College, Gagliano spoke with Steve Paulson about her experiments, the emerging field of plant intelligence, and her own experiences of talking with plants.