TADEAS:

More Evidence for UAPs! Scientists Afraid to Speak Out
https://youtu.be/lYVxRHk258g?si=eW4QGqjMEyKGARSH

[2604.18799] Machine Learning Supports Existence of Previously Unrecognized Transient Astronomical Phenomena in Historical Observatory Images
https://arxiv.org/abs/2604.18799

M Comper
https://www.facebook.com/share/p/1B6x3QrxnF/

Something was orbiting Earth before Sputnik. Or at least, something was showing up on photographic plates from the Palomar Observatory between 1949 and 1957 that behaves exactly as an orbiting, highly reflective object should.

A machine learning model trained on 107,875 transient point sources from those plates, all captured before anything artificial was supposed to be in orbit, has produced results that are difficult to dismiss and difficult to explain. The transients are brief, star-like flashes that appear on one photographic plate and vanish from every subsequent image. A classifier scored each one's probability of being a real object rather than a plate defect (dust, scratches, emulsion errors). Only about 10% scored above 0.80 probability. What makes the surviving high-confidence transients remarkable is their physical behaviour.

They avoid Earth's shadow. The top-scoring transients show a 55.2% shadow deficit relative to geometric expectations, significant at the 5-sigma level. That's exactly the pattern you'd expect from highly reflective objects in orbit that go dark when sunlight can't reach them.

They also cluster around US nuclear weapons tests. Transient counts were significantly elevated within one day of above-ground tests conducted at the Nevada test site, just 435 km from Palomar. The association gets stronger the more likely the transient is to be real: the highest-confidence group showed a 62.7% higher rate of nuclear-window appearances than the lowest. The signal was temporally specific, concentrated on the day of each test and the preceding night, with no significant effect at longer lags.

The study directly addresses the main criticism, that all these transients are just plate defects. If that were true, the ML model would have performed at chance level (0.50), not 0.81. And neither the shadow deficit nor the nuclear correlation would show any systematic relationship with the model's confidence scores. Both do.

The authors acknowledge the uncomfortable implication. If both findings are real, the explanations that can account for both simultaneously are, as they put it, "very small" in number and "highly implausible by most" standards: either secret satellite launches predating Sputnik by years and timed to nuclear tests, or a non-human technosignature. They call for replication using plates from other observatories worldwide.

https://vimeo.com/254693464

This is the documentary about Lynn Margulis- Symbiotic Earth

Margulis was strongly critisizing neo-darwinism and prove that evolution of life had not been driven by competition, but cooperation. She described her theory as endosymbiosis. And together with James Lovelock she formulated the Gaia theory (Lovelock’s work is also breafly mentioned in the book Syntropic agriculture according to Ernst Götsch).

Password: LynnMargulisGaia

These Mini Brains Just Learned to Solve a Classic Engineering Problem
https://singularityhub.com/2026/03/24/these-mini-brains-just-learned-to-solve-a-classic-engineering-problem/


Attaching living brain tissue to computers sounds like science fiction. But brain organoids have already made it reality.


These blobs of brain cells often start life as skin cells that have been turned back into stem cells. After bathing in a special cocktail of nutrients, they develop into various types of brain cells that self-organize into intricate three-dimensional structures similar to parts of the brain. Neurons form networks, ripple with electrical waves, and when connected to other tissues—such as an artificial spinal cord and lab-grown muscles—can control them.


Bioengineers have taken notice, envisioning organoids as potential living processors. Our brains use far less power and are more adaptable than the most advanced neuromorphic chips and brain-inspired AI. Brain organoids linked together into computers could theoretically enable computation in a dish at a fraction of the energy cost.


There are hints this blue-sky idea could work. Scientists have taught hundreds of thousands of isolated neurons to play the video games Pong and, more recently, Doom. Separately, researchers used cultured neurons to control the simple movements of a vehicle.


But mini brains are different. Unlike isolated neurons, organoids’ 3D structures and connections are harder to decipher. Yet predictable learning is essential to realizing “organoid intelligence.” Their electrical activity needs to rapidly adapt to inputs, strengthening or weakening circuits.

Home page | Rangelands ATLAS
https://www.rangelandsdata.org/atlas/

Rangelands are areas of grasses, grass-like plants, forbs, shrubs and sometimes trees that are grazed or have the potential to be grazed by livestock and wildlife. They are diverse in their vegetation highly influenced by rainfall, temperature and other climate phenomena, and habitat for a wide range of wildlife, many species of which are found nowhere else.

Rangelands are home to millions of people, from pastoralists to hunter-gatherers to ranchers to conservationists. Rangelands feed millions of people worldwide. Rangelands have significant cultural and aesthetic value too, and for many, are places of inspiration and beauty.

This Rangelands Atlas has been developed to raise awareness on the importance of rangelands and highlight the changes taking place which are having significant impacts on rangelands, demanding their protection and restoration

This week, Google DeepMind proposed an important cognitive framework for measuring progress toward AGI by decomposing intelligence into ten human cognitive faculties and evaluating systems against human baselines.

This is a major advance over vague, single-number claims about “AGI,” because it makes capability claims empirically testable, human-comparable, and diagnostically rich.

We argue, however, that a human-comparative cognitive profile, while necessary, is not sufficient for the next phase of AGI evaluation. Three shortcomings motivate the extension:

- First, a highly general system may operate in a deeply non-human style while still exhibiting powerful abstraction, transfer, world-modeling, and self-improvement.

- Second, social fluency and ethical polish do not by themselves constitute beneficial moral agency.

- Third, many of the most consequential deployment failures stem not from static capability deficits but from behavioral shifts that emerge under stress, competition, temptation, or self-preservation pressure.

Learn more:

Beyond Human Comparison - by Ben Goertzel - Eurykosmotron
https://bengoertzel.substack.com/p/beyond-human-comparison

Ben Goertzel discussed a recent paper by Professor Yann LeCun and co-authors proposing Superhuman Adaptable Intelligence (SAI) as an alternative framing to AGI, which they describe as an overloaded and flawed concept.

The paper defines SAI as intelligence capable of adapting to exceed humans at any task humans can perform, while also adapting to tasks outside the human domain that have utility.

Dr. Goertzel assessed SAI as a specific parameterization within existing theoretical frameworks of general intelligence, particularly Efficient Pragmatic General Intelligence (EPGI).

He suggested that extending the concept with explicit safety constraints and mechanisms for open-ended capability growth would better address key questions in long-term AGI development.

Read Dr. Goertzel's analysis and explore why SAI is a special case of AGI, not an alternative:

LeCun’s “Superhuman Adaptable Intelligence” Is a Special Case of AGI, Not an Alternative
https://bengoertzel.substack.com/p/lecuns-sai-is-a-special-case-of-agi

Intelligence is not a collection of skills nor an accumulation of declarative knowledge.

Intelligence is the ability to accomplish new tasks with no prior training or with fast training.

This points to the necessity of System 2, world models, and planning.

[2602.23643v1] AI Must Embrace Specialization via Superhuman Adaptable Intelligence
https://arxiv.org/abs/2602.23643v1

NASA Study: Non-biologic Processes Don't Fully Explain Mars Organics - Astrobiology
https://astrobiology.com/2026/02/nasa-study-non-biologic-processes-dont-fully-explain-mars-organics.html

In a new study, researchers say that non-biological sources they considered could not fully account for the abundance of organic compounds in a sample collected on Mars by NASA’s Curiosity rover

Y LeCun
https://www.facebook.com/share/1BDH2cXmnz/

At the AI Impact Summit in Delhi, Yoshua Bengio says that AI systems should make predictions without any goal.
He says goals would bias the systems in possibly dangerous ways by giving it drives and desires.
He claims that the proper template to use is idealized human scientists.

I completely disagree with the whole premise.
I don't think any system can do anything useful without an objective.

One point we agree on is that LLMs are intrinsically unsafe. But they are unsafe precisely because they don't have any objectives and merely emulate the humans who produced the text they've been trained on.

My recommendation is the exact opposite of Yoshua's.
AI systems *should* have goals. They should be designed so that they can do nothing else but fulfilling the goals we give them.

Naturally, these goals and objectives must include safety guardrails.

But the point is that, by construction, the system *must* fulfill the goal we give it and *must* abide by the safety guardrail constraints.

I call this objective-driven AI architectures.

TADEAS:
TADEAS:

Adam Marblestone – AI is missing something fundamental about the brain
https://youtu.be/_9V_Hbe-N1A?si=nrXVwPrLHAVWwsY_