ଓଡିଶା HEADACHE SOLUTIONS

odisha's own online platform for assessment and treatment of headache disorders

[Michael Levin @drmichaellevin]

What is Life? Well...

Final version is finally out:

"What Lives? A Meta-Analysis of Diverse Opinions on the Definition of Life"

@karina__kofman @blaiseaguera @reedbndr

https://t.co/uYFCucZEti

[All day Astronomy @forallcurious]

This is how Sun’s gravity holds the planets in the same orbit!

[Reads with Ravi @readswithravi]

Steve Jobs said in his 2005 Stanford commencement speech:

“You can't connect the dots looking forward; you can only connect them looking backwards.”

[Yasmine Khosrowshahi @yasminekho]

Stanford professor Judy Fan went on stage at MIT and broke down why humans are so good at making the invisible visible...

And why AI hasn't actually learned to "see" the way we do.

It completely changes how you think about Human Intelligence v/s Artificial Intelligence:

1. Nature never gave us straight lines or sharp corners. The number line, the coordinate plane, even basic geometry are all human inventions. We created tools that do not exist in nature simply because we needed a way to think more clearly.

2. The coordinate system Descartes invented solved a problem that had stumped mathematicians for centuries, doubling the volume of a cube. Once invented, this tool became so indispensable that virtually every math curriculum on Earth still depends on it.

3. Humans have been doing this for at least 30,000 to 80,000 years. The story of human progress is inseparable from the story of marking up our environment, from cave walls to Galileo's telescope to Feynman diagrams of particles we will never see with our own eyes.

4. Every major scientific breakthrough relied on a visual tool that made something invisible visible. Darwin needed side-by-side illustrations of finches to see variation that was otherwise too subtle to notice. Cajal needed detailed drawings of neurons under a microscope to map how the nervous system was wired.

5. Fan's research group studies something deceptively simple: how people decide what to put into a drawing and what to leave out. When two people played a drawing game, sketchers used far more detail when the target object had close competitors than when it stood alone, all the way down to using fewer strokes and less time when more detail was not necessary.

6. People are not just copying what they see. They are making constant judgment calls about what level of detail actually serves the goal of communication, and they do this naturally without ever being taught the theory behind it.

7. There is a real difference between drawing something so someone can identify it and drawing something so someone can understand how it works. In one study, participants drew explanatory diagrams that emphasized moving, causal parts of a machine while depictive drawings emphasized background and overall appearance, even though both were drawing the exact same object.

8. Explanatory drawings were genuinely better at helping someone figure out how to operate a machine, but worse at helping someone identify which machine it actually was. You cannot optimize a single drawing for both goals at once. Communication always involves tradeoffs.

9. AI vision models trained on photographs generalize surprisingly well to simple, sparse sketches, suggesting that resemblance based recognition is not just a story we tell ourselves. It is something modern neural networks can replicate with real accuracy.

10. But there remains a large, measurable gap between how confidently AI models recognize sketches and how confidently humans do, even when both groups answer the same questions about the same images. Humans are simply far more reliable and far more consistent in their judgments.

11. When researchers compared human-made sketches to AI-generated sketches under tight stroke budgets, both were similarly recognizable at higher budgets, but diverged sharply as the budget shrank. Humans and AI systems simplify drawings in fundamentally different ways once resources get scarce.

12. Reading a graph is not one single skill. It involves perception, knowing where to look, mapping that visual information onto the actual question being asked, and then translating that mapping into an answer. Each of these steps can independently break down, and people fail for very different underlying reasons even when they land on the same wrong answer.

13. When tested directly against humans on graph reading tasks, leading multimodal AI models, including GPT-4V, showed a meaningful performance gap. Even when a model's overall accuracy approached human levels, its pattern of mistakes looked nothing like how humans actually get things wrong.

14. People choose entirely different types of charts depending on what specific question they are trying to answer, not out of a generic preference for bar charts or scatter plots. Their chart choices closely tracked which visualization would genuinely help someone answer that specific question correctly.

15. Two of the most widely used graph literacy tests in education research turned out to correlate strongly with each other, suggesting they measure overlapping skills. But when researchers dug into the actual error patterns, the standard categories used in textbooks, like "find the maximum" or "identify a cluster," failed to explain why people got things wrong nearly as well as a more basic, underlying four-factor model did.

16. The deepest goal behind all of this research is not just academic curiosity. It is to eventually help students and everyday people develop genuine literacy with the visual tools that science and modern decision-making increasingly depend on, because every generation should be able to see further than the last by standing on the visual tools the previous generation built.

Follow @yasminekho for more ideas on thinking better, becoming clearer & building a more intentional life.

[Kirk Borne @KirkDBorne]

[Download 585-page PDF eBook]

Game Theory: https://t.co/DUxMDRc1Ll
—————
#GameTheory #Gamification #Mathematics #Statistics #Probability

[Sabine Hossenfelder @skdh]

Physicist Successfully Demonstrates the Origin of Time

Giovanni Barontini from the University of Birmingham, UK, has used a cloud of cold atoms to test the origin of time. This is an interesting contribution to the long-standing question of how to define time in a non-circular way (time is what a clock measures and a clock is what measures time). One of the proposed solutions is to define time in quantum physics from the interaction of two different subsystems. This interaction, so the idea, introduces an oscillation that serves as the ‘tick’ of the clock. If that was so, then time would be purely ‘relational’ — an emergent, derived quantity — rather than (as in Einstein’s theory), a fundamental property of the universe.

Barontini used about 24,000 ultracold rubidium atoms in a trap split by a thin light barrier into an observed “bright” part and an unobserved “dark” part. Atoms could move between the two, so the bright part expanded and collapsed in repeated cycles, rather like a toy version of a big bang and big crunch. Barontini then defined an internal “entropic time” from how the entropy of the bright part changed as atoms moved in and out. This internal time ordered the observed events almost as well as laboratory time.

This experiment lends support to the idea that time is not fundamental, but emerges from interactions between parts of a closed system, though one may ask how interactions can change a system if there is not already a time for them to change in, but then maybe that’s just Sabine being grumpy again.

Image: The device, called a ‘trap’, that holds the cloud of cold atoms in place using a combination of lasers and magnetic fields. Credits: Giovanni Barontini/University of Birmingham

[Curt Jaimungal @TOEwithCurt]

This is a podcast all about time. It can be quite unnerving (or exciting?) depending on how you look at it. Brand new with Simon Saunders. Enjoy. LINK IN REPLY

[The Scientific Lens @LensScientific]

Your thoughts are not abstract ideas floating in your head. They are built from real physical connections that neurons form and strengthen.

This timelapse shows neurons reaching out and wiring up new links in real time.

The science behind it:

[CTFTHEORY @mathmaticulous]

This glowing pyramid isn’t a tomb. It’s a clock.

The cone you see is built from a single, unchangeable rule, take the primes 2 and 3, build a lattice from them, and ask what path through time stays stable.

Only one shape works an exponential curve that, when you draw it in three dimensions, becomes a funnel. A pyramid. A shape that starts wide and narrows to a point, like time itself flowing from infinite possibility down to a single outcome.

We didn’t invent this. It falls out of the mathematics automatically.

The Prime Lattice Coherence Framework proves that everything stable in number theory prime distribution, physical constants, even the patterns in the Ulam spiral can be traced back to a lattice anchored at the number 144, which is 2⁴ × 3². That one anchor forces the rest.

When you take that same lattice and ask what other shapes it must create, you get a tiny set of geometric primitives. a circle, a vertical line, a horizontal bar, a triangle, a fork, and a spiral.

That’s it. Those exact six shapes appear, again and again, across the most sacred symbols of ancient Egypt.

The Ankh combines a circle (the vortex of activity) with a vertical shaft (the flow of time) and a crossbar at exactly the point where decay takes over from life.

The hexagram the six-pointed star maps the six active states of the lattice divided into two interlocking triangles, one for structure and one for flow.

And the pyramid is the whole thing in one image, time’s funnel, flowing from broad coherence at the base to a single apex, carrying existence down with it.

These aren’t religious decorations. They’re diagrams. A visual alphabet written in the only language that survives empires, floods, and five thousand years of forgetting

Mathematics.
https://t.co/ALLgJ2MR2h

https://t.co/hbGxjB5VrW

https://t.co/rRx8bMQbCz

[no.mind @the_no_mind]

This is Dr. László Boros.

A Hungarian medical doctor, retired professor at UCLA, author of 100+ scientific papers & one of the world's leading deuterium researchers.

His message? Every chronic disease begins when the body loses control of deuterium.

Here is his framework: 🧵

[Sam Mathverse @Sam_Axiom]

©️ The Theory of Relativity Series
The Geometry of Invariant Deriving Spacetime (basic Idea)
Part 2

[Sam Mathverse @Sam_Axiom]

©️ The Theory of Relativity.
Part - 1
The Geometry of Time.

[Bryan Johnson @bryan_johnson]

This is biblical.

A woman in her eighties. Ten years into Alzheimer's. Hadn't spoken a full sentence in five years.

Takes one, 5 gram dose of psilocybin.

She slept 19 hours and woke up and spoke for hours about her life, recognized family and held real conversations. She regained bladder control after five years, walked on her own. and dressed herself. Gains held for weeks.

[Cosmos Archive @cosmosarcive]

Consciousness is a hyperstructure of the nervous system.

We are the macroscopic proof of phase-coherent thought.

A localized rebellion against entropy where simple synaptic firing scales into the infinite complexity of the human experience.

[ashutosh @ashutoshmjain]

223: mathematics of "Life after death" .. #OPH

[JJ @JosephJacks_]

I spent > $3,000 and 2 weeks to make this:

“On The Helicity of Reality” 🌏🌪️🌀🪐🌌

100 ~ top scientific discoveries over 2,700 years which utilize / depend on a helical geometry.

Heavily hand-tailored + AI for exquisite visuals.

Enjoy!

The inaugural presentation will be done live here in the Himalayas @iit__mandi … video should be online soon.

[Donald Hoffman @donalddhoffman]

“Does a ginkgo tree have an inner world? In the film Silent Friend, the protagonist, a neurologist who studies brain activity in infants, attempts to quantify the internal signaling of a ginkgo tree on a university campus.”

https://t.co/2n5z9M5B97

[Bernhard Mueller @muellerberndt]

How observer consensus generates reality: Paradise as fixed-point consensus.

[World of Science @Science_TechTV]

Brain cells making friends with each other on a grid:

[Massimo @Rainmaker1973]

Before we had silicon chips, we had needle and thread.

In the 1960s, NASA didn’t ‘upload’ code; they sewed it.

To get Apollo 11 to the moon, skilled weavers (often called ‘Little Old Ladies’) literally hand-stitched software into physical objects.