John von Vithelli

ieee.

[John von Vithelli @Vithelli]

Cuando estén en un cóctel lo que tienen que decir es que se ha demostrado que las ecuaciones de Naiver Stokes eran incorrectas, o la definición de fluido es incorrecta, y que se requiere otro modelo.

[John von Vithelli @Vithelli]

La buena noticia es que las ecuaciones de Navier Stoques dejan de existir, o sea, una ecuación menos, y la gente puede decir: "20 años que salí del bachillerato y nunca he usado las ecuaciones de Naiver Stoques, menos mal eran un error."

[John von Vithelli @Vithelli]

Entiendo que eso de ecuaciones de Navier Stoques quedan pues como no válidas de forma general, o sea, un fluido como generalmente se modelaba pues es equivocado. Esto tal ves sea porque el espacio dentro el cual se co sidera tal fluido no es como se imaginaba antes.

[John von Vithelli @Vithelli]

Se apropian de mis ideas y descubrimientos.

[Big Brain AI @realBigBrainAI]

Terence Tao, Professor of Mathematics at UCLA and Fields Medalist, on why nobody can fully explain why LLMs work:

Tao starts with the mechanics, which are no mystery at all. You gather an enormous amount of text and you fit a curve to it.

"The magic of LLMs is that if you train these LLMs on enough data — so trillions and trillions of data points — and you really try to fit as good a curve as possible, and this takes like millions and millions of dollars of computing power and months and months of time, then suddenly, even when you iterate, it stays coherent. It begins to sound not like monkeys but it actually sounds like a human speaking."

That is the entire recipe: data, compute, time, curve-fitting. None of it obviously adds up to fluent English.

Then Tao says the part that most people building these systems move past quickly:

"And somehow we don't fully understand why that's the case."

The admission comes from one of the most capable living mathematicians, and the gap he describes sits at the centre of the field.

His best account of what's happening puts the mystery in the language rather than the machine:

"What seems to be true is that language, like English or other natural languages, contains a lot of hidden patterns that we're not consciously aware of. I mean, we know some of the laws of English, there's laws of grammar and things, but there are sort of unspoken, unwritten rules of language that humans pick up."

It relocates the question: the structure was always latent in the text, and the model found it. Why enough curve-fitting surfaces that structure is still unanswered.

Tao reaches for a child to explain it:

"A human child, even though they're not taught what a noun is, what a verb is or whatever, they can pick up what order English words go in just by continual exposure to the language."

Which is honest about the limits of the explanation, because we can't fully account for how children do it either.

From there, the unexplained behaviour compounds. Exposure to language turns out to be enough to produce something that looks like reasoning:

"It seems like you can teach these models to also pick up patterns in language to the point where you can give them math questions. The answer to 2 plus 3 is — and they will say five."

And once a model handles language at all, you can push it into resembling self-correction:

"Once you have a little bit of ability to speak English, you can kind of go in loops and sort of check your work and make fewer mistakes, and you can prompt these models to proceed step by step and not say something unless it's been double checked and so forth. And so they become a little bit smarter, quote unquote, to the point where they can solve many, many complicated tasks."

The scare quotes around "smarter" carry the whole argument. Tao does not concede that the unexplained fluency implies anything underneath it:

"But they're still just guessing the next word to say. It's not really grounded in any deep understanding of the real world. It's just that they have seen the patterns in the English language or other language that they've absorbed so well."

[John von Vithelli @Vithelli]

Las ecuaciones de Navier-Stokes- Vithelli en macroeconomics.

[John von Vithelli @Vithelli]

🥃🥃🥃

[Jazz Is Dead @jazzisdeadco]

“Sofrito” by the great Mongo Santamaría. Live in 1973.

[John von Vithelli @Vithelli]

🥃🥃🥃

[John von Vithelli @Vithelli]

Y hay como errores graves por parte de las autoridades en el concepto.
Y ojalá lleguen los de espacio público a quitarme mis 4 guacales de mango para tirar esos mangos a la calle y decirles ahí tienen sus hijueputas mangos hambrientos malparidos...

[John von Vithelli @Vithelli]

Hay como errores en la comprensión de qué es 'Espacio público', y no me referire a la teoría de bienes públicos
en economics.
En la puta calle se pueden hacer cosas, caminar, estar parado, hablar... y entre otras se puede 'vender', porque vender y comprar no son delitos...

[John von Vithelli @Vithelli]

La idea es no obstruir el espacio, ni usar carpas o estructuras fijas, ni menos dejarlas por la noche... igual que alguien con un 4 puertas, no puede obstruir el ep de caminar ni armarle garaje al carro en el ep, pero si puede estacionar... porque estacionar no es delito.

[John von Vithelli @Vithelli]

Te quiero mucho Maclovia.

[John von Vithelli @Vithelli]

Si es niño Javier Estokes.

[Daniel Gómez Gaviria @dgomezco]

Esto no tiene ningún sentido y refleja un profundo desconocimiento de las funciones y papel del DANE.

[John von Vithelli @Vithelli]

Esto nos da una idea de 'qué es el espacio', solo que nuestro cerebrito al evolucionar en la superficie del planeta no está acostumbrado a percibirlo así.

[John von Vithelli @Vithelli]

Esto es importante. El espacio alrededor de masas forma especie de túneles. En este caso si soltamos una partícula a cierta distancia de la tierra 'caerá' en un 'tunel' que recorre la tierra y la luna. Y este túnel es permanente, dadas las masas que tienen efecto...

[Shane Ross @RossDynamicsLab]

What if a spacecraft could cycle between Earth and Moon orbits, performing multiple circuits of each, naturally and indefinitely, with zero propulsion?

We’ve discovered a new class of stable, prograde, low-energy cycler orbits that do just that.

Why these orbits matter:

Ballistic → fuel-free
Stable → long-term ready
Near-chaotic → agile with low ΔV
Low-energy → access to Earth/Moon, Lagrange points, Sun–Earth L1/L2, even heliocentric space

At the AAS/AIAA Astrodynamics Specialist Conference in Boston next week, I’ll present on a new family of ballistic Earth-Moon cycler orbits that are stable, prograde, and mission agile—unlike any cyclers in the current literature.

The example below is shown in both the Earth-Moon rotating frame and inertial frame.

Conference Paper: https://t.co/v3VDPIT0X4

[John von Vithelli @Vithelli]

Ahora, supongamos estamos en la superficie del planeta... si soltamos una partícula caerá en un túnel que la lleva a estrellarse con la tierra. No es que exista una fuerza que atraiga una masa a la otra... sino que el espacio para esa partícula es dicho túnel.