Luis Lo

Yann LeCun just told the most well-funded industry in human history it is solving the wrong problem. LeCun: “Babies learn this around the age of eight or nine months, that objects don’t float, they fall.” No dataset. No labels. No reward signal. A nine month old drops a spoon and builds a physics engine no machine can match. LeCun: “Most of us can learn to drive in about 20 or 30 hours of training without ever crashing, causing any accident.” Twenty hours. Tesla has built the most capable driving system on the road. It took billions of miles of data to get there. A sixteen year old gets there over a long weekend. Not because the teenager is the better driver. Because the teenager is not learning to drive. They are deploying a model of reality they have been building since birth. LeCun: “If we drive next to a cliff, we know that if we turn the wheel to the right, the car is going to run off the cliff and nothing good is going to come out of this.” You simulate the crash. You see the wreckage. You feel the fall. You turn the wheel. None of it was real. All of it was intelligence. Every AI has to crash a thousand times to learn what you imagined once and never did. That is not a performance gap. That is an architecture gap. LeCun: “The main problem we need to solve is how do we learn models of the world.” Not bigger models. Not more compute. Not another trillion tokens. World models. A machine that can run reality forward before it acts. The industry is scaling language. LeCun says language is a compression of thought. Not thought itself. You understood gravity before you could say the word. You grasped cause and effect before your first sentence. The deepest intelligence you will ever possess was built in total silence. And every lab on Earth is trying to reconstruct the mind from words alone. Physics does not care about your context window. A baby who learns that cups fall in a kitchen already knows that rocks fall off cliffs. No retraining. No fine-tuning. One model. Every environment. That is what intelligence actually is. Not prediction. Not pattern matching. Not scale. A simulation of reality so precise you rehearse the future before it exists. Every infant on Earth builds one. No machine ever has.

🚉 | La indeleble huella de carbono del Tren Maya Para abrir paso a las vías, se talaron 11,500 mil has. de selva (equivalentes a 11 mil campos profesionales de fut) 🏟️ Un estudio científico dimensiona por primera vez la huella de carbono de la obra: se liberaron a la atmósfera un millón 727 mil toneladas de CO2. No es poca cosa: equivale a las emisiones de 375 mil autos de gasolina durante todo un año. ⛽️ ✒️ @Orkydya fabricadeperiodismo.com/reportajes/la-…

¡¡PERO QUÉ VERGÜENZA!! En unos cuartos de final de un Mundial, el VAR ha llamado al árbitro para revisar una jugada que ha terminado en una segunda amarilla a Embolo por una SUPUESTA simulación en el CENTRO DEL CAMPO. Pero es que el VAR ha entrado para avisar ¡POR UNA FALTA AL JUGADOR EQUIVOCADO! Una revisión por confusión de identidad ha terminado en una expulsión de Suiza. Esto es desproporcionadísimo. Es una locura lo que está pasando. No he visto esto en mi vida. Tiene que ganar Argentina sí o sí

@Calvin_256 @FabrizioRomano use your eyes lil bro

López Obrador tiró la Corte autónoma, porque los ministros tenían 50 asesores. Hizo ministra a Lenia Batres por vivir rentada en una vecindad: eso le daba autoridad moral. Pero ella tiene 94 asesores y vota en contra de que el IMSS dé prótesis a derechohabientes.   Y resultó que a la autollamada “Ministra del Pueblo” (porque vivía rentada en una vecindad) ya no le gustó vivir como el pueblo: acaba de contratar 14 ayudantes más y se transporta en camionetas 4x4 y SUVs de lujo, cuyos precios rebasan los dos millones.   El juego de espejos de la ministra es notorio: votó contra la entrega de anteojos, lentes de contacto, aparatos auditivos, implantes cocleares, así como prótesis y órtesis externas para los derechohabientes del IMSS.   ¿Por qué voto en contra la declaratoria general de inconstitucionalidad del artículo 42 del Reglamento de Prestaciones Médicas del IMSS? Porque dice que el gobierno carece de dinero y materiales para garantizar la entrega de estos aditamentos a derechohabientes.   Ajá, afirma en la Corte que el Estado no tiene lana, pero usó 40 mil pesos del dinero del Estado para pagarse un “retrato vivo”. Recordemos que el Estado no produce nada, y sus recursos salen del cobro de cinco billones a quienes pagan impuestos.

One of my longest-standing arguments is that we are not living in Orwell’s 1984, where truth is centrally suppressed and censored by force (that’s former communist societies, modern-day China, Russia, North Korea). We are living in something much closer to Huxley’s Brave New World. The truth is not hidden - it is almost always readily available. But it is buried beneath an industrial quantity of noise: propaganda, outrage, half-truths, conspiracy theories, influencer theatre, algorithmic rage bait and an endless stream of content designed not to inform us, but to keep us emotionally stimulated. The modern information system does not need to censor the truth when it can simply drown it in noise. A fact no longer has to be disproven - it only has to be surrounded by a hundred competing claims, stripped of context and nuance, turned into partisan ammunition and pushed into the same feed as celebrity gossip, memes and 15 second videos engineered to deliver the fastest possible dopamine hit. By the time the truth reaches us, it appears as just another piece of content competing for our attention. That is the more sophisticated form of control: not preventing people from knowing, but exhausting their capacity to care. Orwell feared a world in which people would be deprived of information. Huxley feared a world in which they would be given so much distraction, stimulation and triviality that they would lose the desire to seek it. The defining struggle of our age is therefore not simply between truth and censorship, but between truth and indifference.

The Well Just Dropped: 15 Terabytes of Pure Physics Gold Is Now Open Source The scientific AI world just got a massive upgrade.Polymathic AI, in collaboration with the Flatiron Institute and researchers from Princeton, Cambridge, NYU, Berkeley, Los Alamos, and more, has released The Well: a staggering 15TB collection of high-fidelity physics simulations. This isn’t toy data. These are real, expensive-to-run simulations across 16 different physical domains, including turbulent fluid dynamics, supernova explosions, magneto-hydrodynamic cosmic flows, acoustic scattering, and active biological matter. Until now, reproducing this level of data required weeks on national supercomputers and grant money most teams will never see. The Well changes everything. It’s purpose-built for training PDE surrogate models the AI systems that can replace slow, costly physics solvers with a single fast neural network forward pass. Everything is fully open source, easy to load with PyTorch, and ready to drop straight into your training pipeline. Researchers and builders can now train on world-class physics data without the insane compute barriers that used to stand in the way. This is more than just another dataset drop. It’s a serious accelerator for scientific machine learning.The future of physics-informed AI just got a whole lot more accessible.Get it here: polymathic-ai.org/the_well/

One way of seeing climate change in action is looking at 1200 year continuous data from Japan about cherry tree blossoming.

⚠️SALVADOR CIENFUEGOS supo y OCULTÓ que NORMALISTAS continuaban CON VIDA días después del 26 de septiembre de 2014. El GIEI se fue del país dejando EVIDENCIA técnica, documental y de inteligencia sobre el ocultamiento. Lo que acaba de hacerle la @CNDH a Ayotzinapa para encubrir al ejército y aliados de la presidenta es lo más CRIMINAL que han hecho Piedra, Estrada, Pomposo y el resto de delincuentes que secuestraron la institución. 43 veces miserables. 135 mil veces malditos.

Just released: An El Niño tracker for the world! See how strong El Niño is getting, intensity forecasts and maps that show the risk of extreme climate conditions in the months ahead — all updated regularly. 🔖 Bookmark this one! wapo.st/4vXkix2 Here's what's inside 🧵

40 out of 86 Brown students scored a perfect 100 on their midterm. Then the professor moved the final in person, and 22 of those perfect scorers never showed up again. He'd suspected AI cheating from the start. The take-home midterm was deliberately harder than usual, yet the class averaged 96 when the historical range is 65 to 80. Some answers contained odd phrasing that matched what ChatGPT produced when he ran the questions through it himself. Roberto Serrano has taught economics at Brown for 34 years. He filed no accusations. He announced the final would be in person, count for half the grade, and that if the two distributions didn't match, the final alone would determine grades. Then the exodus. 27 students never showed up. 22 of them had perfect midterms. Of the 59 who did show, 19 failed. Several signed the exam and turned it in blank. The average fell from 96 to 48, the lowest in the course's history. He never needed a plagiarism detector. The cheaters identified themselves by walking away. A grade distribution became a confession. Here's the part nobody's sitting with. Serrano proved it. He sent the distributions to Brown's dean and provost. The provost never responded. The academic committee's reply amounted to calling it "a wake-up call." The students who bailed before the final walked away clean. Every university in America is now grading two populations, students and students plus ChatGPT, on one curve. The honest kids in Serrano's class watched a 96 average get set by machines, then sat a real final against it. The cheaters lost nothing. That's the incentive structure now, and it grades itself.

Yo tan feliz pensando que Parakeet era para notas de voz, y la gente metiéndole reuniones de una hora. Se ahogaba a los 6 minutos y me lo reportaron, claro, así que senté el culo y ahora traga las horas que te dé la gana. El mejor SST para transcribir github.com/achetronic/par…

"La probabilidad de que un libro influya en las respuestas de la IAG no depende de su relevancia intelectual intrínseca, sino de su grado de estructuración e integración en el ecosistema digital". educacion.nexos.com.mx/el-libro-como-…

🗳️📌 MORENA BLINDA A ROCÍO NAHLE CONTRA REVOCACIÓN DE MANDATO El Congreso de Veracruz aprobó nuevos candados para la revocación de mandato. A nivel federal, para pedir la revocación presidencial se necesita el respaldo del 3% de la Lista Nominal. En Veracruz el requisito es del 10%. Eso equivale a unas 750 mil firmas. Además, esas firmas deberán reunirse en al menos 107 municipios. Y en cada municipio se tendrá que juntar al menos el 3% de su Lista Nominal. También precisaron que solo tendrán un mes para juntar las firmas, del 1 al 30 de noviembre de 2027. Morena defendió la reforma diciendo que busca respaldo amplio. La oposición votó en contra y acusó que el procedimiento queda casi imposible.

Me asombra (cero me asombra) y me intriga (todavía menos) cómo es que esos premios nacionales de periodismo, tan progres, tan feministos y tan imparciales han evitado a toda costa mencionar siquiera el escándalo de Víctor Rodríguez Padilla. Quizás es porque están muy ocupados defendiendo a la asistente de Maru Campos que la mugroseó en público o al Pan que se quiso hacer rico con unos tinacos a costa del pueblo. O denunciando las estupideces de la hermana con sueño de la Alcaldesa de la Cuauhtémoc. Ya saben. Salvando a México pues. Debe ser eso. Pero ya mero dicen algo. Chanza y mañana.

MIT published a paper arguing that every AI model on earth is secretly converging on the same "brain." The paper is called "The Platonic Representation Hypothesis." The claim inside it is one of the strangest ideas in modern machine learning, and once you see it you cannot unsee it. For years, everyone assumed that a model trained on images and a model trained on text were building fundamentally different things inside themselves. Different data. Different architecture. Different world. A vision model learns what a cat looks like. A language model learns what the word "cat" sits next to. Two separate universes with no reason to line up. The researchers checked whether that was actually true. They took 78 vision models and a stack of large language models, and measured how each one organized concepts internally, not what they output, but the shape of the relationships between ideas in their heads. Which things they treat as close together. Which things they treat as far apart. Then they compared the shapes across models that had never seen each other's data. The shapes were lining up. And here is the part that should stop you cold. The bigger and more capable the models got, the more their internal maps agreed with each other. A better vision model and a better language model don't drift apart. They converge. As if they were both climbing toward the same summit from opposite sides of a mountain. The authors put it in a line that sounds almost like a joke, borrowed from Tolstoy: all strong models are alike, each weak model is weak in its own way. Then they took it one step further, and this is where it stops being a curiosity and starts being unsettling. They found that how closely a language model's internal map lined up with a vision model's internal map actually predicted how good that language model was at reasoning and at math. The models that saw the world more like the other modality did better at problems that had nothing to do with images at all. So the question the paper asks is the obvious one. If a model that only reads text, and a model that only sees pixels, and a model trained on a completely different objective are all drifting toward the same internal representation as they get smarter, what is that representation a representation of? Their answer is the thing that gives the paper its name. They argue the models are all converging on a single shared statistical model of the reality that generated the data in the first place. Text is a shadow of the world. Images are a shadow of the world. Sound, touch, everything, different shadows cast by the same underlying thing. And a big enough model, trained on enough of any one type of shadow, starts reconstructing the object casting it. Plato said this in 375 BC. The allegory of the cave. Prisoners chained facing a wall, watching shadows, mistaking the shadows for reality, while the real forms exist outside the cave, casting everything they see. The MIT team took his allegory literally and pointed it at neural networks. The training data is the shadows on the wall. The model, they argue, is slowly turning around toward the fire. They even proved a version of it mathematically. Under certain conditions, a whole family of learning algorithms is provably pulled toward representing the same underlying statistical structure, the co-occurrence relationships baked into reality itself, regardless of whether they're fed words or pixels. Different sensors, same answer. The implications the paper draws are the part that should matter to anyone building this stuff. If it's true, then to build a better language model you should train it on images, because pictures carry information about the same reality that words are trying to describe. They cite evidence this already works. It means translation between any two modalities gets easier the smarter models get, because they're all speaking dialects of the same underlying language. And it means, their words, that hallucination might decrease with scale, because a model converging on an accurate model of reality has less room to invent things that reality doesn't contain. Now the honest part, because the authors are honest about it and a viral thread that skips this is lying to you. This is a hypothesis, not a verdict. On their own measurement, the alignment between vision and language models climbs clearly with scale but only reaches about 0.16 on a scale where 1.0 is perfect. They flat-out ask in their own paper whether that number means strong convergence with noise on top, or weak convergence with a mountain still left to explain. The clean math only holds in an idealized world where nothing is lost between reality and observation, which is not the world we live in. Some things a picture can show that a sentence never will, and vice versa. And in domains like robotics, they see no convergence yet at all. So it might not go all the way. The cave might be deeper than one paper can measure. But sit with the shape of what they found anyway. Systems built by different labs, on different continents, trained on different senses, for different reasons, with no coordination, are independently drifting toward the same internal picture of the world. The smarter each one gets, the more they agree. And the thing they seem to be agreeing on is reality itself.

@JOLIBUTH @Aeromexico A @Aeromexico no le interesa, no van a responder. Solo dicen que lo harán de cara al público.

@Aeromexico Les respondí y no me han contestado!!!!

¿Cómo es posible que un desconocido pueda solicitar el reembolso del boleto de avión de un menor de edad y que ese reembolso sea autorizado sin el consentimiento del comprador? Eso fue exactamente lo que hoy nos ocurrió con @Aeromexico. Después de meses planeando un viaje familiar, llegamos al aeropuerto y nos informaron que el boleto de mi hijo menor de edad había sido reembolsado a solicitud de un tercero, por lo que mi hijo ya no tenía derecho a abordar. Nosotros jamás solicitamos ese reembolso. El resultado fue devastador: toda mi familia perdió el vuelo, además de hotel, renta de auto, entradas a museos y reservaciones en restaurantes que ya estaban pagadas. Más allá de las pérdidas económicas, me siento vulnerable, desconcertada, decepcionada y profundamente triste. Si esto pudo pasar con el boleto de un menor de edad, ¿qué tan seguros están realmente los datos y las compras de los clientes? Ya existe un caso abierto con el folio 06814581, pero necesitamos respuestas, una investigación seria y una reparación por todos los daños ocasionados. Aeroméxico, espero que este caso no quede como una estadística más. Mi familia merece una solución y todos los pasajeros merecemos la tranquilidad de que nadie puede modificar o cancelar nuestros viajes sin autorización. Agradeceré mucho que compartan este mensaje para que llegue a quien pueda resolver este caso!!! @Aeromexico @Profeco #atencionalclienteAEROMEXICO #RT @Aeromexico

@NikoMcCarty "The real reason these experiments are fascinating is because they refuted the idea that heredity solely arises from DNA." No. What they showed is that not all DNA is in the nucleus. Plasmids, which were found to convey drug resistance, are DNA that lives in the cytoplasm.

Guía de la Biblioteca UNED → uned.libguides.com/ia/inicio Página institucional sobre IA educativa en la UNED → uned.es/universidad/in…

La Biblioteca de la @UNED ha publicado una guía sobre el uso de la inteligencia artificial generativa como apoyo a la investigación. Es una buena noticia, pero el motivo que me parece relevante va más allá del repositorio en sí. La guía no presenta la IA como una solución para investigar mejor. La sitúa como un conjunto de herramientas útiles en tareas muy concretas: buscar bibliografía, proponer palabras clave, construir ecuaciones de búsqueda, revisar textos, sintetizar información o analizar documentos. Y pone el foco en los usos, las limitaciones y la necesidad de un uso crítico y responsable. Eso, en el contexto universitario actual, no es poco. Porque el problema no es que falte acceso a herramientas de IA. El problema es que muchas instituciones no han acompañado ese acceso con ningún tipo de criterio. Cada persona improvisa, cada departamento decide por su cuenta, y nadie ha definido qué nivel de uso es aceptable en qué contexto. La IA puede ayudar mucho en procesos de investigación. Pero no sustituye el juicio académico, la trazabilidad de las fuentes ni la responsabilidad sobre lo que se escribe o se publica. Aquí añadiría un matiz crítico. Las universidades están haciendo un esfuerzo por publicar guías y marcos de uso. Pero en IA estos documentos no pueden funcionar como normativas que se publican una vez y permanecen inamovibles. Deben ser documentos vivos: actualizables, versionados, revisados con frecuencia. La propia UNED lo ilustra bien: ya tiene una guía más reciente sobre integridad académica en el uso de IA en los Trabajos Fin de Título, publicada en diciembre de 2025. Ese es el camino. Pasar de orientaciones iniciales a documentos cada vez más específicos, conectados con la evaluación, la autoría y las evidencias de aprendizaje. La pregunta ya no es si la IA va a entrar en la investigación universitaria. Ya está dentro. La pregunta es si las instituciones van a liderar ese uso con criterio, o si van a dejar que avance de forma desigual e invisible. Guías como esta son un paso concreto. No porque resuelvan el problema, sino porque ayudan a construir cultura institucional alrededor de la IA. Y ahora mismo, eso importa más que cualquier herramienta. En el primer comentario dejo el enlace de acceso.