Alejandro Piscitelli

Internetómano. Felinómano. Bibliómano, Tigrenómano @datosuba (1996-2021) @EscenariosUdeSA (2020- ) @UNTREF (2018- ) Obsesionado con/por la Tercera Cultura

[Alejandro Piscitelli @piscitelli]

@narodowski No van, no estudian, no repiten, no aprenden. Girar 180 grados y cambiar el eje de la decisión de las burocracias educativas a las escuelas, que los gobiernos las apoyen en vez de decirles a los docentes qué es lo que tienen que hacer. https://t.co/hSBUvwLSUQ

[Miguel Ángel | GptZone @MiguelMaestroIA]

Han pasado solo 10 horas desde que se lanzó Claude Fable 5.

El mundo está sorprendido por su potencial y la gente está creando apps, maravillas en 3D y cosas que hace meses parecían imposibles.

25 ejemplos LOCOS 👇

[Naief Yehya @nyehya]

Escribí este ensayo para la fenomenal revista CTXT, sobre la voracidad destructiva de las empresas de IA y su proceso de entrenamiento
https://t.co/qAlsWd3Vmd

[Daniel 🚀 @daniherrera___]

🧵ABRO HILO sobre la separación de Los Redondos desde la mirada del INDIO #IndioEterno

"Fuimos a la casa de Poli y Skay, a seguir conversando sobre las cosas de rigor que debían resolverse antes de una fecha. Porque ya teníamos una en camino, a tiro de piedra: el 8 de diciembre en el Club Atlético Unión de Santa Fe. La publicidad ya circulaba, las entradas estaban a la venta.

Lo que pasó esa noche me sorprendió. Puede que la embriaguez haya tenido que ver, estábamos medio picoteados… Veníamos hablando de los asuntos pendientes y salió el tema de los videos de Racing: aquellos que habíamos grabado con tantas cámaras y que algún día, cuando estuviésemos en las condiciones ideales, queríamos editar y posproducir."

[Carlos A. Scolari @cscolari]

The rise of remote astronomy has reshaped amateur astrophotography #mediaevolution 😎👇

[Evan Luthra @EvanLuthra]

🚨JEFF BEZOS JUST PUT $100 MILLION INTO A STARTUP WITH NO PRODUCT.

Their entire pitch is that the AI industry is doing everything wrong.

And that your brain already solved the problem everyone is burning billions trying to fix.

The whole AI industry is trapped in one brutal equation:

Smarter models need more compute.

More compute needs more power.

Companies are now building dedicated nuclear plants just to run AI data centers.

A single AI server GPU burns over 1,500 watts.

Your brain runs on 20.

Yet your brain is doing something far more complex than answering a chat prompt. It's learning, reasoning, controlling your body, and processing everything you see and hear on less power than a light bulb.

A startup called Flourish just raised $500 million at a $2.5 billion valuation to exploit that gap.

Their thesis is simple but radical:

The AI industry is optimizing at the wrong layer.

Everyone is racing to build more powerful chips. Flourish says the real problem isn't the hardware. It's the architecture.

Today's AI models activate enormous portions of their networks for almost every task.

Your brain doesn't.

It's sparse. Only the neurons needed for a task light up. Everything else stays quiet, consuming almost no energy.

Flourish wants to copy that.

Their approach comes from a field called connectomics: mapping biological brains neuron by neuron to understand how intelligence actually works.

In 2024, scientists fully mapped a fruit fly brain.

And here's the punchline:

That tiny brain appears dramatically more efficient than modern AI systems performing similar tasks.

Flourish wants to extract that efficiency and turn it into software.

The man leading the effort has one of the strangest résumés in tech.

Thomas Reardon created Internet Explorer at Microsoft.

Then he left software, earned a PhD in computational neuroscience, built a brain-computer interface company, and sold it to Meta for up to $1 billion.

Now he's trying to reverse-engineer the core algorithm of human intelligence itself.

If it works, the implications are staggering.

Advanced AI could run locally on laptops and phones instead of massive data centers.

The energy requirements of AI could collapse.

And the $30,000 GPUs the industry depends on today could become optional.

For 70 years, we've built computers that think nothing like brains.

Flourish is betting the answer was sitting inside our skulls the whole time.

[Carlos A. Scolari @cscolari]

Esperando con ansiedad haidtiana (de Jonathan Haidt) el próximo libro de Sherry Turkle. Mientras llega, les recomiendo leer esto que escribí en 2012: "Las tecnologías conversacionales y la crítica de la razón digital" (https://t.co/xGQYJqWdGJ). También viene al caso repasar este texto de @nigropatricia publicado en plena pandemia: "En defensa de la conversación, pero ¿de cuál de ellas?" https://t.co/UfdVtF2fgn Durante años Sherry Turkle se quejó de la falta de conversación por culpa de las tecnologías digitales. Ahora que hablamos con las máquinas, ¿seguirá ese lamento? 🤔 @piscitelli @julitoalonso @unfernandezmas @sangerchu @borovinsky @sandvaldettaro @AlexValverde_V @mateomortola @christilcara @edmundogs @FAlbarello @alvaroliuzzi @mdlt @jlori @ciberfan @juliussinmundo

[Carlos A. Scolari @cscolari]

APOCALYPSE NOW. LOS SAPIENS CONTRA LA TECNOLOGÍA en #Hipermediaciones 👉 ¿De dónde viene el visceral rechazo de los sapiens a la tecnología? Vivimos en un entorno plagado de artilugios, a estas alturas imposibles de escindir de la humanidad, aunque seguimos venerando lo natural como si fuera un espacio puro, bueno y originario. En "Platón contra las máquinas" Marcos Alonso no se pregunta si la inteligencia artificial es inteligente, sino algo más radical: ¿tiene sentido seguir hablando de lo ‘artificial‘? https://t.co/ruG3DKOt92 #MediaEvolution #IA #AI

[VISION IA @vision_ia]

Incroyable ! Elon Musk, considéré comme "fou" par les "experts" de l'industrie, rafle tout. Encore une fois. GOOGLE signe et demande à ELON MUSK d'utiliser Colossus pour faire tourner son IA pour 1 milliard de $ par mois.

Cette annonce fait suite à Anthropic, créateur de l'IA Claude, qui annonçait devoir faire la même chose pour survivre.

"Never bet against Musk", c'est dingue à quel point c'est vrai de tout temps. Laissez-moi vous expliquer :

Il y a un an, Elon Musk construisait un datacenter de 220 000 GPU dans un champ à Memphis. Tout le monde s'est moqué. "C'est n'importe quoi." "Ça marchera jamais." "Il est devenu fou."

Personne ne comprenait pourquoi un mec qui envoie des fusées dans l'espace se mettait à empiler des GPU Nvidia dans un hangar.

Puis le mois dernier, on apprend qu'Anthropic, le créateur de Claude, signe pour TOUTE la capacité de Colossus 1. Prix : 1,25 milliard de dollars par mois. Sur 3 ans. Soit 45 milliards de dollars.

Le concurrent direct de Grok paye Musk pour faire tourner son propre modèle par manque de puissance de calcul.

Et là, cette nuit, coup de tonnerre : Google signe aussi. 920 millions de dollars par mois. 110 000 GPU. Jusqu'en 2029.

Google. La boîte qui possède le plus de puissance de calcul IA au monde. Qui fabrique ses propres puces TPU. Qui a investi 180 milliards cette année en infrastructure.

Même Google n'a pas assez de GPU. Alors Google paye Musk. Total des contrats compute de SpaceX : +70 milliards de dollars.

Et la semaine prochaine ? IPO de SpaceX !

Je m'en souviens très bien, car j'y avais dédié plusieurs vidéos il y a 1-2 ans. Pendant que tout le monde débattait de savoir si l'IA était une bulle, Musk a construit l'infrastructure comme un affamé. Il a poussé ses équipes pour réaliser une prouesse de vitesse de construction. Et maintenant, bulle ou pas bulle, tout le monde lui paye un loyer.

[Marian Ferrarelli @FerrarelliM]

Terapia y acompañamiento siguen siendo los usos predominantes de la IA.... señal de que los sistemas insisten con su antropomorfización por diseño

Me gustó el concepto de #Thinkslop... y este tip sobre hacer consciente el workflow.... con metacognición e intencionalidad cognitiva 👇🏼👇🏼

"Draw boundaries. Think about what parts of your workflow you should retain for yourself and which are indeed better suited for AI. How might you systematically ensure that you and your AI then stay in your respective lanes?"

https://t.co/p2MagQ78vG

[Carlos Iván Moreno @carlosivanmoren]

El gran pensador Edgar Morin nos enseñó que la incertidumbre no es una anomalía que deba corregirse. Es la condición natural humana.

Los grandes desafíos de nuestro tiempo -IA, cambio climático, desigualdad, polarización- no admiten respuestas simples ni miradas únicas. Por eso defendió el pensamiento complejo.

Cuando una ideología, una disciplina o ahora un algoritmo promete explicarlo todo, conviene desconfiar.

Hoy es urgente transformar la universidad, orientada en disciplinas aisladas, en una “multiversidad”, espacio que conecte saberes y forme personas capaces de hacerse las preguntas relevantes y resolver los problemas complejos.

El principal legado de Morin: educar no es transmitir certezas, sino formar personas para abrazar y dar sentido a la incertidumbre.

Comparto mi columna en @Milenio

https://t.co/HNcniBNQTW

[Alejandro Piscitelli @piscitelli]

https://t.co/wFOgymTnX9 En las últimas semanas hemos perdido talentos únicos: Morin, Le Parc, Aristarain, Puenzo, actores de todo, ninguno me ha dolido tanto como la partida del Indio Solari QEPD @EscenariosUdeSA @julitoalonso @cscolari @unfernandezmas https://t.co/61f0OKSzZF

[Carlos A. Scolari @cscolari]

Restos descubiertos en una cueva de Sudáfrica revelan que el uso “oportunista” del fuego se remonta a hace 1,8 millones de años. El Homo erectus sacó provecho de las llamas causadas por incendios 700.000 años antes de lo que se creía.
https://t.co/g59jXh7FXl

[Christian Schwarz @christilcara]

Un país que aún valora la cultura y el pensar.

[Millie Marconi @MillieMarconnni]

A Chicago philosopher wrote one book in 1940 proving that 95% of the books you have read in your life, you didn't actually read, and Charlie Munger has been telling people to read it for 50 years.

His name was Mortimer Adler.

He spent 40 years at the University of Chicago, ran the editorial board of the Encyclopædia Britannica, and built his entire career on one uncomfortable observation about the people around him.

Most adults who called themselves well-read had not actually read a book in the real sense even once. They had run their eyes over the pages, registered the words, formed a vague impression, and put it back on the shelf.

The book had passed through them without ever entering them.

In 1940 he wrote How to Read a Book. It has stayed in print for 86 years.

Charlie Munger recommends it. Naval Ravikant recommends it. Fareed Zakaria recommends it.

Every serious thinker who builds a career on absorbing information eventually finds their way to this book, and the reason is that Adler had isolated something nobody else was naming clearly.

There are four levels of reading. Almost everyone is stuck on the second one. The fourth level is so different from what most people call reading that you have probably never done it in your entire life.

Level one is elementary.

You learn it as a child. You decode the letters into words and the words into sentences. You finish the sentence and understand roughly what it said. This is reading the way a 7-year-old reads, and almost every adult on earth has stopped developing past this point in some quiet way.

Level two is inspectional.

This is skimming. You move through a book quickly to figure out what it is broadly about. You read the back cover, scan the table of contents, glance at a few paragraphs, and form an opinion. Most adults who claim to have read 50 books a year are actually doing this. They are inspecting books, not reading them. They walk away with a vague sense of the argument and almost none of the evidence that supports it.

Level three is analytical.

This is the level Adler said most people have never properly experienced. You take one book and you wrestle with it for as long as it takes. You identify the question the author is trying to answer. You map their argument from front to back. You write your disagreements in the margins. You force yourself to articulate, in your own words, what the author is claiming and why. The point is not to finish the book. The point is to argue with it as if the author were sitting across the table from you. Most people never do this once in their life, because it is exhausting and slow and feels nothing like the reading they were taught as children.

Level four is the one almost nobody knows exists. Adler called it syntopical reading. The word means "across topics," and the technique is something closer to running a small private research lab in your own head.

You pick a single question that actually matters to you. How does power corrupt people. Why do civilizations collapse. What makes a marriage last. How does a person change their own mind. Then you assemble five or ten or twenty books from different authors, different centuries, different traditions, all of them taking a swing at the same question.

You do not read any of them cover to cover. You move between them. You find the chapter in book three that addresses the same question as the chapter in book seven. You force those two authors to argue with each other inside your own head.

The book stops being the unit of reading. The question becomes the unit. And the authors become voices in a conversation you are now hosting.

This is the level where reading stops being consumption and starts being construction.

You are no longer absorbing what someone else thinks. You are building a position of your own out of the friction between people who disagreed.

Adler argued that this is the only level of reading where you stop being a passive receiver of other people's ideas and start being someone who can produce ideas of their own.

The reason Charlie Munger has been recommending this book for 50 years is that this is exactly how Munger has always thought. He calls it building a latticework of mental models. The technique he is describing is just syntopical reading applied for a lifetime.

You take the strongest insight from psychology, the strongest insight from biology, the strongest insight from economics, and you stack them against the same problem until something new falls out the bottom.

The reason most people never reach level four is not that it is intellectually difficult. It is that it is logistically uncomfortable. It requires you to keep multiple books open at once.

It requires you to take notes that nobody is going to grade. It requires you to abandon the goal of finishing books and replace it with the goal of answering questions.

This is also why AI just changed everything Adler was teaching.

NotebookLM, Claude, and tools like them let you do syntopical reading at a speed that would have looked like magic to a Chicago philosopher in 1940.

You upload 10 books on the same question. You ask the AI to surface every place those authors agree and every place they contradict each other.

The technique Adler said almost nobody on earth had reached can now be run on a Sunday afternoon by anyone with a laptop and one good question.

The technique was always the unlock. The bottleneck used to be time. The bottleneck is now curiosity.

Most people will keep reading the way they always have. A book at a time. Eyes over the pages. No question driving it. No other authors in the room. Adler called that level two for a reason.

You are not behind on your reading list.

You are behind on the level you are reading at.

[Carlos A. Scolari @cscolari]

Muere Edgar Morin, sociólogo y filósofo francés, a los 104 años.
Su trabajo se situó a más allá de la sociología tradicional y se presentó como una reflexión sobre el ser humano y la sociedad. Abordó la complejidad de las redes desde la orillas de las ciencias sociales, incluso antes del estallido de las teorías matemáticas centradas en las conexiones y los fenómenos emergentes.
https://t.co/MhN3wEeAvm

[Daron Acemoglu @DAcemogluMIT]

A post about Pope Leo XIV's encyclical on AI. Why the Pope is right, but perhaps not right enough.

Artificial intelligence is reshaping the world in front of our eyes: how we communicate, how we access information, how we work, how income and status are distributed among us, and soon how we fight and kill each other. Yet the public conversation about AI remains stuck on the minutiae of competition between labs, or on a false dichotomy between AI as a “stochastic parrot” with no real capabilities and AI as an alien superintelligence poised to take command of humanity.

The more important questions are about what we want from AI, and whether our current mindset, institutions, and control mechanisms are equal to the task of steering it toward our welfare.

It is refreshing, then, that a bold and powerful voice has weighed into this debate: Pope Leo XIV. As an economist who has long argued that technology is a matter of choice rather than fate, I find Leo’s intervention welcome and, on most points, on target. But on the most consequential question of what AI should actually be designed to do, Leo stops short.

Secular readers may bristle at the encyclical’s opening invocation of the Tower of Babel. They would be mistaken to stop reading there. Leo goes much further than most pundits, journalists and policymakers in the United States by recognizing that what happens to AI, and hence to humanity, is a under our control. There are multiple possible paths for AI, and which one we take will have sweeping consequences. He is also ahead of many commentators when he writes forcefully and unequivocally that “technology is never neutral, because it takes on the characteristics of those who devise, finance, regulate, and use it.”

These were the central themes of the book I wrote with Simon Johnson, Power and Progress: Our Thousand-Year Struggle over Technology and Prosperity. It is heartening to hear them taken up by a voice with Leo's reach.

The Pope is also right to question the current trajectory of AI in warfare and law enforcement. What was taboo only a few years ago – AI-driven mass surveillance, algorithms selecting targets for killing – has become routine. Many in Silicon Valley are now calling openly for a new military-algorithmic complex centered on AI as an instrument of American hard power. Leo captures something deep and too often ignored: “Any technology that facilitates attacks without seeing the face of human beings lowers the moral threshold of conflict.”

His call for the “disarmament of AI” follows directly from these observations. As he explains, disarming AI means “freeing it from the mentality of ‘armed’ competition, which today is not limited simply to the military context, but is also an economic and cognitive phenomenon.” His moral clarity in stating that “there is no algorithm that can make war morally acceptable” should be a warning to technologists rushing to design new weapons of mass destruction.

Underneath these specific concerns lies a more fundamental claim: that what is technically feasible is not the same as what is good for humanity, and that the difference depends on who controls the technology and what ideology and interests guide them.

Leo edges toward what I take to be the most important point about AI's future when he observes that “while AI promises to boost productivity by taking over mundane tasks, it frequently forces workers to adapt to the speed and demands of machines, rather than designing machines to work with those who work.”

But here he does not go far enough. He stops short of questioning the prevailing design philosophy of AI itself: a philosophy centered on mimicking human capabilities and automating human tasks, with the ultimate goal of artificial general intelligence (AGI) that can do everything a person can.

This philosophy rests on a mistake. It assumes that artificial intelligence and humanintelligence are fundamentally similar, and therefore machines should naturally take over whatever humans currently do. Yet these intelligences are fundamentally different.

Humans are “one-shot” learners. We form hypotheses from a few examples, mentally simulate possibilities, and refine our understanding through a social process of trial and error. This is how children learn language - imitating a few words, generalizing, and adjusting based on how others respond. We are not, however, very good at absorbing massive volumes of information or sifting through unstructured data for relevant patterns.

AI models are almost the opposite. They thrive on enormous training sets and excel at pattern recognition at scale. But they have, as yet, no genuine creativity, no real-world embodiment, and no capacity for trial-and-error learning grounded in interaction with the physical and social world.

When two things are different – you shouldn’t, and typically you couldn’t – use one to mimic the other. If you did, you would end up with suboptimal, disappointing results. It would have been a colossal mistake, and the Chicago Bulls’s legendary coach Phil Jackson would have gone down in the annals of basketball as one of the worst coaches in history, if he decided in the 1990s that because Michael Jordan was the better player, Jordan should mimic everything that Scottie Pippen and Dennis Rodman were doing in the team. The team went from championship to championship because these players worked together and complemented each other.

The same applies to AI and human skills.

The more productive path is complementarity – using AI to do what humans cannot, so that humans can do what they do best. An electrician aided by AI diagnostics, a nurse supported by AI in interpreting symptoms, a teacher using AI to personalize instruction for each student; these are the contours of a different AI future, one that raises rather than displaces human capability.

Optimists and industry insiders will respond that automation-first AI can still benefit everyone, provided redistributive policy keeps pace. But this argument has a poor track record. Forty years of digital automation have already concentrated gains at the top, hollowed out middle-skill work, and produced disappointing aggregate productivity growth. There is little reason to expect that an even more powerful round of automation, deployed by even more concentrated firms, will end differently. We can and must demand a different design.

The global stakes from the future of AI are even larger than those we can see around us in the United States. For the developing world, where billions still depend on the prospect of decent jobs as a path out of poverty, an automation-centric AI agenda is not merely suboptimal. It is simply transferring to foreclose the most important route to broad-based prosperity.

The biggest failing of today's AI industry is its refusal to recognize any of this. It is guided instead by an ideology of control (the industry’s own over humanity) and by a conviction that machines are uniformly better than humans.

As Leo rightly notes, this failure is enabled by the fact that a handful of companies now command the future of AI.

What we need is a combination of moral clarity and a serious, society-wide debate about what AI can do and what we want it to do. That debate must move beyond exhortation toward concrete choices: antitrust action against the dominant platforms, public investment in human-complementary AI, regulation of surveillance and autonomous weapons, and meaningful rights for workers and citizens over the data on which these systems are built.

The Pope's intervention makes such a debate a little more likely today than it was before.

It is now up to the rest of us to carry it further than he was willing to go.

[Alex Prompter @alex_prompter]

Let me trace the timeline here because nobody's connecting it.

Step 1: Scrape the entire internet. Every book, every article, every conversation, every piece of art, every forum post. Do it without asking. Do it without paying.

Step 2: Train a model on all of it. Call it "artificial intelligence."

Step 3: Go to BlackRock's Infrastructure Summit and announce: "We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter."

Step 3 is where you sell people's own knowledge back to them. On a meter.

They took the collective output of human thought, compressed it into a model, and now they want to charge you by the token to access a version of what you and everyone you know already created.

One Reddit user put it perfectly: "They stole all this data from us, the people, our life's work, creativity, art, by devouring the internet and blowing through all copyright laws. Now they want to sell it back to us in the form of a utility."

Imagine if someone photocopied every book in the public library, burned the library down, and then opened a subscription service for the copies.

That's the metered intelligence business model.

And they're pitching it to infrastructure investors as though they invented water.

[Marian Ferrarelli @FerrarelliM]

Sobre el anuncio de un supuesto gemelo digital presentado por el Ministerio de Capital Humano: no tengo suficiente información sobre este proyecto en particular ni sobre qué significaría usarlo en política social con el agregado de IA. Sólo sé que los gemelos digitales ya existen hace rato en varias industrias (la aeronáutica y la espacial, entre otras) para simular situaciones que de otro modo sería imposible evaluar/ medir por su complejidad y riesgo.

Si “gemelo digital en política social con IA” implica eventualmente el despliegue de sistemas de asignación de planes sociales, asistencia médica e incluso educación en diversas poblaciones, dejo por acá dos casos emblemáticos que nos recuerdan que es muy difícil que esto salga bien cuando un algoritmo decide sobre la trayectoria de vida de las personas:

Inglaterra, caso “F*ck the algorithm”,  2020: El uso de un algoritmo para estimar las notas escolares en el Reino Unido durante la pandemia desató protestas masivas, ya que el software penalizó desproporcionadamente a los alumnos de escuelas públicas y de sectores más vulnerables. 

https://t.co/ds747Q44Kz

[Julio Ernesto Lopez @julitolopez]

https://t.co/pZD2rCXMhm