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[!summary]AI changes how people work by making tasks easier but also increasing expectations and multitasking. Some people use AI to think less and lose mental skills, while others work harder to grow and improve with AI. The future will depend on our will to learn and struggle, not just on how smart we are.
Highlights
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Researchers from ActivTrak analyzed the digital activity of more than 10,000 workers and found that when people adopted AI, their work life became more intense, not less. The time that these early adopters spent on email, messaging, and chat apps more than doubled. Their use of business software rose by 94 percent.
Reportas sobre lo contraintuitivo que es esto.
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The general pattern that the research points to is that many people don’t use the time they save using AI to do less; they use the time to take on new tasks. AI also seems to shift workers’ expectations, and their boss’s expectations, about how much they should accomplish in a day. Every hour feels more crowded, but also more frazzled. The ActivTrak researchers found that the time people spent on focused, uninterrupted work fell by 9 percent. There’s even a name for this mental state: “AI brain fry.”
AI Brain Fry Nuevo concepto vinculado al agotamiento mental consecuencia del constante cambio de contexto y al expectativa de mayor productividad en base al uso de IA
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A study by Michael Gerlich from SBS Swiss Business School found “a significant negative correlation between frequent AI tool usage and critical thinking abilities.” At first, AI sucks you in. You really do become more productive when using it. But then it threatens to hollow you out, as you become less capable and less knowledgeable. The saddest cases are people who get used to the AI crutch for a bit and then have it taken away. Researchers led by Grace Liu of Carnegie Mellon University put subjects through that experience and concluded, “After just ~10 minutes of AI-assisted problem solving, people who lost access to the AI performed worse and gave up more frequently than those who never used it.” A study of physicians who specialize in endoscopy—using flexible probes to examine the inside of the body—found that before they started using AI they located precancerous intestinal lesions in 28.4 percent of colonoscopies. After they started using AI, and then had it taken away, they located lesions in only 22.4 percent of colonoscopies. Their detection skills had seriously declined.
Deskilling. Estudios que demuestran su existencia. ¿Qué significa esto? ¿Qué implicancias tiene? ¿Como prevenirlo y enfrentarlo? ¿Cuáles son las prevenciones especiales que se deben tener desde el mundo de la enseñanza-aprendizaje? Idea de que las escuelas deben parecerse más a un gimnasio que a un lugar de trabajo, y que los programas que buscan desarrollar habilidades deben adaptarse a las condiciones mediáticas de nuestro nicho tecnológico del día de hoy, que incluye IA, y ajustar sus métodos de enseñanza y evaluación de aprendizajes para evitar sucumbir ante el aligeramiento nocivo de la fricción.
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The MIT developmental psychologist Laura Schulz has found that if a teacher offers instruction on how to use an object, she is unintentionally limiting children’s curiosity about it. But if she deliberately restrains from offering instructions, they become more curious. AI is like the instruction-offering teacher.
Esta es otra forma de plantear lo mismo que la investigación con la cátedra de física. Está como para investigar el trabajo de esta tipa y ver si tiene algo de nuevo o valioso en la forma de presentar le información. Es algo que puede ser un reel en sí mismo, y conectarse luego con el efecto que la IA tiene en el outsourcing del esfuerzo cognitivo.
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People in this group will also become less and less able to stand up to the bot. The technology is asking you to be a competent conversation partner with a highly intelligent but imperfect entity. But what if you’ve never done the work to form your own worldview or build your own knowledge base? You’re going to engage in what the experts call “cognitive surrender.” You’re going to believe everything the bot tells you, head off in whatever direction the bot suggests. Researchers at the University of Pennsylvania’s Wharton School programmed an AI to occasionally give wrong answers. The humans accepted its errors as true 80 percent of the time.
Capitulación cognitiva. Un complemento tenebroso al deskilling. Si no desarrollo mi musculatura cerebral, no voy a ser capaz de evaluar críticamente el output del modelo y voy simplemente a aceptar lo que declara. Este es un problemón gigante desde la perspectiva de la confianza epistémica, considerando que serán generaciones completas quienes estarán en esta condición… ¿y si un régimen totalitario empieza a modificar ciertos aspectos de la realidad? Por esto es importante que hayan instituciones públicas dedicadas al desarrollo de los modelos, o al menos que haya public oversight. Está también el desafío de las brechas por NSE.
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Sibben argues that the comment “AI could have done it in five minutes” is not really about speed. “It is a moral revaluation. It assumes that what matters is the output, not the ordeal; the image, not the seeing; the product, not the person becoming capable of making it.”
Está interesante la propuesta de que la adopción masiva de la IA puede movilizar cambios en la ética del involucramiento con el esfuerzo, el trabajo y sus productos. Habría que profundizar más en ella.
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The Mental Marathoners. Now we get to the high-need-for-cognition people and how they will fare in the coming age: kind of like marathoners, I suspect. The automobile is a perfectly good technology for traveling 26.2 miles. There is no practical reason that any person should train themselves to run that distance. But some people do. They want to put in the effort because they want to accomplish things—they want to expand their capacities.
Los maratonistas mentales. Se puede plantear el argumento haciendo referencia al caso de la mariposa de los abedules, en el sentido de que un cambio repentino y masivo de las condiciones del entorno hace que otro fenotipo sea el m con más ventajas competitivas. En este caso, si bien el esfuerzo no es necesario para producir trabajo, si lo es para desarrollar capacidades para apalancar los recursos disponibles, y sólo aquellos que voluntariamente se esfuercen por sesgos de carácter, son los que “heredarán el cielo”.
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In this age, cultural output will feel ever more familiar, as writing, songs, and movies become syntheses of what has already been produced. Marathoners are going to want to produce work, by contrast, that feels personal, that reflects their unique self. They’re going to want to find ways to use AI to increase their agency, rather than diminish it.
El aplanamiento de la producción cultural como consecuencia de la creciente dependencia en el mismo repositorio creativo. Se podría utilizar como analogía lo que pasa con la variabilidad genética cuando poblaciones que estaban separadas se entremezclan.
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The crucial task before us is to cultivate people’s desire to seek out cognitive complexity. Not to go all Joseph Campbell on you, but the essential challenge is: How do we train people to see their life as a hero’s journey in which they take on difficult missions that they may fail at and that will certainly involve pain and suffering? How do we form people so they have an explorer’s heart, a willingness to endure, an ability to struggle on, even when their body and mind are telling them to give up, to reach new destinations and figure stuff out?
Este es un súper buen prompt para escribir al respecto. Dado lo que sé yo sobre desarrollo humano y el sistema educativo… ¿cuál sería mi propuesta en concreto? De entrada, creo que es algo que podría relacionar con la crítica de Aftab sobre cómo los psiquiatras se hacen los locos con el trabajo emocional. Por ahí he escuchado observaciones similares en docentes, que no estaban preparados o interesados en trabajar con el sufrimiento humano, sino que sólo en enseñar. Creo que ahí hay un cambio de framing que hacer: que importe mucho menos cuánto dominio del contenido lograste, y mucho más tu actitud, cuánto te esforzaste, etc. Hoy en día los docentes no están preparados para valorar ese componente de las competencias, ni tampoco para evaluarlo claramente.
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What AI can’t do is hunger for things. Yes, a few reward-like mechanisms are in the thin layer of the models built through reinforcement learning, but the models are overwhelmingly about predicting, not desiring. AI can’t hunger, in the first place, because it doesn’t have biological needs—the needs that push living things to grow and explore. More important, AI doesn’t have a self. A bot doesn’t have a past person that it used to be or a future person that it wishes to become. A bot does not have a structure of cares and an order of loves, as a person does. A bot doesn’t have a personal history, a particular set of wounds, joys, and exhilarations experienced in regions deeper than rational calculation, and it doesn’t have a succession of dreams and hopes, which emerge from those regions as well. Despite what the rationalists used to tell us, life is not mostly about solving problems. Any computer can do that. Life is a pilgrimage, a journey—it’s going somewhere, growing from experience, expanding yourself, reaching for some possibility that you do not yet possess. The defining human features therefore are propulsions—the drives that push us to take on mental effort and overcome difficulty—and aspirations: knowing where you want to go, what purpose you serve, what kind of person you’d like to be. If we can help people learn to want more, hunger more, they’ll be willing to undertake the mental effort to do hard things, and we’ll avoid the cognitive polarization that is staring us in the face. If we can educate people to be clear and wholehearted about what they truly love, then AI will do the calculating and the synthesizing, but humans will still define what matters, what is worth exploring, what missions we go on, and where we end up. That would produce a bot-filled society in which human dignity is preserved, and perhaps even enhanced.
Continuando el anterior, creo que este agrega la idea de que lo que distingue a las máquinas de las personas es el telos y el deseo. Me gusta mucho l idea de que la educación puede estar enfocada principalmente en crear ese relato que genera trayectoria en los niños, construir una narración que, respetando las posibilidades de sus circunstancias, construye justo aquellas vías libidinales que son adaptativas y están disponibles.