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This teacher-turned-cognitive scientist shared a disturbing reality that left the room stunned. “Our kids are LESS cognitively capable than we were at their age.” Every previous generation outperformed its parents since we began recording in the late 1800s. So, what happened? Screens. Dr. Jared Horvath explained: “Gen Z is...

207,495 views • 2 months ago •via X (Twitter)

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Dr. Jared Cooney Horvath just delivered the brutal truth parents and educators need to face: “Even in schools, it doesn’t matter what the size of the screen is… and it doesn’t matter who bought it… All of these things are going to hurt learning, which in turn are going to hurt our kids’ cognitive development.” His core warning: Gen Z is the first modern generation to be less cognitively capable than their parents — despite more years in school. Attention, memory, literacy, numeracy, executive function, even general IQ — all declining. The culprit isn’t school itself. It’s the widespread introduction of screens and digital tools for learning. Across 80 countries, once tech floods classrooms, performance drops sharply. Kids using computers ~5 hours/day for schoolwork score over 2/3 of a standard deviation lower than those who rarely touch tech. US NAEP data mirrors it: states adopt 1:1 devices → scores plateau, then fall. The biological reality: Humans evolved to learn deeply from other humans, not screens. Screens circumvent the natural mechanisms of attention, memory consolidation, and deep processing. When the tool fails to deliver, we don’t remove it — we redefine success to fit the tool (e.g., SAT reading comprehension reduced to skimming short sentences instead of deep passages). That’s not progress. That’s surrender. The cost is a generation losing cognitive sharpness at the exact moment the world needs them sharpest. Parents, teachers, policymakers: How much longer do we let screens dictate what “learning” looks like?

Camus

179,935 views • 5 months ago

I can tell almost instantly when I meet a young man or woman, whether he or she is a deep thinker. They may not be at the top of their class, but that doesn’t necessarily make them less intelligent than the kid who scored 1500 on his SAT or the guy with an IQ of 134. They may be more intelligent. Much more intelligent, but the methods we have for quantifying that intelligence do not adequately capture the breadth and depth of brilliant minds that exist in the world. So they go unrecognized while the kids who excel on answer-based examinations get the best grades, attend the best schools, earn the best degrees, and, more often than not, go on to have mediocre lives. Why? There is one thing that the most brilliant and accomplished people I have ever met all share in common, and it isn’t pedigree or IQ. It’s curiosity. And not just any curiosity—it’s the inexhaustible kind. It’s the kind that will never be satisfied. In my experience, this is the sort of curiosity that breeds humility and most often coincides with a questions-based mindset. And it’s this type of mindset, not the answers-based mindset our educational system selects for, that is the actual prerequisite for brilliance. I’ve seen this kind of brilliance in physical therapists, plumbers, and pretty much any profession you can imagine that we don’t typically associate with brilliance. But we do associate it with excellence. And that’s because to become excellent at something, you have to become your own teacher. This means going from learning how to give the right answers to learning how to ask the right questions. And that requires curiosity and an almost psychotic commitment to excellence. So, while the person in this video is correct that less intelligent people than he are far more successful than he has been, the more interesting and less remarked upon insight is that people like him are not as brilliant as the system tells us they are.

Demetri Kofinas

76,349 views • 6 months ago

The most interesting part for me is where Andrej Karpathy describes why LLMs aren't able to learn like humans. As you would expect, he comes up with a wonderfully evocative phrase to describe RL: “sucking supervision bits through a straw.” A single end reward gets broadcast across every token in a successful trajectory, upweighting even wrong or irrelevant turns that lead to the right answer. > “Humans don't use reinforcement learning, as I've said before. I think they do something different. Reinforcement learning is a lot worse than the average person thinks. Reinforcement learning is terrible. It just so happens that everything that we had before is much worse.” So what do humans do instead? > “The book I’m reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no equivalent of that with LLMs; they don't really do that.” > “I'd love to see during pretraining some kind of a stage where the model thinks through the material and tries to reconcile it with what it already knows. There's no equivalent of any of this. This is all research.” Why can’t we just add this training to LLMs today? > “There are very subtle, hard to understand reasons why it's not trivial. If I just give synthetic generation of the model thinking about a book, you look at it and you're like, 'This looks great. Why can't I train on it?' You could try, but the model will actually get much worse if you continue trying.” > “Say we have a chapter of a book and I ask an LLM to think about it. It will give you something that looks very reasonable. But if I ask it 10 times, you'll notice that all of them are the same.” > “You're not getting the richness and the diversity and the entropy from these models as you would get from humans. How do you get synthetic data generation to work despite the collapse and while maintaining the entropy? It is a research problem.” How do humans get around model collapse? > “These analogies are surprisingly good. Humans collapse during the course of their lives. Children haven't overfit yet. They will say stuff that will shock you. Because they're not yet collapsed. But we [adults] are collapsed. We end up revisiting the same thoughts, we end up saying more and more of the same stuff, the learning rates go down, the collapse continues to get worse, and then everything deteriorates.” In fact, there’s an interesting paper arguing that dreaming evolved to assist generalization, and resist overfitting to daily learning - look up The Overfitted Brain by Erik Hoel. I asked Karpathy: Isn’t it interesting that humans learn best at a part of their lives (childhood) whose actual details they completely forget, adults still learn really well but have terrible memory about the particulars of the things they read or watch, and LLMs can memorize arbitrary details about text that no human could but are currently pretty bad at generalization? > “[Fallible human memory] is a feature, not a bug, because it forces you to only learn the generalizable components. LLMs are distracted by all the memory that they have of the pre-trained documents. That's why when I talk about the cognitive core, I actually want to remove the memory. I'd love to have them have less memory so that they have to look things up and they only maintain the algorithms for thought, and the idea of an experiment, and all this cognitive glue for acting.”

Dwarkesh Patel

1,051,605 views • 9 months ago