> Human cognition was basically bruteforced by evolution-- "Brute forced" implies having a goal of achieving that and throwing everything you have at it until

> Human cognition was basically bruteforced by evolution-- "Brute forced" implies having a goal of achieving that and throwing everything you have at it until it sticks. That's not how evolution by natural selection works, it's simply about what organisms are better at surviving long enough to replicate. Human cognition is an accident with relatively high costs that happened to lead to better outcomes (but almost didn't). > why would it be impossible to achieve the exact same result in silicon I personally don't believe it'd be impossible to achieve in silicon using a low level simulation of an actual human brain, but doing so in anything close to real-time requires amounts of compute power that make LLMs look efficient by comparison. The most recent example I can find in a quick search is a paper from 2023 that claims to have simulated a "brain" with neuron/synapse counts similar to humans using a 3500 node supercomputer where each node has a 32 core 2 GHz CPU, 128GB RAM, and four 1.1GHz GPUs with 16GB HBM2 each. They claim over 126 PFLOPS of compute power and 224 TB of GPU memory total. At the time of that paper that computer would have been in the top 10 on the Top500 list, and it took between 1-2 minutes of real time to simulate one second of the virtual brain. The compute requirements are absolutely immense, and that's the easy part. We're pretty good at scaling computers if someone can be convinced to write a big enough check for it. The hard part is having the necessary data to "initialize" the simulation in to a state where it actually does what you want it to. > especially after we already demonstrated some parts of those results (e.g. use of language) that critically set us apart from other animals? Creating convincing text from a statistical model that's devoured tens of millions of documents is not intelligent use of language. Also every LLM I've ever used regularly makes elementary school level errors w/r/t language, like the popular "how many 'r's are there in the word strawberry" test. Not only that, but they often mess up basic math. MATH! The thing computers are basically perfect at, LLMs get wrong regularly enough that it's a meme. There is no understanding and no intelligence, just probabilities of words following other words. This can still be very useful in specific use cases if used as a tool by an actual intelligence who understands the subject matter, but it has absolutely nothing to do with AGI.