studies show a clear trend – output is up (more code, more commits, bigger diffs), but outcomes don’t reflect that trend. If anything, the average team is taking longer to ship worse software
studies show a clear trend – output is up (more code, more commits, bigger diffs), but outcomes don’t reflect that trend. If anything, the average team is taking longer to ship worse software
That’s better, but you’re still just defining things such that you have to be correct.
Humans spend 90% of their time just predicting the next token. All we need is to mix in a little bit more reasoning and bam, AGI!
See how annoying and hollow that rhetorical technique is?
The fact is that we have no idea whatsoever. We might reach AGI next week. We might never reach AGI. The Chinese government might be sitting on AGI right now. We have no idea, and this is completely new territory that has caused us to question our very understanding of intelligence.
The problem is that you’re not arguing anything. Your first comment is an ad hominem at best, and your second is an appeal to the unknown. You haven’t argued anything.
Here, here’s my argument. LLMs are built on neutral networks. They use a ridiculous amount of compute to “simulate neurons”, but their neurons are not even a 100th of the power of an actual neuron, which is why this is a dead-end for AGI. You’ve probably seen this diagram before
And had it explained that each of the nodes in this image represents a virtual neuron. Assuming that AGI is achieved via the means we know right now, namely being human-like, then you’d probably expect creating a neural net with the complexity of a human brain, minus all the stuff needed for steering a meat-suit, would get us there.
The problem is that those little nodes are input-output machines that are ridiculously simple compared to real neurons. They take in inputs from every connection they have, based on internal weights and measures, and then they output a single value to all their output neurons. That value is a float between 0 and 1, representing some percentage of activation.
Real neurons, meanwhile, use neurotransmitters to communicate to their neighbors, not simple electrical signals. There are hundreds of those, but even if there were 10, then neural networks would be completely incapable of modeling a simple brain, let alone a human one.
Why? Because that means each of those nodes in the neural net would need 10 separate inputs and outputs connected to all their existing nodes to even come close. That’s a 100x more connections for every part of the node. And we’re currently consuming basically every chip on earth to feed to this thing, and it’s not even 1/100th the complexity needed for even a dumbed down neuron, let alone a realistic one.