That’s a very interesting take. I hadn’t really considered evolution
I guess if you really wanted to start from scratch, you could figure out how to evolve the whole system from a single cell or something like that. In some ways neural networks have kind of evolved in that way, assisted by humans. They started with a single perceptron, and have gone all the way to deep learning and convolutional networks
I also remember a long time ago studying genetic and evolutionary algorithms, but they were pretty basic in terms of what they could learn and do, compared to modern LLMs
Although recently I saw some research in which they were applying essentially genetic algorithms to merge model weights and produce models with new/evolved capabilities
It's this take on the situation which I think needs more emphasis.
Whether anyone likes it or not, these systems have co-evolved with us.
Hundreds of researchers contributing and just like English for example, it's ever-changing and evolving.
Given this trend, it's highly unlikely we won't achieve ASI.
It's not like hardware engineers stop innovating or venture capital stops wanting more. There might be a massive dip or even another AI winter but like the last one, eventually it picks up momentum again because there's clearly utility in these systems.
I've been coding for 25+ years and only a couple of days ago did it hit me that my profession has changed in a very dramatic way - I'm very critical of AI output, but I can read and comprehend code much quicker than I can write it relative to these systems.
Of course, that creates a barrier to holding a system in your head so going slow is something that should be pushed for when appropriate.
How much compute does simulating the earth for 4.7 billion years at atomic precision take? Why would that be more efficient than current approaches? Evolutionary algorithms work but are extremely inefficient, we don't have the compute to evolve even a single bacteria, let alone the whole history of the planet so we can arrive at human-like species.
I guess if you really wanted to start from scratch, you could figure out how to evolve the whole system from a single cell or something like that. In some ways neural networks have kind of evolved in that way, assisted by humans. They started with a single perceptron, and have gone all the way to deep learning and convolutional networks
I also remember a long time ago studying genetic and evolutionary algorithms, but they were pretty basic in terms of what they could learn and do, compared to modern LLMs
Although recently I saw some research in which they were applying essentially genetic algorithms to merge model weights and produce models with new/evolved capabilities