The primary purpose of this blog post is to create a public record. Some technical insights are deliberately omitted.
I think we’re in an algorithmic AI overhang.
Today’s neural networks (including GPT) are so data inefficient they will not strictly outcompete human brains’ performance in all domains (excluding robotics, which is hardware-limited) no matter how much data we shove into them and how big we scale them.
The human brain uses a radically different core learning algorithm that scales much better as a function of its training data size.
The core learning algorithm of human beings could be written in a handful of scientific papers comparable to the length and complexity of Einstein’s Annus Mirabilis.
Once the mathematics behind the human brain’s learning algorithms are made public, they will be running at scale on silicon computers in less than 10 years.
Within 10 years of getting these algorithms to scale, they will be cheap enough for a venture-backed startup to run them at a scale outstripping the smartest human alive―assuming civilization lasts that long. (A world war could destroy the world’s semiconductor fabricators.)
I’ve been thinking about this idea for a long time. What finally pushed me to publish were two fire alarms in sequence. First, a well-respected industry in the leader of AI stated in a private conversation that he believed we were algorithmically limited. Secondly, Steven Byrnes wrote this post. The basilisk is out of Pandora’s Box.
[Prediction] We are in an Algorithmic Overhang
The primary purpose of this blog post is to create a public record. Some technical insights are deliberately omitted.
I think we’re in an algorithmic AI overhang.
Today’s neural networks (including GPT) are so data inefficient they will not strictly outcompete human brains’ performance in all domains (excluding robotics, which is hardware-limited) no matter how much data we shove into them and how big we scale them.
The human brain uses a radically different core learning algorithm that scales much better as a function of its training data size.
The core learning algorithm of human beings could be written in a handful of scientific papers comparable to the length and complexity of Einstein’s Annus Mirabilis.
Once the mathematics behind the human brain’s learning algorithms are made public, they will be running at scale on silicon computers in less than 10 years.
Within 10 years of getting these algorithms to scale, they will be cheap enough for a venture-backed startup to run them at a scale outstripping the smartest human alive―assuming civilization lasts that long. (A world war could destroy the world’s semiconductor fabricators.)
I’ve been thinking about this idea for a long time. What finally pushed me to publish were two fire alarms in sequence. First, a well-respected industry in the leader of AI stated in a private conversation that he believed we were algorithmically limited. Secondly, Steven Byrnes wrote this post. The basilisk is out of Pandora’s Box.
Part 2 here