I want to differentiate between categories of capabilities improvement in AI systems, and here’s the set of terms I’ve come up with to think about them:
Infrastructure improvements: Capability boost in the infrastructure that makes up an AI system. This involves software (Pytorch, CUDA), hardware (NVIDIA GPUs), operating systems, networking, the physical environment where the infrastructure is situated. This probably is not the lowest hanging fruit when it comes to capabilities acceleration.
Scaffolding improvements: Capability boost in an AI system that involves augmenting the AI system via software features. Think of it as keeping the CPU of the natural language computer the same, but upgrading its RAM and SSD and IO devices. Some examples off the top of my head: hyperparameter optimization for generating text, use of plugins, embeddings for memory. More information is in beren’s essay linked in this paragraph.
Neural network improvements: Any capability boost in an AI system that specifically involves improving the black-box neural network that drives the system. This is mainly what SOTA ML researchers focus on, and is what has driven the AI hype over the past decade. This can involve architectural improvements, training improvements, finetuning afterwards (RLHF to me counts as capabilities acceleration via neural network improvements), etc.
There probably are more categories, or finer ways to slice the space of capability acceleration mechanisms, but I haven’t thought about this in as much detail yet.
As far as I can tell, both capabilities augmentation and capabilities acceleration contribute to achieving recursive self-improving (RSI) systems, and once you hit that point, foom is inevitable.
I want to differentiate between categories of capabilities improvement in AI systems, and here’s the set of terms I’ve come up with to think about them:
Infrastructure improvements: Capability boost in the infrastructure that makes up an AI system. This involves software (Pytorch, CUDA), hardware (NVIDIA GPUs), operating systems, networking, the physical environment where the infrastructure is situated. This probably is not the lowest hanging fruit when it comes to capabilities acceleration.
Scaffolding improvements: Capability boost in an AI system that involves augmenting the AI system via software features. Think of it as keeping the CPU of the natural language computer the same, but upgrading its RAM and SSD and IO devices. Some examples off the top of my head: hyperparameter optimization for generating text, use of plugins, embeddings for memory. More information is in beren’s essay linked in this paragraph.
Neural network improvements: Any capability boost in an AI system that specifically involves improving the black-box neural network that drives the system. This is mainly what SOTA ML researchers focus on, and is what has driven the AI hype over the past decade. This can involve architectural improvements, training improvements, finetuning afterwards (RLHF to me counts as capabilities acceleration via neural network improvements), etc.
There probably are more categories, or finer ways to slice the space of capability acceleration mechanisms, but I haven’t thought about this in as much detail yet.
As far as I can tell, both capabilities augmentation and capabilities acceleration contribute to achieving recursive self-improving (RSI) systems, and once you hit that point, foom is inevitable.