Comment by physicallyIllfr
21 hours ago
How is it improving, that would require rearranging its weights and biases which it cannot do easily or quickly.
21 hours ago
How is it improving, that would require rearranging its weights and biases which it cannot do easily or quickly.
Self improvement during training, and AI self training are already happening. Easily/quickly are seemingly a factor of how much power/hardware you want to use at once.
With the level of compute they have they aren't stuck with frozen models like you are.
The infrastructure provisioning alone to train is heavily dependent on humans, as is dealing with failures (training runs fail a ton). Its not as simple as adding another ec2 on your dashboard. < 1k people in the world know how to do this, there will not be "recursive" or looped continual training for a long long long time. There are so many delicate inputs and controls. Not to mention the chains of businesses and the people required to operate them just to obtain the data needed, clean it and hand it to the llms.
The llms are supervising rlhf and creating synthetic data (to an extent) but they're nowhere close to being able to operate the full training stack end to end. This is a fantasy being sold to investors to create fomo.
Remember they're also limited by an effective memory of like 500k words a turn. Memory systems are lossy, so are swarm/sub agent mechanism. Im not worried about llms becoming self powered super entities anytime soon.
Is easily and quickly a requirement? Isn't it enough that over time it improves itself even if the process is complex and slow?
Do we know it's actually improving itself? Perhaps it's just opaquely sorting all ones and zeros for better lookup efficiency.