> scaling to 1T parameters significantly enhances sample efficiency and performance ceilings;
Man, I find SOTA deep learning somewhat hilarious. We scale models to absurd proportions, burning through a shitload of resources just to achieve (slightly above) human intelligence.
The human brain has a few billion neurons and uses as much power as a light bulb.
When for the training part you have to consider brains had like billions of years to develop. Maybe one of the reasons llms seem to be so expensive to train is because we are "compressing" in far less time that learning part
> scaling to 1T parameters significantly enhances sample efficiency and performance ceilings;
Man, I find SOTA deep learning somewhat hilarious. We scale models to absurd proportions, burning through a shitload of resources just to achieve (slightly above) human intelligence. The human brain has a few billion neurons and uses as much power as a light bulb.
Similarly, all the components in an audio amplifier are super dumb because you can just have 4 guys play in your living room amirite
When for the training part you have to consider brains had like billions of years to develop. Maybe one of the reasons llms seem to be so expensive to train is because we are "compressing" in far less time that learning part
The human brain is estimated to have approaching 10^11 neurons (most of them in the cerebellum).
However, a neuron is much more than a single parameter. The brain is estimated to have from 10^14 to 5x10^14 synapses.
Do you find cars similarly hilarious?
the active layers concept seems to be experiencing convergent evolution to how synapses in a human function
huge parameter models with many small but efficient layers can work quickly on low resource hardware
similar to how neurons experience chemical spiking to activate small portions of the brain at once