> 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.
True although a lot of those neurons and synapses are in the cerebellum, responsible for motor coordination and or in the visual cortex and so forth. Only a portion are in the language and reasoning areas. LLM's are comparable to human scale now, i think, and if trends continue will swiftly pass us by in the future.
If I had a magic button I would not only pause AI development but set it back 10 years. Sadly I have no influence on events and those who do, don't care about the future of humankind or actively wish us dead.
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
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.
However, a neuron is much more than a single parameter. The brain is estimated to have from 10^14 to 5x10^14 synapses.
If I had a magic button I would not only pause AI development but set it back 10 years. Sadly I have no influence on events and those who do, don't care about the future of humankind or actively wish us dead.
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