MoE introduces a dynamic way of scaling model capacity without proportionally increasing computational cost. Instead of activating every parameter for every input, the model selectively routes tokens through specialized 'expert' networks.
Each of these achievements would have been a remarkable breakthrough on its own. Solving them all with a single technique is like discovering a master key that unlocks every door at once. Why now? Three pieces converged: algorithms, computing power, and massive amounts of data. We can even put faces to them, because behind each element is a person who took a gamble.
Neural networks are some of the most promising artificial intelligence (AI) models. These systems process data similarly to the human brain, passing information through a complex network of nodes in the same way information goes through layers of neurons. That makes them capable of solving complicated problems in minimal time, which is particularly advantageous in the medical industry. Drug discovery is a vital but challenging process.
Dong et al. (2019) and Tay et al. (2022) train on a mixture of denoising tasks with different attention masks (full, causal and prefix attention) to bridge the performance gap with next token pretraining on generative tasks.
