#neural-networks

#neural-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.

"AI agents will very quickly develop two subgoals, if they're smart. One is to stay alive, and the other subgoal is to get more control."

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.

If the chilled water isn't actually chilled, the chiller fans are adding to the problem rather than helping solve it, leading to rising temperatures.

AI models are more than just a block box of data; they are neural networks that recognize specific patterns from massive datasets.