> It could also mean "wait" is the best possible action I can take now. And instead of being perturbed by waiting it is an active decision to wait.
As an example: I worked on a PhD in applying machine learning to certain tasks in programming and mathematics. I ended up burning-out and had to quit.
When I started in 2014, most cutting-edge ML research was on image processing like convolutional neural networks. That's a very bad fit for the sorts of tree-structures and text sequences I wanted to use. The state of the art for the latter were RNNs which are notoriously slow (hard to parallelise), suffer exploding/vanishing gradients (needing e.g. LSTM), etc.
Transformers and LLMs solve the issues I was facing; so in hindsight it would have been better to wait a few years (I believe the Attention Is All You Need paper came out in 2017?)
As an example: I worked on a PhD in applying machine learning to certain tasks in programming and mathematics. I ended up burning-out and had to quit.
When I started in 2014, most cutting-edge ML research was on image processing like convolutional neural networks. That's a very bad fit for the sorts of tree-structures and text sequences I wanted to use. The state of the art for the latter were RNNs which are notoriously slow (hard to parallelise), suffer exploding/vanishing gradients (needing e.g. LSTM), etc.
Transformers and LLMs solve the issues I was facing; so in hindsight it would have been better to wait a few years (I believe the Attention Is All You Need paper came out in 2017?)