The Rate of Progress of Deep Learning Is the Slowest in 5 Years
Today more people are working on deep learning than ever before -- around two orders of magnitude more than in 2014.
Time for something new!
Time for something new!
It's on AskHN and you don't have a question. Also...
- How did you define and measure progress?
- How did you measure the number of people working in the field?
- If true, why would any of this be a problem?
- Why does a field need to constantly progress? For example, you could say that there's been even less progress in linear regression or random forest methods over the past five years - this doesn't take away from their usefulness in certain applications.
- Why would the assumed slow rate of progress mean that DL needs to be replaced?
- If you find more suitable solutions, you're always free to implement them in place of DL.
1. What is slowest based on?
2. Is the comment about people working in the field meant to demonstrate something about slowness or some other point?
What do you suggest?