> to train a state of the art ASR AI, you need roughly 100x A100 for a month, 100,000+ hours of audio recordings, and math knowledge to find a maximum likelihood path through a logit matrix.
Do you think there is a distinction between the kinds of problems that take some kind of "raw" signal data (audio, images etc) as input, where deep learning approaches appear to be fruitfully applied, and other kinds of problems that appear in many places in business and the public sector where the input space is not some kind of raw signal data but instead tabular data.
I have heard some people argue that the latter kinds of tabular-data style problems can be effectively tackled with a variety of statistical methods, and that deep learning style approaches do not offer an advantage.
Maybe fast.ai is commenting on the latter class of problem.
I agree with you :) Most business-style "automate my Excel" problems can be solved pretty well with regular statistical models, meaning you don't need AI to solve them.
So my impression is that fast.ai teaches you how to use AI methods, but with example problems that didn't need AI to be solved well.
Do you think there is a distinction between the kinds of problems that take some kind of "raw" signal data (audio, images etc) as input, where deep learning approaches appear to be fruitfully applied, and other kinds of problems that appear in many places in business and the public sector where the input space is not some kind of raw signal data but instead tabular data.
I have heard some people argue that the latter kinds of tabular-data style problems can be effectively tackled with a variety of statistical methods, and that deep learning style approaches do not offer an advantage.
Maybe fast.ai is commenting on the latter class of problem.