Bullshit. Write me a better cat detector in SQL, or protein folder, or super-human board game player. And, by the way, ML does write SQL [1].
Deep learning has all these disadvantages and difficulties because we moved the goalposts too many times and now want so much more out of it than regular software. A model has to be accurate, but also unbiased, updated timely and explainable and verified in much detail; also efficient in terms of energy, data, memory, time and reuse in other related tasks (fine-tuning).
We already want from an AI what even an average human can't do, especially the bias part - all humans are biased, but models must be better. And recently models have been made responsible with fixing societal issues as well so they've become a political battleground for various factions with different values - see recent SJW scandals at Google.
With such expectations it's easy to pile on ML but ML is just a tool under active development, with practical limitations, while human problems and expectations are unbounded.
Deep learning has all these disadvantages and difficulties because we moved the goalposts too many times and now want so much more out of it than regular software. A model has to be accurate, but also unbiased, updated timely and explainable and verified in much detail; also efficient in terms of energy, data, memory, time and reuse in other related tasks (fine-tuning).
We already want from an AI what even an average human can't do, especially the bias part - all humans are biased, but models must be better. And recently models have been made responsible with fixing societal issues as well so they've become a political battleground for various factions with different values - see recent SJW scandals at Google.
With such expectations it's easy to pile on ML but ML is just a tool under active development, with practical limitations, while human problems and expectations are unbounded.
[1] a neural SQL patent: https://patentimages.storage.googleapis.com/af/78/be/92ee342...