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Eh, if you boil all research in AI/ML down to the binary of "AGI or bust," then sure, everything is a failure. But, if you look at your smartphone, virtually e

Eh, if you boil all research in AI/ML down to the binary of "AGI or bust," then sure, everything is a failure. But, if you look at your smartphone, virtually every popular application the average person uses--Gmail, Uber, Instagram, TikTok, Siri/Google Assistant, Netflix, your camera, and more--all owe huge pieces of their functionality to ML that's only become feasible in the last decade because of the research you're referencing.

going_ham 4y ago
These are engineering marvel! This is engineering at it's finest. Applied math at it's finest. So it's not a failure. The way people hype AGI/AI/ML whatever undervalues the actual effort behind these remarkable feat. There is so much effort being made to make this work. Deep learning works when it is engineered properly. So it is just another tool in the toolbox! Look at how graphics community is approaching deep learning. They already had sampling methods but with MLPs (NeRFs), they are using it as glorified database. So it's engineering! I want to underscore that AI/ML/DL research requires ground breaking innovation not only in algorithms but also in hardware and software engineering.
samhw 4y ago
Sorry, I should have been clearer. I obviously concede that stuff like applying kNN over ginormous datasets to find TV shows people like, or doing some matrix decomposition to correlate ('recognise') objects in photographs, is obviously useful in the trivial sense. It has uses. It wouldn't exist otherwise. I was more thinking on a higher level, about whether it has led to any truly epochal technological advances, which it hasn't. Machine learning / neural nets also (like I said) get to claim credit for a hell of a lot of things which are just products of colossal advances in hardware – simply of its becoming possible to run statistical methods over very very large '1:1 scale' sample sets – and not due to a specific statistical technique (NN) which is not remotely new and has been heavily researched for about 40-50 years now.

going_ham 4y ago

These are engineering marvel! This is engineering at it's finest. Applied math at it's finest. So it's not a failure. The way people hype AGI/AI/ML whatever undervalues the actual effort behind these remarkable feat. There is so much effort being made to make this work. Deep learning works when it is engineered properly. So it is just another tool in the toolbox! Look at how graphics community is approaching deep learning. They already had sampling methods but with MLPs (NeRFs), they are using it as glorified database. So it's engineering! I want to underscore that AI/ML/DL research requires ground breaking innovation not only in algorithms but also in hardware and software engineering.

samhw 4y ago

Sorry, I should have been clearer. I obviously concede that stuff like applying kNN over ginormous datasets to find TV shows people like, or doing some matrix decomposition to correlate ('recognise') objects in photographs, is obviously useful in the trivial sense. It has uses. It wouldn't exist otherwise. I was more thinking on a higher level, about whether it has led to any truly epochal technological advances, which it hasn't. Machine learning / neural nets also (like I said) get to claim credit for a hell of a lot of things which are just products of colossal advances in hardware – simply of its becoming possible to run statistical methods over very very large '1:1 scale' sample sets – and not due to a specific statistical technique (NN) which is not remotely new and has been heavily researched for about 40-50 years now.