While it's true that training a new DL model requires lots of computation power, I personally feel that such activity mentioned in the article is more of "application" of ML instead of "research". I personally think University should move in the direction of "pure" research instead.
For example, coming up with a new DL model that has improved image recognition accuracy would mean it has to be trained through the millions of samples from scratch, which requires a lot of time and money. But I'd argue that such thing is more of an "application" of DL instead of "research". Let me explain why... Companies like FAANG have the incentives to do that, because they have tens or hundreds of immediate practical use cases once model is completed, hence I call such activity more of an "application" of ML rather than "research", because there's a clear monetary incentives of completing them. What about University, what sort of incentives do they have by creating a state-of-the-art image recognition other than publication? The problem is publication can't directly produce the resources needed to sustain the research (i.e. money)
I think ML research in the university should move in the direction of "pure" research. For example, instead of DL, is there any other fundamentally different ways of leveraging current state-of-the-art hardware to do machine learning? Think how people moved out approaches such as SVM to neural network. Neural network was originally a "pure" research project. At the moment of creation, neural network wasn't taking off because hardware wasn't capable to keep up with its computational demand, but fast forward 10-15 years later, it becomes the state-of-the-art. University ML research should "live in the future" instead of focusing on what's being hyped at the moment
For example, coming up with a new DL model that has improved image recognition accuracy would mean it has to be trained through the millions of samples from scratch, which requires a lot of time and money. But I'd argue that such thing is more of an "application" of DL instead of "research". Let me explain why... Companies like FAANG have the incentives to do that, because they have tens or hundreds of immediate practical use cases once model is completed, hence I call such activity more of an "application" of ML rather than "research", because there's a clear monetary incentives of completing them. What about University, what sort of incentives do they have by creating a state-of-the-art image recognition other than publication? The problem is publication can't directly produce the resources needed to sustain the research (i.e. money)
I think ML research in the university should move in the direction of "pure" research. For example, instead of DL, is there any other fundamentally different ways of leveraging current state-of-the-art hardware to do machine learning? Think how people moved out approaches such as SVM to neural network. Neural network was originally a "pure" research project. At the moment of creation, neural network wasn't taking off because hardware wasn't capable to keep up with its computational demand, but fast forward 10-15 years later, it becomes the state-of-the-art. University ML research should "live in the future" instead of focusing on what's being hyped at the moment