Despite all this promise—or perceived promise—one thing that machine learning isn’t is magic. “Let’s be realistic,” says George Dahl, a computer scientist at Google. “Machine learning is nonlinear regression,” a simple type of statistical analysis in which collected data are “fit” with model parameters. Dahl won a Merck & Co. machine-learning competition while a graduate student in Geoffrey Hinton’s group at the University of Toronto.
Making machine learning sound like something it’s not yet could be bad for the technique itself. If it can’t live up to the bar that’s been set, funders and scientists may decide machine learning isn’t worth their time. “We need the most brilliant minds to feel enticed” to study it and explore its benefits for it to be successful, says Nuno Maulide, an organic synthetic chemist at the University of Vienna.
To explore the space between what some have promised and what machine learning might actually deliver—and to discern among chemists a consensus about the much-ballyhooed tool—C&EN has examined some of the fields where it’s generating the most enthusiasm and skepticism.
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Whether that’s overhyping it depends on your perspective. For those chemists who work most closely with machine learning, the excitement they see in press releases and casual conversation can get tiresome. These experts have great faith that machine learning will have a real and lasting impact on chemistry, especially if more people are trained to use it. At the same time, some worry that this tool can’t possibly live up to the highest expectations and that disappointment might hurt progress.
Cronin puts it this way: “Although I say machine learning is overhyped and annoying, I think it’s underused by chemists.”
- More Here
Making machine learning sound like something it’s not yet could be bad for the technique itself. If it can’t live up to the bar that’s been set, funders and scientists may decide machine learning isn’t worth their time. “We need the most brilliant minds to feel enticed” to study it and explore its benefits for it to be successful, says Nuno Maulide, an organic synthetic chemist at the University of Vienna.
To explore the space between what some have promised and what machine learning might actually deliver—and to discern among chemists a consensus about the much-ballyhooed tool—C&EN has examined some of the fields where it’s generating the most enthusiasm and skepticism.
[---]
Whether that’s overhyping it depends on your perspective. For those chemists who work most closely with machine learning, the excitement they see in press releases and casual conversation can get tiresome. These experts have great faith that machine learning will have a real and lasting impact on chemistry, especially if more people are trained to use it. At the same time, some worry that this tool can’t possibly live up to the highest expectations and that disappointment might hurt progress.
Cronin puts it this way: “Although I say machine learning is overhyped and annoying, I think it’s underused by chemists.”
- More Here
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