Pankaj Sharma is one of the world's premier speakers on Leadership and Personal Mastery. As a presenter, Sharma has the rare ability to electrify an audience yet deliver uncommonly original and useful insights that lead to individuals doing their best work, teams providing superb results and organizations becoming unbeatable.
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The 2024 Nobel Prize in Physics was given to John Hopfield and Geoffrey Hinton for development of techniques that laid the foundation for revolutionary advances in artificial intelligence
A Nobel Prize in Physics for... AI?
For science journalists such as myself, covering the Nobel Prizes can be a nerve-wracking experience. In October of each year as we dutifully await the announcement of the latest prizewinners, many of us wonder if the Nobel Committee will throw us a topical curveball. And this year's Physics Nobel for breakthroughs in machine learning was certainly a curveball, as the research involved scarcely concerns physics at all, but rather dwells in the realm of computer science. The tech-heavy trend continued with this year's Chemistry Nobel, which also went to machine-learning approaches—this time for predicting and designing protein structure and function.
Besides inspiring some obvious jokes ("Will next year's Nobel Prize in Literature honor the inventors of Microsoft Word's autocomplete function, perhaps?"), the trend does highlight a very real concern: Just as "one device to rule them all" smartphones have rendered most other general consumer electronic gadgets obsolete, are artificial intelligence and machine learning going to do the same thing for scientific research? Is this trend of tech-heavy Nobels a signal of The End of Science?!
Dear reader, I won't pretend to really know the answer, but my guess is that the much-hyped rise of AI isn't the existential threat to old-fashioned flesh-and-blood ingenuity and intuition that some pundits proclaim. Not yet, anyway. These new technologies are first and foremost tools for human use, much like their predecessors the integrated circuit and the optical fiber (both of which also received Nobels). In most cases, actually making them useful will still require having humans involved. The relationship is symbiotic. Real science demands a lot of simple-but-hard work at which humans excel.
The real imminent threat science now faces from this notional rise of the machines is the prospect of too many scientists and their sponsoring institutions prematurely buying in to the hype, favoring and funding AI-infused projects and proposals at the expense and exclusion of all others. And in this respect, at least, 2024's tech-heavy science Nobels may be an ill portent indeed.
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