However, Hodgkinson worries that researchers in the field will pay attention to technique, rather than science, when trying to reverse engineer why the trio won the award this year. “What I hope this doesn’t do is cause researchers to use chatbots inappropriately, mistakenly thinking that all AI tools are equivalent,” he says.
The fear that this could happen is based on the explosion of interest around other supposedly transformative technologies. “There are always hype cycles, the most recent being blockchain and graphene,” says Hodgkinson. After the discovery of graphene in 2004, between 2005 and 2009, 45,000 academic articles mentioning this material were published, according to Google Scholar. But after Andre Geim and Konstantin Novoselov won the Nobel Prize for their discovery of the material, the number of published papers skyrocketed to 454,000 between 2010 and 2014, and to more than one million between 2015 and 2020. This increase in research has likely had only one modest real-world impact so far.
Hodgkinson believes that the energizing power of multiple researchers recognized by the Nobel Prize panel for their work in AI could cause others to begin congregating around the field, potentially resulting in science of changing quality. “Whether the proposals and applications (of AI) have substance is another question,” he says.
We have already seen the impact of media and public attention towards AI in the academic community. The number of publications on AI has tripled between 2010 and 2022, according to Stanford University researchwith almost a quarter of a million articles published in 2022 alone – more than 660 new publications a day. That was before the launch of ChatGPT in November 2022 started the generative AI revolution.
To what extent academics are likely to follow the media attention, money and plaudits of the Nobel Prize committee is a question that vexes Julian Togelius, an associate professor of computer science at New University’s Tandon School of Engineering. York who works in AI. “Scientists generally follow some combination of path of least resistance and greatest profitability,” he says. And given the competitive nature of academia, where funding is increasingly scarce and directly linked to researchers’ job prospects, it seems likely that the combination of a trending topic that, as of this week, has the potential of making high achievers win the Nobel Prize It might be too tempting to resist.
The risk is that this could hinder innovative new ideas. “Getting more fundamental data from nature and proposing new theories that humans can understand are difficult things to do,” Togelius says. But that requires deep reflection. It is much more productive for researchers to carry out AI-enabled simulations that support existing theories and involve existing data, producing small advances in understanding, rather than giant steps. Togelius predicts that a new generation of scientists will end up doing exactly that, because it’s easier.
There is also a risk that overconfident computer scientists, who have helped advance the field of AI, begin to see AI work receiving Nobel Prizes in unrelated scientific fields (in this case, physics and chemistry) and decide to follow. their steps, invading other people’s territory. “Computer scientists have a well-deserved reputation for sticking their nose into fields they know nothing about, injecting some algorithms and calling it a breakthrough, for better or worse,” says Togelius, who admits to having previously been tempted to add deep learning to another field of science and “advance it,” before thinking better of it, because you don’t know much about physics, biology or geology.
