But Hodgkinson worries that researchers in the field will focus on the technology rather than the science when trying to reverse engineer why the trio won the award this year. “What we hope doesn’t happen is that researchers mistakenly think all AI tools are equivalent and use chatbots inappropriately,” he says.
Concerns that this could happen are based on an explosion of interest in other potentially transformative technologies. “There are always hype cycles, the most recent ones being blockchain and graphene,” Hodgkinson says. According to Google Scholar, after graphene’s discovery in 2004, 45,000 academic papers mentioning the material were published between 2005 and 2009. However, after Andre Geim and Konstantin Novoselov won the Nobel Prize for the discovery of this substance, the number of papers published was 454,000 between 2010 and 2014, and between 2015 and 2020. In that time, sales have rapidly increased to over 1 million copies. This proliferation of research has led to probably I only had modest Real world impact so far.
Hodgkinson said the vibrant force of several researchers whose work in the field of AI has been recognized by the Nobel Prize committee could begin to attract other researchers to the field, which could result in an increase in science. I think the quality may change. “Does the proposal or application have content?” [of AI] “That’s a different issue,” he says.
We have already seen the impact of media and public attention on AI on the academic community. According to , the number of publications on AI tripled between 2010 and 2022. Research by Stanford Universitynearly 250,000 papers were published in 2022 alone, with more than 660 new publications per day. That was before the generative AI revolution began with the release of ChatGPT in November 2022.
The extent to which academics are likely to submit to media attention, funding, and praise from the Nobel Prize committee is a question that worries Julian Togerius, an associate professor of computer science at New York University’s Tandon School of Engineering who works on AI. “Scientists generally follow the path of least resistance and most cost-effective combination of methods,” he says. And given the competitive nature of academia, where funding is increasingly scarce and is directly tied to researchers’ job prospects, as of this week, top performers have the potential to win a Nobel Prize. A combination of popular themes seems likely. You may want to resist.
The risk is that this can inhibit innovative new thinking. “It’s difficult to get more fundamental data from the natural world and come up with new theories that humans can understand,” Togelius says. But it requires deep thinking. It is much more productive for researchers to run AI-enabled simulations that support existing theory and include existing data, creating small hops rather than large leaps in understanding. You can. Togelius predicts that a new generation of scientists will eventually do just that because it’s easier.
Also, overconfident computer scientists who have contributed to advances in the field of AI begin to see their AI work winning Nobel Prizes in unrelated scientific fields (in this example, physics and chemistry). There is also a risk that we may decide to follow in their footsteps and compromise science and technology. someone else’s territory. “Computer scientists have a well-deserved reputation, for better or worse, for sticking their noses into fields they don’t know, introducing some kind of algorithm, and calling it progress,” Togelius says. He doesn’t know much about physics, biology, or geology, so he used deep learning to “advance” it into another scientific field before thinking much about it.
