Majority of AI Researchers Say Tech Industry Is Pouring Billions Into a Dead End

Founded in 1979, the Association for the Advancement of AI is an international scientific society. Recently 25 of its AI researchers surveyed 475 respondents in the AAAI community about "the trajectory of AI research" — and their results were surprising. Futurism calls the results "a resounding rebuff to the tech industry's long-preferred method of achieving AI gains" — namely, adding more hardware: You can only throw so much money at a problem. This, more or less, is the line being taken by AI researchers in a recent survey. Asked whether "scaling up" current AI approaches could lead to achieving artificial general intelligence (AGI), or a general purpose AI that matches or surpasses human cognition, an overwhelming 76 percent of respondents said it was "unlikely" or "very unlikely" to succeed... "The vast investments in scaling, unaccompanied by any comparable efforts to understand what was going on, always seemed to me to be misplaced," Stuart Russel, a computer scientist at UC Berkeley who helped organize the report, told New Scientist. "I think that, about a year ago, it started to become obvious to everyone that the benefits of scaling in the conventional sense had plateaued...." In November last year, reports indicated that OpenAI researchers discovered that the upcoming version of its GPT large language model displayed significantly less improvement, and in some cases, no improvements at all than previous versions did over their predecessors. In December, Google CEO Sundar Pichai went on the record as saying that easy AI gains were "over" — but confidently asserted that there was no reason the industry couldn't "just keep scaling up." Cheaper, more efficient approaches are being explored. OpenAI has used a method known as test-time compute with its latest models, in which the AI spends more time to "think" before selecting the most promising solution. That achieved a performance boost that would've otherwise taken mountains of scaling to replicate, researchers claimed. But this approach is "unlikely to be a silver bullet," Arvind Narayanan, a computer scientist at Princeton University, told New Scientist. Read more of this story at Slashdot.

Mar 22, 2025 - 17:33

Majority of AI Researchers Say Tech Industry Is Pouring Billions Into a Dead End

Founded in 1979, the Association for the Advancement of AI is an international scientific society. Recently 25 of its AI researchers surveyed 475 respondents in the AAAI community about "the trajectory of AI research" — and their results were surprising. Futurism calls the results "a resounding rebuff to the tech industry's long-preferred method of achieving AI gains" — namely, adding more hardware: You can only throw so much money at a problem. This, more or less, is the line being taken by AI researchers in a recent survey. Asked whether "scaling up" current AI approaches could lead to achieving artificial general intelligence (AGI), or a general purpose AI that matches or surpasses human cognition, an overwhelming 76 percent of respondents said it was "unlikely" or "very unlikely" to succeed... "The vast investments in scaling, unaccompanied by any comparable efforts to understand what was going on, always seemed to me to be misplaced," Stuart Russel, a computer scientist at UC Berkeley who helped organize the report, told New Scientist. "I think that, about a year ago, it started to become obvious to everyone that the benefits of scaling in the conventional sense had plateaued...." In November last year, reports indicated that OpenAI researchers discovered that the upcoming version of its GPT large language model displayed significantly less improvement, and in some cases, no improvements at all than previous versions did over their predecessors. In December, Google CEO Sundar Pichai went on the record as saying that easy AI gains were "over" — but confidently asserted that there was no reason the industry couldn't "just keep scaling up." Cheaper, more efficient approaches are being explored. OpenAI has used a method known as test-time compute with its latest models, in which the AI spends more time to "think" before selecting the most promising solution. That achieved a performance boost that would've otherwise taken mountains of scaling to replicate, researchers claimed. But this approach is "unlikely to be a silver bullet," Arvind Narayanan, a computer scientist at Princeton University, told New Scientist.