The AGI hype train is running out of steam
The AGI train hype has hit some heavy traffic.
While messengers and fundraisers would make bullish predictions about fake general intelligence, they have been quieter lately. Peter Thiel - the tech billionaire and rumored vampire - says Silicon Valley's big brains have lost enthusiasm for AGI.
"Elon is no longer talking about it and Larry [Page] off to Fiji and they don't seem to be working so hard, ”said Thiel at a recent event.
Thiel described Musk as a “weatherman for the zeitgeist,” who has stopped talking about AGI because interest has waned.
Scientists are also increasingly skeptical. A recent inspection paper suggested that AGI is "in principle impossible," while other researchers have ridiculed supporters of the term.
"I have not yet entered a job on AGI that I can really take on," said Abeba Birhane, a psychologist based at University College Dublin.
I have not yet come across a work on AGI that I can really take on pic.twitter.com/Gjs1QM8BW2
- Abeba Birhane (@Abebab) November 14, 2022
The path to AGI shows more and more - at best - a long one.
Interesting devices with human-like information are still very challenging. As Melanie Mitchell, a professor of computer science at Portland State University, said in an introductory paper published last year on arXiv:
Since its inception in the 1950s, the artificial intelligence field has cycled several times between periods of optimistic forecasting and large investment ('AI spring') and times of disappointment, loss of confidence, and reduced funding ('AI winter'). . Even with today's fast paced AI breakthrough, the development of long - promised technologies such as self - driving cars, home robots, and chat companions has been far more difficult than many expected. people. One reason for these repetitive cycles is our limited understanding of the nature and complexity of information itself.
Critics fear another winter of AI is coming. Hyping AGI has helped to encourage major investment in artificial intelligence, but predicting predictions could be devastating in the future - if the world's advances proof of disappointment.