You see this "pulling up the ladder after you've ascended" behavior in mature industries, even in licensing requirements for jobs such as hair-braiding. To see it in emergent—not new, we've been tracking AI on the blog for over a decade,* emergent—technology is one more aspect of the "rich get richer" flywheel effects. In this case, those who have the money to lobby (or buy into Anthropic or OpenAI or like Elon, build your own supercomputer) **
From Business Insider via Yahoo Finance, October 31:
- Big Tech is lying about some AI risks to shut down competition, a Google Brain cofounder has said.
- Andrew Ng told The Australian Financial Review that tech leaders hoped to trigger strict regulation.
- Some large tech companies didn't want to compete with open source, he added.
A leading AI expert and Google Brain cofounder said Big Tech companies were stoking fears about the technology's risks to shut down competition.
Google Brain was a deep-learning AI research team that merged with the DeepMind division earlier this year.
Andrew Ng, an adjunct professor at Stanford University who taught OpenAI CEO Sam Altman, told The Australian Financial Review that the biggest tech companies hoped to trigger strict regulation with the "bad idea that AI could make us go extinct."
"There are definitely large tech companies that would rather not have to try to compete with open source, so they're creating fear of AI leading to human extinction," he told the news outlet. "It's been a weapon for lobbyists to argue for legislation that would be very damaging to the open-source community."
In May, AI experts and CEOs signed a statement from the Center for AI Safety that compared the risks posed by AI with nuclear war and pandemics. OpenAI CEO Sam Altman, DeepMind CEO Demis Hassabis, and Anthropic CEO Dario Amodei all put their names to the public statement.
Other AI heavyweights have issued several warnings about the accelerated development of advanced generative AI models, with many urging regulators to act quickly....
I don't know if it is going to work out as well as 2013's "Why Is Machine Learning (CS 229) The Most Popular Course At Stanford?"—which was followed by 2014's Deep Learning is VC Worthy—which was followed by 2015 to date "Saaaay, this Nvidia may be on to something."
But we shall see....
