Ex-Google CEO Compares AI as Dangerous as Nuclear Weapons

Eric Schmidt, who used to be the CEO of Google, recently said that artificial intelligence (AI) is like nuclear weapons. Mr. Schmidt talked about his job at Google and what was going on 20 years ago when he spoke at the Aspen Security Forum on Friday.

In the early days of Google, the man who used to run it said that he was “naive” about the power of information. He then said that technology should better match the ethics and morals of the people it helps, and he compared AI to nuclear weapons.

Mr. Schmidt thought that in the near future, the US and China would need to talk about an AI agreement. “We eventually came up with a “no surprise” rule for nuclear tests in the 1950s and 1960s, and they were eventually banned,” Mr. Schmidt said.

"We are not ready for the negotiations that we need." – @ericschmidt #AspenSecurity pic.twitter.com/As749t6ZyU

— Aspen Security Forum (@AspenSecurity) July 22, 2022

“It’s an example of a balance of trust or a lack of trust; it’s a “no surprises” rule. I’m very worried that the way people in the US see China as corrupt or Communist or whatever, and the way people in China see the US as failing, will lead people to say, “Oh my god, they’re up to something,” and then start some kind of mess. Start something that, when you’re getting ready or arming it, sets off the other side. We don’t have anyone working on that, but AI is that powerful “the former head of Google said.

In simpler terms, Mr. Schmidt is worried that there is no plan for how AI is made or used. He is worried that this could make it more likely that artificial intelligence will be used in bad ways.

Notably, AI’s abilities have been talked about many times over the years. Elon Musk, the CEO of Tesla, told the Independent that AI is likely to be a threat to humans. Recently, Google fired a software engineer who said that the company’s AI had become self-aware and intelligent.

But experts have said that the problem with AI is what it is taught to do and how people use it. This means that if the algorithms that train these systems use bad data, the results will show that.

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