Existential risk from AI is admittedly more speculative than pressing concerns such as its bias, but the basic solution is the same. A robust public discussion is long overdue, says David Krueger
IN A recent White House press conference, press secretary Karine Jean-Pierre couldn’t suppress her laughter at the question: Is it “crazy” to worry that “literally everyone on Earth will die” due to artificial intelligence? Unfortunately, the answer is no.
While AI pioneers such as Alan Turing cautioned that we should expect “machines to take control”, many contemporary researchers downplay this concern. In an era of unprecedented growth in AI abilities, why aren’t more experts weighing in?
Before the deep-learning revolution in 2012, I didn’t think human-level AI would emerge in my lifetime. I was familiar with arguments that AI systems would insatiably seek power and resist shutdown – an obvious threat to humanity if it were to occur. But I also figured researchers must have good reasons not to be worried about human extinction risk (x-risk) from AI.
Yet after 10 years in the field, I believe the main reasons are actually cultural and historical. By 2012, after several hype cycles that didn’t pan out, most AI researchers had stopped asking “what if we succeed at replicating human intelligence?”, narrowing their ambitions to specific tasks like autonomous driving.
When concerns resurfaced outside their community, researchers were too quick to dismiss outsiders as ignorant and their worries as science fiction. But in my experience, AI researchers are themselves often ignorant of arguments for AI x-risk.
One basic argument is by analogy: humans’ cognitive abilities allowed us to outcompete other species for resources, leading to many extinctions. AI systems could likewise deprive us of the resources we need for our survival. Less abstractly, AI could displace humans economically and, through its powers of manipulation, politically.
How this moment for AI will change society forever (and how it won't)
There is no doubt that the latest advances in artificial intelligence from OpenAI, Google, Baidu and others are more impressive than what came before, but are we in just another bubble of AI hype?
But wouldn’t it be humans wielding AIs as tools who end up in control? Not necessarily. Many people might choose to deploy a system with a 99 per cent chance of making them phenomenally rich and powerful, even if it had a 1 per cent chance of escaping their control and killing everyone.
Because no safe experiment can definitively tell us whether an AI system will actually kill everyone, such concerns are often dismissed as unscientific. But this isn’t an excuse for ignoring the risk. It just means society needs to reason about it in the same way as other complex social issues. Researchers also emphasise the difficulty of predicting when AI might surpass human intelligence, but this is an argument for caution, not complacency.
Attitudes are changing, but not quickly enough. AI x-risk is admittedly more speculative than important social issues with present-day AI, like bias and misinformation, but the basic solution is the same: regulation. A robust public discussion is long overdue. By refusing to engage, some AI researchers are neglecting ethical responsibilities and betraying public trust.
Big tech sponsors AI ethics research when it doesn’t hurt the bottom line. But it is also lobbying to exclude general-purpose AI from EU regulation. Concerned researchers recently called for a pause on developing bigger AI models to allow society to catch up. Critics say this isn’t politically realistic, but problems like AI x-risk won’t go away just because they are politically inconvenient.
This brings us to the ugliest reason researchers may dismiss AI x-risk: funding. Essentially every AI researcher (myself included) has received funding from big tech. At some point, society may stop believing reassurances from people with such strong conflicts of interest and conclude, as I have, that their dismissal betrays wishful thinking rather than good counterarguments.
David Krueger is an assistant professor in machine learning at the University of Cambridge