Various parties concerned about the risks that artificial intelligence (AI) pose to humanity have called for a pause in its development. But such a call faces a difficult collective action problem.
Can it be solved?
I have been skeptical (and suggested an alternate type of approach to AI safety here). But a new report argues that a pause is indeed possible.
Drafted by the Working Group on AI Pause Feasibility, an interdisciplinary group led by statistician William Fithian and philosopher Wesley H. Holliday (both of Berkeley), the report‘s focus is:
a scenario in which world leaders would prefer pausing to not pausing if they could be confident that others were also pausing or that defections from a pause would be detected within a reasonable timeframe. In such a pause-willing scenario, is a pause for at least a decade feasible?
Their answer is that a “hardwired pause” is feasible. What is a hardwired pause?
A hardwired pause would stop the production of training-capable AI chips, which are needed to develop more powerful AI models. Over time a hardwired pause would also phase out most or all of the pre-pause stock of such chips. It would replace them with inference-only AI chips, which are capable of fast, energy-efficient AI inference on approved models but are not practically useful for training new frontier AI models, even if stolen or seized, due to the way the chips are constructed.
Such a pause is feasible, they argue, mainly because of two sets of facts. The first concerns chip production:
The supply chain for AI chips is highly concentrated with several chokepoints, bolstering governments’ abilities to enforce rules on the production of such chips. In addition, bypassing existing supply chains is extraordinarily difficult.
The second concerns the demands of training new models:
Because training new models requires large numbers of training-capable chips, the feasibility of verification is an inherently quantitative question: while it is next to impossible to detect an evader who is using a single computer to train a small AI model, it is far easier to detect an evader who is training a frontier-scale AI model using hundreds of thousands or millions of chips... To research and train a new model that would significantly advance the frontier after a hardwired pause, absent an unforeseen methodological breakthrough, an evader would need to marshal a significant fraction of the world’s total computing capacity for months or years at a time.
The above quotes are from the executive summary of the report. The full report is here, and a press release from Berkeley about it is here.
Discussion welcome.
(via Wesley Holliday)
So people talk about this as a collective action problem. But it isn’t actually clear that’s what’s going on. Based on their AI policy, I think China just doesn’t take the more extreme predictions seriously.
An additional problem. From what I can tell—though others should correct me if I’m wrong—the proposal would also make it impossible for people to fine tune their own open-weight models. It would also create a large bureaucratic obstacle for fixing alignment problems in existing models.
From what I can tell the proposed policy would also lock in US dominance of AI. I’m not sure why other countries would go along with this.