Pausing AI development might go wrong. How to mitigate the risks?
We’re pushing for a pause of the development of large, general AI models. See our proposal for more details.
This measure is not without risks. In this article, we’ll address some of these risks and how to mitigate them.
Pausing too early
If an AI pause happens before the risks are large enough, we might miss out on the benefits of AI. Ultimately, we need to balance the risks with the costs of pausing.
In our view, the chance that AI will cause catastrophic risks soon is already large enough to warrant a pause at this moment. As stated by Stuart Russell, when faced with an uncertain deadline, one should take the action that would be optimal given the shortest time constraint.
Also, pausing early give us breathing room for the enforcement of the pause to not be perfect and to other improvements hard to regulate, like algorithmic breakthroughs, to not make us all fall into the precipice.
Pausing for too short / only 6 months
The pause we propose is of indeterminate length. We shouldn’t unpause until there is a good enough consensus that we know how to develop aligned AIs, not matter how powerful they are, and that we have the systems in place to do it carefully and democratically. It is NOT like the six-month pause asked by the open letter published by the Future of Life Institute .
Pausing for too long
Nick Bostrom, one of the first AI alarmists, worries that at some point we might worry too much about AI risks , although that moment is not yet here. If concern about AI keeps rising, and we get a Pause, it might lead to a situation where the entirety of AI development becomes taboo or illegal. If that happens, we will never reap the benefits of AI, and in the meantime, we might encounter other existential risks that we could have avoided with the help of AI.
We can address this risk by clearly stating under what conditions AI development should resume. As we said, we suggest that AI development should resume when building provably safe AI becomes possible. Additionally, we only propose to ban the development of very specific kinds of models: only the largest, general models.
Centralization of AI might make takeover risks worse
Some proposals, like ”MAGIC ”, suggest that we should centralize AI development in a single organization. This makes AI development more controllable, but it also creates a single point of failure. But this is not a PauseAI proposal.
National/local pauses might fail
If one country pauses AI development, other countries will continue to develop AI. We might end up in a world where the first AGI is developed by a non-cooperative actor, which is likely to be a bad outcome. The incentives to pause individually are weak, because the benefits of AI development are large, and the risks of AI development are global. This is a classic tragedy of the commons situation.
The solution to this is to make the pause international through a treaty, which is what we’re proposing. This also requires a strong enforcement mechanism. Countries that don’t comply with the treaty should be punished. Economic sanctions may be sufficient, but military intervention might be necessary in extreme cases.
AI development might go underground
If AI development (above a certain threshold) is banned, it might go underground. The potential benefits are so large that a rogue (state) actor might decide to develop AI in secret. That means the first to achieve superintelligence would be a non-cooperative actor, which is likely to be a bad outcome.
By tracking GPU sales, we can detect at least some underground AI development.
Western powers (US, Netherlands and Taiwan), control the GPU supply chain strongly enough to prevent uncooperative states from obtaining GPUs. Non-state actors are unlikely to be able to gather sufficient resources in secret to train an AGI for at least a decade after AGI becomes possible by Big Tech companies. Also, the fact that there no longer is a business incentive would help to reduce the amount of underground AI development.
Hardware overhang could cause a fast takeoff
If we don’t include hardware R&D in the pause, the price-performance of GPUs will continue to double every 2.5 years, as it did between 2006 and 2021. This means AI systems will get at least 16x faster after ten years and 256x faster after twenty years, simply due to better hardware. If the pause is lifted all at once, these hardware improvements would immediately become available for training more powerful models more cheaply—a hardware overhang. This would cause a rapid and fairly discontinuous increase in AI capabilities, potentially leading to a fast takeoff scenario and all the risks it entails.
This is a serious concern, although there are strong arguments to be made that overhang is unlikely to occur .
PauseAI supports a pause on relevant compute improvements as well. Also, as we said, the ‘play’ button should not be pressed if we still don’t know how to build safe AI. And that includes the training and deployment of models with more advanced hardware.
AI development is necessary for learning how to make AIs safe
Most people believe that some level of prosaic/ incremental alignment is necessary, so if a full pause without exceptions is implemented, not enough progress on alignment would be done and eventually actors who wouldn’t care about safety and the pause would develop an unaligned powerful AI.
That’s one reason we propose having a way of approving certain training runs. That would let us learn from bigger systems if we can assure their safety. However, in the worst case in which we can’t assure their safety and progressing enough in alignment, we still have the option of trying to augment our intelligence via other technologies.
Algorithmic or runtime improvements may make smaller models dangerous, too
We’ve seen that changes in training data, training algorithms, or runtime usage can lead to large improvements in model performance. That’s why we’re not just focusing on model size. We’re proposing to pause the development of large, general AI models that are either 1) larger than 10^12 parameters, 2) have more than 10^25 FLOPs used for training or 3) capabilities that are expected to exceed GPT-4. This third condition is added to also include smaller models that can be dangerous. Enforcing a cap on capabilities is tricky, as it is hard to predict the capabilities of a model before it is trained.
Since the stakes are so high, we should be cautious, so we also support a pause on relevant algorithmic and runtime improvements. However, enforcing this will be more difficult than enforcing compute regulations, because hardware is easier to trace than software.
If we only ban general AI models, we might still get AGI through narrow models
We want to restrict dangerous models that can do things like manipulate humans, plan strategically and write code. We don’t want to restrict very narrow models, like image classifiers used in self-driving cars or medical diagnosis. However, this leads to a difficult situation where there are strong incentives to stretch the definition of “narrow” AI.
Therefore, we haven’t defined “narrow” AI in our proposal, but instead, we use the three conditions mentioned above.
We will end up with less safety-minded organizations if we pause
If you dive into the history of OpenAI, DeepMind and Anthropic, you will find that all of them have been founded by people who are quite worried about AI risks. In a way, we are lucky that the biggest AI companies right now have AI safety as part of their culture. Maybe a pause gives a large number of companies the time to catch up, which could lead to a large group of companies that are less safety-minded.
If we were asking for a time-based pause, this would be a fair concern. But what we’re asking for is a pause until we can prove AI can be built safely, so we should not end up with organizations building unsafe AI after the pause is lifted.
If a pause is implemented, we should expect a political compromise
We have a specific proposal that we think is optimal. However, we should not expect to have our proposal implemented exactly as we want it. Politics is messy and unpredictable, so we should expect our lobbying efforts to have vaguely directional effects, rather than precise effects. If we get some form of a Pause, but it’s not exactly what we want, this might end up being worse than having no pause at all. For example:
- A national pause that would let potentially worse actors get to AGI first
- An international pause that is not enforced properly, leading to a similar outcome
We can mitigate this by being consistent and clear in our communications about what we want.
Pausing too late
This is the most obvious and most likely failure risk: if we pause too late, we are likely to encounter catastrophic risks. And that could happen soon, as we explain in our urgency page.
This is why we need your help pushing for a pause right now .