2min chapter

#155 – Lennart Heim on the compute governance era and what has to come after

80,000 Hours Podcast

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CHAPTER

How to Measure Algorithmic Efficiency in Computer Vision

In recent years we've seen very rapid improvements in algorithmic efficiency so with the same amount of compute and data you're able to get significantly improved performance. So i'm excited about what's going to be happening in the future it's like we have to say and this is only for computer vision I hope we have more analysis on there in the future.

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Transcript

Episode notes

As AI advances ever more quickly, concerns about potential misuse of highly capable models are growing. From hostile foreign governments and terrorists to reckless entrepreneurs, the threat of AI falling into the wrong hands is top of mind for the national security community.

With growing concerns about the use of AI in military applications, the US has banned the export of certain types of chips to China.

But unlike the uranium required to make nuclear weapons, or the material inputs to a bioweapons programme, computer chips and machine learning models are absolutely everywhere. So is it actually possible to keep dangerous capabilities out of the wrong hands?

In today's interview, Lennart Heim — who researches compute governance at the Centre for the Governance of AI — explains why limiting access to supercomputers may represent our best shot.

Links to learn more, summary and full transcript.

As Lennart explains, an AI research project requires many inputs, including the classic triad of compute, algorithms, and data.

If we want to limit access to the most advanced AI models, focusing on access to supercomputing resources -- usually called 'compute' -- might be the way to go. Both algorithms and data are hard to control because they live on hard drives and can be easily copied. By contrast, advanced chips are physical items that can't be used by multiple people at once and come from a small number of sources.

According to Lennart, the hope would be to enforce AI safety regulations by controlling access to the most advanced chips specialised for AI applications. For instance, projects training 'frontier' AI models — the newest and most capable models — might only gain access to the supercomputers they need if they obtain a licence and follow industry best practices.

We have similar safety rules for companies that fly planes or manufacture volatile chemicals — so why not for people producing the most powerful and perhaps the most dangerous technology humanity has ever played with?

But Lennart is quick to note that the approach faces many practical challenges. Currently, AI chips are readily available and untracked. Changing that will require the collaboration of many actors, which might be difficult, especially given that some of them aren't convinced of the seriousness of the problem.

Host Rob Wiblin is particularly concerned about a different challenge: the increasing efficiency of AI training algorithms. As these algorithms become more efficient, what once required a specialised AI supercomputer to train might soon be achievable with a home computer.

By that point, tracking every aggregation of compute that could prove to be very dangerous would be both impractical and invasive.

With only a decade or two left before that becomes a reality, the window during which compute governance is a viable solution may be a brief one. Top AI labs have already stopped publishing their latest algorithms, which might extend this 'compute governance era', but not for very long.

If compute governance is only a temporary phase between the era of difficult-to-train superhuman AI models and the time when such models are widely accessible, what can we do to prevent misuse of AI systems after that point?

Lennart and Rob both think the only enduring approach requires taking advantage of the AI capabilities that should be in the hands of police and governments — which will hopefully remain superior to those held by criminals, terrorists, or fools. But as they describe, this means maintaining a peaceful standoff between AI models with conflicting goals that can act and fight with one another on the microsecond timescale. Being far too slow to follow what's happening -- let alone participate -- humans would have to be cut out of any defensive decision-making.

Both agree that while this may be our best option, such a vision of the future is more terrifying than reassuring.

Lennart and Rob discuss the above as well as:

How can we best categorise all the ways AI could go wrong?
Why did the US restrict the export of some chips to China and what impact has that had?
Is the US in an 'arms race' with China or is that more an illusion?
What is the deal with chips specialised for AI applications?
How is the 'compute' industry organised?
Downsides of using compute as a target for regulations
Could safety mechanisms be built into computer chips themselves?
Who would have the legal authority to govern compute if some disaster made it seem necessary?
The reasons Rob doubts that any of this stuff will work
Could AI be trained to operate as a far more severe computer worm than any we've seen before?
What does the world look like when sluggish human reaction times leave us completely outclassed?
And plenty more

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