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Stephen McAleese

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Computer Science student from Ireland who's interested in AI safety research.

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A recent survey of AI alignment researchers found that the most common opinion on the statement "Current alignment research is on track to solve alignment before we get to AGI" was "Somewhat disagree". The same survey found that most AI alignment researchers also support pausing or slowing down AI progress.

Slowing down AI progress might be net-positive if you take ideas like longtermism seriously but it seems challenging to do given the strong economic incentives to increase AI capabilities. Maybe government policies to limit AI progress will eventually enter the Overton window when AI reaches a certain level of dangerous capability.

This is a cool project! Thanks for making it. Hopefully it makes the book more accessible.

Update: the UK government has announced £8.5 million in AI safety funding for systematic AI safety.

Thanks for writing this! It's interesting to see how MATS has evolved over time. I like all the quantitative metrics in the post as well.

I wrote a blog post in 2022 (1.5 years ago) estimating that there were about 400 people working on technical AI safety and AI governance.

In the same post, I also created a mathematical model which said that the number of technical AI safety researchers was increasing by 28% per year.

Using this model for all AI safety researchers, we can estimate that there are now  people working on AI safety.

I personally suspect that the number of people working on AI safety in academia has grown faster than the number of people in new EA orgs so the number could be much higher than this.

One argument for continued technological progress is that our current civilization is not particularly stable or sustainable. One of the lessons from history is that seemingly stable empires such as the Roman or Chinese empires eventually collapse after a few hundred years. If there isn't more technological progress so that our civilization reaches a stable and sustainable state, I think our current civilization will eventually collapse because of climate change, nuclear war resource exhaustion, political extremism, or some other cause.

Thanks for the writeup. I like how it's honest and covers all aspects of your experience. I think a key takeaway is that there is no obvious fixed plan or recipe for working on AI safety and instead, you just have to try things and learn as you go along. Without these kinds of accounts, I think there's a risk of survivorship bias and positive selection effects where you see a nice paper or post published and you don't get to see experiments that have failed and other stuff that has gone wrong.

I'm sad to hear that AISC is lacking in funding and somewhat surprised given that it's one of the most visible and well-known AI safety programs. Have you tried applying for grant money from Open Philanthropy since it's the largest AI safety grant-maker?

"In brief, the book [Superintelligence] mostly assumed we will manually program a set of values into an AGI, and argued that since human values are complex, our value specification will likely be wrong, and will cause a catastrophe when optimized by a superintelligence"

Superintelligence describes exploiting hard-coded goals as one failure mode which we would probably now call specification gaming. But the book is quite comprehensive, other failure modes are described and I think the book is still relevant.

For example, the book describes what we would now call deceptive alignment:

"A treacherous turn can result from a strategic decision to play nice and build strength while weak in order to strike later"

And reward tampering:

"The proposal fails when the AI achieves a decisive strategic advantage at which point the action which maximizes reward is no longer one that pleases the trainer but one that involves seizing control of the reward mechanism."

And reward hacking:

"The perverse instantiation - manipulating facial nerves - realizes the final goal to a greater degree than the methods we would normally use."

I don't think incorrigibility due to the 'goal-content integrity' instrumental goal has been observed in current ML systems yet but it could happen given the robust theoretical argument behind it:

If an agent retains its present goals into the future, then its present goals will be more likely to be achieved by its future self. This gives the agent a present instrumental reason to prevent alternations of its final goals."

Some information not included in the original post:

  • In April 2023, the UK government announced £100m in initial funding for a new AI Safety Taskforce.
  • In June 2023, UKRI awarded £31m to the University of Southhampton to create a new responsible and trustworthy AI consortium named Responsible AI UK.
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