Thanks for the post! I was interested in what the difference between “Semiconductor industry amortize their R&D cost due to slower improvements” and “Sale price amortization when improvements are slower” are. Would the decrease in price stem from the decrease in cost as companies no longer need to spend as much on R&D?
Thanks! What happens to your doubling times if you exclude the outliers from efficient ML models?
I really appreciated the extension on “AI and Compute”. Do you have a sense of the extent to which your estimate of the doubling time differs from “AI and Compute” stems from differences in selection criteria vs new data since its publication in 2018? Have you done analysis on what the trend looks like if you only include data points that fulfil their inclusion criteria?
For reference, it seems like their criteria is ”… results that are relatively well known, used a lot of compute for their time, and gave enough information to estimate the compute used.” Whereas yours is “important publication within the field of AI OR lots of citations OR performance record on common benchmark”. ”… used a lot of compute for their time” would probably do a whole lot of work to select data points that will show a faster doubling time.
Thanks for this! I really look forward to seeing the rest of the sequence, especially on the governance bits.
Came here to say the same thing :)
Thanks for the question. I agree that managing these kinds of issues is important and we aim to do so appropriately.
GovAI will continue to do research on regulation. To date, most of our work has been fairly foundational, though the past 1-2 years has seen an increase in research that may provide some fairly concrete advice to policymakers. This is primarily as the field is maturing, as policymakers are increasingly seeking to put in place AI regulation, and some folks at GovAI have had an interest in pursuing more policy-relevant work.
My view is that most of our policy work to date has been fairly (small c) conservative and has seldom passed judgment on whether there should be more or less regulation and praising specific actors. You can sample some of that previous work here:
We’re not yet decided on how we’ll manage potential conflicts of interest. Thoughts on what principles are welcome. Below is a subset of things that are likely to be put in place:
We’re aiming for a board that does not have a majority of folks from any of: industry, policy, academia.
Allan will be the co-lead of the organisation. We hope to be able to announce others soon.
Whenever someone has a clear conflict of interest regarding a candidate or a piece of research – say we were to publish a ranking of how responsible various AI labs were being – we’ll have the person recuse themselves from the decision.
For context, I expect most folks who collaborate with GovAI to not be directly paid by GovAI. Most folks will be employed elsewhere and not closely line managed by the organization.
Thanks! I agree that using a term like “socially beneficial” might be better. On the other hand, it might be helpful to couch self-governance proposals in terms of corporate social responsibility, as it is a term already in wide use.
Some brief thoughts (just my quick takes. My guess is that others might disagree, including at GovAI):
Overall, I think the situation is quite different compared to 2018, when I think the talk was recorded. AI governance / policy issues are much more prominent in the media, in politics, etc. The EU Commission has proposed some pretty comprehensive AI legislation. As such, there’s more pressure on companies as well as governments to take action. I think there’s also better understanding of what AI policy is sensible. All these things update me against 1 (insofar as we are still in the formative stages) and 2. They also update me in favour of thinking something like: governments will want to take a bunch of actions related to AI and so we should try to steer those actions in positive directions.
I think the AI policy / governance field is mature enough at this point that it’s not that helpful to think of an AI governance regime as one unitary thing. I much prefer thinking about specific areas of AI governance. Depending on the area, I’d likely have different views on 1-3. For example, it seems likely that companies are best placed to help develop standards that may be used to inform legislation further down the line. I wouldn’t expect companies to be best placed to figure out what the US should do wrt updates to antitrust regulation.
On 3, I think it’s true that companies have incentives in favour of acting prosocially and that we can boost these incentives. I’m not sure those incentives outweigh their other incentives, though. The view is not that e.g. Facebook, Amazon, Google, are all-things-considered going to act in the public interest. I also don’t think Jade-2018 held that view.
Happy to give my view. Could you say something about what particular views or messages you’re curious about? (I don’t have time to reread the script atm)
Thanks Michael! Yeah, I hope it ends up being helpful.
I’m really excited to see LTFF being in a position to review and make such a large number of grants. IIRC, you’re planning on writing up some reflections on how the scaling up has gone. I’m looking forward to reading them!
Thanks for pointing that out, Michael! Super helpful.
You can find the talk here.
Thanks for the catch :) Should be updated now
Hello, I work at the Centre for the Governance of AI at FHI. I agree that more work in this area is important. At GovAI, for instance, we have a lot more talented folks interested in working with us than we have absorptive capacity. If you’re interested in setting something up at MILA, I’d be happy to advice if you’d find that helpful. You could reach out to me at email@example.com
That’s exciting to hear! Is your plan still to head into EU politics for this reason? (not sure I’m remembering correctly!)
To make it maximally helpful, you’d work with someone at FHI in putting it together. You could consider applying for the GovAI Fellowship once we open up applications. If that’s not possible (we do get a lot more good applications than we’re able to take on) getting plenty of steer / feedback seems helpful (you can feel to send it past myself). I would recommend spending a significant amount of time making sure the piece is clearly written, such that someone can quickly grasp what you’re saying and whether it will be relevant to their interests.
It definitely seems true that if I want to specifically figure out what to do with scenario a), studying how AI might affect structural inequality shouldn’t be my first port of call. But it’s not clear to me that this means we shouldn’t have the two problems under the same umbrella term. In my mind, it mainly means we ought to start defining sub-fields with time.
A first guess at what might be meant by AI governance is “all the non-technical stuff that we need to sort out regarding AI risk”. Wonder if that’s close to the mark?
A great first guess! It’s basically my favourite definition, though negative definitions probably aren’t all that satisfactory either.
We can make it more precise by saying (I’m not sure what the origin of this one is, it might be Jade Leung or Allan Dafoe):
AI governance has a descriptive part, focusing on the context and institutions that shape the incentives and behaviours of developers and users of AI, and a normative part, asking how should we navigate a transition to a world of advanced artificial intelligence?
It’s not quite the definition we want, but it’s a bit closer.