Wiki Contributions


Forecasting Compute - Transformative AI and Compute [2/4]

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?

What is Compute? - Transformative AI and Compute [1/4]

Thanks! What happens to your doubling times if you exclude the outliers from efficient ML models?

What is Compute? - Transformative AI and Compute [1/4]

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.

Transformative AI and Compute [Summary]

Thanks for this! I really look forward to seeing the rest of the sequence, especially on the governance bits.

The Centre for the Governance of AI is becoming a nonprofit

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.
Some AI Governance Research Ideas

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. 

Jade Leung: Why companies should be leading on AI governance

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. 
Jade Leung: Why companies should be leading on AI governance

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)

Some AI Governance Research Ideas

Thanks Michael! Yeah, I hope it ends up being helpful. 

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