Jaime Sevilla

Director @ Epoch
4706 karmaJoined Mar 2019Working (0-5 years)


Director of Epoch, an organization investigating the future of Artificial Intelligence.

Currently working on:

  • Macroeconomic models of AI takeoff
  • Trends in Artificial Intelligence
  • Forecasting cumulative records
  • Improving forecast aggregation

I am also one of the coordinators of Riesgos Catastróficos Globales, a Spanish-speaking network of experts working on Global Catastrophic Risks.  

I also run Connectome Art, an online art gallery where I host art I made using AI.


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Riesgos Catastróficos Globales
Aggregating Forecasts
Forecasting quantum computing


Our team at Epoch recently updated the org's website.
I'd be curious to receive feedback if anyone has any!
What do you like about the design? What do you dislike?
How can we make it more useful for you?

Note that the AMF example does not quite work, because if each net has a 0.3% chance of preventing death, and all are independent, then with 330M nets you are >99% sure of saving at least ~988k people.

An encouraging update: thanks to the generous support of donors, we have raised $95k in funds to support our activities for six more months. During this time, we plan to 1) engage with the EU trilogue on the regulation of foundation models during the Spanish presidency of the EU council, 2) continue our engagement with policy markers in Argentina and 3) release a report on global risk management in latin america.

We nevertheless remain funding constrained. With more funding we would be able to launch projects such as:

  1. A report on prioritizing and forecasting global catastrophic risk that could be used by stakeholders such as the regional UNNDR office or CEPAL.
  2. Expand our previous work on mapping BSL-3 and BSL-4 labs in Latin America.
  3. Develop a theory of change for how latin america could contribute through multilateral negotiations to the management of AI risk, covering a study of the historic role of latam countries in the nuclear risk treaties and the management of other global risks.
  4. Further our relationship with Uruguay and other countries in the MERCOSUR to invite them to follow Argentina in the formulation of emergency plans for food security in case of nuclear winter.
  5. And other priority projects to improve the management of global catastophic risk in Spanish-Speaking countries.

Each of these projects could cost between $30k and $80k to develop. You can support us with a donation to help us develop these projects. You may also reach out to me through a PM if you are considering donating and want more information.

I have grips with the methodology of the article, but I don't think highlighting the geometric mean of odds over the mean of probabilities is a major fault.  The core problem is assuming independence over the predictions at each stage. The right move would have been to aggregate the total P(doom) of each forecaster using geo mean of odds (not that I think that asking random people and aggregating their beliefs like this is particularly strong evidence).

The intuition pump that if someone assigns a zero percent chance then the geomean aggregate breaks is flawed:

  1. There is an equally compelling pump the other way around: the arithmetic mean of probabilities defers unduly to people assigning a high chance. A single dissenter between 10 experts can bound the lower bound of the probability to their preferred up to a factor of 10.
  2.  And surely if anyone is assigning a zero percent chance to something, you can safely assume they are not taking the situation seriously and ignore them. 

In ultimate instance, we can theorize all we want, but as a matter of fact the best performance when predicting complex events is achieved when taking the geometric mean of odds, both in terms of logloss and brier scores. Without more compelling evidence or a very clear theoretical reason that distinguishes between the contexts, it seems weird to argue that we should treat AI risk differently. 

And if you are still worried about dissenters skewing the predictions, one common strategy is to winsorize, by clipping the predictions among the 5% and 95% percentile for example.

Yeah in hindisght that is probably about right.

It'd be interesting to look at some of these high profile journalists, and see if they are well supported to do impactful journalism or if they have to spend a lot of time on chasing trends to afford working on IJ pieces.

I looked into the impact of investigative journalism briefly a while back.

Here is an estimation of the impact of some of the most high profile IJ success stories I found:

How many investigative journalists exist? It's hard to say, but the International Federation of Journalists has 600k members, so maybe there exists 6M journalists worlwide, of which maybe 10% are investigative journalists (600k IJs). If they are paid like $50k/year, that's $30B used for IJ.

Putting both numbers together, that's $2k to $20k per person affected. Let's say that each person affected gains 0.1 to 10 QALYs for these high profile cases, then that's $200 to $200k per QALY.

Seems to not be competitive with global health interventions, which are around $60/QALY IIUC, though of course this is neglecting that IJ has many important cultural effects (but then again, so does curing children from malaria!). I could also be grossly overestimating how much money goes to investigative journalism, and of course I am neglecting that the marginal dollar is probably much much less impactful than the average dollar.

Do not take this two minute exercise too seriously though! I'd be keen on seeing a more careful approach to it.

What are some open questions in your mind, for potential GCR priorities you haven't had time to investigate?

What risks do you feel are particularly neglected by the EA community?

What opportunities are you most excited about for GCR mitigation outside the Anglosphere?

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