I want to highlight something that I missed on the first read but nagged me on the second read.
You define transformative AGI as:
1. Gross world product (GWP) exceeds 130% of its previous yearly peak value2. World primary energy consumption exceeds 130% of its previous yearly peak value3. Fewer than one billion biological humans remain alive on Earth
You predict when transformative AGI will arrive by building a model that predicts when we'll have enough compute to train an AGI.But I feel like there's a giant missing link - what are the odds that training an AGI causes 1, 2, or 3?
It feels not only plausible but quite likely to me that the first AGI will be very expensive and very uneven (superhuman in some respects and subhuman in others). An expensive, uneven AGI may years or decades to self-improve to the point that GWP or energy consumption rises by 30% in a year.
It feels like you are implicitly ascribing 100% probability to this key step.This is one reason (among others) that I think your probabilities are wildly high. Looking forward to setting up our bet. :)
Any update on when "early 2023" will be?
Absolutely. A few comments:
I work at Netflix on the recommender. It's interesting to read this abstract article about something that's very concrete for me.
For example, the article asks, "The key question any model of the problem needs to answer is - why aren’t recommender systems already aligned."
Despite working on a recommender system, I genuinely don't know what this means. How does one go about measuring how much a recommender is aligned with user interests? Like, I guarantee 100% that people would rather have the recommendations given by Netflix and YouTube than a uniform random distribution. So in that basic sense, I think we are already aligned. It's really not obvious to me that Netflix and YouTube are doing anything wrong. I'm not really sure how to go about measuring alignment, and without a measurement, I don't know how to tell whether we're making progress toward fixing it.
My two cents.