[HN Gopher] Scribble-based forecasting and AI 2027
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Scribble-based forecasting and AI 2027
Author : venkii
Score : 50 points
Date : 2025-06-30 16:15 UTC (6 hours ago)
(HTM) web link (dynomight.net)
(TXT) w3m dump (dynomight.net)
| keeganpoppen wrote:
| this is actually quite brilliant. and articulates the value and
| utility of subjective forecasting-- something i too find somewhat
| underrated-- extremely clearly and convincingly. and same goes
| for the biases we have toward reducing things to a mathematical
| model and then treating that model as more "credible" despite
| there being (1) an infinite universe of possible models, so you
| can use them to "say" whatever you want anyway and (2) it
| complects the thing being modeled with some mathematical
| phenomenon, which is not always a profitable approach.
|
| the scribble method is, of course, quite sensitive to the number
| of hypotheses you choose to consider, as it effectively considers
| them all to be of equal probability, but it also surfaces a lot
| of interesting interactions between different hypotheses that
| have nothing to do with each other, but still have effectively
| the "same" prediction at various points in time. and i don't see
| any reason that you can't just be thoughtful about what "shapes"
| you choose to include and in what quantity-- basically like a
| meta-subjective model of which models are most likely or
| something haha. that said, there's also some value in the low-res
| aspect of just drawing the line-- you can articulate exactly what
| path you are thinking without having to pin that thinking to some
| model that doesn't actually add anything to the prediction other
| than fitting the same shape as what is in your mind.
| groby_b wrote:
| At least for me, the core criticism of AI 2027 was always that it
| was an extremely simplistic "number go up, therefore AGI", with
| some nice fiction-y words around it.
|
| The scribble model kind-of hints at what a better forecast
| would've done - you start from the scribbles and ask "what would
| it take to get that line, and how'd we get there". And I love
| that the initial set of scribbles will, amongst other things,
| expose your biases. (Because you draw the set of scribbles that
| seems plausible to you, a priori)
|
| The fact that it can both guide you towards exploring
| alternatives and exposing biases, while being extremely simple -
| marvellous work.
|
| Definitely going to incorporate this into my reasoning toolkit!
| ben_w wrote:
| To me, 2027 looks like a case of writing the conclusion first
| and then trying to explain backwards how it happens.
|
| If _everything_ goes "perfectly", then the logic works (to an
| extent, but the increasing rate of returns is a suspicious
| assumption baked into it).
|
| But everything _must_ go perfectly to do that, including all
| the productivity multipliers being independent _and_ the USA
| deciding to take this genuinely seriously (not fake seriously
| in the form of politicians saying "we're taking this
| seriously" and not doing much), and therefore no-expenses-
| spared rush the target like it's actually an existential
| threat. I see no way this would be a baseline scenario.
| MarkusQ wrote:
| Another useful trick: plot the same data several ways (e.g. if
| you were playing with Moore's law you might plot (log)
| {transistors/cm2,"ops/sec","clock speed","ops/sec/$" etc.} their
| inverses vs time, as well as things like "how many digits of p
| can you compute for $1", "multiples of total world compute in
| 1970") and do the same extrapolation trick on each.
|
| You _should_ expect to see roughly comparable results, but often
| you don't and when you don't it can reveal hidden
| assumptions/flawed thinking.
| crabl wrote:
| Interesting! My first thought looking at the scribble chart was
| "isn't this Monte Carlo simulation?" but reading further it seems
| more aligned with the "third way" that William Briggs describes
| in his book Uncertainty[1]. He argues we should focus on direct
| probability statements about observables over getting lost in
| parameter estimation or hypothesis testing.
|
| ^[1]: https://link.springer.com/book/10.1007/978-3-319-39756-6
| empiko wrote:
| To be honest, I expected the punchline to be about how randomly
| drawing lines is the same nonsense as using simplistic
| mathematical modeling without considering the underlying
| phenomenon. But the punchline never came.
|
| Predicting AI is more or less impossible because we have no idea
| about the its properties. With other technologies, we can reason
| about how small or how how a component can get and this gives us
| psychical limitations that we can observe. With AI we throw in
| data and we are or we are not surprised by the behavior the model
| exhibits. With a few datapoints we have, it seems that more
| compute and more data usually lead to better performance, but
| that is more or less everything we can say about it, there is no
| theory behind it that would guarantee us the gains for the next
| 10x.
| Fraterkes wrote:
| Im sorry, I think the line scribbling idea is neat but the most
| salient part of this prediction (how longs this going to take)
| depends utterly on the scale of the x-axis. If you made x go to
| 2200 instead of 2050 you could overlay the exact same set of
| "plausible" lines.
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