Steven Byrnes comments on Dissolving the Deep Learning Sample Efficiency Gap

[Steven Byrnes]

What people take this to mean varies widely. Steven Byrnes appears to read the gap as evidence that current algorithms are far from what the brain is doing, such that much better algorithms must be waiting to be found. His guess is that human-level, human-speed AGI will require not a datacenter but “one consumer gaming GPU,” even for training from scratch. Yarrow Bouchard on the EA Forum, reads the same gap as evidence that AGI isn’t close at all, precisely because nobody knows how to close it. Nearly opposite conclusions from the same starting observation.

I’m confused, these don’t sound “nearly opposite” to me, they sound very compatible. Did you misread something? Or maybe you’re noticing that Yarrow & I have opposite vibe and emphasis, even when we’re saying basically the same thing?

(I very strongly disagree with Yarrow about all kinds of things, but I don’t think this paragraph is pointing to an example. Here’s an example where I was partly agreeing and partly disagreeing with Yarrow on something in the vicinity of this topic.)

[Samuel Knoche]

Hmm, yes, that was poorly phrased. You both think we need a new paradigm, and that something fundamental is missing, but your conclusion as to what it means for how worried we should be is opposite.

Changed that last sentence to:

> They both agree that current algorithms are missing something fundamental, yet draw opposite conclusions about what that means for AGI risk and timelines.