I really enjoyed this article. My only critique is that the 2055 predictions are "meta-linear". In other words: the author avoids the (probable) mistake of taking our current tech and linearly regressing the numbers 33 years forward, but the predictions still suggest a kind of "worldline symmetry" with the present date at the origin.
It's quite possible that none of these predictions will come true simply because the timeframe is large enough for many unanticipated breakthroughs and roadblocks.
Maybe someone will figure out a much, much simpler foundational architecture than "perceptrons++", maybe we'll all be training clouds of 3D gaussians, maybe quantum computers will finally take off and we don't even have the nouns for the building blocks we'll use.
On the negative side perhaps we hit a hard scaling limit (in hardware or training) that we didn't see coming. Or a civilizational setback.
All that said, though, if I were a betting man I wouldn't exactly wager against the article's conclusions; they're probably the best we can extrapolate knowing only the past and present state of affairs.
I think you are right, the next 33 years are likely to be very different.
I would lean to them being even more dramatic, due to the opportunity to advance algorithms, not just resources.
On the more obvious side, most libraries are not yet taking full advantage of many known gradient optimization techniques. It’s been so much easier to just add data & processing that there is an overhangs of tools to still apply.
And large successful models are telling us important things.
For instance, it is clear that language models are learning a kind of logic of language similar to how we process thoughts, allowing highly disparate types information to be woven together sensibly.
At some point, identifying the nature of that processing could radically simplify language processing.
That is just one opportunity for radical architecture and algorithm advances, and it would be revolutionary.
It's quite possible that none of these predictions will come true simply because the timeframe is large enough for many unanticipated breakthroughs and roadblocks.
Maybe someone will figure out a much, much simpler foundational architecture than "perceptrons++", maybe we'll all be training clouds of 3D gaussians, maybe quantum computers will finally take off and we don't even have the nouns for the building blocks we'll use.
On the negative side perhaps we hit a hard scaling limit (in hardware or training) that we didn't see coming. Or a civilizational setback.
All that said, though, if I were a betting man I wouldn't exactly wager against the article's conclusions; they're probably the best we can extrapolate knowing only the past and present state of affairs.