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A brand new AI lab known as Flapping Airplanes launched on Wednesday, with $180 million in seed funding from Google Ventures, Sequoia, and Index. The founding staff is spectacular, and the aim — discovering a much less data-hungry technique to prepare massive fashions — is a very attention-grabbing one.
Primarily based on what I’ve seen up to now, I’d charge them as Degree Two on the trying-to-make-money scale.
However there’s one thing much more thrilling concerning the Flapping Airplanes venture that I hadn’t been capable of put my finger on till I learn this submit from Sequoia accomplice David Cahn.
As Cahn describes it, Flapping Airplanes is without doubt one of the first labs to maneuver past scaling, the relentless buildout of knowledge and compute that has outlined a lot of the business up to now:
The scaling paradigm argues for dedicating an enormous quantity of society’s sources, as a lot because the economic system can muster, towards scaling up at this time’s LLMs, within the hopes that this may result in AGI. The analysis paradigm argues that we’re 2-3 analysis breakthroughs away from an “AGI” intelligence, and in consequence, we should always dedicate sources to long-running analysis, particularly tasks that will take 5-10 years to return to fruition.
[…]
A compute-first method would prioritize cluster scale above all else, and would closely favor short-term wins (on the order of 1-2 years) over long-term bets (on the order of 5-10 years). A research-first method would unfold bets temporally, and needs to be keen to make numerous bets which have a low absolute chance of working, however that collectively increase the search area for what is feasible.
It may be that the compute people are proper, and it’s pointless to concentrate on something apart from frenzied server buildouts. However with so many corporations already pointed in that route, it’s good to see somebody headed the opposite manner.
