we find that the performance on held out tasks decreases dramatically. Whereas if we um just take out a random 20% of the data that's less diverse than the most diverse subset, the performance um only decreases a little bit. And so this suggests that actually having really diverse data plays an important role in enabling it to generalize to new tasks.
it's also enabling engineers who have seen the light and believe the curves are going to continue to go up to actually get together in groups of two and five and 10 and 20 and 30 people and start to do things that rival the output of these big companies that are tripping over themselves.
The Broken Earth Trilogy by NK Jemison (The Fifth Season, The Obelisk Gate, The Stone Sky): Triple-Hugo winner. Stunning world-building. Really fun to escape into a series—haven't done that in years.
The scale word gets a lot of attention in this. The interpretation that I use is effectively to avoid adding the human priors to your learning process. And if you read the original essay, this is what it talks about is how researchers will try to come up with clever solutions to their specific problem that might get them small gains in the short term while simply enabling these deep learning systems to work efficiently, and for these bigger problems in the long term might be more likely to scale and continue to drive success.
The problem with these grand ambitions—saving humanity from extinction and enabling our species to reach its full potential millions of years from now—is that they ostensibly justify any cost in the present.
You thus can see that the prosperity of the modern world does not refute Malthus. We faced the Malthusian dilemma and opted for one of his options, namely vice.
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