Unlocking a Million Times More Data for AI Through Improved Data Accessibility
By
williamtrask
Hand-rolled, kettle-boiled, baked to perfection. Worth every minute at the bakery.
Summary
This article argues against the 'peak data' theory in AI development, proposing that we can unlock a million times more data through improved data accessibility. It suggests an ARPANET-style program to create a national data infrastructure that would make scientific and other valuable data more accessible for AI training, potentially accelerating AI progress significantly.
Key quotes
· 4 pulledEvery major leap forward in AI progress has been accompanied by a large increase in available data to support it.
AI leaders often warn that we have reached 'peak data' — that all human data for training AI has been exhausted.
Our analysis suggests that this view may not capture the whole picture.
How a new ARPANET-style program could solve the data accessibility problem
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