AI progress depends on us using less data, not more

A minimal-data practice will enable several AI-driven industries — including cyber security, which is my own area of focus — to become more efficient, accessible, independent, and disruptive.

1. AI has a compute addiction. The growing fear is that new advancements in experimental AI research, which frequently require formidable datasets supported by an appropriate compute infrastructure, might be stemmed due to compute and memory constraints, not to mention the financial and environmental costs of higher compute needs.

MIT researchers estimated that “three years of algorithmic improvement is equivalent to a 10 times increase in computing power.”

2. Big data can mean more spurious noise. 

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more on infodemic in this IMS blog
https://blog.stcloudstate.edu/ims?s=infodemic