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AI progress depends on us using less data, not more

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In the data science community, we’re witnessing the beginnings of an infodemic — where more data becomes a liability rather than an asset. We’re continuously moving towards ever more data-hungry and more computationally expensive state-of-the-art AI models. And that is going to result in some detrimental and perhaps counter-intuitive side-effects (I’ll get to those shortly).

To avoid serious downsides, the data science community has to start working with some self-imposed constraints: specifically, more limited data and compute resources.

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.

When data becomes a curse rather than a