AI’s Nobel Win: Science Needs Better Data, Not Just Bigger Models
If AI is supposed to revolutionize science, why are we drowning it in garbage data?
David Baker, a biochemist and newly-minted Nobel laureate, warns that AI's impact on science will stall unless the data fed into these models improves. Alongside Demis Hassabis and John Jumper from Google DeepMind, Baker was awarded the Chemistry Nobel for AI tools revolutionizing protein research.
Their success relies heavily on the high-quality Protein Data Bank (PDB), a rare example of well-curated data essential for meaningful scientific progress. However, as AI models increasingly rely on bloated, internet-scraped datasets, the risk of producing biased, erroneous results grows.
The roadblock isn’t just model size but data quality:
- Garbage in, garbage out: AI outcomes depend on clean, curated inputs.
- Unique data sources: Few datasets match the PDB’s rigor and utility.
- AI's potential: New tools enable breakthroughs, but without solid data, progress falters.
As AI models scale, will science keep up by curating more high-quality data, or will noisy inputs undermine the breakthroughs we expect?
Read the full article on MIT Technology Review.
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