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AI-first Biotech

The Road to Biology 2.0 Will Pass Through Black-Box Data

This year marks perhaps the zenith of expectations for AI-based breakthroughs in biology, transforming it into an engineering discipline that is programmable, predictable, and replicable. Drawing insights from AI breakthroughs in perception, natural language, and protein structure prediction, we endeavour to pinpoint the characteristics of biological problems that are most conducive to being solved by AI techniques. Subsequently, we delineate three conceptual generations of bio AI approaches in the biotech industry and contend that the most significant future breakthrough will arise from the transition away from traditional “white-box” data, understandable by humans, to novel high-throughput, low-cost AI-specific “black-box” data modalities developed in tandem with appropriate computational methods.

Michael Bronstein
TDS Archive
Published in
46 min readMar 18, 2024
“Biology 2.0” imagined by DALL-E 3.

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TDS Archive
TDS Archive

Published in TDS Archive

An archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication.

Michael Bronstein
Michael Bronstein

Written by Michael Bronstein

DeepMind Professor of AI @Oxford. Serial startupper. ML for graphs, biochemistry, drug design, and animal communication.

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