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AI-first Biotech 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. 46 min read

This post was co-authored with Luca Naef.

The release of ChatGPT by OpenAI in November 2022 has thrust Artificial Intelligence into the global public spotlight [1]. It likely marked the first instance where even people far from the field realised that AI is imminently and rapidly altering the very foundations of how humans will work in the near future [2]. A year down the road, once the limitations of ChatGPT and similar systems have become better understood [3], the initial doom predictions ranging from the more habitual panic about future massive job replacement by AI to declaring OpenAI as the bane of Google, have given place to impatience why is it so slow?, in the words of Sam Altman, the CEO of OpenAI [4]. Familiarity breeds contempt, as the saying goes.

We are now seeing the same frenetic optimism around AI in the biological sciences, with hopes that are probably best summarised by DeepMind

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