While the public’s attention has been captured by AI chatbots, a quiet revolution has been brewing in the sciences. One of the most promising fields AI is impacting is biology—long dominated by the tradition of the “wet lab,” which favors pure experimental data over computer simulation. Deep learning is changing that. It enables computers to understand complex patterns in data and generate ideas based on those patterns, and that’s making AI more and more central to experimental biology. There is no field more complex in its patterns than biology, so AI is the perfect tool for understanding it. And—as a host of new companies are showing—engineering it.
Oddly enough, even the success of large-language models, or LLMs, like ChatGPT could be seen as evidence of AI’s ability to understand biological complexity. After all, language is a product of a particular biology—ours. The text on which ChatGPT is trained is just one of many complex forms of data produced by biological systems. In nature, this complexity reaches up from the smallest working parts—our proteins, our DNA—but also extends up through cells, organs, to physiology, disease, and behavior.
AI, with the right data, can span all of these scales and make sense of the data we collect on all of them. It’s poised to accelerate basic science, the business of biotechs, the behemoth pharmaceutical companies, and the broader bioeconomy.
The most exciting aspect of wet AI is that it promises to turn the molecular world into a programmable medium. That could have far reaching impacts, giving us the ability to imagine new categories of products based on engineered biology.https://t.co/Cb5mziIFaF
— proto.life (@protolifemedia) January 20, 2024