Dario Amodei, the CEO of Anthropic, recently published an essay called “Machines of Loving Grace.” It sketches out his vision for how AI could radically transform neuroscience, economics, diplomacy, and the meaning of work.
Although Amodei does acknowledge some real-world issues limiting scientific progress — such as the slow growth of organisms and tedious clinical trials — he mostly passes over the more general tools that will be required to accelerate research in the near term. Still, many of the bottlenecks slowing biology today are biophysical, rather than computational. Therefore, I’m using Amodei’s essay as a rallying cry for researchers to innovate their way past existing bottlenecks in wet-lab biology, which, if achieved, would help scientists actually build more powerful AI models in the future.
It wasn’t easy for me to write this essay because it’s often difficult to predict exactly where a solution to a given problem will emerge. That’s why researchers hoping to accelerate biology at large should strive to build “platform tools” that “can apply in multiple contexts,” as Adam Marblestone has written, rather than narrow solutions to short-term problems.
The balance between AI advances on the one hand and wet-lab innovations on the other is also a bit like the chicken and egg problem. Yes, AI will accelerate biological progress, but first we must make it easier and faster to run experiments while creating better methods to study biology in a more holistic and less reductionist way. Solving the latter challenges will, oddly enough, require both machine learning and wet-lab innovations.