The number of scientific papers that cite AI has quadrupled over the past 10 years, according to research published in preprint form last year, meaning AI is increasingly being used across academia.
The dream of AI scientists of creating tools smart enough to accelerate humanity’s endless drive to acquire new knowledge is becoming a reality.
For example, Google’s DeepMind’s Alphafold is being used to predict the shape of every protein known to science, revolutionising biomedical research, while its Brain Metaphor project has created the most detailed map of human brain connections to date.
Microsoft has also created MatterGen, a model that uses diffusion models to produce novel inorganic crystals, while the company has also partnered with the EU to use AI for weather forecasting.
This AI-based acceleration of scientific progress is also transforming fields such as physics and materials science.
However, neural networks are black boxes whose internal workings are hard to decipher, which can make results challenging to interpret.
Nonetheless, with judicious application, the use of AI in scientific research could accelerate humanity’s understanding of the world.
