The article also explores the potential of AI in discovering new scientific principles by observing the behavior of the system and looking inside the neural networks. The author suggests that large multi-modal foundation models, transfer learning, and zero- and few-shot learning could make AI models surprisingly good at addressing messy problems with only a little prior data. The author concludes by stating that AI systems may eventually systematically change the entire practice of science, potentially speeding up scientific discovery and bringing both risks and benefits.
Key takeaways:
- AlphaFold 2, a deep learning system, has made significant progress in predicting the 3-dimensional structure of proteins from their amino acid sequences, revolutionizing molecular biology.
- AlphaFold's success in the CASP competition, where it outperformed other modeling groups, suggests its ability to generalize beyond its training data.
- While AlphaFold is a complex model with 93 million learned parameters, there is ongoing research into understanding its inner workings and potentially discovering new high-level principles about protein structure.
- The author suggests that large multi-modal foundation models, transfer learning, and zero- and few-shot learning could help AI models address messier problems with limited prior data in the future.