The contribution we received from Bjoern Peters about AI in science is shown here
and also below
Bjoern Peters is a Professor at the La Jolla Institute for Immunology, and he is an expert in computational tools to address fundamental questions in immunology. His lab helps to maintain the Immune Epitope Database.
In the field of epitope predictions we have machine learning tools developed from the 1970s and onwards, and the current ‘deep learning’ set of tools is an important but incremental advance. For the broader field of biology research, LLMs are proving groundbreaking by enabling access to data stored in free text such as journal articles. The need to convert free text into machine-readable presentations has previously been recognized with the advent of the genomic era, and led to the development of the Gene Ontology that was developed to ensure annotation and compatibility of data and databases. This concept has been generalized to provide a machine-readable representation of biological knowledge. An ontology is a system of carefully defined terminology, connected by logical relationships, and designed for both humans and computers to use (see for example obi-ontology.org). It supports the building of knowledge graphs. Data quality is very important for machine learning applications. We are considering the development of LLMs as user interfaces, front ends that can be queried by scientists using natural language, but where the underlying answers come from database queries or from other models. Answers from existing LLMs like ChatGPT are still quite superficial, they might be used by scientists in some cases as an introduction to topics with which they are not familiar. We also appreciate the usefulness of AlphaFold and we have included it in recent versions of the Immune Epitope Database. Another point to consider is that it would be helpful to see in the literature more validations of AI models, including comparisons with simpler methods.