Research team unveils ProSyno, a context-free prompt-learning model that taps Wiktionary descriptions and a dynamic matching encoder to spot synonyms across domains without KGs or corpora, setting new benchmarks on four datasets.

From text generation in academic settings to the ethical challenges of using artificial intelligence in the publishing industry: Professor Christoph Bläsi takes a comprehensive look at AI in book studies – and far beyond. Since 2019, he has been one of three spokespersons for the KI@JGU network.

AI has an "Achilles' heel": it often tries to decipher the cell like a person trying to understand a single word without reading the entire sentence. For example, seeing the word "Apple" makes it hard to know if the reference is to a fruit or a tech company. But through context, such as "I took a bite of..." versus "I bought shares of...", the meaning becomes clear. Previous in silico labeling methods looked at individual pixels without understanding the "story" behind the cell.

Nitzan Almalem and Prof. Assaf Zaritsky from the Computational Cell Dynamics lab at the Institute for Interdisciplinary Computational Science of the Stein Faculty of Computer and Information Science at Ben-Gurion University of the Negev developed a computational solution to this problem. Instead of the computer just learning from the image of the cell, Nitzan taught it to use the context of the cell. Contextual information refers to metadata or environmental factors, such as cell shape, its neighbors, or its position in a colony, that help the AI "generalize" and understand the image better. By understanding this context, the computer accurately stained rare processes like cell division, which look very different from the norm and often cause other systems to fail.

The Future: A Language Model for Cells

This ability to decipher context is just the beginning. The lab plans to expand this "context" to include information such as cell type, microscope type, disease state, and even the drugs the cell has received.

The vision is to build an extensive "dictionary" that eventually becomes a complete foundation model (a "language model") of the cellular world. Such a system could understand complex biological "texts" from any microscope and any cell type, providing scientists with a vivid, accurate picture of life without ever disturbing it.