Strategies for Building AI-ready Data Sources and (Semi)autonomous Reasoning Agents Operating on Top of Them
Strategies for Building AI-ready Data Sources and (Semi)autonomous Reasoning Agents Operating on Top of Them

Abstract: 

Here, we present a prototype Translator framework and architecture, which we have developed for integrating semantically, annotated Knowledge Sources (over 40) and for creating a data platform to support automated reasoning and serendipitous discovery of new ‘facts’ or interesting and testable hypotheses. We also discuss the strategies of how to integrate and provide high-value AI-ready data sources as well as how to develop (semi) autonomous reasoning agents that would advance reasoning through innovative uses of these knowledge sources.

Bio: 

Marcin von Grotthuss is a Senior Computational Scientists at the Broad Institute of MIT and Harvard. He earned the Ph.D. in bioinformatics at Radboud University Nijmegen, Netherlands and gained his professional experiences at Sanford-Burnham Institute (La Jolla), University of Washington (Seattle), Harvard University, University of Cambridge, University of California Irvine, and the Brigham and Women’s Hospital / Harvard Medical School. In his work, initially, he applied machine-learning technics to estimate the biomedical properties of small molecules. Next, he worked on distant sequence-structure-function relationships in proteins. After that, he was focused on the genomics of model organisms and human genetics (1000 Genomes Project). Currently, he develops knowledge portals for complex traits like type 2 diabetes, stroke, and cardiovascular diseases. And he is one of the instrumental scientists in the consortium that builds a Biomedical Data Translator.