Input Tailored Concepts Maps - A Way to Facilitate AI for Navigating Scientific Knowledge
Iris AI uses non-semantic models to apply text understanding techniques to a scientific body of knowledge. The current algorithm is a mixture of neural topic models, keyword extraction and heuristics functions to form an input tailored concept map filled in with scientific articles. The current approach facilitates both existing unsupervised as well as supervised techniques for AI training. To verify our current progress we use state-of-the-art metrics, but moreover we conduct real life experiments comparing our tool to existing tools in the field through sci-thons. The results show great potential for new techniques in text understanding, and will change the way people navigate scientific knowledge.
Victor Botev is the CTO of Iris AI. Before joinin Iris he was an Artificial Intelligence Research Engineer at Chalmers University, in Gothenburg, Sweden. He has conducted research on clustering and predictive neural networks models, as well as usage of signal processing techniques in studying Big Data. As a Masters Thesis Student at CPAC Systems AB he has worked in the development of an autonomous compactor for pavement. Previously he was a senior software developer at Pinexo, a tech lead at Skrill and a web developer at Seedburger AG. He also has a second Masters (Artificial Intelligence) and BSc. (Software Development) degrees from Sofia University.