IRIS AI

Project "IRIS AI" data sheet

The following table provides information about the project.

 Partnership

Take a look of project's partnership.

Map

+-

Leaflet | Map data © OpenStreetMap contributors, CC-BY-SA, Imagery © Mapbox

 Project objective

More than 4000 research papers are published every day. Sadly, our human brains can only process a fraction of this knowledge. As a result, 50% of papers published are read by less than three people and as many as 90% of papers published are never cited. Currently, researchers rely on a lengthy manual process when reviewing scientific literature or trying to find a solution to a complex questions, generally only looking for papers within their expertise area, when the answer may be in an unrelated area. At Iris AI AS, we decided to overcome such challenges by giving birth to Iris.ai, the first Artificial Intelligence-powered researcher. Iris.ai uses a neural network algorithm to understand context and document similarity. It automates the process of finding relevant scientific literature and creating new hypothesis, saving up to 90% time of the research process and increasing accuracy with a 85% precision. As a result, it will accelerate the progression of knowledge and problems solving. Iris.ai is quickly becoming effective thanks to our team of data scientist experts and the invaluable help from our community of AI trainers, who aid Iris.ai make sense of science. Iris.ai is also becoming famous! We have been featured in the $5M IBM Watson AI XPRIZE or the prestigious Science journal and were selected by Fast Company in 2017 as one of the top 10 most innovative companies in AI (sharing ranking with Google, IBM and Baidu). Furthermore, we have big corporate clients such already on board. With Iris.ai, we will be first targeting the materials science R&D market, estimated to be worth €85 billion by 2024 and growing at a 10.4% CAGR (period 2015-2024) although our long-term goal is R&D at large globally valued at €1400 billions per year. By using a freemium business model approach, we expect to have a cumulative net profit of €55.8M and have hired 100 new professionals by the end of 2023.