The (up to) €14.4 million in capital is aimed at supporting Iris.ai’s mission of making scientific research more actionable, namely through accelerating research across every field, mapping the vast amount of research available, and helping to broaden researchers’ understanding beyond their specific domains.
Quite simply Iris solves a fundamental problem for researchers, there are only 24 hours in one day, and only so much information the human mind can acquire and draw connections to/from in any given time period. With the advancement of a “data-driven” society, the problem then arises, what and how do we segment and process all of this data to make good use of it?
Particularly pressing for researchers, as locating relevant research is akin to finding a needle in a haystack. So much data, so little time. As a result, researchers in both academia and industry are missing relevant published papers that could advance their knowledge, or, quite frankly, simply wasting time reading irrelevant research.
This is where Iris steps in and puts Artificial Intelligence to use, dramatically reducing the time required to carry out scientific research through categorisation, navigation, summaries, and systematisation of data from academic papers, patents, and all other technical or research documentations.
According to Iris.ai, its technology is already being used by hundreds of universities and companies, eliminating the weeks and months spent manually wading through patents and research.
The backing arrives at a time when Europe is facing stiff academic research and practical application competition from the US and China. With this in mind, the EIC decided on backing Iris.ai, with the jury reviewing its application commenting, “A strong AI/ML European player is of utmost importance for the EU, given the evident progress in US and Chinese-driven AI developments and the potential biases that could therefore be implemented in the algorithms.”
With tools including ChatGPT making headlines on an increasing frequency, Iris.ai CEO and co-founder Anita Schjøll Abildgaard explained, “The current generation of large language models, including ChatGPT, don’t work for science today. They hallucinate, generate mistruths, and misunderstand scientific text due to a lack of domain-specific knowledge. What we’re doing differently is we are working on factuality validation, and injection of externally validated knowledge, creating a trustworthy system that can be relied upon for analysing scientific research.”