Project description
The first AI-powered researcher
Thousands of research papers are published every day. Half of these will never be read by more than three people, while only 10 % will be cited. One of the obstacles is related to the lengthy process of researchers needing to manually sift through the papers. The EU-funded IRIS AI project has the solution. Powered by AI, this computer researcher uses a neural network algorithm to understand context and document similarity. It automates the process of finding relevant scientific literature and creating new hypotheses. It can save up to 90 % time of the research process and increase accuracy with an 85 % precision.
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences databases
- social sciences economics and business business and management business models
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.2.3. - INDUSTRIAL LEADERSHIP - Innovation In SMEs
MAIN PROGRAMME
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H2020-EU.3. - PRIORITY 'Societal challenges
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H2020-EU.2.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
SME-1 - SME instrument phase 1
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-EIC-SMEInst-2018-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
1368 Brum
Norway
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.