Project description
Making machine learning an easier process
With AI proving to be immensely useful for sectors and industries around the globe, many seek to further research in this field. As such, many are focusing on the process of machine learning, a process that is currently proving to be very costly as well as prone to errors and other issues. It also reduces productivity and requires a large amount of human interaction, vastly hampering the commercialisation and general use of AI systems. The EU-funded BrainMatter project aims to develop a novel lean AI platform that combats this problem by providing businesses and data scientist teams with fully customisable data processing solutions that allow for easier and cost-efficient preparation of AI from beginning to end.
Objective
Artificial Intelligence (AI) appears to be the next big thing that will contribute immensely to businesses’ growth and change the rules of competition, as demonstrated by the growing AI for enterprise applications, a market expected to reach €5.8 billion by 2024. Current AI and ML models are trained via supervised learning, in which manually labeled data is fed into AI models such as regression algorithms. Labeling huge datasets, however, need a lot of human interaction, attention, and effort, which slows down productivity, is prone to errors and, more importantly, very costly.
Our company, BrainCreators, is well paired to address this issue. We deliver innovative AI solutions and to date have accelerated >40 companies in their root cause analysis, language interpretation, predictive maintenance, and quality control related projects. Our innovation, BrainMatter, is a unique enterprise-grade lean AI platform that will offer businesses, and their data scientist teams, a fully customizable data processing solution, that streamlines the full AI lifecycle from the creation of high quality, large data sets to the training and deployment of AI models. It is applicable for a diverse range of data - image recognition, audio, text, video, time series, sensor data. Our innovation is customizable, self-learning, and in line with lean principles, improves accuracy with continuous model training. At the management level, BrainMatter will enable companies to save costs, speed up the labeling process, give control over the process with a customizable dashboard, and improve security through on-premise usage. At the implementation level, it will support data scientists and empower them to dedicate more time towards high-value-added tasks.
After five years of exploitation, we expect BrainMatter to boost our revenues to €17.6 million (with an ROI above 3 euro per euro invested), creating 78 direct and over 2,000 indirect jobs in the process.
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 artificial intelligence machine learning supervised learning
- natural sciences computer and information sciences data science big data
- natural sciences computer and information sciences artificial intelligence computer vision image recognition
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors
- natural sciences computer and information sciences data science data processing
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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.
1017 JV AMSTERDAM
Netherlands
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.