APRICOT SIGNED
Artificial PeRceptIon Chip for the Intelligence Of Things
Total Cost €
0EC-Contrib. €
0Partnership
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0APRICOT project word cloud
Explore the words cloud of the APRICOT project. It provides you a very rough idea of what is the project "APRICOT" about.
Project "APRICOT" data sheet
The following table provides information about the project.
| Coordinator |
ANOTHER BRAIN
Organization address contact info |
| Coordinator Country | France [FR] |
| Total cost | 3˙547˙817 € |
| EC max contribution | 2˙483˙472 € (70%) |
| Programme |
1. H2020-EU.3. (PRIORITY 'Societal challenges) 2. H2020-EU.2.3. (INDUSTRIAL LEADERSHIP - Innovation In SMEs) 3. H2020-EU.2.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies) |
| Code Call | H2020-SMEInst-2018-2020-2 |
| Funding Scheme | SME-2 |
| Starting year | 2018 |
| Duration (year-month-day) | from 2018-12-01 to 2020-11-30 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | ANOTHER BRAIN | FR (PARIS) | coordinator | 2˙483˙472.00 |
Map
Project objective
The goal of APRICOT project is to accelerate the industrialisation and commercialisation of a disruptive artificial intelligence (AI) technology, that goes beyond the current mainstream of Deep Learning/Neural Networks. This new technology will have a world-scale impact in many domains as it will enable efficient, transparent and explainable, data preserving and GDPR compliant, controllable and adaptable AI-enhanced products and services in our everyday life. The technology is fully owned by the European start-up Another Brain. Almost all solutions proposed by current AI key players rely on artificial neural networks optimised through deep learning. Despite their significant improvement with respect to prior solutions, critical problems remain unsolved. Another Brain develops a New generation AI chip with a disruptive approach that solves these problems. Instead of considering the human brain at a neuron level, we replicate the brain’s behaviour on a more macroscopic level where large neuronal groups have a dedicated function. Perceived data is transformed into invariant semantic representations that are memorised in one size in associative memory without supervision. Learning is fast, incremental and continuous. Algorithms’ decisions are self-explanatory. The solution is easily customisable, low power, generic to all human senses and well suited to the automation of tasks performed by human beings. For the first applications, we will connect our chips to sensors creating smart modules able to understand the surrounding world in real-time. This addresses the needs of many markets, the automotive industry especially autonomous driving being our most important one. Our total addressable market will exceed €5billion in 3 years, with €2billion for the automotive segment. This project can contribute to meet many Horizon 2020 societal challenges from Health and Wellbeing to Secure Societies including Smart, Green and Integrated transport.
Publications
| year | authors and title | journal | last update |
|---|---|---|---|
| 2020 |
David Dehaene, Oriel Frigo, Sébastien Combrexelle, Pierre Eline ITERATIVE ENERGY-BASED PROJECTION ON A NORMAL DATA MANIFOLD FOR ANOMALY LOCALIZATION published pages: , ISSN: , DOI: |
2020-02-06 |
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