Projektbeschreibung
Ausschöpfung der Rechenleistung des menschlichen Gehirns
Künstliche Intelligenz und maschinelles Lernen breiten sich immer weiter in zentralen Gebieten wie dem Gesundheits- oder Finanzwesen, bei autonomen Fahrzeugen oder der Spracherkennung aus. Doch die enormen Investitionen in aktuelle Ansätze des maschinellen Lernens und neuromorphischen Computing in diesen Bereichen haben durch steigende Anforderungen an Rechenleistung und zunehmenden Energieverbrauch feste Obergrenzen. Das EU-finanzierte Projekt NEU-ChiP will das Gebiet revolutionieren und untersucht, wie Stammzellen des menschlichen Gehirns, die auf einem Mikrochip gezüchtet wurden, beigebracht werden kann, Probleme anhand von Daten zu lösen. Mithilfe von fortschrittlicher 3D-Computermodellierung wird ein interdisziplinäres Konsortium die Veränderungsprozesse der Zellen und deren Plastizität beobachten, um die Technologie des maschinellen Lernens grundlegend zu verändern.
Ziel
The EU and the rest of the world increasingly rely on artificial intelligence (AI) and machine learning (ML) for everyday functioning. Applications range from decision making in areas such as health and finance, face recognition, autonomous vehicle control, speech recognition and interaction with the internet and social media platforms. Estimated annual global spend on ML and AI is $77.6B in 2022 with a business value of $3.9T. However, current deep-learning machines suffer from inherent and difficult limitations: architectures not adaptable, ineffective learning rules, long training times and computing power, making advances unsustainable.
The NeuChiP project will tackle this issue. We will use emerging stem cell technology to make human neuronal networks that self-organise developmentally using the rules that form the brain. Networks will be made of layered cortical structures and hubs, with guided directional network connections and housed in a fabricated assembly. Input will be by patterned light at cells expressing optogenetic actuators, and output recorded via high resolution 3D multielectrode arrays. Intrinsic physiological mechanisms will enable them to undergo plasticity to designated input patterns. NeuChip will surpass the abilities of conventional artificial neural networks by conducting tasks in dynamically changing environments, exploiting the adaptive, complex and exploratory nature of biological human neural systems. To achieve this we have assembled a cross-disciplinary consortium of neuroscientists, stem cell biologists, bioelectronics developers, statistical physicists, together with machine learning and neuromorphic computing experts. We expect that within 15 years NeuChiP technology, using biological learning rules and powerful human-brain-based circuits will lead to novel and widespread advances in machine learning abilities and beyond, leading to a paradigm-shift in AI technology and applications to benefit society.
Wissenschaftliches Gebiet
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- natural sciencescomputer and information sciencesinternet
- medical and health sciencesmedical biotechnologycells technologiesstem cells
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Schlüsselbegriffe
Programm/Programme
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenUnterauftrag
H2020-FETOPEN-2018-2019-2020-01
Finanzierungsplan
RIA - Research and Innovation actionKoordinator
B4 7ET Birmingham
Vereinigtes Königreich