Descrizione del progetto
Sfruttare la potenza di calcolo del cervello umano
L’uso odierno dell’IA e dell’apprendimento automatico sta diventando sempre più diffuso in ambiti cruciali come la sanità, la finanza, i veicoli autonomi e il riconoscimento vocale. Tuttavia, gli enormi investimenti negli attuali approcci del campo all’apprendimento automatico e al calcolo neuromorfico hanno seri limiti, perché richiedono una potenza di calcolo sempre maggiore e un’elevata richiesta di energia. Per raggiungere una svolta in questo campo, il progetto NEU-ChiP, finanziato dall’UE, studierà in che modo le cellule staminali del cervello umano coltivate su un microchip possono essere istruite a risolvere problemi a partire dai dati. Utilizzando una sofisticata modellazione computerizzata 3D, un consorzio interdisciplinare condurrà un’osservazione dei processi di modifica delle cellule e della loro plasticità per consentire un importante cambiamento nella tecnologia di apprendimento automatico.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
Programma(i)
Invito a presentare proposte
Vedi altri progetti per questo bandoBando secondario
H2020-FETOPEN-2018-2019-2020-01
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
B4 7ET Birmingham
Regno Unito