Descripción del proyecto
Aprovechamiento de la potencia computacional del encéfalo humano
El uso actual de la inteligencia artificial y el aprendizaje automático es cada vez más frecuente en ámbitos cruciales como el sanitario, financiero, de vehículos autónomos y de reconocimiento del habla. Sin embargo, las enormes inversiones en los métodos de aprendizaje automático y la computación neuromórfica actuales en este campo presentan serias limitaciones, al necesitar una potencia computacional cada vez mayor y altas exigencias energéticas. Para lograr un gran avance en este campo, el proyecto financiado con fondos europeos NEU-ChiP estudiará cómo se puede enseñar a las células madre del encéfalo humano cultivadas en un microchip a resolver problemas a partir de datos. Mediante el uso de una modelización informática tridimensional sofisticada, un consorcio interdisciplinar llevará a cabo una observación de los procesos de los cambios y la plasticidad de las células para permitir un cambio radical en la tecnología de aprendizaje automático.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-FETOPEN-2018-2019-2020-01
Régimen de financiación
RIA - Research and Innovation actionCoordinador
B4 7ET Birmingham
Reino Unido