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2023
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THE D.8.8.1 - State of the art for digital models of cultured neural networks
Lagani G, Falchi F, Amato G
THE deliverable 8.8.1 is a technical report about current state-of-the-art approaches in the field of bio-inspired technologies for Artificial Intelligence (AI)
Lagani G, Falchi F, Amato G
THE deliverable 8.8.1 is a technical report about current state-of-the-art approaches in the field of bio-inspired technologies for Artificial Intelligence (AI)
2023
Other
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THE D.3.2.1 - AA@THE User needs, technical requirements and specifications
Pratali L, Campana M G, Delmastro F, Di Martino F, Pescosolido L, Barsocchi P, Broccia G, Ciancia V, Gennaro C, Girolami M, Lagani G, La Rosa D, Latella D, Magrini M, Manca M, Massink M, Mattioli A, Moroni D, Palumbo F, Paradisi P, Paternò€ F, Santoro C, Sebastiani L, Vairo C
Deliverable D3.2.1 del progetto PNRR Ecosistemi ed innovazione - THE
Pratali L, Campana M G, Delmastro F, Di Martino F, Pescosolido L, Barsocchi P, Broccia G, Ciancia V, Gennaro C, Girolami M, Lagani G, La Rosa D, Latella D, Magrini M, Manca M, Massink M, Mattioli A, Moroni D, Palumbo F, Paradisi P, Paternò€ F, Santoro C, Sebastiani L, Vairo C
Deliverable D3.2.1 del progetto PNRR Ecosistemi ed innovazione - THE
2023
Conference article
Open Access
AIMH Lab for a susteinable bio-inspired AI
Lagani G, Falchi F, Gennaro C, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Sustainable AI. In particular, we discuss the problem of the environmental impact of AI research, and we discuss a research direction aimed at creating effective intelligent systems with a reduced ecological footprint. The proposal is based on bio-inspired learning, which takes inspiration from the biological processes underlying human intelligence in order to produce more energy-efficient AI systems. In fact, biological brains are able to perform complex computations, with a power consumption which is orders of magnitude smaller than that of traditional AI. The ability to control and replicate these biological processes reveals promising results towards the realization of sustainable AISource: CEUR WORKSHOP PROCEEDINGS, pp. 575-584. Pisa, Italy, 29-30/05/2023
Lagani G, Falchi F, Gennaro C, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Sustainable AI. In particular, we discuss the problem of the environmental impact of AI research, and we discuss a research direction aimed at creating effective intelligent systems with a reduced ecological footprint. The proposal is based on bio-inspired learning, which takes inspiration from the biological processes underlying human intelligence in order to produce more energy-efficient AI systems. In fact, biological brains are able to perform complex computations, with a power consumption which is orders of magnitude smaller than that of traditional AI. The ability to control and replicate these biological processes reveals promising results towards the realization of sustainable AISource: CEUR WORKSHOP PROCEEDINGS, pp. 575-584. Pisa, Italy, 29-30/05/2023
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ceur-ws.org 
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2023
Other
Open Access
AIMH Research Activities 2023
Aloia N, Amato G, Bartalesi V, Bianchi L, Bolettieri P, Bosio C, Carraglia M, Carrara F, Casarosa V, Ciampi L, Coccomini Da, Concordia C, Corbara S, De Martino C, Di Benedetto M, Esuli A, Falchi F, Fazzari E, Gennaro C, Lagani G, Lenzi E, Meghini C, Messina N, Molinari A, Moreo A, Nardi A, Pedrotti A, Pratelli N, Puccetti G, Rabitti F, Savino P, Sebastiani F, Sperduti G, Thanos C, Trupiano L, Vadicamo L, Vairo C, Versienti L
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.DOI: 10.32079/isti-ar-2023/001
Metrics:
Aloia N, Amato G, Bartalesi V, Bianchi L, Bolettieri P, Bosio C, Carraglia M, Carrara F, Casarosa V, Ciampi L, Coccomini Da, Concordia C, Corbara S, De Martino C, Di Benedetto M, Esuli A, Falchi F, Fazzari E, Gennaro C, Lagani G, Lenzi E, Meghini C, Messina N, Molinari A, Moreo A, Nardi A, Pedrotti A, Pratelli N, Puccetti G, Rabitti F, Savino P, Sebastiani F, Sperduti G, Thanos C, Trupiano L, Vadicamo L, Vairo C, Versienti L
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.DOI: 10.32079/isti-ar-2023/001
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS