Summary
In the last decade, deep learning algorithms have enabled AI systems to perform a series of tasks, like speech and image recognition, as well as or better than humans. However, this technology is not going to be enough to deliver human-level intelligence without consciousness. Without understanding the subjective awareness element, it may be impossible to build AI that has a human-like ability to focus its computational resources and intelligently control that focus and interact with people in a socially competent manner. ASTOUND proposes an Integrative Approach For Awareness Engineering to establish consciousness in machines. The approach consists of an AI architecture for Artificial Consciousness based on the Attention Schema Theory (AST), a novel approach to social cognition that reconciles some of the current most debated cognitive neuroscience theories of consciousness. According to the AST, the brain constructs subjective awareness as a schematic model of the process of attention, suggesting that an information-processing machine could attribute consciousness properties to others in a similar way. The AST-based architecture proposed by ASTOUND will combine an Attention Mechanism provided by the attentional layers in a deep neural architecture and a Long Term Memory module allowing interplay between internal and external stimuli (data) with an Attention Schema that will determine empathic and trustworthy decision-making. ASTOUND will first implement this architecture into a virtual conversational agent (i.e. chatbot) to verify the hypothesis that an artificial consciousness based on AST will improve performance in a task of natural language understanding. ASTOUND will provide insights into consciousness that are concrete enough, and mechanistic enough, that engineers can build upon it to facilitate future technologies. The study has the potential to be a toolbox for the construction of an EIC Portfolio in conscious AI.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101071191 |
| Start date: | 01-12-2022 |
| End date: | 30-11-2025 |
| Total budget - Public funding: | 3 330 897,50 Euro - 3 330 897,00 Euro |
Cordis data
Original description
In the last decade, deep learning algorithms have enabled AI systems to perform a series of tasks, like speech and image recognition, as well as or better than humans. However, this technology is not going to be enough to deliver human-level intelligence without consciousness. Without understanding the subjective awareness element, it may be impossible to build AI that has a human-like ability to focus its computational resources and intelligently control that focus and interact with people in a socially competent manner. ASTOUND proposes an Integrative Approach For Awareness Engineering to establish consciousness in machines. The approach consists of an AI architecture for Artificial Consciousness based on the Attention Schema Theory (AST), a novel approach to social cognition that reconciles some of the current most debated cognitive neuroscience theories of consciousness. According to the AST, the brain constructs subjective awareness as a schematic model of the process of attention, suggesting that an information-processing machine could attribute consciousness properties to others in a similar way. The AST-based architecture proposed by ASTOUND will combine an Attention Mechanism provided by the attentional layers in a deep neural architecture and a Long Term Memory module allowing interplay between internal and external stimuli (data) with an Attention Schema that will determine empathic and trustworthy decision-making. ASTOUND will first implement this architecture into a virtual conversational agent (i.e. chatbot) to verify the hypothesis that an artificial consciousness based on AST will improve performance in a task of natural language understanding. ASTOUND will provide insights into consciousness that are concrete enough, and mechanistic enough, that engineers can build upon it to facilitate future technologies. The study has the potential to be a toolbox for the construction of an EIC Portfolio in conscious AI.Status
SIGNEDCall topic
HORIZON-EIC-2021-PATHFINDERCHALLENGES-01-01Update Date
31-07-2023
Images
No images available.
Geographical location(s)
Structured mapping
Unfold all
/
Fold all
