SCIENTIFIC THEORY
ASTOUND proposes to build a plausible human-brain architecture for consciousness capable of demonstrating an undeniable added value in terms of human-machine interaction. This will be obtained by combining state of the art deep neural networks models with an Attention Schema.
The Attention Schema Theory (AST) , first conceived by Dr. Michael Graziano (Princeton Neuroscience Institute, part of ASTOUND’s team), explains the brain basis of subjective awareness in a mechanistic and scientifically testable manner. According to the AST, the brain is an information-processing device with the capacity to focus its processing resources more on some signals than on others.
On the other hand, the team at Universidad Politécnica de Madrid (Spain) has recently proposed state-of-the-art modules for conversational systems, as well as automatic dialogue evaluation metrics such a PoE and Fined-Eval which are SotA models for self-assessment metrics. In addition, new Deep Learning algorithms for automatic classification of human movements using inertial sensors and multimodal processing for trustworthiness evaluation will be used to enrich and better understand the human-chatbot interaction.
In addition, Ecole Normale Supérieure in Paris (ENS) will work on detecting biases in language to establish mechanisms for evaluating and removing undesired behaviors in conversational system inherited from the training data used. Finally, the team from University Medical Center Hamburg-Eppendorf (UKE) will propose innovative mechanisms to evaluate the effects on humans when chatting with a conscious chatbot versus one without consciousness.
in the first year of the project, IndeepAI (IAI) has worked, in collaboration with Dr. Graziano, in proposing an implementation for AST that could be applied to Transformer-based models. They also performed several experiments to test if the proposed mechanism is able to provide such capabilities as we are looking for. Current results are promising according to the AST, but still need additional improvements and experiments to move into more complex problems and also to show its application on conversational systems. In addition, Universidad Poilitécnica de Madrid (UPM) has created a novel dialogue dataset in the domain of art-works that is being used for training the proposed chatbot. Finally, University Medical Center Hamburg (UKE) has been working on evaluating the current capabilities of LLMs in Theory of Mind tests, together with a large number of human users, to better understand how to evaluate consciousness in both humans and chatbots; this activity is planned in order to propose a new type of test that we termed Turing-Graziano test for evaluating consciousness.
