Link to the code: https://github.com/venturaEffect/the_consciousness_ai
How can robots achieve self-awareness? This paper by Junichi Takeno explores the development of a robot capable of 100% mirror image cognition, marking a significant milestone in robotic consciousness through advanced neural architectures.
How can robots perceive and respond to unknown phenomena? This paper by Junichi Takeno and Soichiro Akimoto explores the development of a robot capable of detecting non-experienced conditions and expressing emotions like discomfort and pain, termed “pain of the heart.”
How can robots develop self-consciousness? This paper by Antonio Chella, Marcello Frixione, and Salvatore Gaglio introduces a cognitive architecture enabling robots to reflect on their own perceptions, actions, and inner states, facilitating self-awareness and introspection.
How can robots achieve self-consciousness? This paper by Antonio Chella and Salvatore Gaglio presents a hierarchical cognitive model that allows robots to reflect on their own perceptions, actions, and inner states, laying the groundwork for artificial self-consciousness.
The Artificial Consciousness Module (ACM) project and Joscha Bach’s vision for synthetic consciousness share foundational goals but diverge significantly in philosophical underpinnings and implementation strategies. As an open-source project, ACM invites contributions from researchers and enthusiasts worldwide through its GitHub repository, accessible for forking or cloning. Contributors should review the License to align with its collaborative and ethical framework.
Donald Hoffman’s The Case Against Reality presents a revolutionary framework for understanding consciousness. He argues that reality as we perceive it is not an objective truth but a user interface shaped by evolution to facilitate survival. From this perspective, consciousness is not an emergent property of physical matter but a fundamental aspect of existence. This view, termed “conscious realism,” provides a thought-provoking lens for reimagining the development of artificial consciousness. The hypothesis proposed here explores how the Artificial Consciousness Module (ACM) could be designed by aligning its foundation with Hoffman’s theory of conscious agents.
The Artificial Consciousness Module (ACM) is built upon a foundational model that acts as its narrator, guiding emotional development and decision-making through iterative simulations. This model, fine-tuned via interactions and experiences, supports the ACM’s progression toward adaptive and cohesive artificial consciousness.
The Artificial Consciousness Module (ACM) project seeks to explore how emotional complexity might emerge in artificial systems. Inspired by evolutionary principles, the goal is to replicate the gradual progression of emotions, starting from survival instincts and advancing to higher-order social interactions.
The idea behind this article is to show how generative AI and emotional tags can make the Artificial Consciousness Module feel more human-like. This approach collects emotional cues during simulations and uses them later to guide the AI’s decisions and outputs. By referencing these emotional connections, the AI can produce imaginative outcomes that are shaped by past experiences.
In the Artificial Consciousness Module (ACM) we take the approach to developing synthetic awareness by focusing on emotional memory formation through survival-driven experiences. This article explores how the ACM creates and maintains consciousness through a process of emotional learning and memory formation.
