The need for collaboration to establish a unified language in all AI-related spaces, particularly focusing on ‘diverse intelligent systems’ incorporating AI, LLMs, and biological intelligences, is a pressing issue. Cortical Labs, a biological computing startup behind “Dishbrain,” spearheaded a study that brought together experts from various countries to propose a pathway for standardizing terminology in this rapidly evolving and contentious field. The objective is to create a comprehensive field guide for researchers engaged in developing generally intelligent systems, a resource that is currently lacking in the industry.
The complexity and contentious nature of the language used in scientific and public discourse on emerging technologies, especially those aimed at creating generally intelligent systems, present significant challenges. With disagreements, confusion, and ambiguity surrounding the semantics used to describe these technologies, the need for a common language becomes imperative. Ongoing disagreements and multiple definitions of terms like “intelligence” highlight the urgency of establishing a shared vocabulary to facilitate clearer communication and collaboration among researchers across different disciplines.
Previous attempts at proposing nomenclature guidelines have been limited in scope and participation, often developed by a select group of experts without broader community engagement. The current collaborative effort aims to involve researchers and scientists from diverse fields to collectively agree on and adopt nomenclature specific to AI, autonomous systems, consciousness research, machine learning, organoid intelligence, and robotics. The goal is to provide clarity and consistency in language use, allowing authors to communicate effectively across interdisciplinary boundaries while accommodating the rapid advancements in the field.
The collaboration intends to employ a mixed-method approach, incorporating a modified Delphi method to reach consensus on key terms and definitions. This approach involves an initial round of open-ended questions, followed by iterative refinement and categorization through collaborative consultation until agreement is reached. In cases where consensus is challenging to achieve, a weighted majority voting system will be utilized to make final decisions on specific terms.
While the primary focus of the collaboration is on establishing nomenclature guidelines for specific fields like artificial intelligence and robotics, the outcomes are expected to have broader implications for a range of related disciplines. By creating a standardized language for discussing generally intelligent systems, the collaboration aims to facilitate cross-disciplinary dialogue, enhance clarity in scientific communication, and support the development of innovative technologies in the field. The adoption of theory-agnostic nomenclature will allow for greater precision and consistency in describing complex systems and concepts, ultimately advancing research and collaboration in diverse intelligent systems.
The call for collaborative nomenclature in AI and diverse intelligent systems represents a significant step towards fostering coherence and consensus in a rapidly evolving field. By engaging researchers, scientists, and experts from various disciplines in the process of defining key terms and concepts, the collaboration is poised to shape the future of language use in AI research and development. Through a collective effort to establish a common language, the field can overcome semantic challenges, promote interdisciplinary collaboration, and drive innovation in the realm of generally intelligent systems.