The field of Artificial Intelligence (AI) is rapidly expanding and evolving, with new technologies and concepts being developed at an unprecedented pace. In order to keep up with these advancements, it is crucial to establish a common language and nomenclature that can be universally understood and accepted by researchers in the field. A recent study led by Cortical Labs has highlighted the need for collaboration in defining the language used in AI-related spaces, with a focus on ‘diverse intelligent systems’ that encompass AI, Large Language Models (LLMs), and biological intelligences.
The study brought together scientists, ethicists, and researchers from various countries to propose a pathway forward towards unifying the language in AI research. The rapidly growing and controversial nature of AI research necessitates a community-based approach to consensus on nomenclature. With advancements in silicon-based technologies like large language models and synthetic biology methods such as organoid development, a common language is essential for effective communication and understanding within the field.
Describing the complex phenomena in AI research poses challenges, as emerging technologies and disciplines contribute to the creation of generally intelligent systems. Disagreements, confusion, and ambiguity surrounding the terminology used in AI research make it difficult for researchers to effectively communicate their ideas. The lack of a common language leads to diverse definitions of key terms, hindering progress and collaboration within the field.
To address the challenges in language development, the study aims to establish nomenclature guidelines that are theory-agnostic and broadly applicable across various fields. By inviting researchers and scientists to collaborate on defining key terms, the study seeks to provide utility and nuance to discussions in AI research. The adoption of nomenclature adhering to a standard will enable authors to use language explicitly, unambiguously, and consistently in their work.
Methodology for Collaboration
The collaborative effort will involve a mixed method approach, including the use of a modified Delphi method. This approach will facilitate iterative consultation among experts in the field, allowing for strategic refinement and categorization of key terms. In cases where consensus is not reached, a weighted majority voting system will be implemented to reach a conclusion. The study will focus on AI-related fields such as artificial intelligence, autonomous systems, consciousness research, machine learning, organoid intelligence, and robotics, while also inviting input from other related disciplines.
Collaborative language development is essential for advancing AI research and fostering greater understanding and communication within the field. By establishing a common nomenclature that is universally accepted, researchers can effectively communicate their ideas and findings, leading to progress and innovation in the rapidly evolving field of AI. The efforts of Cortical Labs and other researchers involved in this study represent a significant step towards unifying language in AI research and creating a framework for future collaboration and discovery.
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