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Toward a nomenclature consensus for diverse intelligent systems: Call for collaboration.
Brett J Kagan1,2, Michael Mahlis1, Anjali Bhat3
1Cortical Labs, Brunswick, VIC 3056, Australia.
Establishing common language is vital for diverse intelligent systems research, whether silicon-based like large language models or biology-based like organoids. A community approach is needed to agree on nomenclature for advancing this field.
Area of Science:
- Artificial Intelligence
- Synthetic Biology
- Neuroscience
Background:
- Disagreements in scientific language hinder multidisciplinary collaboration and societal impact.
- Advancements in diverse intelligent systems, including large language models and organoids, necessitate clear communication.
- The development of intelligence across different substrates (silicon and biological) presents unique linguistic challenges.
Purpose of the Study:
- To address the critical need for consensus on nomenclature in the rapidly evolving field of diverse intelligent systems.
- To propose a collaborative pathway for establishing shared terminology.
- To identify key terms and relevant fields for nomenclature development.
Main Methods:
- Reviewing current terminology in artificial intelligence and synthetic biology.
- Identifying areas of potential linguistic conflict and overlap.
- Proposing community-based methods for achieving consensus on nomenclature.
Main Results:
- Highlighted key terms and fields requiring standardized nomenclature.
- Identified the urgency for a unified approach to naming diverse intelligent systems.
- Suggested potential consensus-making strategies for the scientific community.
Conclusions:
- A community-driven consensus on nomenclature is essential for the progress of diverse intelligent systems research.
- Standardized terminology will facilitate collaboration and understanding across disciplines.
- Adopting proposed methods can lead to a shared language for artificial and biological intelligence.
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