Artificial Intelligence (AI) Evolutionary Lifecycle Model
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An Artificial Intelligence (AI) Evolutionary Lifecycle Model is an evolutionary lifecycle model (that describes the systematic progression inherent in the development and evolution of) for AI systems.
- Context:
- It can illustrate the stages from the early conceptualization of AI to its current state and potential future advancements.
- It can (typically) include stages like:
- AI Evolutionary Emergence/Origination: Marking the initial development of AI concepts and primitive systems.
- AI Evolutionary Expansion/Proliferation: Indicating rapid growth in AI research and applications across various domains.
- AI Evolutionary Explosion/Diversification: Highlighting a significant increase in AI technologies and methodologies diversity.
- AI Evolutionary Maturation/Consolidation: Denoting a period of stabilization where AI becomes integral in many technological solutions and industries.
- AI Evolutionary Stabilization/Equilibrium: Reflecting a state where AI technologies achieve a balanced and sustained level of development.
- AI Evolutionary Extinction/Obsolescence: Hypothetical stage marking potential decline or transformation of AI technologies into something beyond current understanding.
- It can provide insights into AI's impact on society, economy, and technology.
- It can vary from a Narrow AI Evolutionary Lifecycle Model to General AI Evolutionary Lifecycle Model.
- ...
- Example(s):
- A Narrow AI Evolutionary Lifecycle Model, such as:
- one that accounts for recommender systems.
- one that accounts for expert systems.
- ...
- General AI Evolutionary Lifecycle Model, such as:
- one that accounts for Large Language Model.
- one that accounts for expert systems.
- ...
- A Narrow AI Evolutionary Lifecycle Model, such as:
- Counter-Example(s):
- A Static AI Development Model, which assumes a non-evolving, constant state of AI technology.
- A Linear AI Progression Model, which fails to account for the complex, multi-stage evolution of AI systems.
- See: Artificial Intelligence, Machine Learning, Evolutionary Computation, AI Ethics.
References
2024
- (GPT-4, 2024) ⇒ GPT-4. (2024). “Artificial Intelligence Evolutionary Lifecycle: A Comprehensive Overview.”
- The Artificial Intelligence (AI) Evolutionary Lifecycle Model provides a framework for understanding the complex and dynamic progression of AI technologies. This model encompasses various stages, beginning with the emergence and origination of basic AI concepts, followed by rapid expansion and proliferation across multiple domains. The explosion or diversification stage witnesses a significant increase in AI technologies and methodologies, leading to a maturation or consolidation phase where AI becomes a staple in numerous technological solutions. This is followed by a stabilization or equilibrium stage, where AI development reaches a sustained and balanced level. A hypothetical final stage of extinction or obsolescence suggests a future where AI technologies may either decline or transform into something beyond our current understanding. This model not only charts the historical progression of AI but also provides insights into its future trajectory, touching upon aspects like AI ethics, the impact on various industries, and the evolution from narrow to