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Why is artificial general intelligence still considered theoretical?
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artificial intelligence
Image generated by the Stable Diffusion model from the prompt “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings,” which is the definition of artificial intelligence in the Encyclopædia Britannica article on the subject. Stable Diffusion is trained on a large set of images paired with textual descriptions and uses natural language processing to generate an image.
Artificial general intelligence (AGI), also known as strong AI, remains theoretical because it aims to replicate the full spectrum of human intellectual abilities, a goal that has proven exceptionally difficult to achieve. While current AI systems excel at specific, narrow tasks, AGI would require the capacity for autonomous reasoning, the ability to solve problems outside of its training data, and the flexibility to learn from experience across diverse domains.1234
Several factors contribute to its status as a theoretical concept:
- Lack of Definition: There is no universally accepted, precise criterion for what constitutes "intelligence" in an artificial system. Without an objective benchmark, it is difficult to determine whether a system has reached human-level capability or if a research program has truly succeeded.2
- Scaling Challenges: The difficulty of scaling the modest achievements of current AI models to reach human-like generality cannot be overstated. It remains a subject of debate whether AGI can emerge from existing large-language models or if an entirely different technological approach is required.21
- Complexity of Human Cognition: AGI seeks to mirror the human mind, which involves complex processes that are not yet fully understood or replicable in machines.12