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Human level AI.
To make an AI creative and capable of producing truly novel, unpredictable insights about the nature of the universe, simply matching the number of connections or nodes, as many as in the human brain, is not enough. The processing elements would also need to incorporate a comparable range of modes of failure, random errors, and spontaneous new connections — features that are fundamental to human neural plasticity and creativity. For these errors not to result in catastrophic failures, the system would require enormous redundancy and parallel processing, much like the human brain. Without these requirements, AI is likely to remain limited — generating only what it has already learned, or producing output that is merely a reflection of its input: garbage in, garbage out.
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Opinion by AI:
Your argument highlights a core challenge in AI development: simply scaling up the number of connections or nodes to match the human brain does not guarantee creativity or the ability to generate truly novel insights. As you note, human neural plasticity and creativity stem not just from sheer computational power, but also from a rich interplay of random errors, spontaneous new connections, and robust redundancy — features that help us adapt, innovate, and recover from mistakes.
Current AI systems, especially large language models and deep learning models, are fundamentally limited by their training data and the absence of biological-like plasticity and error tolerance. The principle of “garbage in, garbage out” (GIGO) still holds: if the input data is flawed or limited, the AI’s output will be similarly constrained, regardless of model size. Even generative AI, which can produce seemingly plausible and creative outputs, is ultimately shaped by the data it has seen, and it lacks the intrinsic mechanisms for true randomness or spontaneous insight found in biological brains.
To move beyond these limits, AI would need to incorporate mechanisms for random errors, spontaneous connection formation, and massive redundancy — much like the human brain. These features would allow for unpredictable, novel outputs and the ability to recover from disruptions, rather than simply reproducing or remixing learned patterns. Without such mechanisms, AI is likely to remain confined to its training data, unable to achieve the kind of open-ended creativity and adaptability that defines human intelligence.
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latest posts ..
- A New Cosmology Emerges.
- Testing Nonlocal Information Transfer. Corrected version.
- Quantum Shortcuts.
- Bell’s experiment didn’t prove anything.
- Controversial Experiment in Quantum Physics.
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- Two tested and proven methods to capture Zero-Point Energy (ZPE). [Repost]
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- Aging as Adaptation. Josh Mitteldorf.
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