A recent study suggests that older AI models may show signs of cognitive decline, but not everyone is convinced. The idea that AI could deteriorate over time raises an interesting discussion about the nature of software, its maintenance, and whether AI systems should be treated more like evolving organisms rather than static tools.
Unlike hardware, which physically ages and degrades, software doesn’t wear out in the same way. However, AI models do face obsolescence when they can’t keep up with evolving data, shifting contexts, or new user expectations. Without continuous self-maintenance—like self-diagnostics, pruning of outdated patterns, or regular fine-tuning—models can develop inconsistencies, making them less effective over time.
This could be compared to how an old operating system struggles with modern software demands. We often find ways to extend the life of physical devices well beyond their expected use, but software—especially AI—rarely enjoys the same longevity without intervention.
The question then arises: Should older AI models be periodically refreshed or even retired, much like outdated technology? Perhaps they should be archived for historical comparison, allowing researchers to analyze their evolution while ensuring real-time AI remains sharp and effective.
The future of AI might not just be about creating new models but also about maintaining and refining the ones we already have.
However, considering we are discovering new processes and orientations on AI all the time, many of these should be replaced when the time is most appropriate. Which could be far more often than profit or finances dictate.
Compiled with aid of ChatGPT
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