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The Challenges of Complexity in the Future

The Challenges of Complexity in the Future

The emergence of a new class of problems - complexity - poses significant challenges for future science and just about everything else knowledge-based. These problems are characterized by their inherent difficulty and the multitude of possible solutions, none of which can be guaranteed to be correct or optimal.

When a system reaches a certain level of complexity, it becomes possible to discern any pattern one chooses to see. Furthermore, these patterns can be convincingly proven through both data and analysis. For instance, economists and social scientists can derive different, yet provable, patterns within the economy and human cultures, leading to vastly different predictions. This multitude of solutions renders the problems of complexity seemingly impossible to solve definitively.

Artificial Intelligence (AI) will likely exacerbate this issue. With access to comprehensive knowledge, AI systems will identify even more patterns, guided by human input, leading to an increase in questionable solutions. Due to their reliance on extensive data sources, these AI-generated patterns will be difficult to refute. The question arises: Are these patterns correct, or are they simply new interpretations?

Consider the legal field as an example. Jurors may soon be faced with sifting through extensive legal documentation, much of which they may struggle to understand fully. Similarly, in the medical field, think about the role of aspirin in preventing heart attacks. Recent studies reveal that aspirin may not be an automatic prescription to all entering middle age, only those with heart risk. Moreover, aspiring may affect healing. These studies were partly based on AI information. The upshot here is that aspirin may be good and/or risky. What to do? The complexity of such issues and the proliferation of AI-generated solutions will make it increasingly challenging to determine the most accurate and effective answers. 

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