{"id":15748,"date":"2024-06-04T20:17:53","date_gmt":"2024-06-04T20:17:53","guid":{"rendered":"https:\/\/aurelis.org\/blog\/?p=15748"},"modified":"2024-06-04T20:17:55","modified_gmt":"2024-06-04T20:17:55","slug":"are-llms-parrots-or-truly-creative","status":"publish","type":"post","link":"https:\/\/aurelis.org\/blog\/artifical-intelligence\/are-llms-parrots-or-truly-creative","title":{"rendered":"Are LLMs Parrots or Truly Creative?"},"content":{"rendered":"\n

Large Language Models (LLMs, such as GPT) are, at present, just mathematical distillations of human-made textual patterns \u2014 very many of them.<\/h3>\n\n\n\n

They are, therefore, frequently described as parrots.<\/p><\/blockquote>\n\n\n\n

Size matters.<\/strong><\/p>\n\n\n\n

The parrot feature may be applied when there is little input or little diversity in input. Then, clearly, the result is a pattern-based average of the input \u2015 parrot-like.<\/p>\n\n\n\n

But A.I. is also in the size. With more significant amounts of input, it becomes less evident where the \u2018inspiration\u2019 comes from. With massive and hugely diverse (also multi-modal) input, it\u2019s even less evident to the point that it becomes impossible to see what caused the result, even when one knows that there is no other input than the textual patterns.<\/p>\n\n\n\n

One can still debate whether such a system is genuinely creative. In any case, it\u2019s less evidently parrot-like.<\/p>\n\n\n\n

Heightening the complexity makes it even less evident.<\/strong><\/p>\n\n\n\n

\u2015 for instance, at RAG-time<\/a> using procedures that are themselves LLM-imbued or using multiple soft constraints in specific knowledge bases.<\/p>\n\n\n\n

The combination of such with \u2013 one or more \u2013 vast LLMs brings us into the creative domain to such a degree that we need to acknowledge the creativity or else start denying our own.<\/p>\n\n\n\n

Let\u2019s agree then we are past the borders into the truly creative domain. Moreover, creativity is not merely a matter of generating novel combinations but involves depth and meaningfulness, which will also apply to super-A.I. systems.<\/p>\n\n\n\n

There is also a more profound implication involved.<\/strong><\/p>\n\n\n\n

If such a system is genuinely creative, then if it exhibits intelligent behavior, this should be seen as truly intelligent. In the same vein, if it exhibits emotional behavior, this should be seen as truly emotional.<\/p>\n\n\n\n

This is, of course, different from the human kind of intelligence\/emotion. Still, more abstractly seen, it deserves the same qualifications.<\/p>\n\n\n\n

Conceptual emergence<\/strong><\/p>\n\n\n\n

In human creativity, sometimes concepts emerge that were not part of the initial thought process. For instance, the concept of \u2018gravity\u2019 emerged from observing falling objects.<\/p>\n\n\n\n

We could design LLMs not just to process data but to observe and hypothesize, suggesting or creating entirely new concepts based on non-conscious cues picked up from the human user\u2019s behavior or input text,<\/p>\n\n\n\n

Imagine an A.I. given the task of understanding climate patterns. It might not only predict weather but could potentially generate new theories about climate behavior by integrating data from unrelated fields like biology, economics, and social sciences. This cross-disciplinary \u2018thinking\u2019 could lead to innovations that a narrowly focused A.I. might miss.<\/p>\n\n\n\n

Such a system or systems will soon be more creative than people.<\/strong><\/p>\n\n\n\n

There is no intrinsic barrier to this \u2015 nothing that prevents it for the sake of our being so very special. Eventually, we\u2019re not. We shouldn\u2019t see this as degrading but realistic.<\/p>\n\n\n\n

It may lead to the end of humanity if we fight it by all means.<\/p>\n\n\n\n

Otherwise, the future \u2013 including ours \u2013 is brightly creative with systems that are also capable of expanding the frontiers of human creativity and understanding in unprecedented ways in fields like art, science, and literature.<\/p>\n

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Large Language Models (LLMs, such as GPT) are, at present, just mathematical distillations of human-made textual patterns \u2014 very many of them. They are, therefore, frequently described as parrots. Size matters. The parrot feature may be applied when there is little input or little diversity in input. Then, clearly, the result is a pattern-based average Read the full article…<\/a><\/p>\n

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