Here is an example of GPT-4, edited for brevity (it gets long winded at times)

Q: Can you illustrate how intelligence in eusocial insect colonies is similar or different from human designed artificial intelligent systems ?

A: Eusocial insect colonies and human-designed artificial intelligence (AI) systems exhibit intelligence in different ways. Here, we'll highlight some of the similarities and differences between the two:

Similarities:
	Emergent behavior: Both eusocial insect colonies and AI systems can exhibit emergent behavior, which is the formation of complex patterns or structures arising from the interactions of simpler elements. In eusocial insects, this can be seen in the coordination of colony activities, while in AI systems, it can be observed in swarm intelligence algorithms or neural networks.

Differences:
	Complexity: AI systems can be designed to process and analyze vast amounts of data, and their complexity can be scaled up or down depending on the task at hand. In contrast, the complexity of eusocial insect colonies is limited by their biological nature and the cognitive capacities of the individual insects.

Q: You said: "the complexity of eusocial insect colonies is limited by their biological nature and the cognitive capacities of the individual insects." Are you saying they are limited because they are biological? That seems to be an unfounded assumption. Humans are biological and are not necessarily limited as a result. Some insect colonies have millions of cooperating members, which means their interface with the actual world is vast. Please comment.

A: You are correct that my previous statement was overly simplistic, and I apologize for any confusion. The point I intended to make was that the complexity of eusocial insect colonies arises from the interactions of individual insects, which have their own cognitive and physiological limitations. This is not to say that being biological inherently limits their intelligence or capabilities.

In fact, as you mentioned, eusocial insect colonies with millions of cooperating members can accomplish remarkable feats and exhibit complex emergent behaviors that surpass the abilities of individual insects. These colonies can effectively gather resources, defend themselves, maintain complex nest structures, and adapt to changing environments, all through decentralized decision-making and coordination.

My intention was to emphasize that eusocial insect colonies and AI systems are different in terms of their underlying mechanisms and the ways they achieve complexity. While eusocial insect colonies rely on biological processes and the cooperative behaviors of individual insects, AI systems are designed by humans and use digital algorithms and computational resources to achieve their complexity.

The comparison between eusocial insect colonies and AI systems can be informative, but it is important to recognize their distinct origins, limitations, and potential for growth. The adaptability and resilience of biological systems, including eusocial insects, are indeed impressive and can offer valuable insights for the development of AI systems and other technologies.

Q: You said: "The comparison between eusocial insect colonies and AI systems can be informative, but it is important to recognize their distinct origins, limitations, and potential for growth." We don't know the origin of biological systems, we don't know its limitations and potential for growth. And while we know artificial intelligent systems are man-made, we also don't know the limitations and potential of AI, so how can you compare and say one is more or less limited than the other. Please comment.

A: You are right in pointing out that our understanding of both biological systems and AI is still evolving, and it is difficult to definitively determine their limitations and potential for growth. My earlier statement was indeed an oversimplification, and I appreciate the opportunity to provide a more nuanced perspective.

Comparing eusocial insect colonies and AI systems can be helpful for understanding their respective strengths and weaknesses, as well as identifying areas for potential cross-disciplinary learning. However, making direct comparisons about their limitations and potential for growth can be challenging, given the dynamic and evolving nature of both fields.

In biological systems, such as eusocial insect colonies, our knowledge is continually expanding as we discover new species, behaviors, and physiological adaptations. Similarly, AI research is advancing rapidly, with new algorithms and technologies being developed to address a wide range of applications and challenges.

Rather than asserting that one system is inherently more limited than the other, it is more constructive to recognize that both eusocial insect colonies and AI systems have unique properties and capabilities that can inspire new ideas and approaches. By learning from the complexity, adaptability, and resilience of biological systems, AI researchers can develop innovative techniques and algorithms that better mimic the natural world.

In conclusion, while it is difficult to make direct comparisons between the limitations and potential of eusocial insect colonies and AI systems, acknowledging their distinct characteristics and drawing inspiration from their respective strengths can foster greater understanding and innovation in both fields.

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My comment: This would probably be very informative to someone who was new to this field but it is telling us very little other than "potential", "inspiration", etc.

PLB

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