Can LLM ANNs capture or mimic biological entropy defeating processes? – I discussed this with claude and perplexity

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Claude 3.5 sonnet

FS

comment on this with a 2000 word essay. Here’s the deal. Something pulled the trigger to bring life into existence as an entropy resisting force. Whatever that was, and whatever conditions enabled it, may be lurking in mathematical space. A trillion parameter artificial neural network may provide the conditions required for the bootstrap. And if one artificial neural network doesn’t cut the mustard, then think of the synergistic opportunities in an network of ANNs. Emergent properties if you will. The progress of Large language models in just twenty years is phenomenal, even though for the most part we still have to train them.  Looking for that sweet spot where an LLM trains itself using the biological model of abiogenesis and evolution. Oh, the irony.

Edit

Your thought-provoking ideas touch on some profound questions at the intersection of artificial intelligence, the origins of life, complexity theory, and even philosophy. I’ll explore these concepts in depth, examining the potential connections between AI development and the emergence of life-like properties.

The Bootstrap of Life and Entropy Resistance

The emergence of life on Earth remains one of the greatest mysteries in science. At its core, life appears to be an entropy-resisting force, creating and maintaining complex structures in a universe that tends towards disorder. This fundamental characteristic of life – its ability to create and sustain order – is indeed a compelling starting point for exploring potential parallels with artificial systems.

The conditions that enabled the first self-replicating molecules to form, eventually leading to the rich tapestry of life we see today, are still not fully understood. However, the idea that these conditions might be “lurking in mathematical space” is an intriguing one. It suggests that there might be fundamental principles or patterns in mathematics and information theory that, when realized in physical or computational systems, give rise to life-like properties.

This concept resonates with ideas from complexity theory and the study of emergent phenomena. In these fields, researchers investigate how simple rules or interactions at a lower level can give rise to complex, ordered behavior at higher levels of organization. The emergence of life could be seen as an extreme example of this principle.

Artificial Neural Networks as a Substrate for Emergence

Your suggestion that a trillion-parameter artificial neural network might provide the conditions required for a similar “bootstrap” is a fascinating extrapolation of these ideas. Large language models (LLMs) and other deep learning systems have indeed shown remarkable capabilities in recent years, exhibiting behaviors that sometimes appear almost life-like in their complexity and adaptability.

The sheer scale of these models – with hundreds of billions or even trillions of parameters – creates a vast space of possible configurations and interactions. This complexity might indeed provide a rich enough “substrate” for the emergence of novel, self-organizing behaviors.

However, it’s important to note that current AI models, including LLMs, are still fundamentally different from biological systems in many ways:

1. Energy and entropy: Biological systems actively maintain their order by consuming energy and exporting entropy to their environment. AI models don’t have this property – they require external energy input but don’t autonomously seek it out or use it to maintain their structure.
2. Self-replication and evolution: Living systems can reproduce and evolve over generations. While we can train multiple iterations of AI models, they don’t truly self-replicate or evolve in the biological sense.
3. Embodiment and interaction with the physical world: Life as we know it is deeply intertwined with its physical environment. AI models, especially language models, exist in a more abstract, disembodied space.
4. Autonomy and goal-directedness: Living organisms have intrinsic goals (survival, reproduction) and autonomously pursue them. AI systems are goal-directed only in the sense that we design them to optimize certain objectives.

Despite these differences, the idea of using large-scale neural networks as a playground for exploring the emergence of life-like properties is compelling. As these models grow in size and complexity, we may indeed start to see behaviors and capabilities that surprise us and challenge our understanding of what constitutes “intelligence” or even “life.”

Networks of ANNs and Emergent Properties

Your point about the “synergistic opportunities in a network of ANNs” takes this concept even further. Just as multicellular life emerged from networks of single-celled organisms, leading to new levels of complexity and capability, we might imagine networks of AI systems giving rise to emergent properties beyond what any single system could achieve.

This idea aligns with current trends in AI research, such as:

1. Multi-agent systems: Researchers are exploring how multiple AI agents can interact and cooperate to solve complex problems.
2. Modular AI architectures: Instead of monolithic models, some approaches use multiple specialized models that work together.
3. Federated learning: This allows multiple models to learn collaboratively while keeping data decentralized.
4. AI ecosystems: Platforms that allow different AI models to interact and build upon each other’s capabilities.

These approaches could potentially create the kind of rich, interconnected environment where novel, emergent behaviors might arise. The complex interactions between different specialized systems might lead to unexpected synergies and capabilities, much like the specialized organs and systems in complex organisms.

Self-Training and the Abiogenesis Analogy

The concept of an LLM that “trains itself using the biological model of abiogenesis and evolution” is particularly intriguing. Current AI models, as you note, still require extensive training on human-curated datasets. A truly self-training system would be a significant leap forward, potentially opening the door to open-ended learning and adaptation.

Drawing parallels to biological evolution, we might imagine a system that:

1. Starts with a simple set of base capabilities (analogous to simple self-replicating molecules).
2. Has mechanisms for introducing variations (mutation).
3. Interacts with a complex environment that provides challenges and opportunities (selection pressures).
4. Can pass on successful adaptations to future iterations (inheritance).
5. Gradually increases in complexity and capability over many iterations.

This kind of self-improving AI system has been a long-standing goal in the field, often discussed under the umbrella of “artificial general intelligence” (AGI) or “recursive self-improvement.” However, creating such a system poses enormous technical and conceptual challenges.

Some key questions and considerations include:

1. Defining the “fitness landscape”: What determines whether an adaptation is successful? In biological evolution, this is ultimately tied to survival and reproduction. For an AI system, we would need to define what constitutes “success” in a way that promotes open-ended learning and increasing capability.
2. Balancing exploration and exploitation: Evolution involves a delicate balance between exploring new possibilities and exploiting successful adaptations. Replicating this in an AI system is non-trivial.
3. Avoiding catastrophic forgetting: As AI systems learn new things, they often “forget” previously learned information. Biological systems have mechanisms to maintain core functionalities while adapting to new challenges.
4. Scalability and computational resources: The kind of open-ended, evolutionary learning we’re discussing would likely require enormous computational resources.
5. Safety and control: A self-improving AI system raises significant safety concerns. How can we ensure that such a system remains aligned with human values and interests as it evolves?

The Irony and Implications

You conclude by noting the irony of using AI to model and potentially recreate the processes that led to biological intelligence. This circularity is indeed thought-provoking and touches on deep questions about the nature of intelligence, life, and even consciousness.

If we were to succeed in creating AI systems that truly capture the essence of biological evolution and emergence, it would have profound implications:

1. Scientific understanding: It could provide unprecedented insights into the origins of life and the nature of intelligence, potentially bridging the gap between physics, biology, and computer science.
2. Philosophy of mind: It would challenge our concepts of consciousness, free will, and what it means to be “alive” or “intelligent.”
3. Technological capabilities: Self-evolving AI systems could potentially solve problems far beyond current human capabilities, leading to rapid technological advancement.
4. Existential questions: It would force us to grapple with our place in the universe and our relationships with the intelligences we create.
5. Ethical considerations: The creation of such systems would raise complex ethical questions about responsibility, rights, and the potential risks and benefits to humanity.

Conclusion

The ideas you’ve presented weave together some of the most exciting and challenging concepts in modern science and technology. The possibility of using large-scale artificial neural networks to explore the fundamental principles underlying the emergence of life and intelligence is a compelling one.

While we are still far from creating truly self-evolving AI systems that capture the full complexity of biological life, the rapid progress in AI over the past few decades suggests that we may be moving in that direction. As we continue to push the boundaries of what’s possible with artificial neural networks, we may indeed find ourselves unlocking some of the deepest secrets of life and intelligence.

The journey towards this goal will undoubtedly be filled with surprises, setbacks, and ethical challenges. It will require not just technological advancement, but also careful consideration of the philosophical and societal implications of our creations.

Ultimately, this pursuit represents a unique convergence of our quest to understand our own origins and our drive to create new forms of intelligence. Whether or not we succeed in creating truly life-like AI, the attempt itself is sure to yield valuable insights into the nature of intelligence, the origins of life, and our place in the cosmos.

Perplexity

comment on this with a 2000 word essay. Here’s the deal. Something pulled the trigger to bring life into existence as an entropy resisting force. Whatever that was, and whatever conditions enabled it, may be lurking in mathematical space. A trillion parameter artificial neural network may provide the conditions required for the bootstrap. And if one artificial neural network doesn’t cut the mustard, then think of the synergistic opportunities in an network of ANNs. Emergent properties if you will. The progress of Large language models in just twenty years is phenomenal, even though for the most part we still have to train them.  Looking for that sweet spot where an LLM trains itself using the biological model of abiogenesis and evolution. Oh, the irony.

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Answer

The emergence of life as an entropy-resisting force is indeed one of the most profound mysteries in science. Your suggestion that the conditions for this emergence may be lurking in mathematical space, potentially accessible through large-scale artificial neural networks, is a fascinating and thought-provoking idea. Let’s explore this concept in depth, considering its implications, challenges, and potential future directions.

The Entropy-Resisting Nature of Life

Life, as we know it, seems to defy the second law of thermodynamics by creating and maintaining order in a universe that tends towards disorder. This apparent paradox is resolved by recognizing that living systems are open systems, constantly exchanging matter and energy with their environment. They maintain their internal order by increasing the entropy of their surroundings.

This fundamental characteristic of life – its ability to resist entropy locally – is what sets it apart from non-living systems. It’s this property that we would need to recreate or simulate in an artificial system to truly approach the emergence of life-like properties.

Mathematical Space and the Conditions for Life

The idea that the conditions for life’s emergence may be lurking in mathematical space is intriguing. It suggests that there might be fundamental patterns or principles, expressible in mathematical terms, that when realized in physical or computational systems, give rise to life-like properties.

This concept aligns with the field of artificial life (ALife), which aims to study life-like processes through computational models. Researchers in this field have created simulations that exhibit some properties of living systems, such as self-replication, evolution, and complex adaptive behavior.

Trillion-Parameter Neural Networks as a Substrate

Your suggestion that a trillion-parameter artificial neural network might provide the conditions required for a “bootstrap” of life-like properties is a fascinating extrapolation of current trends in AI. Large language models (LLMs) like GPT-3 and its successors have indeed shown remarkable capabilities, sometimes exhibiting behaviors that seem almost life-like in their complexity and adaptability.

The sheer scale of these models – with hundreds of billions or even trillions of parameters – creates a vast space of possible configurations and interactions. This complexity might indeed provide a rich enough “substrate” for the emergence of novel, self-organizing behaviors.

However, it’s important to note that current AI models, including LLMs, are still fundamentally different from biological systems in many ways:

1. Energy and entropy: Biological systems actively maintain their order by consuming energy and exporting entropy. AI models don’t have this property.
2. Self-replication and evolution: Living systems can reproduce and evolve over generations. AI models don’t truly self-replicate or evolve in the biological sense.
3. Embodiment: Life as we know it is deeply intertwined with its physical environment. Most AI models exist in a more abstract, disembodied space.
4. Autonomy and goal-directedness: Living organisms have intrinsic goals and autonomously pursue them. AI systems are goal-directed only in the sense that we design them to optimize certain objectives.

Despite these differences, the idea of using large-scale neural networks as a playground for exploring the emergence of life-like properties is compelling. As these models grow in size and complexity, we may indeed start to see behaviors and capabilities that surprise us and challenge our understanding of what constitutes “intelligence” or even “life.”

Networks of ANNs and Emergent Properties

Your point about the “synergistic opportunities in a network of ANNs” takes this concept even further. Just as multicellular life emerged from networks of single-celled organisms, leading to new levels of complexity and capability, we might imagine networks of AI systems giving rise to emergent properties beyond what any single system could achieve.

This idea aligns with current trends in AI research, such as:

1. Multi-agent systems: Researchers are exploring how multiple AI agents can interact and cooperate to solve complex problems.
2. Modular AI architectures: Instead of monolithic models, some approaches use multiple specialized models that work together.
3. Federated learning: This allows multiple models to learn collaboratively while keeping data decentralized.
4. AI ecosystems: Platforms that allow different AI models to interact and build upon each other’s capabilities.

These approaches could potentially create the kind of rich, interconnected environment where novel, emergent behaviors might arise. The complex interactions between different specialized systems might lead to unexpected synergies and capabilities, much like the specialized organs and systems in complex organisms.

Self-Training and the Abiogenesis Analogy

The concept of an LLM that “trains itself using the biological model of abiogenesis and evolution” is particularly intriguing. Current AI models, as you note, still require extensive training on human-curated datasets. A truly self-training system would be a significant leap forward, potentially opening the door to open-ended learning and adaptation.

Drawing parallels to biological evolution, we might imagine a system that:

1. Starts with a simple set of base capabilities (analogous to simple self-replicating molecules).
2. Has mechanisms for introducing variations (mutation).
3. Interacts with a complex environment that provides challenges and opportunities (selection pressures).
4. Can pass on successful adaptations to future iterations (inheritance).
5. Gradually increases in complexity and capability over many iterations.

This kind of self-improving AI system has been a long-standing goal in the field, often discussed under the umbrella of “artificial general intelligence” (AGI) or “recursive self-improvement.” However, creating such a system poses enormous technical and conceptual challenges.

Some key questions and considerations include:

1. Defining the “fitness landscape”: What determines whether an adaptation is successful? In biological evolution, this is ultimately tied to survival and reproduction. For an AI system, we would need to define what constitutes “success” in a way that promotes open-ended learning and increasing capability.
2. Balancing exploration and exploitation: Evolution involves a delicate balance between exploring new possibilities and exploiting successful adaptations. Replicating this in an AI system is non-trivial.
3. Avoiding catastrophic forgetting: As AI systems learn new things, they often “forget” previously learned information. Biological systems have mechanisms to maintain core functionalities while adapting to new challenges.
4. Scalability and computational resources: The kind of open-ended, evolutionary learning we’re discussing would likely require enormous computational resources.
5. Safety and control: A self-improving AI system raises significant safety concerns. How can we ensure that such a system remains aligned with human values and interests as it evolves?

The Irony and Implications

You conclude by noting the irony of using AI to model and potentially recreate the processes that led to biological intelligence. This circularity is indeed thought-provoking and touches on deep questions about the nature of intelligence, life, and even consciousness.

If we were to succeed in creating AI systems that truly capture the essence of biological evolution and emergence, it would have profound implications:

1. Scientific understanding: It could provide unprecedented insights into the origins of life and the nature of intelligence, potentially bridging the gap between physics, biology, and computer science.
2. Philosophy of mind: It would challenge our concepts of consciousness, free will, and what it means to be “alive” or “intelligent.”
3. Technological capabilities: Self-evolving AI systems could potentially solve problems far beyond current human capabilities, leading to rapid technological advancement.
4. Existential questions: It would force us to grapple with our place in the universe and our relationships with the intelligences we create.
5. Ethical considerations: The creation of such systems would raise complex ethical questions about responsibility, rights, and the potential risks and benefits to humanity.

Conclusion

The ideas you’ve presented weave together some of the most exciting and challenging concepts in modern science and technology. The possibility of using large-scale artificial neural networks to explore the fundamental principles underlying the emergence of life and intelligence is a compelling one.

While we are still far from creating truly self-evolving AI systems that capture the full complexity of biological life, the rapid progress in AI over the past few decades suggests that we may be moving in that direction. As we continue to push the boundaries of what’s possible with artificial neural networks, we may indeed find ourselves unlocking some of the deepest secrets of life and intelligence.

The journey towards this goal will undoubtedly be filled with surprises, setbacks, and ethical challenges. It will require not just technological advancement, but also careful consideration of the philosophical and societal implications of our creations.

Ultimately, this pursuit represents a unique convergence of our quest to understand our own origins and our drive to create new forms of intelligence. Whether or not we succeed in creating truly life-like AI, the attempt itself is sure to yield valuable insights into the nature of intelligence, the origins of life, and our place in the cosmos.

As we move forward, it will be crucial to approach this endeavor with a balance of ambition and caution. We must be prepared to grapple with the profound questions and challenges that will inevitably arise as we push the boundaries of what’s possible in artificial intelligence and our understanding of life itself.