On the topic thinking about it for yourself and posting further examples as comments...
This is GPT4 thinking about convergent properties, using the post as a prompt and generating 20 plausibly relevant convergences.
Modularity: Biological systems, like the human brain, display modularity in their structure, allowing for functional specialization and adaptability. Modularity is also found in industries and companies, where teams and departments are organized to handle specific tasks.
Hierarchical organization: In biological systems, hierarchical organization is common, with higher-level structures built from lower-level components. Hierarchies are also observed in companies and organizations.
Recurrent connections: Neural networks in the brain have recurrent connections, which allow for feedback and information processing over time. Recurrent structures are also seen in supply chains and communication networks.
Redundancy: Redundancy in biological systems provides robustness and fault tolerance. This concept is also utilized in industries for backup systems and fail-safe mechanisms.
Adaptation: Biological systems adapt to changing environments to survive. Adaptation is also a crucial aspect for businesses and industries, where they must adjust to market trends and demands.
Error correction: Biological systems have mechanisms to detect and correct errors (e.g., DNA repair mechanisms). Error correction is an essential aspect of modern communication systems and data storage.
Network robustness: Biological networks (e.g., metabolic networks) exhibit robustness against perturbations. Similar robustness is desirable in communication and transportation networks.
Small-world networks: Biological networks often display small-world properties, with short path lengths and high clustering. These properties are found in social networks and the internet.
Scale-free networks: Biological networks often exhibit scale-free properties, with a few highly connected nodes (hubs) and many less connected nodes. Scale-free networks are also found in the internet, social networks, and citation networks.
Sparsity: Neural networks in the brain are sparse, with many fewer connections than theoretically possible. Sparsity is also utilized in machine learning algorithms and data compression techniques.
Decentralization: Biological systems often rely on decentralized control mechanisms. Decentralization can also be seen in blockchain technology and peer-to-peer networks.
Homeostasis: Biological systems maintain internal stability through feedback mechanisms. Homeostasis is also relevant to industries, where maintaining stable operating conditions is essential.
Oscillations: Oscillatory behavior is common in biological systems, such as circadian rhythms. Oscillations can also be observed in economic cycles and traffic patterns.
Synchronization: Synchronization occurs in biological systems, such as the firing of neurons. Synchronization is also essential in distributed computing and communication systems.
Division of labor: Division of labor is observed in biological systems (e.g., cells within multicellular organisms) and is a fundamental principle in industries and organizations.
Cooperation and competition: Biological systems display a balance of cooperation and competition. These dynamics are also observed in economic systems, business strategies, and social interactions.
Plasticity: Plasticity in biological systems allows for learning and adaptation. In industries, plasticity is important for innovation and adaptation to changing market conditions.
Evolvability: Biological systems can evolve through mutation and selection. Evolvability is also relevant in industries, where companies must be able to innovate and adapt to survive.
Self-organization: Self-organization occurs in biological systems, such as pattern formation in developing organisms. Self-organization is also observed in swarm intelligence and decentralized control systems.
Energy efficiency: Biological systems are optimized for energy efficiency, as seen in metabolic pathways. Energy efficiency is also a crucial consideration in industries and technology development.
In my view a) it broadly got the idea b) the result are in my view in a better taste for understand agents than e.g. what you get from karma-ranked LW frontpage posts about AIs on an average day
On the topic thinking about it for yourself and posting further examples as comments...
This is GPT4 thinking about convergent properties, using the post as a prompt and generating 20 plausibly relevant convergences.
Modularity: Biological systems, like the human brain, display modularity in their structure, allowing for functional specialization and adaptability. Modularity is also found in industries and companies, where teams and departments are organized to handle specific tasks.
Hierarchical organization: In biological systems, hierarchical organization is common, with higher-level structures built from lower-level components. Hierarchies are also observed in companies and organizations.
Recurrent connections: Neural networks in the brain have recurrent connections, which allow for feedback and information processing over time. Recurrent structures are also seen in supply chains and communication networks.
Redundancy: Redundancy in biological systems provides robustness and fault tolerance. This concept is also utilized in industries for backup systems and fail-safe mechanisms.
Adaptation: Biological systems adapt to changing environments to survive. Adaptation is also a crucial aspect for businesses and industries, where they must adjust to market trends and demands.
Error correction: Biological systems have mechanisms to detect and correct errors (e.g., DNA repair mechanisms). Error correction is an essential aspect of modern communication systems and data storage.
Network robustness: Biological networks (e.g., metabolic networks) exhibit robustness against perturbations. Similar robustness is desirable in communication and transportation networks.
Small-world networks: Biological networks often display small-world properties, with short path lengths and high clustering. These properties are found in social networks and the internet.
Scale-free networks: Biological networks often exhibit scale-free properties, with a few highly connected nodes (hubs) and many less connected nodes. Scale-free networks are also found in the internet, social networks, and citation networks.
Sparsity: Neural networks in the brain are sparse, with many fewer connections than theoretically possible. Sparsity is also utilized in machine learning algorithms and data compression techniques.
Decentralization: Biological systems often rely on decentralized control mechanisms. Decentralization can also be seen in blockchain technology and peer-to-peer networks.
Homeostasis: Biological systems maintain internal stability through feedback mechanisms. Homeostasis is also relevant to industries, where maintaining stable operating conditions is essential.
Oscillations: Oscillatory behavior is common in biological systems, such as circadian rhythms. Oscillations can also be observed in economic cycles and traffic patterns.
Synchronization: Synchronization occurs in biological systems, such as the firing of neurons. Synchronization is also essential in distributed computing and communication systems.
Division of labor: Division of labor is observed in biological systems (e.g., cells within multicellular organisms) and is a fundamental principle in industries and organizations.
Cooperation and competition: Biological systems display a balance of cooperation and competition. These dynamics are also observed in economic systems, business strategies, and social interactions.
Plasticity: Plasticity in biological systems allows for learning and adaptation. In industries, plasticity is important for innovation and adaptation to changing market conditions.
Evolvability: Biological systems can evolve through mutation and selection. Evolvability is also relevant in industries, where companies must be able to innovate and adapt to survive.
Self-organization: Self-organization occurs in biological systems, such as pattern formation in developing organisms. Self-organization is also observed in swarm intelligence and decentralized control systems.
Energy efficiency: Biological systems are optimized for energy efficiency, as seen in metabolic pathways. Energy efficiency is also a crucial consideration in industries and technology development.
In my view
a) it broadly got the idea
b) the result are in my view in a better taste for understand agents than e.g. what you get from karma-ranked LW frontpage posts about AIs on an average day