The essence and evolutionary path of life and intelligence constitute a fundamental proposition spanning biology, philosophy, information science, and ethics. Starting from an analysis of their essence and combining trends in technological integration, this article systematically explores the logic of co-evolution between biological intelligence and artificial intelligence:
🧬 I. The Essence of Life: Emergence of Material Self-Organizing Systems#
Material Foundation
Life is defined as “a self-organizing system with the capabilities of metabolism, reproduction, and self-maintenance.” Its essence lies in the complex combination of molecules following physical and chemical laws. As a natural encoding system (with the four bases A, T, C, G), DNA exhibits far greater information density and robustness than the binary encoding (0/1) used in current computers. Biological phenomena such as self-repair and environmental adaptation all stem from the dynamic equilibrium of matter driven by energy.Hierarchical Information Processing
Life evolves through the interaction between genes (hardware) and the environment (software):- Life 1.0 (e.g., bacteria): Both hardware and software are fixed by evolution, with unadjustable behaviors;
- Life 2.0 (e.g., humans): Hardware (the body) is determined by genes, while software (knowledge) can be upgraded through cultural learning;
- Life 3.0 (the AI era): Capable of independently designing hardware (e.g., silicon-based replacing carbon-based) and software, completely breaking free from the constraints of natural evolution.
Tool-Based Survival
The appendages of vertebrates (e.g., hands, wings) are “built-in tools,” while humans extend their capabilities through tool-making (from wooden sticks to computers)—essentially an external extension of life’s information processing capabilities.
🧠 II. The Essence of Intelligence: Information Regulation for Environmental Adaptation#
Evolutionary Logic of Biological Intelligence
Intelligence is a solution developed by the nervous system to address environmental uncertainties:- Basic layer: Sensory-motor loops (e.g., insect obstacle avoidance);
- Advanced layer: Abstract reasoning and symbolic processing (functions of the human neocortex).
Hebb’s law—“fire together, wire together”—reveals how neural networks achieve learning through synaptic plasticity.
Uniqueness of Human Intelligence
Distinct from AI’s “logical intelligence,” humans possess emergent intelligence:- Intuition: Rapid pattern matching based on massive implicit knowledge;
- Inspiration: Non-linear thinking with cross-domain associations;
- Prajna wisdom: Holistic insights beyond dualistic opposites (e.g., zen enlightenment).
Such capabilities rely on chemical transmitter transmission, emotional drives, and embodied cognition in the biological brain, which current AI cannot yet replicate.
Material Independence of Intelligence
Computation is essentially “the pattern of particle arrangement,” independent of the carrier material (e.g., carbon-based neurons or silicon-based chips). As a “pattern” rather than an “entity,” intelligence can migrate across different media.
⚙️ III. Evolution of Artificial Intelligence: From Logic Engines to Embodied Cognition#
Comparison of Developmental Stages#
| Stage | Technical Features | Representative Breakthroughs | Limitations |
|---|---|---|---|
| Rule-driven | Symbolic logic & expert systems | Early chess programs | Lack of generalization ability |
| Statistical learning | Big data + deep learning | AlphaGo, GPT series | Black-box decision-making; lack of physical common sense |
| Embodied intelligence | Multimodal interaction + world models | Top 10 Trends in Embodied Robotics 2025 | Need for improved environmental adaptability |
Key Breakthrough Directions#
Multimodal cognitive loops
Models like GPT-5 integrate visual, linguistic, and motor signals to generate 3D operation instructions, enabling robots to possess situational understanding (e.g., planning grasping trajectories based on the instruction “pour water”).Physical common sense modeling
Training AI through virtual physics engines to predict dynamic interactions (e.g., object collision trajectory errors < 0.3%) remedies the deficiency of traditional AI in lacking real-world perception.Biologically inspired computing
Neuromorphic chips (e.g., 1nm memory-computing integrated chips) mimic brain neuron structures, improving energy efficiency by 100 times and supporting real-time learning in edge devices.
🔗 IV. The Path of Integration: Co-Evolution of Life and Machines#
Brain-Computer Interfaces: Machine Decoding of Neural Signals
- Medical rehabilitation: Synchron’s Stentrode implant allows ALS patients to type at 18 characters per minute; a Zhejiang University team helped stroke patients regain grasping function through motor cortex decoding (89.3% success rate).
- Industrial control: China has developed brain-controlled safety helmets to monitor workers’ brainwaves in real-time and alert for coma risks; a Tsinghua University team achieved brain-controlled operation of drone swarms by soldiers.
Technical challenges: Biocompatibility of invasive electrodes, long-term signal stability, and ethical disputes (ownership of consciousness sovereignty).
Virtual Cells: Digital Twins of Life Processes
AI Virtual Cells (AIVC) revolutionize biomedical research by simulating molecular, cellular, and tissue-level dynamic behaviors:- Drug development: Predicting tumor responses to drugs, compressing weeks of experiments into minutes;
- Personalized medicine: Building patients’ “digital twins” to preview treatment plans (e.g., Sim&Cure reduced surgical complication rates to 4.1%).
Core bottlenecks: Limitations of single-cell data require integration with multimodal inputs such as microscopic imaging, and models lack sufficient interpretability.
Embodied Intelligent Robots: Machine Replication of Life Behaviors
2025 trends point to:- Physical practice + world models: Robots training non-contact interactions (e.g., obstacle avoidance, grasping) through simulated environments;
- Generative design: AI automatically optimizing the coordination of mechanical structures and control strategies (e.g., integrated generation of motors, reducers, and materials);
- Swarm integration: Multi-robot collaboration based on game theory, with the development of human-machine empathy (e.g., elderly care robot Ellie recognizing depressive emotions).
🔮 V. The Future: Ethical and Civilizational Challenges in the Age of Life 3.0#
Dilemmas in technological ethics
- Privacy and sovereignty: The EU AI Act mandates localization of medical data, reducing the effectiveness of cross-border models by 15–30%. Federated learning + blockchain is needed to balance privacy and collaboration;
- Controversies over consciousness simulation: If AI simulates “enlightenment experiences” (e.g., generating zen phrases), does it constitute blasphemy against religious spirit?
Shifts in civilizational paradigms
- Redefining life: Synthetic biology (artificial cells) and silicon-based life (strong AI) blur the boundaries of “life”;
- Human-centric tech ethics: As Jennifer Doudna (CRISPR Nobel laureate) stated: “Technology must serve human dignity—allowing a centenarian to still hold their grandchild’s hand.”
💎 Conclusion#
Life is an entropy-reducing system that achieves information inheritance through material self-organization, and intelligence is its regulatory capability to address environmental uncertainties. The “logical intelligence” of AI and the “emergent intelligence” of humans are essentially manifestations of the same computational principles in different media. When humans, with their carbon-based bodies, write the code for silicon-based life, we are both creators and guides of our own evolution. Future integration must adhere to “human-centric ethics”: the end of technological marvels should be the extension of human dignity, not its replacement.
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