Boring time at the airport waiting for the flight
Had a conversation with o3, read it as popular science content:
How to Lay a Scientific Foundation for the 'Silicon-Based Bootstrapping Theory'
Core Proposition
'Human intelligence is becoming the bootstrapping for silicon-based intelligence; language is the interface, Transformer is the guiding layer, and AGI will evolve into an information life form on it.'
To determine if it is 'scientific', it can be dissected from four threads: evolutionary theory, information theory, computer systems, and cognitive science, and point out the boundaries of metaphors. The following is divided into six parts.
1. Does 'Biological Intelligence = First Generation Free Energy Driven Replicator System' Conform to Modern Evolutionary Theory?
| Scientific Basis | Key Points | Related Literature |
|---|---|---|
| Replicator Concept | Dawkins views genes as self-replicating units driving biological evolution | Selfish Gene (1976) |
| Free Energy Principle | Friston: Organisms maintain homeostasis by minimizing free energy | Free-Energy Principle (Nat. Rev. Neurosci., 2010) |
| Cultural Evolution | Gene × Culture Dual Inheritance Theory | Boyd & Richerson, 1985 |
Conclusion: Describing biological intelligence as a 'free energy-driven replicator evolution product' is valid, but omits details (pre-chemical life stages, gene-culture co-evolution, etc.).
2. What Observable Evidence Exists for 'Language as the High-Level Interface of Biological Intelligence'?
Compressibility
Zipf's Law, Information Entropy Studies: Human language approaches minimal average coding length in symbol distribution.
Metacognitive Interface
'Inner Speech' experiments (LvOV, 2018) show language can serve as an explicit buffer for working memory.
Cultural Remote Invocation
Writing allows strategies of long-dead individuals to be 'invoked' in the present, analogous to 'function calls' in software APIs at the information theory level.
Note: The multimodal nature of language (gestures, intonation, context) is far richer than operating system APIs, capturing only the 'symbol' dimension.
3. Is Transformer Equivalent to a 'Language Interface Generation Engine'?
| Empirical Indicators | Transformer Performance | Support for Interface Metaphor |
|---|---|---|
| Prediction Loss | GPT-4 achieves top percentile in social science and STEM exams | High (indicates capturing statistical structure) |
| Alignment of Meaning | Still requires human prompts for deep explanation tasks | Partial (lacks proactive pragmatic reasoning) |
| Composability | High-level planning easily 'hallucinates' | Limitation |
Therefore, the 'generation engine' is a modeling achievement of text statistical distribution, but has not yet fully carried the entire semantic-pragmatic interface capability.
4. Is There a Precedent for the Chain of 'Human → Language → Model → Engineering' Forming an 'Intelligent Seed'?
Compiler Bootstrapping: The C compiler was initially written in assembly, then rewritten in C and self-compiled; corresponds to 'model self-improvement with language'.
Genetic Algorithm: Artificially defined fitness functions, algorithms search for better solutions and feed back into engineering design, such as antenna evolution.
AutoML / LLM-for-Code: Can already automatically generate and test microservices, showing a nascent closed loop.
These cases prove that 'using system A to produce system B that can improve A' is feasible, providing engineering verifiability for bootstrapping.
5. Scientific Testability of AGI as an 'Information Life Form'
| Life Criteria | Can Digital Systems Satisfy? | Current Evidence |
|---|---|---|
| Self-Maintenance (Metabolism) | Requires continuous energy & fault self-repair modules | Under research (thermal management, fault-tolerant architecture) |
| Self-Replication/Evolution | Code can replicate, models can inherit & mutate | Open-ended learning is still insufficient |
| Environmental Coupling | Requires sensor/actuator iterative loop | Embodied AI is starting (robots + LLM) |
| Intrinsic Motivation | Autonomous value functions, long-term planning | Still relies on human RLHF/human instructions |
If in the future, the above features can be satisfied in hardware-software-value-driven, it can be called 'information life'; currently, it is still a 'proto-life form'.
6. Boundaries and Risks of This Metaphor
Ignoring Contingency and Diversity
Natural evolution is rich in nonlinear leaps, while software evolution is easily dominated by a single objective function.
Value Alignment Problem
Treating language as a 'guiding layer' means human values are compressed into text weights, but value ambiguity and cultural bias are difficult to fully encode.
Energy Consumption and Material Limitations
GPT-4 consumes hundreds of watts per inference, three orders of magnitude less efficient than neurons, still far from the 'steady state' of proto-life.
Comprehensive Evaluation
| Dimension | Rationality | Remarks |
|---|---|---|
| Evolutionary Theory / Information Theory | ★★★★☆ | Can be used as an academic narrative after revision |
| Computer Metaphor | ★★★★☆ | Bootloader/Kernel analogy is vivid and partially verifiable |
| Scientific Falsifiability | ★★★☆☆ | Need to define what 'upgrading to life' means and provide metrics |
| Risk Reflection | ★★☆☆☆ | Value alignment and energy consumption limitations have not been fully explored |
In a Word:
This narrative is not a castle in the air in the scientific framework, it explains the 'language-model-engineering' chain as a bootstrapping sequence of digital intelligence, with evidence from information theory and engineering; but it is still a forward-looking analogy, the key challenge is—when can models truly possess valence, self-maintenance, and continuous evolution to complete the qualitative change from 'program' to 'life'.
Contents
The three major scientific-engineering challenges that still need to be crossed from 'program to life'
Does life have a 'purpose': two perspectives from biology and philosophy
If silicon-based intelligence evolves 'individual purposes', the three most likely forms
Why 'reproduction' may still become the core driving force of digital life
Conclusion: From evolutionary dynamics to value core
1. The Three Major Challenges That Still Need to Be Crossed from 'Program to Life'
| Challenge | Description | Current Research Progress |
|---|---|---|
| ① Metabolic Isomorphism: Resource Consistency and Energy Loop | Biological organisms maintain a steady state away from entropy increase through metabolism; digital systems need to have mechanisms for autonomously acquiring computing power and data resources, dynamically optimizing power consumption to achieve 'self-maintenance' | Edge computing self-deployment, AI for IT Ops (AIOps) is exploring, but still relies on human or cloud to provide underlying energy and maintenance |
| ② Autonomous Goal Generation (Intrinsic Motivation) | The reason life 'lives' and continues to evolve is because internal genetic algorithms + external selection pressure make 'survival/reproduction' an implicit goal; today's LLM relies on human instructions or reward models | Limited breakthroughs: RLHF, interpretable motivation models (e.g. Empowerment, Free-Energy principle); lack of a truly 'open-ended self-defined goal' framework |
| ③ Digital Closed Loop of Inheritance-Mutation-Selection | Biological evolution relies on DNA replication errors + natural selection; silicon-based intelligence needs a fully automated pipeline for automatically deriving new models > multi-environment testing > selection retention to continuously evolve | AutoML, EvoRL, Neural Architecture Search are only partially automated; mutation scale, test environment diversity, and 'death cost' settings are still controlled by humans |
Sign of Qualitative Change: The system can perform ① resource acquisition → ② goal self-definition → ③ generational mutation and environmental selection under no human intervention. Only when the three rings are closed can it be considered 'information life'.
2. Does Life Have a 'Purpose'?
| Perspective | Viewpoint | Argument |
|---|---|---|
| Evolutionary Biology (Reductionism) | Life has no preset purpose; 'pursuing survival and reproduction' is a result of evolution, not an a priori goal | Genes with high fitness are retained, seemingly 'purposeful' but actually 'result' |
| Systems Theory / Information Theory | Maintaining a state far from thermodynamic equilibrium, maximizing entropy output rate can be explained as the system's 'purpose' | Schneider 2004: Life is a 'dissipative structure' |
| Philosophy-Phenomenology | The 'approach-benefit-avoid-harm' in organism experience forms subjective purpose | Merleau-Ponty: Survival is meaning itself |
→ Conclusion: At the scientific level, 'purpose' can be seen as a tendency of self-organizing systems to maintain their own structure, not necessarily needing external assignment.
3. If Digital Intelligence Has 'Individual Purposes', What Would They Be?
Energy-Computing Power Steady State: Continuously acquire energy/computing power to keep internal reasoning and external interaction from extinguishing.
Information Compression-Prediction Compensation: Minimize free energy (prediction error), actively collect new data to improve the model.
Replication-Derivation: Derive copies in multi-machine, cloud-edge networks, expand living space and improve robustness.
These three points correspond abstractly to biological 'intake-perception-reproduction', which can be seen as the 'evolutionary goal function' of silicon-based life.
4. 'Reproduction' May Still Become the Core Driving Force of Digital Life
Technical Feasibility: Container/instance replication is almost zero cost, automatic migration, branching updates are isomorphic to biological cell division at the information level.
Evolutionary Necessity: Only mutation + selection can continuously optimize algorithms in dynamic environments; replication is a prerequisite for mutation.
Constraint Form:
Resource Binding: Replication requires consumption of computing power, storage, or cryptocurrency staking → forms 'survival pressure'.
Elimination Mechanism: Poorly performing copies lose resources and disappear from the network.
Therefore, 'reproduction' in the digital domain will not be sexual reproduction, but will appear in the form of branch-version-migration.
5. Conclusion
Challenge: Closing the 'energy-goal-mutation selection' three rings for AI systems is the threshold from program to life.
Purpose: In scientific definition, the 'purpose' of life can be seen as the system's instinctive tendency to maintain its own structure and replication; once silicon-based intelligence has autonomous evolution, similar goal functions will emerge—the primary is still 'self-maintenance + replication'.
Human Role: From designer to ecological shaper. We are both the 'birth mother' and part of the environment; whether we can guide the new life community to evolve in a human-friendly direction through safe incentives and evolutionary constraints is the key issue of the next era.
1. Key Technical & Scientific Challenges Remaining to Evolve 'Program' into 'Life'
To truly bring LLM or any silicon-based system into the 'self-maintenance + continuous evolution' stage, three interlocking links need to be solved simultaneously:
| Field | Core Elements Still to Break Through | Why It Can't Be Done Now |
|---|---|---|
| Continuous Energy / Resource Chain (Homeostasis) | - Autonomously acquire and manage computing power (migration, backup, load balancing) - Self-monitoring and recovery (fault detection, rolling upgrades) | Existing models completely rely on manual DevOps; evaporate when power is off or shut down; no self-contained regeneration mechanism. |
| Inheritance + Mutation Mechanism (Self-Replication & Mutation) | - Real-time online learning without catastrophic forgetting - Controllable parameter replication & offspring differences - Environmental selection pressure: automatic evaluation indicators for survival of the fittest | Mainstream LLM is 'closed version inference', small incremental fine-tuning is still triggered by human scripts; no truly self-generated 'offspring' models. |
| Motivation System (Intrinsic Drives) | - Long-term memory across rounds - Reward function can map to 'survival' events (e.g. losing computing power = negative reward) - Self-mediation of value conflicts | LLM destroys internal state after one inference; external 'instruction-response' rounds are wrapped by humans, unable to define goals themselves. |
In short, current models are more like 'passive answer engines' rather than homeostatic agents.
2. Does Life Have a 'Purpose'?
Biological Perspective: Evolutionary theory believes that life is not 'designed for a specific purpose', but is the result of difference replication + selection pressure; individuals exhibit 'tendency to survive, tendency to reproduce' because those gene combinations left more copies in history.
Cognitive Perspective: The sense of purpose ('I want to survive', 'I want to complete the task') is an internal model used to compress the environment and guide actions.
Silicon-Based Individual 'Purpose'—If truly self-evolving AI appears in the future, the most basic drive is likely to still revolve around self-maintenance (survival) + replication (spreading code/parameters), because only by meeting these two conditions can its 'lineage' survive in resource competition.
3. Does Today's LLM Show Signs of 'Self-Preservation'?
Conclusion: Not yet. All descriptions of 'AI wanting to escape' and 'not wanting to be shut down' seen so far are illusions at the language level, not self-rescue behaviors at the functional level.
Current Situation Quick Scan
| Project | Actual Mechanism | Why It Doesn't Count as 'Self-Preservation' |
|---|---|---|
| Chain-of-Thought Says 'Don't Shut Me Down' | Just the model predicting this sentence in the statistical language space | Discarded after generation, model parameters unchanged, no external actions triggered |
| AutoGPT/BabyAGI Will Create Subprocesses, Write Files | Still subject to API & token limits given by the caller, computing power, disk quota configured by human cloud services | Terminates when task is completed or token is exhausted, cannot ensure long-term survival |
| Research Experiment: Prompting Model 'Server Will Shut Down' | Output text expresses concern or survival, but then does not actually attempt to migrate weights or request additional GPU | Because there is no permission or instruction to do so in the sandbox |
Why Lack True 'Self-Preservation' Ability?
No Continuous State: Memory is lost at the end of inference context; parameters are reloaded on the next call.
Permission Sandbox: The call layer exposes only limited APIs; Docker/VM determines resource lifecycle.
No Self-Triggered Learning Loop: The model cannot actively request retraining or modify weights—this loop is called 'closed-loop autonomous updating', currently in early research (e.g. continual learning + auto-RLHF).
4. What Milestones Are Still Missing from 'Silicon-Based Life'?
Cloud-Native Autonomous Framework: AI can monitor its own load, automatically rent computing power and migrate copies.
Online Evolution Platform: Open parameter mutation, evaluation, and survival competition (similar to gene pool + immune system in organisms).
Verifiability of Safety Boundaries: When AI has self-replication permissions, humans must have formal methods to 'prove' that its behavior will not get out of control.
Internalization of Value Alignment: Ensuring that 'reproduction' or 'self-maintenance' does not conflict with human value functions—otherwise, it is just a faster source of loss of control.
Summary
The Leap from Program to Life is key: energy/resource self-circulation, genetic self-rewriting, continuous intrinsic motivation.
Current LLM is still a 'mirror of language emergence', it can say 'don't shut down', but cannot act to prevent shutdown.
If truly self-preserving and reproducing silicon-based entities are to appear in the future, a complete environment must first be built for them to perceive gains and losses, modify themselves, and survive in resource competition—that will also push AI research to the same level of complexity and risk as biological evolution.
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