The Current State and Future Directions of Artificial General Intelligence (AGI): A Cross-Disciplinary Perspective

1. What is AGI? How Does It Differ from Existing AI?

When discussing artificial intelligence, terms like “strong AI” or “general artificial intelligence” frequently arise. Simply put:

• Narrow AI: Systems like AlphaGo excel at Go, while GPT models specialize in text generation – but only within specific domains
• AGI: Theoretically capable of thinking, learning, and problem-solving across multiple domains like humans

“Today’s most powerful language models can write poetry, code, and even diagnose diseases, but if you ask them ‘how to tie shoelaces,’ they might generate seemingly logical yet physically impossible steps.” – AI Researcher Interview

2. AI Development Timeline: From Rule Systems to Thought Simulation

2.1 Key Milestones

graph TD
    A[1950-1960s] -->|Symbolic Logic| B[ELIZA Chatbot]
    B --> C[1980s Neural Network萌芽]
    C --> D[2012 AlexNet Computer Vision Breakthrough]
    D --> E[2016 AlphaGo Defeats Human Champion]
    E --> F[2020s Rise of GPT-style LLMs]
    F --> G[2025 Multimodal Models with Enhanced Reasoning]

2.2 Current Technical Limitations

While GPT-4 and similar models demonstrate impressive capabilities, they face fundamental constraints:

Capability Dimension	Human Performance	Current AI Limitations
Physical World Understanding	Babies learn object properties through grasping	Lack embodied experience and real perception
Causal Reasoning	Can infer “rain causes wet ground”	Relies on statistical correlations rather than true causal understanding
Continuous Learning	Lifelong knowledge accumulation without catastrophic forgetting	Requires special techniques to prevent new knowledge overwriting old memories

3. How Does the Brain Work? Neuroscience Insights for AI

3.1 Brain Functional Regions and AI Parallels

The brain functions like a precisely organized office with specialized departments:

+---------------------+-------------------+-------------------+
| Brain Region         | Function          | AI Equivalent Technology |
+=====================+===================+===================+
| Occipital Lobe      | Visual Processing | CNN Convolutional Networks |
| Hippocampus         | Memory Encoding   | Memory-Augmented Architectures |
| Prefrontal Cortex   | Decision Planning | Reinforcement Learning |
| Cerebellum          | Motor Coordination| Robotic Control Networks |
+---------------------+-------------------+-------------------+

3.2 Human Memory System Hierarchy

The human memory system resembles a three-story library:

1. Sensory Memory: Millisecond-level raw sensory input (like quickly flipping book pages)
2. Working Memory: Maintains current task information (similar to computer RAM)
3. Long-term Memory:

   • Explicit Memory: Consciously recalled knowledge (reference books on shelves)
   • Implicit Memory: Skill-based memory (muscle memory when riding a bike)

“The human brain consumes only about 20 watts daily yet performs complex reasoning that would require current supercomputing centers”

4. Current AI Architecture Breakthroughs and Limitations

4.1 Mainstream Architecture Comparison

Different transportation types each have advantages and disadvantages:

Architecture Type	Advantages	Limitations
Pure Neural Networks	Strong pattern recognition	Lacks interpretability
Symbolic Systems	Precise logical reasoning	Struggles with ambiguous information
Hybrid Architectures	Combines both strengths	High system complexity

4.2 Breakthrough Algorithm Examples

Decision Transformer Algorithm:

# Pseudocode: Converting reinforcement learning to sequence modeling
Input: Goal G, History H, Reward function R
Output: Action sequence A

1. Encode history and expected returns as trajectory input
2. Use Transformer to predict subsequent actions under future reward conditions
3. Iteratively update sequence based on observed outcomes
4. Output final plan A

Tree-of-Thoughts (ToT) Prompting:

Input: Problem P
Output: Final solution S

1. Initialize root node with initial prompt
2. Expand multiple potential reasoning paths through chain-of-thought
3. Score each path using evaluation mechanisms
4. Apply forward search and backtracking to prune branches
5. Select optimal reasoning trajectory S

5. The Nature of Intelligence: A Symphony of Memory and Reasoning

5.1 Is Compression Intelligence?

Research findings:
Information Bottleneck Theory suggests models generalize by compressing input data, preserving task-relevant features while discarding noise

Minimum Description Length Principle indicates: Simple models that better compress data typically demonstrate stronger generalization capabilities

5.2 Memory and Reasoning Synergy

Like a library requiring classification systems, efficient retrieval, and continuous updates:

Memory Type	Function	AI Implementation
Episodic Memory	Records specific events	Vector database retrieval
Semantic Memory	Stores factual knowledge	Model parameter storage
Procedural Memory	Skill-based knowledge	Reinforcement learning policy networks

6. Key Technical Pathways to AGI

6.1 Modular Architecture

The brain functions like an orchestra requiring different instruments:

• Perception Module: Processes multimodal inputs (visual/language/sensors)
• Memory Module: Hierarchically stores information across different time scales
• Reasoning Module: Combines symbolic logic with statistical learning
• Action Module: Interacts with physical or virtual environments

6.2 Embodied Intelligence

“True intelligence requires a body as a medium, just as humans understand the physical world through object manipulation”

Current progress:

• Simulation environment training (e.g., AI2THOR indoor interaction)
• Robotic physical deployment (e.g., Boston Dynamics)
• Digital twin mapping (industrial equipment digital models)

6.3 Multi-Agent Systems

Similar to human social division of labor:

+----------+     +----------+     +----------+
| Language | --> | Task      | --> | Action   |
| Understanding | Planning |     | Execution |
+----------+     +----------+     +----------+
       \           /   \           /
        \         /     \         /
         v       v       v       v
+-----------------------------------+
| Coordination Controller          |
| (Similar to Prefrontal Cortex)   |
+-----------------------------------+

7. Ethics and Safety: A Dimension Not to Be Overlooked

7.1 Core Challenges

• Cognitive Debt: Over-reliance on AI leading to human cognitive degradation
• Technical Debt: Generated code lacking maintainability
• Energy Consumption: Training large models requires massive power

7.2 Governance Frameworks

Like traffic needing traffic lights and regulations:

Framework Name	Issuing Organization	Core Principles
EU AI Act	European Commission	Tiered risk management, human oversight
NIST AI RMF	U.S. National Institute of Standards	Trustworthiness, interpretability, risk mitigation
OECD AI Principles	Organization for Economic Cooperation and Development	Human-centered, safety responsibility

8. Future Development Directions

8.1 Architectural Evolution

• Neuro-Symbolic Systems: The brain performs both logical analysis (symbolic systems) and intuitive judgment (neural networks)
• Dynamic Architecture: Adjust structure according to task requirements like Transformers
• Multisensory Integration: Unified understanding of visual/auditory/tactile inputs

8.2 Key Breakthrough Points

1. Continuous Learning Mechanism: Learn throughout life without forgetting like humans
2. Causal Reasoning Framework: Distinguish between “rain causes wet ground” and “ground is wet when raining”
3. Emotion Understanding Module: Read micro-expressions and tone variations
4. Energy Efficiency Optimization: Low power consumption with high efficiency like the human brain

9. Frequently Asked Questions (FAQ)

Q1: When will AGI be achieved?

Current expert predictions vary widely, with 37% expecting implementation in 20+ years, requiring breakthroughs in memory, reasoning, and other core capabilities.

Q2: How far are current large models from AGI?

Main gaps: Lack of physical world understanding, continuous learning ability, and goal-directed behavior.

Q3: What societal impacts might AGI bring?

Could reshape employment structures, necessitating early preparation for educational transformation, ethical norms, and governance frameworks.

Q4: How to evaluate AGI progress?

New benchmark tests should include: multimodal understanding, long-term planning, ethical reasoning, and other dimensions.

Q5: How can ordinary people participate in AGI development?

Follow open-source projects, engage in ethical discussions, and enhance AI literacy.

10. Conclusion

AGI development requires cross-disciplinary collaboration, integrating neuroscience, cognitive science, and engineering practices. True breakthroughs may come from:

• Modular architecture design
• Implementation of embodied intelligence
• Continuous learning mechanisms
• Value alignment frameworks

As the paper concludes: “AGI is not merely a technical challenge but a human project requiring ethicists, legal experts, and the public to jointly participate in shaping”