Can the human brain, with its “crude and cumbersome” components (atoms, molecules, neural impulses), achieve higher performance than existing AI? This is a profound and challenging question that touches on the essential differences between biological intelligence and artificial intelligence. A comparison of the performance between the “crude and cumbersome” components of the human brain (atoms, molecules, neural impulses) and AI’s lighter, more agile “advanced” electronic bits (silicon chips, binary computing) requires analysis from multiple dimensions:
I. Surface Disadvantages vs. Deep Advantages: Secrets of Biological Systems#
1. Components that seem “primitive” are actually extremely sophisticated#
- Atoms and molecules:
Carbon-based molecules in the brain (such as proteins and lipids) possess self-organizational ability, enabling them to form dynamic structures (e.g., synaptic plasticity) at room temperature, whereas silicon chips require ultra-clean, nanoscale artificial manufacturing. - Neural impulses (action potentials):
Though they are slow electrical signals on the millisecond scale (a million times slower than electrons), they achieve efficient information integration through parallel asynchronous computing (synchronous firing of hundreds of billions of neurons), while AI relies on synchronous clock cycles. - Bases (DNA):
DNA is not merely genetic code; it also participates in epigenetic regulation, enabling lifelong learning (e.g., DNA methylation modifications during memory formation). In contrast, AI model parameters remain fixed after training.
2. Core advantages of biological systems#
| Capability | How the brain achieves it | AI’s implementation bottlenecks |
|---|---|---|
| Energy efficiency ratio | Complex cognition with 20 watts of power | Training GPT-3 consumes ≈ annual electricity of 120 households |
| Robustness | Functional compensation despite local damage (neural plasticity) | A single transistor failure may crash the system |
| Unsupervised learning | Infants independently construct world models through sensory input | Relies on massive labeled data |
| Causal reasoning | Understanding physical laws through embodied experience | Susceptible to adversarial examples (e.g., misclassifying pandas as gibbons) |
II. Why hasn’t AI’s “performance advantage” been realized?#
1. Limitations of electronic bits#
- Poverty of information expression:
Binary (0/1) can only express discrete logical states, while neural impulses in the brain contain multi-dimensional information such as frequency coding (e.g., 40Hz gamma waves associated with consciousness) and temporal coding (information transmitted via precise pulse timing differences). - Von Neumann bottleneck:
The separation of computation and storage leads to massive energy consumption in “data movement” (accounting for over 60% of AI chip energy consumption), whereas in the brain, memory and computation occur synchronously at synapses.
2. Fundamental differences at the architectural level#
| Dimension | Human brain | Current AI |
|---|---|---|
| Computing paradigm | Event-driven Spiking Neural Networks (SNN) | Clock-driven Artificial Neural Networks (ANN) |
| Connection mode | Small-world network (high clustering + short paths) | Regular layered structure |
| Learning mechanism | Hebbian plasticity (“neurons that fire together wire together”) | Backpropagation (requires global error feedback) |
💡 Key gap: The brain’s hundreds of billions of neurons form sparse connections through hundreds of trillions of synapses (each neuron connects to only thousands of neighbors), while AI’s dense fully connected structures lead to parameter explosion (e.g., GPT-3 has 175 billion parameters).
III. The essence of intelligence: A comparison of world model construction capabilities#
1. Human brain: An embodied cognitive engine#
- Multimodal integration:
The visual cortex, somatosensory cortex, vestibular system, etc., integrate in real time to construct an egocentric spatial map (e.g., perceiving limb positions even with eyes closed). - Predictive processing theory:
The brain continuously generates predictions about the external world (e.g.,预判ing trajectories when catching a ball) and revises models through sensory feedback—a process relying on hierarchical predictive loops between the brainstem-thalamus-cortex.
2. AI: A prisoner of statistical patterns#
- Lack of physical intuition:
Even after training on massive videos, AI struggles to understand causal chains like “a knocked-over cup will make the table wet” (requiring injection of symbolic logic). - Fragility of world models:
AI’s “world model” is essentially a statistic of data correlations (e.g., associating the text “cat” with image features) rather than an understanding of the essence of physical entities.
IV. Path to breakthrough: Next-generation AI inspired by biology#
1. Three directions drawing on the brain#
- Neuromorphic chips:
Examples like IBM TrueNorth and Intel Loihi simulate neuronal pulses, improving energy efficiency by a thousandfold, though learning algorithms remain immature. - Spiking Neural Networks (SNN):
Directly process spatiotemporal pulse sequences, suitable for dynamic perception (e.g., real-time obstacle avoidance in autonomous driving). - Bayesian predictive coding framework:
Treat AI as an “Active Inference” system that learns world models by minimizing prediction errors.
2. Potential disruption from quantum computing#
- Quantum neural networks:
Using quantum superposition for parallel information processing (e.g., Google Sycamore chip), theoretically capable of simulating quantum effects in the brain (referencing Penrose’s theory). - Challenges:
Quantum bits are highly prone to decoherence (requiring -273°C environments), far less stable than biological systems at room temperature.
Conclusion: Re-defining performance advantages#
- In the short term: AI outperforms humans in specific closed tasks (e.g., image classification, Go) due to the speed and precision advantages of electronic bits.
- In the long term: The brain remains the benchmark for open-environment intelligence (cross-modal reasoning, few-shot learning, creativity), and its atom-level “crude” components are actually the ultimate optimization polished by evolution.
For future AI to truly surpass human intelligence, it must move beyond the paradigm of “more computing power + more data” and learn from the brain how to construct dynamic world models with limited resources—as neuroscientist Carlo Rovelli put it: “Intelligence is not a product of computation, but a dance with uncertainty.”