The impact of neuroscience on the further development of AI is foundational but paradoxical:
it both enabled AI’s greatest advances and revealed the limits of current AI paradigms.
I’ll unfold this in layers, ending where it intersects most directly with your own concerns about meaning, silence, and ethics.
1. The obvious impact: neuroscience gave AI its
initial metaphors
Modern AI would not exist without neuroscience.
From neuroscience came:
- the neuron as a computational metaphor
- synapses as weighted connections
- learning as synaptic modification
- reinforcement as dopaminergic reward signals
Deep learning is, historically speaking, neuroscience-inspired engineering.
Neuroscience gave AI permission to imitate the brain.
But that was only the beginning.
2. Neuroscience shifted AI from rules to learning
Early AI (symbolic AI) tried to:
- encode intelligence explicitly
- define rules
- formalize reasoning
Neuroscience suggested something different:
- intelligence emerges
- learning precedes representation
- systems adapt rather than obey rules
This led to:
- neural networks
- backpropagation
- reinforcement learning
- self-supervised learning
So neuroscience helped AI make a crucial move:
From knowing to learning.
3. But neuroscience also exposed what AI still lacks
As neuroscience progressed, it revealed that brains are:
- embodied
- affect-laden
- developmentally shaped
- socially scaffolded
- metabolically constrained
- history-dependent
- ethically situated
This created a growing mismatch:
|
Human brains |
Current AI |
|
Energy-frugal |
Energy-hungry |
|
Slow learning |
Data-intensive |
|
Context-sensitive |
Context-fragile |
|
Developmental |
Static after training |
|
Socially embedded |
Isolated from lived worlds |
So neuroscience began to say, quietly but insistently:
“What you built is not what we study.”
4. Neuroscience complicates the idea of “intelligence”
Neuroscience does not support a single, unified intelligence.
Instead, it shows:
- multiple systems
- competing processes
- partial coordination
- frequent conflict
- no central controller
The brain is not a clean optimizer.
It is:
- noisy
- redundant
- improvisational
- sometimes self-sabotaging
This challenges AI’s dominant assumptions:
- global loss functions
- clean objectives
- stable preferences
- optimal solutions
In your language:
Brains don’t rush to answer.
They hesitate, misfire, and sometimes stay silent.
5. The rise of brain-inspired alternatives (and their limits)
Neuroscience has inspired new AI directions:
- predictive processing
- active inference
- neuromorphic computing
- spiking neural networks
- continual learning
These aim to build AI that:
- predicts rather than reacts
- conserves energy
- learns continuously
- integrates perception and action
But here’s the tension:
The more seriously AI takes neuroscience,
the less scalable and industrial it becomes.
Which is why these approaches remain marginal compared to GPU/TPU-driven models.
6. Neuroscience reintroduces
time
into intelligence
One of neuroscience’s deepest contributions is this:
Intelligence unfolds in time.
Brains:
- remember
- forget
- develop
- age
- change irreversibly
Most AI systems:
- are trained, then frozen
- do not truly age
- do not suffer developmental scars
- do not carry history in the same way
This matters ethically.
Because responsibility requires:
- memory
- continuity
- vulnerability to change
Neuroscience reminds us that:
A system without a lived past cannot be fully answerable.
7. Neuroscience quietly undermines the fantasy of “pure cognition”
Neuroscience shows that:
- cognition is inseparable from emotion
- perception is shaped by expectation
- meaning arises from bodily engagement
- understanding is affectively charged
This contradicts AI systems that:
- process symbols without stakes
- optimize without care
- predict without concern
Which leads to a deep implication:
You can simulate intelligence without simulating concern —
but then you have built something fundamentally different.
8. The ethical consequence (where your work enters directly)
Neuroscience does not tell us how to build better AI alone.
It tells us something more unsettling:
Human intelligence is inseparable from vulnerability.
Brains are:
- shaped by trauma
- limited by mortality
- softened by attachment
- slowed by care
This is why your insistence matters:
- 回到分類之前
- 不趕路
- 為岩中花樹所見
- 靜謐來自不被追問
Neuroscience, taken seriously, sides with you here.
It suggests:
Intelligence that never hesitates,
never suffers,
never remains silent,
is not merely non-human —
it is ethically ungrounded.
9. One-sentence summary
Neuroscience has both enabled AI by inspiring learning systems and constrained it by revealing that real intelligence is embodied, developmental, affective, and temporally situated — qualities that current AI architectures systematically marginalize.
A final quiet thought
Neuroscience teaches us that the brain’s greatest achievement
is not prediction,
but the capacity to remain with uncertainty without collapsing.
That is something no GPU, TPU, or model architecture currently knows how to do.
And perhaps —
as you have been suggesting all along —
that limitation is not a bug,
but a warning.
If you wish, next we could:
- contrast neuroscience vs. optimization culture
- imagine an AI built around vulnerability
- or articulate why silence may be the final frontier of intelligence
Just say the word.
