hslu-ki-h2501

Part 3 — Perception: From Data to Experience

Learning materials

📘 NotebookLM: AI Perception: Berkeley and Merleau-Ponty
- Notebook
- Podcast
⚔️ Miro Board: Maurice vs George
- Board

3.1 Conceptual Introduction — Perceiving Machines?

Artificial Intelligence “perceives” the world through sensors and data inputs — it processes information, recognizes patterns, and makes predictions. But can it truly experience what it perceives?

Philosophers have long debated whether perception creates reality, or merely interprets it. Two thinkers, George Berkeley and Maurice Merleau-Ponty, offer powerful frameworks to question AI perception:

Berkeley: To be is to be perceived.
Merleau-Ponty: The perceiving body and the perceived world cannot be separated.

Guiding question: Does AI perceive the world, or does it merely compute it?

3.2 Application Topics — Connecting Philosophy and AI

1. AI Perception vs. Human Perception

Idea: Compare AI’s pattern recognition with human sensory experience.
Key point: AI collects data; humans interpret meaning through lived experience.
Prompt: If AI has no body or consciousness, can it truly perceive?

2. Virtual Reality and Simulation

Idea: Explore digital “worlds” and how they parallel philosophical idealism.
Berkeley’s link: Objects exist only when perceived — so what happens in a simulated world?
Application: Discuss how AI perceives “objects” in virtual environments that only exist when rendered or computed.

3. Embodied AI and Robotics

Idea: Connect Merleau-Ponty’s phenomenology to embodied AI.
Key point: A robot’s sensors, movement, and feedback create a form of situated perception.
Application: Examine how the body (shape, mobility, sensors) changes what and how AI perceives.

4. AI–Human Interaction

Idea: Human–AI feedback loops blur the line between perceiver and perceived.
Merleau-Ponty’s link: Our perception arises through interaction with the world.
Application: Explore how AI learns from human input — gestures, speech, emotions — and what this means for “shared perception.”

3.3 Philosophical Lenses — Berkeley and Merleau-Ponty

George Berkeley – Idealism and AI Perception

Key Idea:

“To be is to be perceived.” Objects exist only insofar as they are perceived by a mind.

Applications:

AI Perception vs. Human Perception: If AI does not process data, does that data “exist” for it? Compare to Berkeley’s idea that objects cease to exist when unperceived.
Virtual Reality and AI: Explore how virtual environments challenge Berkeley’s immaterialism. When AI operates in simulation, does it “create” or “discover” its world?

Discussion Cue:

Is AI’s “world” real, or only real within its data stream?

Maurice Merleau-Ponty – The Embodied Mind

Key Idea:

The perceiver and the perceived world cannot be disentangled. Perception is embodied — rooted in physical, lived experience.

Applications:

Embodied AI and Robotics: Apply Merleau-Ponty’s view to robotic perception. How does a robot’s body shape its understanding of space, form, and movement?
AI and Human Interaction: Perception happens between perceivers — not just inside one. Examine AI systems that adapt through interaction with humans (voice assistants, adaptive robots).

Discussion Cue:

Can AI achieve a kind of “embodied perception” through interaction, even without human-like consciousness?

3.4 Discussion and Reflection — Thinking Like a Philosopher

Before moving to the practical activities reflect and debate these questions:

Can an AI have an experience of the world, or only data about it?
If an AI stops receiving input, does its “world” disappear — like Berkeley’s unperceived object?
Would giving AI a body (robotic, sensory) make it closer to Merleau-Ponty’s embodied subject?
What does this comparison reveal about human consciousness and perception?

3.5 Synthesis — AI, Perception, and Reality

Aspect	Human Perception	AI Perception
Nature	Embodied, sensory, subjective	Data-driven, computational
Relation to World	Interdependent — shaped by action	One-directional — world as data input
Continuity	Continuous and experiential	Discrete and episodic
Awareness	Conscious and interpretive	Non-conscious and procedural

Takeaway: AI can simulate perception, but it does not live perception.
Berkeley and Merleau-Ponty invite us to question whether “perception” without experience is perception at all.

🧠 Mini-activity idea: Map out “what AI perceives” vs. “what humans perceive” in a diagram — and discuss what’s missing in each.

3.6 Transition to Practice

Reflect on AI perception.
Design an AI inspired by philosophical principles (Berkeley’s idealism or Merleau-Ponty’s embodiment).
Discuss ethical implications of perception and “reality” in AI.