Mind, AI & ConsciousnessDeep Dive #9Explainer

What does it mean for AI to understand?

Can AI Understand?

Date
January 22, 2026
Location
Harbour Centre, Vancouver
Attendance
Not recorded

The explainer

Understanding is a crowded word: competence, grounding, relation, and felt sense get mashed together. This explainer pulls them apart so a reader who skipped the session can still follow why benchmark wins do not settle the meaning question.

Four things people mean by “understand”

Semantic accounts care about symbols and reference. Functional accounts care about what the system can do with a concept. Experiential accounts care about whether there is a felt grasp. Relational accounts care about a signal, a context, and an interpreter: meaning as something that happens between, not only inside.

The Chinese Room remains the stress test. Correct answers can show competence or rule-following without resolving whether the system has intentionality or a world. Fluent behaviour is evidence of skill. It is not proof of meaning, and it is not proof of experience. Embodiment and vulnerability show up here as candidate grounding conditions, not as decorations: a system that never has to be wrong in a shared world may never need the kind of meaning people are pointing at.

What a benchmark cannot close

A leaderboard can show transfer, explanation, or error detection. It cannot show that the system’s tokens are about the world in the way a person’s words are, or that anything is understood from the inside. Keep those claims on separate lines. Otherwise “the model understands” becomes a mood.

Session history

Deep Dive #9 (January 22, 2026, Harbour Centre, Vancouver) tested semantic, functional, experiential, and relational accounts. No single definition won. The public page is based on a recovered, anonymized organizer transcript and uses no participant quotations or attributed views. The Deep Dive #9 dossier on the microsite is the session shelf, not a second copy of this explainer.

Nearby questions in the series

If fluency is doing too much work, read whether LLMs understand or only simulate thinking. If grounding needs a body, see whether consciousness requires a body. If competence might run with no inner life, continue at p-zombies.

Further reading

Use the Library’s Chinese Room trail and the Deep Dive #9 shelf there. The Community Source Index keeps receipts. mac.bc-ai.ca remains a sibling surface.

Key debates

  • Whether formal symbol manipulation is enough for understanding
  • Whether meaning depends on embodiment, vulnerability, or participation
  • How functional skill differs from subjective understanding

Readings

Where the room landed

The room tested semantic, functional, experiential, and relational accounts of understanding. No single definition won: fluent behaviour remained evidence of competence, not proof of meaning or experience.

#meaning#understanding#chinese-room#sensemaking#llm

Try it yourself · 3 interactive

Walk through the experiments from this session

These widgets turn the session questions into thought experiments. They are not evidence of room consensus. State stays in your browser.

Build a four-lens profile of a system without turning unknown experience into a score.

Recap instrument

Calibrating instrument...

Evidence and limits

Capabilities can support functional claims. They do not establish subjective experience.

Photos coming

MAC sessions ran small (~20 people, Chatham House rules). Where event photos surface, they'll be embedded here.

You might also like

Go deeper

The MAC microsite has the interactive version

We built a separate interactive site for this series, with visualizations, thought experiments, and a glossary of consciousness terms. It's the full deep-dive experience for this session.

For #9: try The Grounding Machine, Chinese Room 2.0, Meaning Triangle.

Explore the interactive deep-dive