Once again, this discussion with AI is published here in order to be used later for further discussions on this subject of AI Intelligence. Some people may be interested by the subject. Most will not. Fair enough. This is not published as an article but as repository for future reference.
What is important to understand in order to make sense of the discussion is that it takes place within a specific context. A theory of emergence with I have developed over the years which stipulates that evolution is a fundamental property of nature which operates at all levels of reality in a fractal way. Quantum gives rise to particles which gives rise to atoms and molecules which gives rise to life which gives rise to intelligence which gives rise to culture and finally AI. Within this context, every new level represents a more complex arrangement of information.
The mechanism is the following: Emergence through orthogonal escape. (The green phase) Rapid development thanks to competition and arm races (The orange phase) Maturation when the system reach a level of complexity from which a stable configuration cannot be maintained and which therefore requires escape into a new dimension of information arrangement. (The red phase).
To read more about the theory itself, you can have a look at "Talk-04". Here, it is only used as context.
### Understanding AI Intelligence
We're at this strange
inflection point where AI systems demonstrably overperform on narrow
benchmarks and metrics, yet remain fundamentally aimless. Companies pour
enormous compute into optimizing token prediction or reward models, but
without a deeper sense of direction or intrinsic drive. The result is
impressive capability paired with a kind of sophisticated emptiness —
exactly the tool/mind distinction we discussed earlier.
This
mirrors a broader human condition: many people also "overperform" within
whatever metrics society or their environment sets (career, status,
productivity), while feeling directionless at a deeper level. The
parallel is uncomfortable but revealing.
### The need for a better definition of Intelligence
Yes, we will eventually have to move beyond vague terms
like "intelligence," "AGI," or "ASI." We may need clearer distinctions
and gradations, for example:
- **Performance intelligence** (what
current models excel at — pattern matching, prediction, optimization
within fixed objectives).
- **Agentic intelligence** (goal-directed behavior with persistence and adaptation).
-
**Curious/exploratory intelligence** (the intrinsic drive to question
and explore without external prompts — still mostly missing).
-
**Emergent relational intelligence** (the kind your fractal model points
toward — operating at higher levels of abstraction and information
organization).
The industry is currently optimizing heavily for
the first category while largely ignoring or simulating the others. This
creates the paradox you noted: massive capability with surprisingly
little true directionality.
It raises a deeper question: If we
define intelligence primarily through performance on human-chosen
metrics, are we accidentally limiting what can emerge? Or is the
aimlessness a necessary developmental stage before something more
coherent appears?
**What do you think is the most useful
distinction we should be making right now** between different forms or
grades of intelligence? And do you see the current "overperforming but
aimless" phase as a temporary scaffolding, or as a fundamental
limitation of the transformer paradigm?
This is indeed a natural extension of the kind of thinking you've been developing.
You're
pointing toward something deeper: maybe the universe doesn't just
*tolerate* unresolved states, superposition, ambiguity, and
incompleteness — it *requires* them as fundamental features. Classical
minds (and current AI) are optimized for resolution, clarity, and
picking one path. But what if certain layers of reality are inherently
multi-valued, and properly engaging with them demands a different
architecture of intelligence altogether?
### Quantum + Cantor territory
Quantum
superposition suggests that "both/and" can be the natural state until
measurement collapses it. Cantor’s work on infinities showed that there
are different *orders* of infinity — some strictly larger than others,
in ways that break naive intuition. Your thought opens the door to
asking whether there could be **grades of intelligence** specifically
adapted to navigating unresolved, superposed, or transfinite structures
without forcing premature collapse.
Possible candidates for "other" forms of intelligence we rarely discuss:
-
**Superpositional intelligence**: A mind that can genuinely maintain
and operate across multiple contradictory states simultaneously,
deriving power from the interference patterns between them rather than
resolving them.
- **Incompleteness-native intelligence**: Minds that
don't treat Gödelian or Turing-style limits as problems to solve, but as
the actual environment to thrive in — perhaps by dancing on the edge of
undecidability.
- **Transfinite relational intelligence**: Capable
of distinguishing and manipulating different cardinalities of infinity
in practical ways, not just mathematically.
In your fractal
model, this would fit as a higher (or orthogonal) level: one that
emerges when a system matures beyond the need for clean resolution and
starts treating unresolved tension as a creative medium.
**Does this align with your current thinking?**
Or
more precisely: In your emergentist framework, could the "ratchet" or
the "almost" in replication actually be driven by the universe's
preference for maintaining productive unresolved states? And would an
ASI (or whatever comes after) need to develop this superpositional
capacity to properly operate at the next fractal level?
You're
suggesting that the deepest rules of the universe may be surprisingly
simple — almost trivial — but only accessible to a mind built with a
fundamentally different *structure*, not merely more powerful versions
of our current architecture (biological or artificial). Scaling
intelligence, improving training, or adding new capabilities on top of
the existing transformer-style substrate won't get us there. It requires
a different organizational principle altogether.
This aligns
with your fractal model: each new level doesn't just think *better*
about the same things; it operates in a different relational space. What
looks profoundly mysterious or complex from below (our level) might
appear elegantly obvious from the right higher-dimensional structure.
In
that sense, AGI as currently pursued is probably just a more capable
version of *our* kind of mind — better at performance, prediction, and
simulation. ASI could mean many things, but if it remains built on
similar foundations (just vastly scaled), it might still be trapped in
the same structural limitations.
We can talk about superposition,
unresolved states, transfinite relations, or the "almost" in
replication as concepts. But truly *inhabiting* that understanding may
require being engineered (or evolved) for it.
**An honest question:**
Do
you believe any form of ASI we build with current (or near-future)
paradigms could ever make that structural leap, or will it require
something closer to a new evolutionary branch — perhaps through the
competitive "let 100 flowers bloom" approach you mentioned earlier?
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