Permit me to add a few assumptions behind Kitana's direction:
Incomplete knowledge is a curriculum problem, not necessarily an architectural failure.
Human mistakes don't automatically prove machine mistakes must persist at the same rate.
The model assumes valid, structured, reasonably clear input. Humans themselves struggle when communication is vague or poorly expressed.
If communication quality improves, understanding quality should improve too. We already see this principle in mathematics, programming languages, legal writing, and scientific notation.
Kitana's goal is not to know everything, but to learn, connect, and communicate from what it knows in a traceable way.
Kitana doesn't aim to be more knowledgeable than humans, only close enough to feel alike and reliable.
Error is not impossible even among humans. The objective is simply to reduce it as close to the practical minimum as structure, learning, and verification allow.
And perhaps the most amusing part of the experiment: if successful, Kitana could exist as a small offline system, measured in megabytes rather than billions of parameters. 😊
For further actions, you may consider blocking this person and/or reporting abuse
We're a place where coders share, stay up-to-date and grow their careers.
The circularity is what I'd want to see you tackle next. A dictionary defines "run" using words like "move" and "fast", which are themselves defined by other words, so tracing meaning eventually loops back on itself with no ground floor. Humans break out of that loop with sensory experience, a kid learns "hot" by touching something hot, not by reading the definition. Where does Kitana's grounding actually bottom out, or is the dictionary both the start and the floor? Also curious how it handles a word like "bank", where structure alone can't tell you river or money without the surrounding sentence.
On Grounding:
Meaning is not grounded in sensory experience like "touching hot." It is grounded in structure. A word is understood when traversal through its definition graph closes without hitting a dead end. That closure is the signal of understanding. If traversal fails, the system does not infer or guess — it returns "unknown / not yet learned."
On Context ("bank"):
Words are not resolved in isolation. Meaning is selected through context-driven traversal. Surrounding words activate specific regions of the definition graph and constrain the path that can be taken. For example, in the presence of "river" or "water," the traversal naturally routes through the geographic sense of "bank," not the financial one. Context does not redefine meaning — it selects the correct traversal path within the existing structure. 😊