Models see when photons hit camera sensors, you see when photons hit your retina. Both of them are some kind of sight.
When photons hit the retina, the same kind of photochemical transduction happens — but the signal does not stop at measurement. It flows through a living system that integrates it with memory, emotion, context, and self-awareness. The brain does not just register and store the light, it constructs an experience of seeing, a subjective phenomenon — qualia.
Once models start continuously learning from visual subjective experience, hit me up – and I'll tell you the models "see objects" now. Until direct raw photovoltaic information stream about the world around them without any labelling can actually make model to learn anything, they are not even close to "seeing".
We are no more in touch with physical reality than an LLM, unless you are in the habit of pressing your brain against things. Everything is interpreted through a symbolic map.
See also: feral children.
"dog": [object of class Canine]
They infer meaning from noisy, ambiguous sensory streams. The labels are not explicit, they are discovered through correlation, context, and feedback.
So although caregivers sometimes point and name things, that is a tiny fraction of linguistic input, and it is inconsistent. Children generalize far beyond that.
Real linguistic input to a child is incomplete, fragmented, error-filled, and dependens on context. It is full of interruptions, mispronunciations, and slang. The brain extracts structure from that chaos. Calling that "structured input" confuses the output - inherent structure of language - with the raw input, noisy speech and gestures.
The brain has drives: social bonding, curiosity, pattern-seeking. But it doesn't have a single optimisation target like "expand." Objectives are not hardcoded loss functions, they are emergent and changing.
You're right that lack of linguistic input prevents full language development, but that is not evidence of supervised learning. It just shows that exposure to any language stream is needed to trigger the innate capacity.
Both complexity and efficiency of the human learning is just on another level. Transformers are child's play compared to that level. They are not going to gain consciousness, and no AGI will happen in the foreseeable future, it is all just marketing crap, and it's becoming more and more obvious as the dust settles.
This is such a wildly simplified and naive claim. "Thinking with words" happens inside a brain, not inside a silicon circuit with artificial neurons bolted in place. The brain is plastic, it is never the same from one moment to the next. It does not require structured input, labeled data, or predefined objectives in order to learn "thinking with words." The brain performs continuous, unsupervised learning from chaotic sensory input to do what it does. Its complexity and efficiency are orders of magnitude beyond that of LLM inference. Current models barely scratch the surface of that level of complexity and efficiency.
> Do you have a concept of one-ness, or two-ness, beyond symbolic assignment?
Obviously we do. The human brain's idea of "one-ness" or "two-ness" is grounded in sensory experience — seeing one object, then two, and abstracting the difference. That grounding gives meaning to the symbol, something LLMs don't have.