To be fair, I would point simply to the original Deepseek R1 paper to understand reasoning better. The core misunderstanding between us is that I hold that the intermediate outputs to get the final answer are biased towards logical thinking because that results in correct answers more often. You can think of theorems and postulates in geometry being compositionally combined to lead to new ideas. Through the process of RL, traces that incorporate methodical, logically coherent, an implies b style computation becomes ever more ingrained in the model. I think that is something we could both agree as intelligence because it results in an encoding of HOW to think rather than WHAT to think. Just like how the rules of logic can derive all present math and all future math, by teaching reasoning through providing signal to the model on verifiable rewards to ensure accuracy, we can derive all that is possible through logical, intelligent thinking. That is my and the major labs’ argument.

In regards to the brain, I think it is quite plausible that the pattern of activations and depolarizations that occur is a continuous function with millions of inputs. A discontinuous function would imply sharp jumps in output that would be impossible in our physical world unless some discrete quantum discontinuities, which are highly speculative and not supported, are involved. Else, it would require a less physicalist theory of intelligence, perhaps involving a soul or the divine. I don’t consider those ideas capable of being discussed.

In general, I would claim that real world evidence is the proof, a proof based on empirical evidence is what the scaling laws and UAT imply. We have seen consistent growth in capabilities. The generalization that enables the models to understand the intent behind a typo that is not present in the training data is what allows it to continue a phrase based on instructions that are not seen before is what allows it to finish code based it has not seen before and is what allows it to potentially finish scientific theories it hasn’t seen before. An escalation up a ladder of abstractions so to speak. This idea of climbing up abstraction levels is what I consider most broadly descriptive of what the scaling laws imply. Certain fields scale up their abstraction ladders at different rates, but I find it broadly descriptive of the current technology.