AI doesn't need to learn everything, our LLM Models already contain EVERYTHING. Including ways of how to find a solution step by step.
Which means, you can tell an LLM to translate whatever you want, into a logical language and use an external logic verifier. The only thing a LLM or AI needs to 'understand' at this point is to make sure that the statistical translation from left to right is high enough.
Your brain doesn't just do logic out of the box, You conclude things and formulate them.
And plenty of companies work on this. Its the same with programming, if you are able to write code and execute it, you execute it until the compiler errors are gone. Now your LLM can write valid code out of the box. Let the LLM write unit tests, now it can verify itself.
Claude for example offers you, out of the box, to write a validation script. You can give claude back the output of the script claude suggested to you.
Don't underestimate LLMs
This might be a dogma that needs to die.
Do you? Anything anywhere you could point me to?
The algorithms live entirely off the training data. They consistently fail to "abduct" (inference) beyond any language-in/of-the-training-specific information.
Anyhow, philosophically speaking you are also only exposed to what your senses pick up, but presumably you are able to infer things?
As written: this is a dogma that stems from a limited understanding of what algorithmic processes are and the insistence that emergence can not happen from algorithmic systems.