An important distinction here is you’re comparing skill across very different tasks.

I’m not even going that far, I’m talking about performance on similar tasks. Something many people have noticed about modern AI is it can go from genius to baby-level performance seemingly at random.

Take self driving cars for example, a reasonably intelligent human of sound mind and body would never accidentally mistake a concrete pillar for a road. Yet that happens with self-driving cars, and seemingly here with ARC-AGI problems which all have a similar flavor.

A coworker of mine has a phd in physics. Showing the difference to him between little and big endian in a hex editor, showing file sizes of raw image files and how to compute it... I explained 3 times and maybe he understood part of it now.

Doctors[1] or say pilots are skilled professions and difficult to master and deserve respect yes , but they do not need high levels of intelligence to be good at. They require many other skills like taking decisions under pressure or good motor skills that are hard, but not necessarily intelligence.

Also not knowing something is hardly a criteria , skilled humans focus on their areas of interest above most other knowledge and can be unaware of other subjects.

Fields medal winners for example may not be aware of most pop culture things doesn’t make them not able to do so, just not interested

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[1] most doctors including surgeons and many respected specialists, some doctors however do need that skills but those are specialized few and generally do know how to use email

good nit pick.

A PHD learnt their field. If they learnt that field, reasoning through everything to understand their material, then - given enough time - they are capable of learning email and street smarts.

Which is why a reasoning LLM, should be able to do all of those things.

Its not learnt a subject, its learnt reasoning.