The Top 5 Misconceptions About AI Right Now

Can only read up till the first graf of the first point, but the first point is really good. > Humans are low-data learners. Infants acquire a basic understanding of objects, persistence, containment, support, and causality through relatively sparse but richly structured interaction with the environment. They are not ingesting terabytes of text. They are embedded in the world, acting in it, failing in it, and updating on the basis of feedback. Their concepts emerge not from passively absorbing records of prior linguistic behavior but from tightly coupled perception-action loops. That matters. > > If intelligence in biological systems depends on embodied, intervention-rich learning, then treating ever-larger datasets as a substitute for experience is not merely incomplete. It may be fundamentally misguided. We are taking the residue of human cognition—texts, images, labels, annotations—and mistaking it for cognition itself. > > This is the deeper problem with the current paradigm. It assumes that grounding can be deferred, approximated, or eventually washed out by scale. Train on enough data, and perhaps the problem disappears. But there is no good reason to think that. More of what is ungrounded does not become grounded simply by accumulation. Correlation does not turn into reference by getting bigger.