When you store your knowledge and skills as parametric curves (as all deep learning models do), the only way you can generalize is via interpolation on the curve. The problem is that interpolated points *correlate* with the truth but have no *causal* link to the truth. Hence hallucinations. The fix is to start leveraging causal symbolic graphs as your representation substrate (e.g. computer programs of the kind we write as software engineers). The human-written software stack, with its extremely high degree of reliability despite its massive complexity, is proof of existence of exact truthiness propagation.