I think you're invoking hypotheses that are far too general and complex to explain the failure of what was probably a simple program. I would explain the doctor-patient's failure to Transcend the same way I would explain EURISKO's failure. You have a limited set of heuristics, and a limited set of problems those heuristics apply to, and a limited set of improvements those heuristics can achieve. And when the heuristics have made those improvements, then the new heuristics might improve faster, but they can't improve in qualitatively new ways. Looking at Hal Finney's capsule description, it's obvious that Docpat (to coin a name) was even less sophisticated than EURISKO, which explains the less impressive results.
I can't reference the page that would make it all clear, because I haven't posted it yet. But in my technological timeline, there's a long difference between a "self-optimizing compiler" - cool as they are - and a seed AI.
The problem is multiplex, but three of the most important aspects are circularity, scalability, and generality. Deep Blue was scalable; if you piled on speed, you got a qualitative change in the kind of thought that was taking place. With Docpat, piling on speed just got you the same results faster.
The next aspect is circularity; with Docpat, and even EURISKO, the thing being optimized was too similar to the thing doing the optimizing, and both were too simple. The Elisson design partially addresses this by having the total intelligence optimize a single domdule, the operation of which is very different in character from the operation of the total intelligence.
And part of it is that the heuristics or algorithms or whatever simply
weren't general enough, which the Elisson design addresses by having
multiple architectural domdules of sufficient richness to form thoughts
about generic processes, just as our own intuitions for causality and
similarity and goal-oriented behavior apply - wrongly or rightly - in
domains from mathematics to automobile repair. (Note that, in contrast
to the classical AI and Standard Social Sciences Model dogma, thinking
about generic things takes tremendous richness and lots of
special-purpose code, not simplicity and generality - you have to have
so many intuitions that you can draw analogies to the components or
behaviors of almost any Turing-computable process.)
--
sentience@pobox.com Eliezer S. Yudkowsky
http://pobox.com/~sentience/tmol-faq/meaningoflife.html
Running on BeOS Typing in Dvorak Programming with Patterns
Voting for Libertarians Heading for Singularity There Is A Better Way