"Memory theories" attempt to tie our personal identity to an enduring entity that is, ultimately, information.* But if quantum theory is correct quantum information is conserved, it is never destroyed.
* As opposed to "body theories" of personal identity. Early Christians, for example, expected people to be resurrected in the flesh.
Over the years I have tried to keep some statistics on my* searching. I have, for instance, tried to see how many search terms are "needed." I found that the improvement in quality of the search result falls sharply after increasing beyond four terms. I am told that the average Google search is three words/terms long.
Of course there may well be some dependence on subject matter and the search engine used.
* and to a lesser degree A.s.a.'s.
At a presentation on A.s.a. H. I was asked about symbol grounding and for pictures of my robots. I pulled up some pictures on my iPhone similar to the ones I've published here. It occurs to me that I may have shown fewer pictures because I feel that most of our robots are ugly, especially the ones that are more heavily instrumented. (Dangling lead wires also have resulted in occasional snaring and then electromechanical faults.)
Leake and Crandall* argue that issues with deep learning can benefit from its combination with case-based reasoning. A.s.a. H. , and to a lesser degree, chaining of case-based reasoners, are, in fact, deep case-based reasoners.
*On bringing case-based reasoning methodology to deep learning, ICCBR-20, pages 343-348, Springer, Cham, 2020.
The American Chemical Society committee on professional training's ACS Guidelines and Evaluation Procedures for Bachelor's Degree Programs says, under department expectations, that online labs can supplement traditional labs but not replace them. I wish the American Physical Society had published something similar to this.
The A.s.a. H. project has involved work in all but perhaps a half dozen or so of the subfields* (dynamical systems, diagrammatic reasoning, ...) that make up AI research. I had not planned on this, it simply proved necessary. It looks like nearly all of the subfields really are important and work in any of them can be a contribution. This breadth of coverage of the subfields may be the main difference between "weak or narrow AI" and "strong AI."
* See my blog of 9 September 2010.
Stephon Alexander et al* argue that the universe is a learning (and therefore a thinking) machine.
But again, such extraordinary beliefs would require extraordinary evidence.
* The Autodidactic Universe, arXiv:2104.03902v1, 9 April 2021 , see also arXiv:2008.01540 The Universe as a Neural Network by Vitaly Vanchurin, 4 August 2020.
