Saturday, April 25, 2015

Asa H's language of thought

On each level in Asa H's hierarchical memory Asa defines and evolves symbols/words/concepts/categories.  Patterns are developed between levels which link these concepts into a semantic network.  This system is Asa's language of thought.  It differs from typical human languages in the degree to which it is hierarchically structured.

Constructed memory

Like humans, Asa H has a constructed memory. Typically, there is filtering, weak inputs are not retained at all.  When retained, an averaging is usually performed (with previous very similar memories).  Only the more strongly activated cases pass on activation to the next higher level in the Asa hierarchy.  Forgetting may be used in order to clear/maintain space in (limited) memory.

A forgetting heuristic

Retain longer those memories/cases with the highest and lowest utilities.

Thursday, April 23, 2015

Work on automatic programming

At a conference a few weeks ago I was asked if I had done any work on automatic programming.  That set me thinking.  I have done a little with genetic programming, but really very little.  I suppose some of my neural network work (and, for that matter, Asa H work) could be viewed as automatic program generation from data/examples.  As I think about it, however, the most practical work I've done along these lines is probably the assembly and subsequent use of my code library.  Most of this can be viewed as a component library.

Wednesday, April 22, 2015

Asa H as an informal system

Formal systems "have to fix the language and the rules of operating on symbols beforehand....to definitely exclude subjectivity"  (see Problem solving with neural networks, Wolfram Menzel, Institut fur Logik, Univ. Karlsruhe, Germany)  Asa H, then, is an automated INformal system, a system that defines and then refines its symbols and language on each of the levels of its hierarchy.  The rules of operation that apply between levels of the hierarchy are also variable over time.

Tuesday, April 21, 2015

Useful science, useless science

I have commented before on the analysis that suggests that the majority of scientific publications are, in fact, wrong. (see New Scientist, 30 Aug. 2005)  (I'm sure that the exact proportion varies some from one scientific field to another.)

I have observed that there are also a lot of papers that may not be wrong as such but which are just not very useful.  I won't name names but there is, for instance, a lot of work in computer science that involves the same old methods and algorithms but rewritten in whatever programming language happens to be popular at that time.

Monday, April 20, 2015

The reality of the wave function or quantum fields

I have argued before that not all ontological entities are equally real.  If what is real in the world is what has explanatory usefulness then not everything is equally real.  Not all concepts/entities have equal usefulness.  Deutsch and then Wallace (The Emergent Multiverse, Oxford Univ. Press, 2012, page 389) argue that there are not enough atoms in the universe to account for how Shor's quantum algorithm can factorize a number. The required machinery must be seen to be in the form of quantum fields, not matter. This argues strongly for the reality of the high dimensionality quantum realm. (But I would not necessarily say this has to be in the form of a set of emergent, non interacting,  nearly classical, Everettian worlds.)