Creativity experiments with Asa H

I have argued that our concepts and our science is not unique (my blog of 29 August 2010 and of 31 December 2010 , my poster at the American Physical Society meeting Bull. Am. Phys. Soc., vol. 55, #1, 2010 and my website http://www.robert-w-jones.com/). Asa H learns its own concepts at each layer in its hierarchy.  It will be interesting to see what concepts it forms, where they resemble our own, and where they are unique. By adjusting thresholds so as to require finer scale clustering one can produce more and more precise concepts.

Themes

In my blogs of 16 Sept. 2010 I gave advice on how to begin research and then build up a research program. This is a continuation of that advice.

Some researchers are "polymathic," their research is diversified.  A good example would be Leo Szilard who did work in:
thermodynamics
X-rays in crystals
nuclear physics
metallurgy and engineering (his patent of the nuclear reactor with Fermi and his weapons work)
bacteriology
biochemistry

There are disadvantages to this style of work.  More time must be spent on "getting up to speed" in each new research area.  One must learn different tools, methods, and basic knowledge and get to know and be known by different sets of colleagues.  This impacts what journals you can hope to publish in and what funding you can hope to obtain.

Certainly nowadays interdisiplinary work is very important but for many researchers it is better to stick to some research theme.  One theme I followed in my fusion energy work was open system end plugging.  A more popular fusion energy theme is tokamak studies.

Sticking to a research theme has various advantages.  It is easier to keep a steady source of funding.  Once learned, you stick to a common set of tools, methods, and basic knowledge.  You become known in the community and by the journals you need access to in order to publish your results.

More Experiments With Asa H

I have been working on the following abstract for a conference early next year:

Our recently developed "Asa H" software architecture (KAS Transactions 109 (3/4): 159-167) consists of a hierarchical memory assembled out of clustering modules and feature extractors. Various experiments have been performed with Asa H 2.0:
1. prefer extrapolation from real recorded patterns over extrapolation from synthetic cases
2. record signal input only when it changes by several standard deviations
3. include the number of times a pattern has been seen as a contribution to this pattern's utility
4. weight a pattern's feature more if the feature's standard deviation is smaller
5. search for short sequences common to longer patterns which have high utilities
6. search only until a "close enough" match is found
7. if a component of a case vector varies less (as measured by its standard deviation) then value it (weight it) more
8. as a postprocessor Asa outputs can be made the reference input commands to adaptive approximation based controllers
9. after some number of tries if we can't improve all components of a vector utility then approximate with a scalar utility
10. store and update a mean and standard deviation for time warpage and spatial dilation.  Use these as components in the subsequent degree of matching
11. longer memory for very low utility cases (so we can avoid them)
12. prefer pattern changes that involve agent output change
13. try randomness detection as a filter at the input and at other levels in the network hierarchy
14. compression by blending; if V1 and V2 are similar enough replace them with their vector average

(One sort of*) Attention

In Asa H (Trans. Kansas Acad. Sci., vol. 109, pg 159, 2006) a similarity measure (usually a vector dot product) is used to decide if an input vector, IN, falls into some category, Ini. If the dot product of IN with Ini > ThA (some threshold value) then IN is a member of category i. 

One can add a second threshold, ThB < ThA and if IN dot Ini < ThB then IN is not a member of category i.

If, however, ThB < IN dot Ini < ThA then we can look more closely at the degree of match between IN and Ini.  For instance, we can tune the time warping or spatial transformations like scaling, shifting, rotating, etc. to see if a better match is possible (>ThA).

*Various sorts of attention have been employed in Asa H.  As the input comes in as a function of time we may stay with the currently active case/category, i , so long as the match remains strong enough (similarity measure exceeds some threshold TH).  We can avoid search until the match drops below TH.  This is a sort of attention (to category i) present in Asa H 2.0 lite (see my blog of 10 Feb. 2011)

MPCV

NASA's MPCV makes good use of the work done on Orion but water landing seems to me to be a step backward.  I would also add something like Soyuz's orbital compartment.  But that could be added later and launched separately.

Progress

It is difficult to define progress.  If the values of each individual human can be averaged or summed and so extended to all of society then what is desired is long life for the individuals (i.e. an increasing average life expectancy) and a growing population. (See my Theories of Value Change, Trans. Kansas Academy of Sci., 2006 and my blog of 21 Sept. 2010)  If this is the correct definiton of progress then resource limits would demand that mankind soon expand into space.
The situation could be improved if humans could be replaced by nanoscale artificial intelligences. (see google group sci.space.tech , 15 March 1999, R. Jones)
(Life's "purpose" is to perpetuate and expand itself.)

7 Careers

Carl Bialik in The Wall Street Journal (3 Sept. 2010) cites 7 as the likely number of careers one will have in a working lifetime.  I note that is just the number I've had: http://www.robert-w-jones.com/
(And the average career lasts more than 5 but less than 8 years.)