Roomba

We own 2 roombas (1 "roomba" model 4105 and 1 "roomba scheduler" model 4230).  I had thought to hack the roomba (Hacking Roomba, T. Kurt, Wiley, 2006) to produce and study an embodied agent. I have experimented with the roomba as it comes "out of the box."  I have a bookshelf thats a bit over a foot deep and about the same distance from a corner of the room.  Roomba gets stuck in that three sided box.  Roomba also gets stuck half under one cabinet, my rocker, and tries to climb up the base of a floor lamp (placed near the wall and a drape). As it is I don't think roomba will work even in a "slightly" cluttered room. (Most kid's rooms would be much more cluttered than that.) I suspect that if I hack roomba and control it with Asa H I might be able to produce behaviors that would fix these issues.

Sixth sense

There are bacteria that can sense magnetic fields and sharks can sense electric fields.  These are real sixth and seventh senses.  But humans don't have them.

Free will in men and machines

There are a number of different theories of free will (R. Jones, 2002 annual meeting of the Kansas Academy of Science, Fort Hays State University):
Theory #1: Free will is a self-contradictory notion (Richard Double).
Theory #2: Free will, though conceivable, doesn't exist (Marvin Minsky).
#3: Some behavior randomization is used in order to make us unpredictable to adversaries/predators/competitors.
#4: "Free will" is simply a denial that CERTAIN SPECIFIC sorts of influences were operating.
#5: Cognitive system nonlinearity allows multiple responses to identical external stimuli.
#6: Decisions are based on history/path dependent (and approximate) processes.
#7: Different decisions can result under identical conditions if the agent deliberates for different lengths of time. ("Delay libertarian theory." "Anytime algorithms.")
#8: Quantum mechanics makes it impossible for the same decision to be made again under exactly identical conditions.
#9: An agent (using genetic algorithm or other unpatterned generator) creates rules for how it should decide on actions and tests them in its daily life. Such a random algorithm could just as well have created different rules. (We are most free when we are creative, doing something truly original/unique.)
#10: Everett-like multiple worlds, your decision determines which world you enter. (A strong "free will" requires extreme assumptions about physics.)
With our current knowledge I do not see how a strong free will (like that assumed in religion and the justice system) is possible.  On the other hand, a weak free will (like #3 above, for instance) is certainly reasonable.
I think what passes for free will in humans is a combination of a number of processes like #4, #5, #6, #7, #8, and #9 above.  Complex machines would be subject to the same principles.

Media Bias

In watching the Casey Anthony and DSK coverage I note that the US media has a strong pro-prosecution bias. The more rightwing media is the worst. People think they have to have someone to hold responsible.  Do people really believe in innocent until proven guilty?

The Cycle of Theory and Experiment

Science consists of a cycle of theory and experiment.  Experiments are needed to "keep us honest."  I have criticized Hutter's AIXI (Universal Artificial Intelligence, Springer, 2005) for offering no executables. (Joel Veness, Hutter, et al, now have provided pseudocode in "A Monte-Carlo AIXI Approximation", Dec. 2010 and C++ code on Veness' website, jveness.info/)
But it's hard to get something more than qualitative results from AI experiments.  In my Asa H I have a choice of many clustering algorithms (or a combination of several of them), various similarity measures, a variety of feature extraction algorithms, and multiple extrapolation/learning algorithms.  I can also adjust the amount of processing time devoted to learning and to each kind of learning.  There are also a number of thresholds/learning rates/free parameters to set.  I have found that in these ways AI experimentation is much harder than the pure physics experiments I did 20 years ago.

(How) Can Asa H (or other AIs) Exhibit Consciousness?

Scientists have proposed perhaps a hundred different definitions of intelligence.  Asa H (Trans. Kan. Acad. Sci., vol. 109, # 3/4, pg 159, 2006) satisfies most all of these.  But the question of consciousness is a more difficult issue.
There are a number of different theories of consciousness (Some Theories of Consciousness, Ann. Mtg. Kan. Aca. Sci., Hutchinson, April 2000, R. Jones):
#1. There is no consciousness (behaviorism.
#2. Consciousness exists but plays no useful role (Roger Carpenter).
#3. The brain acts like layer after layer of feature detectors (Comm ACM, Nov. 1990, pg 63, fig. 8) starting from things like edge detectors and leading into something like grandmother cells (though these can be distributed).  You are conscious of your grandmother when this cell (or set of cells) is active.
#4. Feedback is the key to consciousness.  As in Elman neural networks you can see your own thoughts/internal signals/internal state as feedback inputs from the hidden layers.
#5. Consciousness is the contents of the global workspace, the blackboard of a blackboard architecture.
#6. Spreading activation in a semantic network.  What are conscious are the active nodes.
#7. Message aware control system (Scientific Approaches to Consciousness, Schneider and Pimm-Smith, pg. 65, Cohen and Schoolers eds., Lawrence Ehrlbaum, 1997)
#8. Metaprocessing.  Modules watching modules.
#9. Self-consciousness is your model of you, which is a part of your model of the world.
#10. Consciousness is a serial algorithm running on parallel hardware (Dennett, Consciousness Explained, Little Brown). Leads to feedback.
#11. Consciousness is the contents of the various modules' buffers (J. R. Anderson, How can the human mind occur in the physical universe?, Oxford U. Press, 2007, pg 243)
#12. Consciousness is emergent.
#13. There are multiple (various kinds of ) consciousness, many of the above.
If theories 1, 2, or 3 are correct there is nothing we need to do with Asa H.  A recurrent network like an Elman network can be unrolled and equated with a purely feedforward network.  This could be equated with a multiple layer Asa H network.  Alternatively, feedback links can be added to Asa H. This might take care of theory 4. A blackboard is a memory with multiple access (and, possibly, access control logic). If the messages between the Asa H layers are equated to blackboards this would take care of theory 5.  Asa H is a kind of semantic network as in theory 6.  As in theory 8 Asa H has certain modules (extrapolators, deduction system, etc.) watching other modules and upper layers watching the contents of the lower layers.
Are these the right kind of modules?  Are they watching the right things? Asa H can form grandmother cells.  One of these could be a "me" cell (as would develop with a robot seeing itself in a mirror for instance).  This takes care of theory 9. We would like to run Asa H on parallel hardware, theory 10.  Asa H has the kind of buffers needed for theory 11.  Do we have the right modules? Theory 12 usually assumes that emergence occurs as the size of the semantic system increases.  We are constantly trying to scale Asa H up.
Theory 7 is not a clear match with Asa H.
Asa H (and other AIs) may be conscious depending on what is the correct theory of consciousness.

ebooks

Of the last 14 books I bought 3 were available in electronic form. (Actually, 1 was kindle and 2 were nook and I only own a kindle reader.)  Also, the price of an ebook is usually a substantial fraction of the price of the book in paper.  In fact, the price of an ebook is sometimes greater than the price of the book in paper (a current example is Minsky's "The Emotion Machine").