Human Rights Violations

If our government is going to oppose human rights violations they should start with american businesses and industries. 

Printer Problems

In debugging Asa H 2.0 I frequently print out a hard copy of code to proof read, trace, and file.  I have 4 or 5 printers that I use but the one on my desk at ESU is an old Epson Stylus color 600.  I find that I have bugs in my code listings that are caused by hardware (printer) failure.  An equal sign in the code, = , gets printed as a minus sign, - , in the printed listing.  The first time I came across this in proof reading I thought I had caught a coding error.  More recently I have had cases where this happened 3 or more times in just a few hundred lines of code.  I may either retire the printer (if our administrators will buy me a new one) or restrict its use to word processing and the like.

Alternate Realities

At the "April" 2010 American Physical Society meeting I argued in favor of alternate realities (Bull. Am. Phys. Soc., vol. 55, #1, 2010) (See my web site http://www.robert-w-jones.com/ , click on philosopher and then alternate realities.) I find that Kevin Kelly has a similar argument on pages 381 and 382 of his book The Logic of Reliable Inquiry, Oxford Univ. Press, 1996.

Preliminary Experiments

I have trouble getting the idea of a "preliminary experiment" across to my students.  They want their first data to be perfect and want to know what to expect before they even take that first measurement.  There's also the issue of not knowing what variations to expect after you've only taken 1 (or a few) measurements.  I wish all students had taken statistics before they enter a physics lab.

They also seem to expect more of physics than of other sciences.  I recall in biology we had trouble even finding some given organ in a frog, fish, etc.  And in chemistry we just wanted to see a reaction occur or a color change, etc.  But in physics lab they don't just want to see two objects fall side by side they want g=9.80 not 10. 

There is also the thought that if you just had better equipment your results would look better.  And they would, but when you have better equipment you are also looking at things that are harder to measure.  It's a sort of arms race, as your research gets more advanced your tools get better but what you are trying to do gets harder.  As a result you mostly "run in place" on a "tredmill."

Blogs, refereed publications, and open science

How should one compare a refereed publication in SCIENCE to a blog on the web?  In the debate over arsenic-associated bacteria can one really prefer Rosie Redfield's blog over Felisa Wolfe-Simon, et al's journal article?  It seems to me that one should prefer the position that has the stronger arguments and/or greater experimental evidence.  When a paper appears in SCIENCE that makes it more likely that its arguments are probably sound (as opposed to some random web post) but in the end it is only the arguments and evidence that counts for anything.

If you're out looking for solid research you will find it more frequently in SCIENCE and less frequently on the web.  But any given paper (or blog or idea) must be judged on its own merit.


On the subject of alternate forms of life:   I believe that mechanical life is far more important to consider as opposed to the simple substitution of arsenic for phosphorus in DNA.

When a search returns a large number of hits it would be good practice to restrict your attention to refereed works.  When a search returns few hits you don't have that luxury and will have to study what you have.

Software Components

My AsaH 2.0 is assembled from standard software components like those for sorting, searching, taking vector dot products, etc. In experimenting with AsaH I have been spending more time and effort on the glue code (integrating the various components) than was spent on developing the components themselves. This makes me wonder if the component software methodology really buys us the advantages that were hoped for. I wonder what experiences others have had.

On War

Starting a war is murder and those who start a war should be tried for murder.  Of course you can plead self-defense but the issue should be decided by a neutral judge and jury.

the importance of Science

1. Our machines,nutritional and agricultural science, electricity, medicine, gas, plumbing, materials, etc. enrich our lives, make them as good as they are.

2. Science drives the progress that we have in our society. As a kid I read the book Scientists Behind the Inventors.  I recommend it.

3. Science is the nearest thing we have to a source of truth.

Imagine

"Imagine there's no countries....
and no religion too...
Imagine all the people sharing all the world."      John Lennon

Theory of Thought

The following is an abstract I am preparing for a conference early next year.

I have decomposed thought into remembering, generalization, comparison, explanation, deduction, organization, induction, classification, concept formation, image manipulation, feature detection, analogy, compression, simulation, and value assessment. (Trans. Kansas Academy of  Sci., vol. 108, pg. 169, 2005, vol. 109, pg 159, 2006, vol. 109, pg 254, 2006, vol. 110, pg 302, 2007, vol. 111, pg 174, 2008, vol. 112, pg 143, 2009, vol. 113, pg 127, 2010) Previous AI experiments like SOAR, ACT-R, and CYC have all lacked some of these capabilities. In order to implement these processes on a computer each of them is, in turn, decomposed into sorting, searching, vector averaging, vector differencing, vector dot product, sensitivity analysis, renormalization, interpolation, extrapolation, concatenation, time warping, and image manipulations like rotation, shifting, scaling, etc.  These later are all well established algorithms for which there already exist efficient, error free, standard software components.  There is no claim that this decomposition is unique.  Hopefully it is adequate, or do we need explicit attention, affect, or language modules for instance?  Also, are the linkage paths between modules correct and adequate? 

In this fashion we hope to build minds which are more rational than humans and which possess better values. (Humans are not rational.  See, for example, Predictably Irrational by D. Ariely, Harper, 2009)  We should also note that we do not expect our AI Asa H to fill the same niche that humans fill.  Asa is better than humans at certain mental tasks but certainly not all.

Asa H 2.0 data structures

The XNOR pattern:

11    1
00    1
01    0
10    0

is recorded as:  (shown unnormalized)

caselist(1) = 1
timeactive(1) = 1
featureactive(1) =1
featurestrength(1) =1

caselist(2) = 1
timeactive(2) = 1
featureactive(2) = 2
featurestrength(2) =1

caselist(3) = 1
timeactive(3) = 2
featureactive(3) = 3
featurestrength(3) = 1

caselist(4) = 2
timeactive(4) = 2
featureactive(4) = 3
featurestrength(4) = 1

caselist(5) = 3
timeactive(5) = 1
featureactive(5) = 2
featurestrength(5) = 1

caselist(6) = 4
timeactive(6) = 1
featureactive(6) = 1
featurestrength(6) = 1

as opposed to the data structures used in Asa H 1.0 (see page 165 of Asa H: A hierarchical architecture for software agents, R. Jones, Trans. Kan. Acad. Sci., vol. 109, # 3/4, pg 159-167, 2006)

Being different

"You can't be better without being different."  John Horak

(But let's not use this as an excuse for anything and everything.)

Solid state Fusion

(Rather than the common term "cold fusion" I will use the term "solid state fusion" since "cold fusion"
was meant to refer to muon catalyzed reactions.)  We should not believe in solid state fusion because it requires at least  THREE "miracles."  First, the extremely good confinement of the fusioning nuclei in the solid state media.
Second, the large production of energy with very much reduced production of radiation (neutrons, gammas, etc.).  Third, the fusion reaction in light hydrogen (not just heavy hydrogen).  Any one of these is a near "miracle" and has a very low probability of occurrence.  The possibility of solid state fusion being true is then the product of these three!  We should not believe it without extraordinary evidence. (i.e., something well beyond the evidence we expect for normal scientific claims.)

Religion

"Religion is poison."  Mao Zedong

"Such extraordinary beliefs would require extraordinary evidence."  Carl Sagan

"If this world had a creator he would have to be an incompetent."  unknown

The Workplace should be more Democratic

The workplace is currently a dictatorship.  This is a mistake and makes a business less efficient.  It has been known for a hundred years that groups can make better decisions than an individual can.  This has been proven in many experiments over the last hundred years.  Businesses must be made more democratic if they are to succeed.  Currently most businesses fail in 3 to 5 years and 90% of businesses fail within about 10 years.
For every business in the United States that makes a profit there are four others that do not.

ESS, an economical space station

Is the international space station, ISS, worth its 100 billion dollar price tag? Unless the alpha magnetic spectrometer finds something spectacular the answer is probably no.  So what would an economical space station, an "ESS", look like?  Numerous trade studies suggest to me that it would have a "permanent" core providing electrical power, cooling, guidance, communications/data transfer, robotics, life support and living facilities for 2 or 3 astronauts, an airlock, propulsion, etc.  The ESS would be man tended, not permanently manned, but might support an experiment on long term "closed cycle" life support systems.  Many of the ESS's experiments would come as temporary/replaceable plug in modules rather like the russian MRM-2 on the ISS.  The ESS would be resupplied by something similar to the russian Progress freighters which would serve to dump garbage as well as provide upmass capability.  A Soyuz like shuttle (but without the Orbital Compartment which is not needed for short duration flight to and from the ESS) would bring up astronauts as well as bring back down mass.  These shuttles could fly up unmanned in order to increase down mass capability.

Freedom in the United States

Americans talk a lot about freedom.  The republicans talked a lot about freedom recently in the run up to the elections.  But most americans have very little freedom in the workplace.  Most of us are wage slaves in our professional lives.

Why are there "so few" physics majors?

Administrators tell us that we have too few physics majors (even when we exceed the national averages).  Why are there fewer physics majors than one might want?

1. First, physics is hard.  Not everyone is capable of doing physics.  And even if you have the ability it takes many years of training to become competent at physics.

2. Second, physicists are poorly paid.  Considering how important physics (and the other sciences are) for national development physicists are poorly paid.  Two of my best friends, one a physics major as an undergraduate and one in engineering, changed to careers in law and medicine, physics and engineering just didn't pay them well enough.  Some of the best physics students just won't put up with it.

3. Third, physicists are poorly treated.  We are treated as hired hands by a management class that has poor values and bad judgement. The old term "wage slavery" is all too true.

Data Mining with Asa H and vector utility

In a recent post (Data mining with Asa H) I assumed that Asa H was using a classical scalar utility.  In my plasma confinement example U = TNt, the Lawson triple product.  T is the plasma temperature, N is the plasma density, and t is the energy confinement time.

But a better model of plasma confinement is the two component vector utility U = (T, Nt). Vector utility was included in my original paper (http://www.robert-w-jones.com , inventor, Asa artificial intelligence) and this is an example where it is useful. (Of course for a DT plasma one could use the energy gain, Q, as the scalar utility rather than TNt.)

When fusion scientists use the triple product they are being value monists, when they use the Lawson conditions they are being value pluralists.

A set of Lawsonlike conditions is also a good way to describe ion source efficiency.

Is "goodness" a vector?

Utility theory in economics is a contribution to formalizing a notion of "good."  But we now know that the traditional scalar theory of utility is an approximation (Beardon, et al, J. of Mathematical Economics, 37, 17-38, 2002) and that utility must, in general, be a vector (Thrall, Decision Processes, 1960, Wiley, NY).  Similarly, any other formalization of good-evil would be a vector quantity.

It has often been a source of confusion to observe that people can be both good AND evil.  This is now explained in terms of the various vector components, one of which might be quite "positive" (strongly good) while another component might be quite "negative" (strongly bad/evil).

Mechanical Life

I wish to argue that web-operated autonomous robots (www.robert-w-jones.com, cognitive scientist, cognitive architectures.) are an artificial life form.  They: 1. use energy, 2. respond to stimuli from their external environment (via touch sensors, webcams, etc.), 3. can grow, repair, and reproduce by file transfer/copy into new processors, bots, etc. 4. are organized and incorporate learning/adaption, 5. their software is a "genetic" blueprint that is downloaded/uploaded to new/additional hardware, 6. they exhibit feedback/homeostasis, 7. they abandon worn out/obsolete  hardware, 8. they can act on their environment.   By most definitions Asa H would be considered alive.

I believe that the invention of mechanical life is probably more important than the biochemical synthesis of "artificial life" will be. (Mechanical life may prove to be more important than biological life.)

Cross-field transport through plasma bridges/spokes

Parker, et al (Applied Physics Letters, vol. 97, 091501, 2010) suggest that electron transport across a
magnetic field in plasmas may be caused by "spokes." This looks to be the same as my Trans. of the Kansas
Academy of Sci., vol. 92, # 3/4, pg 176, 1989, "plasma bridges."

Will Artificial Intelligences be Immortal?

Futurists often argue that artificial intelligences will be effectively immortal, able to file copy themselves into replacement machines as the older ones wear out.  Similarly, if the brain replacement argument (Clark Glymour, Hans Moravec, and others) allows us to upload human intelligences into machines they suggest we too could become immortal (Mind Children, Harvard U. Press, 1988).

But in order to reduce memory size and speed up search an AI needs to delete older, less useful memories in
order to make room for newer more useful ones.  Over a long period of time you simply wouldn't be the same
"person" anymore, even if you inhabited the "same" body.

It would be good for humans to live longer.  We spend too much of our lives preparing for our careers
(typically 22-28 years) and too long in retirement (15-20 years or so).  But the only sort of immortality that
seems possible might be recurrence (Recurrences and Discrete Dynamical Systems, Gumowski and Mira,
Springer, 1980). (One would want recurrence of that portion of the world that is "you" but NOT recurrence of the rest of reality. You don't want to live the same life over again.)

Grading

I believe that grading of students is more useful for motivation than for assessment.
I believe that grading makes students work a bit harder and learn a bit more than
they otherwise would.

Equations must be compatible with their boundary conditions

A system described by the second order linear and homogeneous differential equation:

y" + p(x) y` + q(x) y = r(x)

is not compatible with just any reasonable boundary conditions, for instance the linear and inhomogeneous
boundary conditions at the end points of the interval [0,1]:

a1 y(0) + a2 y`(0) = c

and

b1 y(1) + b2 y`(1) = d

Equations and boundary conditions must not only be reasonable models independently they must also be compatible with one another.

Classroom Learning

I think people overestimate what you can learn in a single course.  Let me tell you how I learned mechanics and electricity and magnetism (and perhaps some other physics subfields as well).  I learned a bit in junior high physical science and my high school physics class.  I was taught the same things again in a first year undergraduate physics course and then in undergraduate mechanics and E&M classes.  (Electronics too.) I learned a bit more in graduate school in several mechanics and E&M classes.  I learned the rest when I taught first year physics, E&M, mechanics, and several electronics courses.  Only with ALL of this did I really know some physics.  No one course would possibly have done the job.

People expect too much of a single course. After all, neural networks typically require many observations of a given pattern before they learn it and learning is, in general, NP hard.

Weapons of Mass Destruction

The United States government and legal system have completely distorted the meaning of the term "weapon of mass destruction."  The proper military definition of a weapon of mass destruction can only be a weapon which is at least capable, in principle, of killing more than a million people at a single shot.  This would include most nuclear weapons, some biological agents, and practically no chemical agents.  A WMD is a strategic weapon, not one that can bring down a building.

The Scientific Notebook, Files, and Blogs

Back in the day we all kept notebooks.  An idealization of what these should be like can be found as chapter 6 in Writing the Laboratory Notebook by Howard Kanare, American Chemical Society, 1985.  I use this in my Advanced Lab course in physics here at ESU.  While I still have some notebooks of my own from as recently as 2001 I have since gone over to keeping files instead.  The files for my philosophical work look something like Working Notes, pg 136 of the Theory of Difference, Douglas Donkel, Ed., SUNY Press, 2001.  Perhaps a blog could become the modern incarnation of "Working Notes."  It would have the advantage (disadvantage?) of being available to everyone at once.
The disadvantage of this speed (and all things on the web) is that they are more likely to have been half baked.

Is Object Oriented Programming a Mistake?

I just read Mordechai Ben-Ari's criticism of OOP in the September issue of the Communications of the ACM
(vol. 53, No 9, pg 32, 2010).  My own criticism was: Is OOP a mistake?, www.comp.lang.C++, 26 May 2000 and www.comp.object, 22 Feb. 2001. (Note I am not criticizing MODULARITY in general.)  Gabriel has also offered: Objects Have Failed (www.dreamsongs.com/Files/ObjectsHaveFailed.pdf).

Data Mining with Asa H

The Asa H (Hierarchical Autonomous Software Agent) AI (Trans. Kansas Acad. Sci., vol 109, No 3/4, pg 159, 2006 and www.robert-w-jones.com, computer scientist, artificial intelligences) can be used for data mining. Data is input as a vector and corresponding utility (V1, U1), (V2, U2), (V3, U3).....

For a plasma confinement device V might be:

V1= (B, Nn, P, Ne, Te, t,...)

where B is the magnetic field strength, Nn is the neutral fill gas density, P is the input power, Ne is the plasma density, Te is the (electron) plasma temperature, and t is the confinement time.

The corresponding utility U might be:

U1= t Te Ne

the Lawson product.

When a set of V,U "pairs" are input to Asa H its extrapolation algorithms will suggest possible parameter changes that may improve U.

One of the extrapolation algorithms Asa H uses is:

V3 = V2 +(V2 - V1)c

where c <= 1 and U2 > U1.

Another application might be to forecast (and then avoid) arcing in an ion source.

Theory of Thought

Stopping undersea oil leaks

When the deepwater horizon incident occurred I sent the following suggestion to British Petroleum and Washington:

"An explosion near the well head on the ocean floor could remove the blowout preventer and shear the pipe off at the sea bed.  A second explosion to one side but near the exposed pipe could push soil over the well and seal it.  It might also be possible to crush/crimp the pipe."

Problems with electronic submissions

Electronic submissions for publication have their problems.  In the old days when you sent in a paper copy of an abstract they might occasionally cut off an edge when it went under the camera.  I will report on my recent experiences with the Bulletin of the American Physical Society because they have been the worst but I have had similar problems with other journals and conferences.

My last submission to BAPS had a % symbol dropped.  This did not occur in the echo to my electronic submission.  I was using the American Physical Society's own software (Latex).

The submission to BAPS just prior to that one had no errors that I noticed but the one just before that had 5 extra characters inserted in various locations throughout the abstract.  In past years I have had as many as 3 or 4 words dropped in an abstract.  Sometimes the sense was lost completely.

Scientific Pluralism

Knowledge is of an approximate character.  Our formalisms abstract and simplify.  Each theory is an idealization, often times approximating in its own DIFFERENT ways, each offering somewhat different coverage of the domain of interest.  Having MULTIPLE overlapping theories of a field is then better than having just one.
(www.robert-w-jones.com, Philosopher, changing what science is and how its done)

Throughout my career I have followed this "scientific pluralism" by trying to work on both sides of the various important questions: (for example)

magnetic confinement of plasmas     -versus-     inertial confinement

low beta plasmas     -versus-     wall confined plasmas (beta>1)

open magnetic traps     -versus-     closed magnetic confinement

adiabatic traps (magnetic mirrors)     -versus-     nonadiabatic traps (cusps)

neural networks     -versus-     g.o.f.a.i. (good old fashioned AI)

scruffy AI     -versus-     neat AI

dualism     -versus-     monism

rockets     -versus-     tethers

rockets     -versus-     space drives

I also try to do theoretical, experimental, AND computational work.

Value system for a mind

Logic of the mind

While present day computers execute classical propositional logic using operators like AND, OR, NOT, and IMPLIES (and thence NAND, NOR, EXOR, ...) a mind would require a fibring with, at least, temporal logic with operators like SINCE, S(A,B), UNTIL, U(A,B) (and thence, F, "in the future", P, "in the past", ...) as well as spatial logic with operators like "in front of", "behind", etc. and a non-monotonic consequence relation. (And probably the logic should be fuzzified.)

In addition to the fibred logic a mind would also need a value function/system.

Computer simulations versus "real" experiments

In order to pursue online instruction there is a desire to use computer simulations in place of "real"
experiments.  We even hear those who claim simulations are "just as good as" real experiments.

There are various important ways in which simulations differ from real experiments but the bottom
line is:

simulations only contain the physics that you understand and were able to program in,
real experiments also contain all the physics you haven't thought of, don't understand,
and haven't included.

Growing your research program

Most work is done by groups.  You likely begin your career working on a PIECE of a project in some
SUBFIELD.  An adviser may have given you your task and will know it is doable with the resources at
hand, is worth doing, and can help with debugging/troubleshooting if you run into trouble.

After a few such efforts and a lot of reading of the literature you will be able to choose and perform
experiments relatively independently.  Your need for the help of others will decrease.

You should first work to become an expert in your particular subfield.  This may take typically  perhaps
5 years. You should try to maintain frequent contact with coworkers both at your institution and
internationally.

One would slowly expand his study to encompass much of his entire subfield and then even explore
other portions of the parent field (but being careful not to spread yourself too thin).

An ideal job would give you the time and freedom to do these things as well as laboratory facilities,
libraries, funding, etc.  (Some time may be required just to secure such a position!)

One will typically keep files on his own work organized by project, subfield, and field.  Similar
reference files would be kept containing the work of others organized following the same system.

Getting started in research

Many graduate students feel that they are doing all the work and their adviser is doing little while putting his/her name on any publications that result.  It is useful to list some of the contributions that an adviser makes (beyond building the laboratory, taking any data himself, writing funding proposals, etc.).

An adviser typically identifies what work remains to be done in the given subfield one is working in.

An adviser identifies what problems can be attempted TODAY with the resources available LOCALLY.

The adviser can often times break the problem into pieces and perhaps even parcel these out to a group
of researchers (perhaps students).

The adviser often times is able to troubleshoot when something goes wrong.

The adviser/senior researcher may know what work is most worthwhile ("publishable").

The senior researcher likely has an extensive file of important/useful references that will help with a given
project.

Much of the adviser's contribution may be years worth of work performed long before the student came
on the scene.

In my case I must thank professors Milos Seidl and Wayne Carr for the start they gave me.

Intelligences

1.  fixed database/rulebase
     no learning
     no value function

     An example would be an expert system.

2.  learning
     no value function

     An example would be a typical neural network.

3.  learning between generations only
     value function used

     An example would be certain genetic algorithms.

4.  learning during the lifetime
     value function used

     An example would be a human (but humans have limited rationality, see, for instance
                                                      Predictably Irrational, Dan Ariely, Harper Collins,
                                                      2008, and a flawed value system founded on a few
                                                      simple drives and aversions).

     Asa H may be a better example (having a better value system and being more rational
                                                      www.robert-w-jones.com, inventor, A.s.a.).

5.  collective intelligences

     An example might be a society (it has been known for 100 years that groups can make
                                                     better decisions than an individual can).

     Another example may be a collection of Asa H agents.                    

Minimum private property

In distinguishing socialism from communism the issue of the amount of allowed private property comes up.
The amount of private property needed would surely depend upon where a person lives, what job they do, etc.  I would think we would all need a few of our own clothes, toiletries like comb, tooth brush, razor, etc.  We might need a watch and cellphone.  Until recently we would need files and notes and specialist's books (in my case for my research and the textbooks I teach from).  With the advent of e-readers like kindle we might instead be able to access books and papers not found in our local libraries.  In certain remote locations one might need private transport.  We might also need glasses (as I do), hearing aids, etc.  We would all have a few personal items like pictures of loved ones, etc.

What constitutes "core AI?"

Core AI is supposed to be the most basic most fundamental areas of AI.  This is not quite the same as the most important areas of AI.  It probably is what should be in any good AI textbook.  Certainly I would include in core AI:

1. search
2. learning
3. memory

as well as something many other researchers would omit:

4. values/utility/fitness

Most people would also throw in:

5. logic(s)
6. representation

I think a good case can be made for adding:

7. pattern recognition
8. complexity
9. heuristics
10. compression
11. statistics

Depending on how broad you want your coverage to be one would also consider (in no special order):

12. natural language
13. neural networks
14. planning
15. consciousness
16. theorem proving
17. parallel computing
18. feature extraction
19. classifiers
20 modularity
21. architectures

How much we know

We have the impression that mankind knows a great deal.  This impression may come from the fact that for 12 to perhaps 24 years we are taught mankind's collected knowledge in our schools.  Year after year we hear of mankind's accomplishments.  But we typically only hear of the things mankind does understand.  As you might expect our teachers (wisely) have only a little to say of those matters we don't understand.  So our impression is biased and may be wrong.

AI Subfields

Large difficult problems are frequently solved by first breaking them up into a set of interrelated smaller problems.  The AI subfields can be a set of such smaller problems into which AI is decomposed.  It is also useful to have a set of subfields that you can go to in order to find methods, algorithms, etc. that can be helpful in your AI work.  No such list is ever complete or unique but here is one I use:

1. weak methods
2. search
3. rules
4. semantic nets
5. logic/deduction
6. heuristics
7. discovery/creativity/induction
8. natural language
9. neural networks
10. distributed AI/collective intelligence
11. robotics/embodiment
12. compression
13. automata/state machines
14. statistics
15. Bayesian statistics
16. planning/scheduling
17. case-based reasoning/memory-based reasoning
18. blackboard systems
19. nonstandard logics (spatial logics, temporal logics, higher order logics, multivalued logics, etc.)
20. representations
21. consciousness
22. learning/data mining
23. theorem proving
24. automatic programming
25. genetic programming
26. qualitative reasoning
27. constraint-based reasoning
28. agents
29. fuzzy logic
30. diagrammatic reasoning (and spatial logic)
31. model-based reasoning
32. emotion
33. ontology
34. quantum computing
35. analogy
36. parallel computing
37. pattern recognition/comparison
38. causality
39. deductive databases
40. language of thought
41. artificial life
42. philosophy of AI and mind
43. innateness/instinct
44. AI languages
45. memory/databases
46. decision theory
47. cognitive science
48. control system theory
49. digital electronics/hardware
50. dynamical systems
51. self-organizing systems
52. perception/vision/image manipulation (and spatial logic)
53. architectures
54. complexity theory
55. emergence
56. brain modeling
57. modularity
58. hybrid AI
59. optimization
60. goal-oriented systems
61. feature extraction/detection
62. utility/values/fitness/progress
63. multivariate function approximation
64. formal grammars and languages
65. theory of computation
66. classifiers/concept formation
67. theory of problem solving
68. artificial immune systems
69. curriculum for learner
70. speech recognition
71. theory of argumentation/informal logic
72. common sense reasoning
73. coherence/consistency
74. relevance/sensitivity analysis
75. semiotics
76. machine translation
77. pattern theory
78. operations research
79. game theory
80. automation
81. behaviorism
82. knowledge engineering
83. semantic web
84. sorting/typology/taxonomy
85. extrapolation/forecasting/interpolation/generalization
86. cooperation theory
87. systems theory
88. semantic computing
89. exploratory programming
90. specialization/decomposition

There is, of course, lots of overlap in these.  Some are certainly more important than others but each might be
a source of help/information/inspiration. (My AI research library is organized into these 89 clusters of books/papers, plus some miscellaneous, textbooks, etc.)

Is our science unique to us?

It has been suggested that aliens come to earth would be more interested in our music and art than in our science. This is based on the idea that science is objective and that there is only one correct ("true") model of reality whereas arts are subjective and unique to their creators.  I think that this is an exaggeration.  I have argued (http://www.robert-w-jones.com/, philosopher, changing what science is) that 2 or more theories are better than one and that we all live in somewhat different realities from those of other people (http://www.robert-w-jones.com/,  philosopher, alternate realities).  Aliens would then like to learn of our unique categories and models of the world.  This is not to say that there aren't views of the world that are wrong.  There are.  Rather, I am just saying that what is true of the world is richer than was once thought and that what we know about the world is always approximate/incomplete.

Value monism

I have argued elsewhere against value monism (http://www.robert-w-jones.com/, philosopher, axiology).

A strong argument against value monism is the fact that the monists can not agree on what is the single thing that is to be valued.  Some suggestions have been:

pleasure

life/health/age

the number of children or grandchildren you have had

money

"utility"

"fitness"

goodness/godliness

time (but one could change this or one's age by relativistic means, and so....)

energy

etc....

Space transportation system

"Drop tanks" could be added to or removed from a spacecraft at a fuel dump in order to increase or decrease its delta v capability.

Space transportation system

Use of present technology suggests a 2 stage launcher to LEO.  Stage 1 would initially be expendable and provide altitude and perhaps about half of orbital velocity.  Stage 2 would enter LEO about empty and link up with an orbiting fuel dump.  By refueling this would allow stage 2 and the payload to depart for moon orbit, a Lagrange point, or Mars.  There stage 2 could again refuel at a fuel dump.

The ideal size would probably be smaller than the HLV designs suggested so far.  The exact optimal size depends on how much the system is to be used.  To make good use of ground facilities launchs should be fairly frequent. 

A growth option would be to develop a reusable stage 1.

Intelligence may be best represented by a vector

Gould, Kitcher, Gardner, and others have all argued against any single generalized factor of intelligence g or "IQ."   I agree that a scalar g or IQ may not be able to represent what we mean when we talk about intelligence. I believe a vector may be needed, consisting of components like:

memory  (amount, accuracy, etc.)

spatial/pictoral

logical/mathematical

speed  (of recall, of deduction, of saving to memory, etc.)

linguistic

creativity

etc.

These components of a vector intelligence measure are NOT assumed to be orthogonal.

Me

My Website

Since I first began to post on the web people have been asking me to establish a website.  I have now done that . My website is at:  http://www.robert-w-jones.com/  That site outlines my work up to 2010.  My work from 2010 onward will be described on my blog (here).