Human-robot interaction

 It is usually argued that robots with a humanoid form factor will more readily adapt to the existing human environment. But I find that in most applications we modify the environment to be more robot friendly*. In making use of Roomba, for example, I reduced the clutter and set up virtual walls. Robot lawn mowers typically have a boundary wire. Similarly, factories and warehouses are modified to accommodate the robots that work there. As robots get smarter they may do more of the adapting and we may do less. But if we cohabitate with robots I expect the result will be a compromise, not our current environment, just as factories have long been tailored to both men and machines.

* I think we also modify our activities/behavior in adapting to the use and limitations of software.

Do you use what you studied in college?

For some careers you will use what you learned in college. For others you may not. In becoming a plasma fusion research scientist I certainly used my undergraduate physics major. When I taught at the National University of Singapore I saw that businesses were eager to hire physics majors because they knew our graduates must be smart, worked hard, and were 4 years more mature than high school graduates. The college degree filtered out the better candidates for the employers.

Pluralism

 Kant argued that one can not know the thing in itself (das ding an sich). Rather, we all create models of the world. I have previously noted* 5 such alternate realities.** 

1. materialism  

2. idealism

3. dualism

4. theism

5. multiverses

In my pluralism I most frequently employ #1. Next most frequently I employ #5. I infrequently use #3 and hardly ever use #4. I never seem to employ #2. I could probably keep detailed statistics on this but it's surely dependent upon what's being studied.

* See my blog of 15 June 2022.

** There are, of course, variations*** and combinations**** of these 5.  (Neither do I insist upon keeping the number to 5.)

*** If ultimate reality was composed of simply quantum fields or the wave function that would be a variation of #1 (provided "materialism" refers to any of the concepts defined and employed by modern physics).  There are, of course, many variations of #4.

**** #3 and #4 are frequently combined, for example.                                                                                                                                                      

Robot disconnecting randomly

I was trying to transfer files from one computer (onboard the robot) to another (external, fixed) computer. My system was disconnecting but apparently only when the robot is moving. Seems to be (USB) connectors. I could hard-wire everything but that makes (subsequent) reconfiguring of things so much harder. Maybe some kind of clips/clamps on the connections to reinforce them, hold them tight? Or something like the old RS232 screw connectors.

Physical theories of everything

 Each of our concepts typically has some limited range of applicability.* Are there suitable** concepts that are universally valid? If not, then it might not be possible to create a single physical theory of everything. Rather, one might need a network of more limited theories, each of which has its own range of applicability. Perhaps a concept hierarchy (like the one A.s.a. H. creates) with theories summarizing the patterns that are seen in various portions of the concept (knowledge) web.

* See, for example, my blog of 30 May 2018.

** Physical? (As opposed to mathematical for example.)

Why should you learn to code?

Jensen Huang, CEO of Nvidia, is claiming that we should no longer learn to code. Suppose he is right. Is writing a specification easier than coding? Some computer scientists will tell you that "Formal specifications are often just as hard to read, and almost as hard to write, as code." I then must ask, is writing prompts easier than coding? Do you get what you intended in each case?

As for me, I needed to know some coding for the same reason I needed to know some digital electronics. I wanted to better understand how the computation actually occurs and in some adequate degree of detail.

Understanding occurs over various levels of abstraction. For some purposes it is sufficient to have a broad overview. For other purposes one needs to deal with finer details on a less abstracted level. I.e., there is a concept hierarchy. Some things can only be understood by working on/at the right level of abstraction using the right concepts.  And concepts that work on one level of abstraction may not be valid on another level.

Embodied AI

 Some researchers believe that embodiment is essential for AGI while others believe it imposes a bottleneck. In providing an AI with some specific sense or a particular concept I have sometimes found simulation to be easier.* In other cases I have found embodiment to be easier.**

Simulations are always an imperfect model of reality. Ultimately we want an AI to have contact with and operate in the real world. It can sometimes be faster to begin AI training on simulations, however.

* Recharging ("feeding") was one example.

** For examples see my blog of  1 Oct. 2015.

Conscious of

 My A.s.a. embodied robots learn concepts like "touch", "smell", "hunger", "pain", "bump", etc.* When one or more of these concepts becomes sufficiently activated** A.s.a. is "aware of"*** their presence. The robot is "conscious of" the sensation. More complex concepts become activated higher up in the concept hierarchy.

* See my blogs of 1 Oct. 2015 and 5 Nov. 2015 for examples.

** By sufficiently strong sensory input.

*** i.e., May react to as appropriate.

Some A.s.a. H. Societies

 I have tried various societies of A.s.a. H. agents. In a pandemonium-like configuration* all agents see the input and the one with the strongest response takes control. Another configuration has a single ("supervisory") agent receive the input and then select which (one or more) specialist agent(s) the input is sent to for response.** A third configuration is similar to Rod Brooks' subsumption architecture but the levels/behaviors and an arbiter are each replaced with an A.s.a. H. specialist agent. The behavior specialist agents all receive the input and propose output responses. The "arbiter" receives the original input and all the proposed outputs from the behavior agents and then generates an eventual output for the society. Details have varied depending on the problems/tasks being attempted.

* Due to Oliver Selfridge in 1959.

** For a large enough society with many specialists the signal can instead go into a tree of supervisory agents which route the input down to the specialist(s).

Even principles of logic are subject to change

Our human experience is very limited.* All of our concepts and models, even our logics/maths, abstract, idealize, and simplify. As our experience grows we are likely to need new logics,** new maths.

As a simple example, A.s.a. H. can be thought of as employing a kind of approximate vector logic where various sensations constitute an input vector which is processed to generate a vector output composed of actions.

* Although, of course, telescopes (and microscopes) do allow us to look away some distance as well as some way back in time.

** See, for example, Richard Epstein, Propositional Logics, Wadsworth, 2001, especially chapter XI and Paul Weingartner, ed., Alternative Logics: Do Sciences Need Them?, Springer, 2004.

There is not one single right way of doing science

 It has been said that "There are as many ways of doing science as there are scientists." That is surely an exaggeration, but there is definitely more than one way of doing science.* I have argued that being a scientist is just being intelligent (plus careful hard work).** I have also argued that intelligence is a vector quantity, one can be intelligent in various different ways.***

A given "scientific method" may also be subdivided and a scientist may specialize in just one of the resulting pieces.**** The scientific endeavor as a whole becomes a group effort. Publications may also be quite specialized/focused. I'll give an example from my plasma confinement work. We had calculated the curves describing particle and energy balance for a linear solenoid dominated by endloss. These had been verified by comparison with experiment. We then calculated (and published) the curves that resulted when other different loss mechanisms/scalings were active. These might possibly describe other confinement devices. 

Not everyone is going to be following "THE scientific method" in every one of their publications. There are different ways to do science as there are different ways in which to think.*****

* Theories of Scientific Method, Nola and Sankey, McGill-Queen's U. Press, 2007.

**See, for example, my blog of 1 September 2012.

*** See, for example, my blogs of 23 August 2010 and 8 September 2011.

**** For example: theory, computation, experiment.

***** For example: induction, deduction, abduction.

Limited reusability in A.s.a. H.

Many of my A.s.a. H. agents are specialists. They can not be reused (without modification/retraining) for some different specialty. It may be possible, however, to reuse some of the various layers (if the concepts used by the two specialists are the same and defined in the same way).   

A pantheism

With the universe as the body of god.

Evolution as the mind of god.

And evolved agents as children of god. 

Will there be yet another AI winter?

 Fully self driving cars that are always a year away.

AI "experts" that have no academic credentials.

Overpriced hardware.

Closed source software.

"Hallucinations"

Fads, hype, and fanboys.

etc., etc..

Why pluralism?

"...absolute certainty is not achievable...the depth of understanding in any field, including philosophy, often comes from exploring multiple perspectives...there are multiple ways of understanding and interpreting the world." ChatGPT 4.0

See, for example, my blogs of 26 September 2010 and 15 June 2022. 

AI curricula again

An AI should not just uncritically read from (or be trained on) the web.* Garbage in, garbage out. A mature intelligent agent would choose what to read/learn depending upon the quality of the reading material and the agent's goals and values. The early learning curriculum for an AI should be carefully chosen. Once it's matured the AI can choose for itself. 

This is one of the problems with gpt. Mainline AI research has not spent enough time/effort on curricula.

* See, for example, my blog of 6 June 2020. 

AIs learning from human publications

Humans do not share a single common world model and ontology.* The concepts that they employ differ from one another even when the same names are being used to label them. An AI model trained off human publications would, at best, learn some kind of fuzzy averaged world model and concepts.

* See, for starters, my blog of 15 June 2022. 

Realistic space launch economics

I do not agree with the optimistic estimates out of Spacex. Back of the envelope calculations, more detailed studies specifically for the Vulcan rocket*, and similar studies done by the European Space Agency are all much more pessimistic (realistic?). (But at least some of our oligarchs are putting some of their money to good use. I'll give them that much.)

* See, for example, Launch Vehicle Recovery and Reuse, M. M. Ragab, et al, American Institute of Aeronautics and Astronautics, 2015.

Adding more actuators

 We have previously noted some of the advantages to giving A.s.a. H. more sensors.* We think that there is also some advantage to its having more actuators.** Giving it two or more mobile arms, for example, rather than just one.

* See my blogs of 1 Sept. 2022 and 1 Nov. 2023.

** And actuators of various different sorts and sizes.