Thursday, January 10, 2019

A.s.a. multitasking

We all know that multitasking while driving is a bad idea but most humans feel free to chat with companions while driving. Similarly, a portion of A.s.a. H.'s hierarchical network can be directing a robot to a recharging station* while another portion of the network may be extrapolating, interpolating, planning, etc. on some completely unrelated problems.

* Or transporting a solar array to a sunny location.

More redesign

I am redesigning A.s.a.'s robots trying to address the problem noted in my 13 Dec. 2017 blog. I want to put as many of the pain sensors (and lead wires) as I can inside the robot bodies. I may try to make greater use of my 3 raspberry pis while I’m at it.

The human brain has no pain receptors in it. A.s.a., however, can carry thermistors* and accelerometers inside its computer brain.

* Raspberry Pis, for instance, might experience overheating issues.

Tuesday, January 1, 2019

Wall confined plasmas

Classical theory greatly overestimates the confinement of beta > 1 plasmas. Intense particle and heat loss in actual experiments has made it difficult to reach (Tn)/(B*B) =1 in order to even study the wall confinement regime.

Robot controller

Many of my robots have been tethered to computers and/or power supplies. This constrains their operation somewhat. Because of its low cost (80 U.S. $) and large number of ports (39) I have bought an EZ-Robot EZ-B V4/2 WiFi robot controller to try out.* Due to limited funds (equipment) and lab work space I typically have to disassemble a robot or two  before I can build a different one.** The New Years break may give me the time that I need to do that.

* We may still need a tether to a power source for some experiments.
** My blog of 9 Nov. 2018 addresses this issue when only the processor needs to be changed out. Modular robot designs can also help.

An argument for alternative logics

Logic is about the formalization of sound reasoning. Since there are different ways of reasoning* there are different logics.

* Those few who might dispute this would argue for a single deductive reasoning. But that would require starting from a set of absolute and eternal truths and these are not available to us.

Medical AI use

Ulloa, et al, report (arXiv:1811.10553, 27 Nov. 2018) that a deep neural network can predict patient 1-year survival from echocardiograms significantly more accurately than trained human cardiologists.

The development of AI in general might take the form of creating specialist AIs like this and adding them to a growing society of agents.* Some of my work with A.s.a. H. has been along these lines. This is the pattern by which machines have taken over other tasks from humans and draft animals, etc. (i.e. automation)**

* Kiva logistics (warehouse) robots would also be an example.

** And see my blog of 20 Sept. 2018.

A danger in education research?

Learning is, in general, NP hard. But some problems are easy or easier. There may be a temptation to simply use education research to identify the easy subject matter (and methods) and then only teach those topics or those examples. This is likely to prove popular with the student community and administration. We might then end up only teaching classical (e.g. Newtonian) physics, for example, and neglect harder things like relativity and quantum mechanics. The difficult foreign language courses have disappeared from many colleges.

I see no problem with identifying and starting the students off with the easier material. I just want to be sure that we eventually cover important subject matter even when it is difficult.