So here we are for a week of teaching online. I did a couple of online lectures earlier in the week. Now when I go to get my email on OUTLOOK my account name and password are accepted just fine. But when I try to access CANVAS or FACULTY and ADVISING tools (to enter grades) my password is not accepted. The IT people had me deal with this by changing my password. That fixed things yesterday but the problem reoccurred today with the new password. I found that when I enter the password the computer changes the account name entry to something else! I was finally able to log on by doing things in reverse order, going back and putting in the account name after entering the password. In that way I got into CANVAS and was able to enter the final exams for my classes. (Next week is online final examination week.) Don't know why the computer wants to change my account name to something incorrect.
Tuesday, December 1, 2020
"As the story goes, Godel was preparing for his US citizenship examination in 1947 when he claimed to have found a 'loophole' in the constitution that allowed the US to elect a dictator." Crazy Donald's current coup attempt* makes it clear that Godel was right to be concerned.
It must be possible to prosecute a sitting president.
* Al Gore is the one who had the US presidency stolen from him, not crazy Donald. (Setting us back twenty years in trying to deal with climate change.)
Thursday, November 26, 2020
In Hume's bundle theory objects/things are simply collections of properties and relationships. But properties may depend upon the observer. In relativity theory mass, color, length, and duration, for instance, depend upon the relative motion of the observer so different observers will not experience identical realities. Andrea Oldofredi also argues for this* in Rovelli's relational interpretation of quantum mechanics where "...the notion of an absolute, unique reality is abolished."
* See arXiv:2011.10295v2 23 Nov. 2020
Monday, November 23, 2020
Perhaps the biggest constraint on me is a shortage of lab and office space. To simplify prototyping with the Raspberry Pi I frequently make use of a Pi-Top . I have now bought an Agilo Research "Evive" in order to simplify prototyping with Arduinos.*
* I also have several Arduino simulators.
Thursday, November 19, 2020
It has been suggested that learning by being told in a natural language may be what makes human intelligence unique.* A.s.a. H. has done this for some time now.** One early example was A.s.a. learning "Obstacle, slow down, turn left." A.s.a. hears/reads this sentence and does what its been told.** A.s.a. also uses natural language to report back to me things that it has learned.
* For example, Deokgun Park, Language Acquisition Environment for Human-Level Artificial Intelligence, arXiv:2011.09410, 19 Nov. 2020.
**Chapter 1 of my book Twelve Papers, www.robert-w-jones.com, Book.
Friday, November 6, 2020
I am hacking the Odyssey "Turbo Runner" in order to give the A.s.a. H. society of agents another flyer. The Turbo Runner is also encaged and is smaller than the DSstyles "Sky Walker" that I have used previously.*
* See my blogs of 6 February and 12 February 2020.
Wednesday, November 4, 2020
Wednesday, October 28, 2020
A.s.a. H.* is a deep learning network in which backprop layers have been replaced by clustering.** When trained to recognize typical human concepts/categories*** A.s.a does not seem to be vulnerable to the kind of adversarial attacks that other deep learning networks fall victim to.**** If and when we use neural networks as preprocessors that may introduce a vulnerability.
* R. Jones, Kansas Academy of Science Transactions, vol. 109, num. 3/4, pg 159-167, 2006 and my blogs of 14 May 2012 and 10 February 2011.
** See Introduction to Artificial Intelligence second edition by W. Ertel, Springer, 2017, pg 280.
*** Learning from a curriculum like that in my 12 September 2020 blog.
**** See Explaining and Harnessing Adversarial Examples, Goodfellow et al, ICLR, 2015.
Monday, October 26, 2020
Even now that we have Raspberry Pis available I do my code development and initial debugging on a desktop. I then transfer the code to a Raspberry Pi where I complete any final debugging*. This may have to be done with the actual robot sensors and actuators. Sometimes this can be done with the robot on a teststand. Sometimes it will require that the robot be operating in its real world environment. An intermediate stage of debugging may be possible with a Raspberry Pi in a Pi-Top or similar prototyping environment.**
* To the degree that debugging is ever final. Really, we simply continue to debug until we have a software package that meets our basic needs. It's never fully debugged.
** The Pi-Top  has been useful for this but is limited to Raspberry Pi 3s.
Friday, October 23, 2020
My colleagues and students at ESU have been doing robotics with Arduinos and C. I have been asked how to interface robotic sensor input with Raspberry Pis using C. Here's one way using GPIO pins 4 and 5:
Friday, October 16, 2020
For the 2020 general election I requested a mail ballot. I got it and voted today for Biden and Harris.
Last time around it felt like it was bad people voting for a bad man.* But, of course, "good" and "evil" must be represented by a vector quantity.** The components of this vector*** are different for different people. A man can simply be both good and bad at the same time but in different ways and as judged by different observers.****
* See my blog of 9 November 2016.
**See my blog of 20 October 2010.
***See my blogs of 12 November 2016 and 6 April 2019 for some possible examples.
****Here I intend to make excuses more for the people doing the voting and less for the man being voted for.
Thursday, October 15, 2020
"This country, with its institutions, belongs to the people who inhabit it. Whenever they grow weary of the existing government, they can exercise their constitutional right to amend it, or exercise their revolutionary right to dismember it or overthrow it." Abraham Lincoln, first inaugural address, 1861
Monday, October 12, 2020
After using mostly Apple and IBM computers and Unix I came to ESU which had adopted Windows PCs. There is so much software out there for Windows that I slowly adopted it too. Similarly, over the last few years, as I look at other people's AI work I have been forced to read more and more Python code. My AI code library previously consisted mostly of BASIC, PROLOG, C/C++, and LISP programs. To this I have now added 50 or 60 PYTHON programs.
Tuesday, October 6, 2020
As students contracted covid and went into quarantine I was asked if "someone might videotape my lectures" for use online. I was happy to say yes since I was thinking to do this very thing if we all went into lockdown again.* It quickly became me that was expected to do the taping. During the first one or two weeks the videos only successfully uploaded to CANVAS** about half the time. The IT people reloaded all of the software, then ultimately changed out the entire computer in the "smart classroom." On the first day using the new hardware I turned the monitor an inch or two toward me, there was a "bloop" "bloop" sound, and the screen went black. Loose plug. I think (hope!) all is now working. There is still a bit of a distraction having to limit myself to perhaps two of my four blackboards, etc.***
*I'm not sure if having videotaped lectures available online makes it more likely healthy students will cut classes. Videos are certainly not as good as the real thing.
** CANVAS is a cluttered mess. Whoever designed it violated the K.I.S.S. principle.
***On top of which I have only half the usual number of lectures available this semester. (Because of Covid limits on the number of people in a classroom.) This means I must go faster. That, in turn, probably means I will make more mistakes.
Wednesday, September 23, 2020
For many years we have been doing much of our interviewing via teleconference calls. We are now doing all of our department meetings via Zoom. Virtual scientific conferences are also on the rise. The American Physical Society's division of plasma physics annual meeting is a virtual meeting this year.
There is a RAND report, Challenges in Virtual Collaboration, TK5105.6.W35, 2005, that shows that things like Zoom and teleconferences are not as good as the real thing.
Saturday, September 12, 2020
The best curriculum for training any given AI agent is probably dependent upon the specialization that that agent will take up. For the case of an artificial general intelligence I've felt that perhaps one should begin by giving the agent something like the set of concepts listed in my blog of 1 October 2015, then filling in the remaining concepts needed for the Toki Pona language. From there one can build up the vocabulary of Ogden's Simplish language. After that, reading of dictionaries and an encyclopedia. (This tends to emphasize human conceptualizations and vocabulary of course while deemphasizing possible alternative concepts.)
Tuesday, September 1, 2020
At one time or another I have taught A.s.a. H. much* of Ogden's Simplish (Basic English)**. Rather than reading the internet perhaps A.s.a. should read a good dictionary, grow its vocabulary, and then read a good encyclopedia.*** The whole issue of AI curriculum again.
Humans typically employ a fairly large vocabulary. What can be done with a small vocabulary like Toki Pona and what requires a larger one? Is greater compression simply placing more demands upon context?
* I don't necessarily want to give A.s.a. concepts of church and religion for example.
** What vocabulary an agent needs depends, of course, on its specialization.
*** There are computer programs to translate English into Simplish. I don't know how good they are.
Wednesday, August 19, 2020
Tuesday, August 11, 2020
A.s.a. H. can make use of various clustering algorithms including Grossberg's adaptive resonance theory. I have an ultralight version of A.s.a., using a.r.t., written in Python, running on Raspberry Pis, which can be carried by and interfaced with mobile robots. I have a second small program like this but using k means clustering, a third using another learning vector quantization algorithm, and a fourth employing Kohonen's self organizing map (allowing some comparison between different clustering algorithms).
Sunday, August 2, 2020
Thursday, July 30, 2020
* Quantum mechanics and relativity, for example.
** And may be used in real emergencies like the covid-19 pandemic.
Friday, July 24, 2020
* See my blog of 1 January 2016 for example and the end of my 23 August 2017 blog.
** This may be further enhanced when we give A.s.a. the very austere Toki Pona language.
Thursday, July 23, 2020
* M Tegmark, Fortsch. Phys., 46: 855-862, 1998.
Thursday, July 16, 2020
* Shown in my blog of 7 January 2012.
Tuesday, July 14, 2020
Monday, July 13, 2020
* See my blog of 12 October 2010.
* See my blog of 23 August 2010.
Monday, July 6, 2020
* registered at a single time step
Monday, June 22, 2020
* See, for example, my blog of 15 Oct. 2010.
Thursday, June 18, 2020
* See, for example, my blogs of 1 Oct 2015 and 5 Nov 2015.
** See, for example, my blogs of 23 Jan 2013, 19 Oct 2015, 21 Feb 2020, and 27 Feb 2020.
Wednesday, June 10, 2020
* See my blog of 20 June 2019.
Saturday, June 6, 2020
Perhaps we can not simply turn an AI loose reading from the internet and expect it to learn.
Thursday, May 28, 2020
Wednesday, May 20, 2020
Tuesday, May 19, 2020
* See, for example, Artificial Intelligence: A Modern Approach, 4th edition, Russell and Norvig, Pearson, 2020, page 252.
Friday, May 15, 2020
Saturday, May 9, 2020
* Any translation software is acting as a preprocessor, in effect performing dimensionality reduction.
** I’ve been using period or pause to signal the end of a temporal sequence. Is this the “correct”/only/“best” thing to do?
Wednesday, April 22, 2020
Thursday, April 16, 2020
Saturday, April 11, 2020
Sunday, March 22, 2020
Friday, March 20, 2020
Thursday, March 19, 2020
* In terms of actual physical agents I now have 40-50 small robots like those in my blog of 8 Jan. 2018.
* See my blog of 28 Oct. 2018.
** See for example my original paper on A.s.a. H, Trans. Kan. Acad. Sci., vol. 109, No. 3/4, 2006.
Tuesday, March 17, 2020
Friday, March 6, 2020
Monday, March 2, 2020
Thursday, February 27, 2020
A.s.a. is hierarchical. Low level regularities are learned more quickly than higher level ones. We have also played with adjusting the learning rates differently on different levels of the concept hierarchy.** When we have done some hand coding of concepts this is equivalent to giving A.s.a. innate concepts. We have sometimes given a layer in the hierarchy a two dimensional memory to allow it to create a spatial map or 2-D vision field. A.s.a. has been given an innate sense of time via time stepping and the time dilation algorithm.
A.s.a. records, updates, and employs probabilities, are they sufficient?
A.s.a.'s hierarchically organized concepts are immediately available for reuse in new combinations. I've emphasized the importance of output/actions, prediction, and extrapolation in addition to simply passively learning sensory input patterns.
A.s.a. may be more comparable to a society of humans rather than one single person.*** Agents can specialize, helping to deal with the combinatorial explosion.**** Various agents can compete against each other in each generation. A.s.a. really can multitask even if individual humans can not.
I have been continuously working on attention mechanisms. How should error correction be propagated between layers of the concept hierarchy? What should a good object concept include? Can consolidation of learning be equated with a society training a specialist agent or is more needed?
* See, for example, How We Learn, Viking, 2020. (Something of a counter argument is in my blog of 21 February 2020.) Dehaene may equate AI to deep learning neural networks and big data, the current fad. There is, of course, a lot more to AI than that.
** And a simulated annealing process.
*** Alternatively, an A.s.a. agent might be likened to one of the specialized regions in a human brain.
**** One sort of attention mechanism.
Monday, February 24, 2020
Friday, February 21, 2020
* See, for example, Stanislas Dahaene, How We Learn, Viking, 2020.
** For example, number neurons that activate when they see 1 thing, or 2 things, or 3 things...
Sunday, February 16, 2020
Wednesday, February 12, 2020
Thursday, February 6, 2020
Saturday, February 1, 2020
* Seeking out things like abundant light for solar panels, moderate temperatures, low clutter environment, etc. in order to maximize utility.
Tuesday, January 28, 2020
I don't think that consciousness is as difficult as the "hard problem" people would have us believe. On the other hand I don't think that Hyper-ConceptNet is as fully conscious as A.s.a. H. is.*****
The attention issue is part of dealing with the curse of dimensionality. Its a problem that must be faced by any machine trying to operate in a large state space.
* arXiv:2001.09442v1, 26 Jan. 2020
** See my blog of 19 Oct. 2016
*** See Trans. Kansas Academy of Sci., 2017, page 108
**** For example, it seems to lack orientation, emotion, and values.
*****But ConceptNet is a large knowledgebase of almost 3 million axioms in first order logic!
Monday, January 20, 2020
- Society requires that most of us work.
- But physics tells us that work is energy. “Labor saving appliances” allow us to replace human labor with other energy sources.
- It might be possible to make energy free. Tesla thought that there might be sources of free cosmic energy. Much of his physics was unsound but solar energy is a possible example. Lewis Strauss, the chairman of the atomic energy commission (1954), thought nuclear energy might become “too cheap to meter.” Plentiful thorium or deuterium fuels, for example.
- No one then need work any longer. Machines would replace all human labor. (Today machines are able to do half of all human jobs. But completing the task might involve the creation of “mechanical life” and the subsequent class struggle between humans and AIs.)
Sunday, January 12, 2020
*A.s.a. H. frequently makes use of a vector value system (see my blog of 19 Feb. 2011) and my criticism of capitalism is based in part on the need to avoid a scalar utility (see my paper www.robert-w-jones, philosopher, Capitalism is Wrong).
Friday, January 10, 2020
It also learns that sweeping the ultrasonic (obstacle) sensor back and forth correlates with having fewer collisions as compared with having a fixed directed ultrasonic sensor. A.s.a. H. then learns to sweep it's sensor, looking for obstacles and spending more time attending to this particular input channel.
Alternatively, if the robot has a single fixed mounted sensor it may learn to make small repeated left and right turns as it advances forward.
Thursday, January 2, 2020
Wednesday, January 1, 2020
Pick and place = (Grasp ball -> Carry ball -> Drop ball)
Grasp ball = (detect ball inside grippers -> close grippers -> sense force against grippers)
Carry ball = (sense force against grippers -> move)
Drop ball = (sense force against grippers -> open grippers -> sense no force against grippers)