Teleportation on ARRL Field Day 2019, a Recapitulation.

Introduction

Given the precarious nature of conditions in ourselves and the world, we never know when the current song is our swansong. Nonetheless we forge ahead, looking for interesting moments of personal discovery. In ham radio everyone is good at something and all the people I know in it are much more qualified in the many subspecialties than myself. One thing I like to do is listen. My wife might dispute this, understanding though she is.

Background

Last year I attempted to monitor our Field Day 2018 communications using a Software Defined Radio that was deployed on site. My hope was that by visualizing ham band traffic, we could make more contacts. Making more contacts is the principal figure of merit for Field Day performance. There is no particular depth of relationship between transcontinental operators. Let me momentarily illustrate the situation. The conversation typically goes like this:

Operator A: CQ Field Day, CQ Field Day, this is operator K1AA.
Operator B: CQ Field Day, CQ Field Day, this is operator K2BB.

Operator A: Roger operator K?BB, My callsign is K1AA, the class is 1A and my section is AR.
Operator B: Roger operator K2??. Could you please repeat your callsign, class and section?

Operator A: Yes, they are K1AA, 1A, Alpha Romeo.
Operator B: Thank you. Please copy K2BB, 2B, and TX. Have a good field day.

Operator A: Roger K2BB. (static and whistling noises)

This terse back and forth is usually the minimum necessary for a two-way contact to be logged and recorded. Because of the pure noise and vagaries of analog single sideband transmission, it usually takes several attempts for each party to provide their callsign, section and class for the logs, data now collected with "logging software". But it remains a century-old fascination that we can communicate over long distances without wires.

Let me amplify one point. In amateur radio Field Day activity is effectively zero relationship. There is no, "Are you okay?" or "How are you doing?" There is no, "Hey, what's the weather where you are?". No, this is the purely distilled state information has been completely sterilized from the contamination of social information. Contrast this with present day social media, which is nothing but personal information, and sometimes a bit too much of it!  Field Day is about Scalps without Faces. It is a Trophy Hunt for Hits, a Safari for Signs, a Contest where only the number of contacts matters, not what content was exchanged. The tacit contract is, "Well we could have done that if we wanted to, the radios, after all, are working! It is an Olympic effort where only numbers matter. As such there is a strange emptiness in the midst of all the chatter, like the weather itself.

Compensating for this angst are the coterie of operators who sit in a circle, away from their radios, eating, drinking and rag chewing about the news of the day, perhaps talking about rigs, antennas and the technology of radio. It is in these local circles that the deepest relationships are built.

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Before Teleportation comes Strategy

There are two well-known strategies in Field Day communications. In the first, an operator sits on a frequency and lets people come to them. This is called, "Running". In the second the operator searches the bands for new contacts in a strategy called "Search and Pounce".

In prior times, this search for contacts was entirely "by ear". The operator would slowly scan a frequency band looking for people who happen to be transmitting at that moment AND who were also unoccupied. If they were busy in a conversation, a waiting line of those ready to pounce would form, adding to the cacophony of noise.

The advent of Software Defined Radio is changing this hit and miss style of brute force search into a more deterministic process, at least, that is my current working belief.

The waterfall displays of SDR (shown above) enables us, at a glance, to observe all the traffic that is in play, "by eye". We can then select potential contacts using criteria such as signal strength and operator consistency. But for operators who have spent years training their ears, change can be hard!

Besides showing all available traffic instantaneously, waterfall displays show how busy the band is, and also how good signal propagation conditions are. These conditions can vary significantly over the course of the day, and over longer time frames due to solar effects on the ionosphere which has its own "weather".

Limitations of Previous Attempts

In my efforts last year to implement SDR Signal Spotting or "fish finding" four limitations were encountered.

1. The SDR hardware required an additional computer and source of power.
2. The SDR loop antenna characteristics did not match those of the main radios which used an antenna called a G5RV. This meant the SDR operator and the contact operators were hearing and seeing completely different views of the signal space. Using a duplicate G5RV's for the SDR was likely to fry the SDR receiver and possibly its host computer due to the strength of nearby transmissions.
3. Even with a different antenna, transmission by any local Field Day operator would completely swamp the SDR input during the call. For those operating in digital modes, their local output obliterated all incoming signals about half the time, significantly limiting the usefulness of the signal spotter to less than a 50% duty cycle.
4. Traditional operators like to search "by ear" rather than using the SDR to search "by eye". Initially operators would be enthusiastic, but under the grind of a long field day, they would revert to habits honed over long years of operating.

A New Approach Based on WebSDR

In an attempt to overcome these limitations a different approach was taken this year. This attempt was greatly facilitated by Tim Lee and John Nordlund. Tim is a Marlboro man of amateur radio appearing at the most helpful of moments. John is an encyclopedia of radio and electronics knowledge, which is dispensed on demand to those like me who were under the mistaken belief that they already knew something. Most of the time some unanticipated nuance is learned.  I had initially planned on running the SDR from my home, and remotely monitoring the signal landscape using an internet link. But Tim and John had suggested I could use WebSDR which would enable me to monitor our signals from several geographically distributed locations. It took me awhile to parse their suggestion into a form I could use, but this approach proved most effective and possibly revolutionary. Further, it has significant headroom for future improvement. It could change amateur radio.

Using WebSDR as the fulcrum for this year's experiment I established five concurrent sessions with Software Defined Radios in:

• Double Oak, Texas, operated by Larry Story, W5CQU
• Washington DC Area operated by Mehmet Ozcan, NA5B
• Dahlonega, GA operated Phil Heaton, NY4Q
• Corinne, Utah, operated by Clinton Turner, KA7OEI
• Half Moon Bay, California USA, operated by Craig McCartney, W6DRZ

The open source WebSDR software can be set up by anyone using inexpensive hardware and a web server. It can also be customized for specific locations and radio configurations. Here is a typical installation operating on the 40-meter band:

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The Teleportation Part

During the course of operations, it was easy to tell if we were heard at the remote locations, and whether operators at those locations were audible by us. This moment of determination resulted in a weird cognitive experience that I can only describe as a sense of teleportation, similar to that which has been described in binaural radios. I could experience the activity of two operators at two locations simultaneously in a peculiar form of binocular vision/audition that is easier and more spectacular to experience than explain. Tweaking the remote vs. local experience is a bit of art I have only begun to explore. It is like being two different people at the same instant, in two different places, hearing two other people and their respective renderings in the fog of noise and single sideband. One distinctly interesting aspect of this experience is that one can recognize a familiar voice even if it has been distorted in tone, timber and frequency. This distortion comes from the signal being encoded, transmitted and decoded by an analog system. The simultaneity of this creates a peculiar cognitive dissonance, the source of the deja vu feelings of teleportation suggested previously.

Let me describe the hardware of this experience. While I, the teloportee, am observing the visual signals of the conversation on the SDR waterfall display, I am wearing headphones with one ear up, like a Cessna pilot who is monitoring an aviation radio simultaneously with conversations in the aircraft. So, in one ear I am hearing our local operator locally, and in the other ear I am hearing them remotely. Then, in complement I hear the remote operator locally on the local operators rig and the remote operator remotely on the software defined radio. All this while tuned to their 3 kHz wide chunk of radio frequency. This whole sonic and visual arrangement pivots back and forth where local and remote conversation halves are experienced from both the local and remote points of view simultaneously!

For our work this weekend, I was surprised by the low-latency of the communication. Conversations quite nearly matched in time, but not in tone, timbre and frequency. It was interesting that despite the aural distortion, you could recognize a familiar operator. The low-latency was especially surprising given the hack job of an internet connection I had cobbled together on short notice. Our ham club facilitator Dick Wallace made efforts to gain a proper wireless connection to a neighboring agency who was sponsoring the event. But due to various (and perfectly reasonable!) security restrictions, no wireless internet connections were available.

The search however did allow us the benefit of a half-hour of undeserved air-conditioning on a hot and muggy Arkansas day. In desperation I finally set up my iPhone as a personal hotspot, using Cellular Data to accomplish the web connection. This web connection enabled the five concurrent SDR sessions with few if any drop-outs of audio or waterfall display. As the afternoon turned to night, I imagined that I would return home to an unhappy wife holding a cell phone data bill of gargantuan proportions. Instead I was pleasantly surprised to find out that after all this DXing and teleportation I was still within the confines of my data plan allowance. My wife is very understanding of my experimentation and I would be remiss in not saying so.

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Summary

In summary, this year's approach of multiple, remote SDR's accessed via a web link solved and eliminated the three of the four limitations encountered in the previous year's exploration.

It partially reduced the fourth limitation as well.

This problem was solved to the point of nearly eliminating the fourth limitation by collocating the SDR fishfinder op and the SSB operator (several) at the same table with our respective hardware. This eliminated the need for 2-meter coordination chatter on the handheld radios and allowed for the simultaneity of the audio that produced the teleportation experience. This also solved the see versus hear problem.

There is more potential for automation of SDR enhanced signal spotting, also known as let the machine log the big numbers.   We might want to consider a local WebSDR installation.

Footnotes and Acknowledgements

There were many contributions made by others that made this Field Day 2019 a success. One that affected me personally was that Roger Fidler provided fabric floors for the tents that provided excellent insect abatement, specifically chiggers. The Carolina Windom antenna was spectacular in the way it was deployed with a portable mast and a flagpole. Roger provided hydration that prevented heat stroke and Tim Lee provided pizza that prevented collapse.

At one point we had a telecommunications wonderland in play. We had the operators and their operations on 7 and 14 Megahertz bands (40 and 20 meters). We had handhelds at 146 Megahertz (2 meters) for tent-to-tent SDR-spotting communication. We had at 2 and 4 Gigahertz bands (15 and 7.5 centimeters) running the internet wireless part of the operation. All these were running simultaneously to accomplish our first signal spotting of this year.

Disclaimer

These comments reflect the experiences, viewpoints and opinions of the author only and not necessarily those of any agency or individual connected with the event. They are offered without warranty of merchantability or fitness for any purpose expressed or implied.

A Fresh New Web Site in 96 lines of Python

It was time to rebuild my website to make it, "responsive", which is code for, "works on phone and desktop".

My site uses a simple structure of three buttons in sequential rows. Three is a good number for remembering and choosing things. It works well for both mobile and desktop applications.

I began forging ahead on this, by accident, after I saw that Adobe Dreamweaver supports bootstrap.

So I started the one hour course on bootstrap, but didn't finish it BECAUSE:

While I was watching scrimba (which is fantastic by the way, because you can stop the video and edit the code they are showing you in real time on the screen) I thought, "What if bootstrap isn't the best way to do this?"

So then I googled some reviews and found the scrimba course on CSS Grid, which is even more fantastic - for my purposes it was cleaner than bootstrap. Thank you Per Harald Borgen Co-founder of Scrimba! He is very clear.

In the meantime, I asked some computer experts, who are also my kids, whether I was headed in the right direction.

By the time they got back to me that same evening, I had already learned CSS Grid on Scrimba and was immediately starting to code up a prototype. I took the course in the listed 62 minutes, but was already producing useable code in 20 minutes. This of course blew my mind.

Then I found out I could transfer my scrimba notes into this JSFiddle and get a working version while viewing the HTML and CSS at the same time. Because CSS Grid is so powerful I didn't need any JavaScript in this case. JSFiddle enabled me to preview my hacks in real time. When I got that running I could just dump the prototype code into Dreamweaver and try it out. (Sad story: Dreamweaver tried to rewrite my code and broke it in the process so I told it not to do that by setting the "Rewrite my code" preferences to "Off"!)

As the complexity of my new site escalated I needed to generate more HTML automatically. The CSS didn't need to be changed at all. To generate said HTML I used Python running in a Jupyter Notebook. This decision was really an important one because I could have written a 'C', Java, or JavaScript program, or I could have written a Unix shell script, or a sed/awk job and so forth. Python in Jupyter was the right decision.

I was able to build a little hack previewer into Jupyter because the libraries for Python are so rich.

So in short the project went a little faster than I expected. My new site, https://wdv.com, is up and running if you want to try it. You can stop reading now unless you like details and statistics.

By way of complexity here are the word, line and character counts:
Old site html file:  134 words     878 lines   14468 characters
New site html file: 237 words    465 lines     8244 characters
New site  CSS file: 120 words   276 lines     2533 characters

Thus the new site is quite a bit simpler if we're counting lines and characters. It loads faster too.

The Python Jupyter Notebook "make-web-site" generates the site from scratch using simple rules:

make-web-site does an external ls, and enumerates a list of directories that contain content.
It prints them in triples, and these can be compared to the current version of the site.

The enumerated list is used to build the index.html for that directory using a couple of Python functions:

These functions are then called to generate each row of buttons for the site:

The resulting html can be copied straight out of the notebook and dumped into Dreamweaver or any other tool. Then the HTML and CSS files are uploaded to the server.

The look and feel of the website uses CSS Grid and is contained in the file index.css.
Watch the scrimba to figure that out.

Assumptions:
1) That each folder is the location of content and contains and index file
2) That there is an images directory that contains an image file for each folder
3) We will assume that each content directory starts with an uppercase letter
4) We will assume that the images directory starts with a lowercase letter and is named, "images".

These assumptions can be violated by giving different arguments to the functions:

That's it!

Now this was just the index file of a thousand page website, so obviously there is more to do, but I was happy with this approach.

Friction In Space: The Statistical Mechanics of a Certain Missing Quora Question

I spent a good chunk of this evening around eight o'clock answer a question on Quora. Quora is a source of a host of bizarre and interesting questions. After answering the question with the most clarity I could, I thought I posted it, but it was sucked into a vacuum, never to be found through any amount of indexing or searching.

The question was, "Is there friction in space?"

I really liked the question, so much that I am devoting a blog entry to it.

The first task is to reconstruct my original answer, which goes something like:

Yes, there is friction in space because space is not empty. There is about 1 hydrogen atom per cubic centimeter of space, more or less.

When a hydrogen atom collides with an object there is an exchange of kinetic energy that obeys the rules of conservation of momentum. Part of this energy is radiated away as heat, as photons whose wavelength, frequency or color is distributed according to the energy associated with the collision. Part of this energy results in a change in direction, however slight of the two parties involved in the collision. The destiny of collision can be represented as a pair vectors with a direction and magnitude.

There is also a 'relative wind' effect. The ensemble of hydrogen atoms that the object collides with has an average velocity that can be moving along with object (running in nautical terms), against the object (upwind), crosswind, or some combination. This relative wind will affect the spectrum of the photons radiated by the travel of the object, red-shifting it when the object is running, blue-shifting it upwind or some combination for the crosswind.

When an object reenters the atmosphere the density of the gases it encounters increases rapidly and heat is dissipated as the, 'searing heat of reentry'.

If the object was initially launched from earth, it must dissipate as heat, the kinetic energy associated with putting it in orbit in the first place. Fire on the way out, fire on the way back.

Curiously only about a tenth of the energy of placing an object in orbit has to do with it achieving the height of its orbit. The other nine-tenths is expended in achieving the velocity of its orbit. This is why if you look at the exhaust plume of a rocket that it gently curves. Early in the launch it is desirable to reduce aerodynamic drag by going straight up. Later in flight the rocket has to tilt towards the horizon to achieve orbital velocity parallel to the surface of the earth at its orbital altitude.

Image Credit

There is a term, "Max Q" which translates "Maximum Dynamic Pressure" on the vehicle that you may have heard in connection with rocket launches. As a rocket ascends vertically two things are happening - the atmosphere is rapidly getting thinner, that is the density is decreasing rapidly, and the speed of the rocket with respect to that atmosphere is increasing rapidly.

There comes a point at which the product of these two quantities reaches its maximum and the structural forces on the rocket are at their maximum. This is the Max Q point. Q stands for dynamic pressure which the half the product of velocity squared times density.

The aft end of the rocket has a characteristic spectrum or heat signature and the friction between the surface of the rocket and the atmosphere can heat it considerably.

Because of staging, much of the energy associated with delivering the spacecraft to orbit is lost to the stages that got it there. The spacecraft only retains the kinetic energy associated with its own mass, and the potential energy associated with its mass and altitude. Typically only 1 to 3% of the mass of the original rocket, the 'throw weight' makes it to orbit. But there is still a lot of energy that has to be radiated as heat when it returns, thus, 'the searing heat of reentry'.

Because of aerodynamic drag, or the 'friction' of the original question, everything is destined to reenter after some period of time.

There is a rule in physics that any time a charged particle is accelerated, it is obligated to emit a photon corresponding to the energy that caused the acceleration.

This means that spacecraft flying through the ionosphere (and nearly all of them are since it extends to 1000 miles above the surface of the earth) are causing emissions of thermal radiation corresponding to the kinetic energy of the collisions or 'friction' occurring at their specific altitude and velocity.

Computing and the Future HW 12 - Final Presentation and Book Review

1. Create a first version of your presentation. The presentation will be 20 minutes. 

Done! The complete video is on YouTube.




2. Grad students only: continue with the book you obtained. Read the next 20 pages. State what page numbers you have read and provide a reminder of the title of the book. Then, discuss those 20 pages. Explain what you agree with, disagree with, and how your views compare with those of other reviewers on Amazon or elsewhere.

Reviewed This Week: 

• Chapter 24 - Sic Transit Humanitas - The Transcent of Man
• Chapter 25 - Floating Prairies of the Seas

The comparison to other Amazon reviewers was done in the last section of this previous homework. I ranked this book highly myself.

I have moved the details answer to my ongoing review of the book, "The Human Race to the Future" a single curated document that is here. It is very detailed with over 400 remarks, many with references and some with illustrations.

Computing and the Future HW 11 - Intelligent Life

1) Do you think we will find intelligent life in the universe? Why or why not?

There are really two questions embedded in this one. The first is, "Is there intelligent life?", the second is, "Will we find it?". I think the answer to the first one is maybe, and the answer to the second one is maybe. 

Quickly sketching - Let's say that the word "maybe", in the absence of better information means, "A 50 percent chance" at each node in the decision tree. My reason for using the 50 percent figure was revealed in a previous assignment where I discovered the perils of false precision. Anyway, running the calculation this means, that there is a 1 in 4 chance that the answer to both questions is, "Yes". There is also a 1 in 4 chance that life is out there but we don't find it. There is a 1 in 2 chance that the answer to both questions is, "No", since if it doesn't exist, we can't find it! There is also an imaginary component to this, where it doesn't exist, but we claim to find it. There is a whole cottage industry devoted to this.

Diving Deeper we could talk about the Drake equation:

Image Credit - Universe Today

or more recently the advent of exoplanet discovery, first by the Kepler spacecraft:

and now by Tess:

With 4023 exoplanets discovered so far, it is clear that exoplanets are abundant but we couldn't see them with land-based telescopes of the past. Most exoplanets are not in a habitable zone that would support, "Life as we know it". But we have discovered several candidate planets that are Earthlike, meaning their mass, and their relative distance from their parent star support liquid water. Given that there are billions of such stars, each with multiple planets, then it becomes more likely than not, that the answer to the first question is, "Yes". But due to the enormous stellar distances, the second question may remain unanswered for some time to come. Also we have to consider that any life that may have existed and tried to communicate with us in the past, may no longer exist. This suggests the follow-up question: "Could intelligent life have existed and now be extinct?" This because we can never see the stars as they are now, only as they were when the light from them left on its journey to Earth. If you haven't tried Galaxy Zoo Citizen Science I highly recommend it.

2) Suppose you had a coupon for a free robot. The catch is it can only do one thing. But you can get a robot that will do whatever one thing you like, just not anything else. What would you want your robot to do, and why?

There is some "wiggle room" in this question depending on what "one thing" means. Consider a Roomba. The "one thing" it does is vacuum the floor, but it executes many actions in order to accomplish that one thing: It docks with its power station, it translates in x and y. It rotates. It goes from room to room. It returns to its dock. It alerts the owner when something is wrong. Recently someone called the police about an intruder and it turned out to be a Roomba. So a Roomba can scare people also.

Image Credit - iRobot

I have a robot called a "Ring Doorbell". It does one thing. It watches my door, 24/7/365. It is my favorite robot because it does that job extremely well, taking video of all comers, and placing that video in the cloud. This prevents anyone from stealing it. Should they try anyway, it bricks itself and calls home when an installation is attempted. It has triggered a new family passtime called, "who came to the door today and what did they do?", a constant source of entertainment with all the draw of a Nature program. It was fairly inexpensive as robots go also:

For me, a robot, is any motorized mechanical device that executes a stored program in any form. From this definition some of the devices in my house are robots and some are not. A dishwasher, clothes-washer, dryer, and microwave all execute stored programs. My refrigerator does not, but the ice maker does. Recently washer and dryers have become smarter, and more autonomous. I can diagnose my dryer from my smartphone. If we lost any single robot in our house, the washer would be the most serious.

But the spirit of the question looks more to the future to a robot I do not yet have. The robot I would really want is a sentry robot. It would screen if a person was friend or foe, and deal with them accordingly. It would summon help if there was danger and it would fix a beverage if things were okay. It would perform the role of a benevolent security guard at the gate to block those whose intentions are harmful and to greet those whose intentions are good. It would use Machine Learning and expression analysis, including facial expressions, voice tone and pattern analysis, and movement patterns such as gait to develop an impression of the intentions of visitors.

The SGR-A1 - Image Credit

Of course if I had one of these for my home, I would also want one for my car. It would ride along in an unoccupied passenger seat taking stock of the traffic, and the people in the vehicles around me. If someone came up playing loud deafening bass tones, it would octave multiply the tones into the pain region and transmit them back to the source. If the source turned down the music, the robot would instantly turn down the transmission. So if such a driver turned down their bass and rolled down the window to ask, "What is that sound", I could just say, "What sound?". I would also want my sentry robot to recommend evasive maneuvers to avoid hazardous conditions and annunciate the presence of bicyclists and pedestrians as a redundant safety check. I would call it, "Back-Seat-Sentry". It would also have an off switch.

3) Imagine a robotic future. Would it possible in such a future for labor to be free? For example, suppose there was a law prohibiting anyone from being paid to do work. Could the human race survive in the face of such a law?

Absolutely. Revenge is a dish best served cold. Work is a task best performed by robots. The appliance singularity has already happened and it is fantastic.

No I'm wrong. For robotic labor in the future to be free, you would have to consent to listen to an advertisement dictated to your robot, or it would not be recharged. After awhile piles of discarded Freebots would accumulate and fill the dumpsters and landfills due to the "Amazon Effect". Hackers would overcome the limitation of forced advertising to build a new race of Hacked Freebots. This would cause Freebot corporation to go out of business and the Freebot to become extinct... or would they?

Image Credit - The Telegraph

The human race has done a good job surviving all kinds of strange laws, so there is not reason to think that anything would change on that front.

4) Comment on the movie Transcendent Man. What do you agree with, disagree with, what do you look forward to, are apprehensive about, etc.

I liked it alot. I made several notes, the gist of which are included below. I had some impressions before I watched the movie and some impressions after so I want to contrast those as well.

Before

• My sense of "The Singularity" that we have been hearing about is that it is like the Hubbert Theory of Peak Oil. A catastrophe of the future that seems like it always will be. With any of the current claims of a technical singularity, there are always moderating, mitigating factors as I wrote about in my first Computing and the Future Assignment. In that assignment I argue against a programmer productivity singularity on the basis of "too many choices". I will say that in my lifetime the appearance of computing has been a singularity and one that I like very much. My relationship with computers is now so long and so deep that I cannot imagine life without them. They are an extension of my brain, my body, and my persona.
• Kurzweil started Singularity University. I wanted to know if this was going to be more like Amway, or more like SIGGRAPH. His claim that, "The Singularity is Near" does feel a bit culty, doesn't it? In class this led us to identify the Hype Cycle - a lifecycle for the appearance of new technologies. Some are fads, some persist.

After

• In class as part of the post-discussion, we identified a 'clone vs. original' principle that emerges from the fact that we can eventually reverse engineer any biological process that we want given enough time and resources. So say that it is possible for us to completely duplicate the functionality of our body. Even if we do that, that does not make this clone, the second-instance of us, the same as the first instance of us? It is not clear how consciousness would be uploaded even if the perfect clone existed, although a hint appeared in the movie. Kevin Warwick in the UK implanted a device in his arm that produced sensations in his brain actions in the real world. He then used that device to move and feel the actions of a remote hand a world away. So he demonstrated that some aspects of volition, of motor effect and sensation CAN be reproduced in the second-instance of ourselves. The question is, in the limit, can all our volition and sensation be thus reproduced and more importantly, is that sum equal to the total of who we are? This is deep.
• Kurzweil articulates the Law of Accelerating Returns which states that the current generation of automation is used to create the next generation and this has a tremendously compounding effect on accelerating technical development. He speaks of, "A billionfold increase in computation in 40 years." That seems singular to me.
• Watching this 2011 movie I noticed how old some of the computers and technology looked, even though the movie is only 9 years old. It reminded me of the fact that humans keep improving things, along a narrow tract until a paradigm change forces them to do something else. An example of this was the advances in Yankee Clipper ships. They kept getting bigger and faster, until the steam engine was invented. Paradigm change.
• Kurzweil identifies the GNR core as Genetics, Nanotechnology, and Robotics. This reminded me of a friend who went to work for Nanotech messiah Eric Drexler. Nanotech was not ready for prime time, and appears to follow a linear rather than exponential growth law. This spells disaster for those who were hoping for more. Machine Learning may change that, especially when combined with robotics, but that could be my own personal bias showing. Machine Learning does seem to benefit more from Moore's Law, the question is will its deployment into the real 4D world reflect that?
• Kurzweil's fight with death caused me to write:
  
  
  "If you don't accept it you are doomed to fight an unwinnable fight"
  
  
• This made me feel sad for Kurzweil, in that he is wasting time that could be used to do things more similar to those he has enjoyed great success in. He seems to have fallen in the same trap as time traveling physicist Ron Mallett, and for identical reasons. Of course these 'traps' can lead to incredible technical progress, but they can also be a source of great personal disappointment. But what if Dr. Mallett succeeded - would he tell anyone or would it be too dangerous to do so?

5) Create at least half of a first draft of your presentation. For example, you could create some slides.

I have created a half first draft using PowerPoint™. Many people belittle or criticize the use of PowerPoint™ but I find it an excellent storyboarding tool for designing and guiding a lecture in a visual format. Almost any kind of media can be included. I suppose any tool can be used to create a boring lecture, or contrariwise an interesting lecture. For me a well prepared presentation prompts my favorite activity, free-associating and brainstorming over an idea, project or topic.

I have finished construction of both vTMS™ units and tested them against their respective power supplies. I have nearly finished the vBrain™ simulation unit, except for the eyes which are drying while I write this. This process has been fun, but far more labor-intensive than I imagined when I thought it up.

6) (Grad students only) continue with the book you obtained. Read the next 20 pages. State the book title, author, and page numbers you have read. Then, discuss those pages. Explain what you agree with, disagree with, and how your views compare with those of other reviewers on Amazon or elsewhere.


Reviewed This Week: 

• Chapter 22 - New Plant Paradigms
• Chapter 23 - Asteroid Apocalypse

I have moved this answer to my ongoing review of the book, "The Human Race to the Future" a single curated document that is here.

Now this week I have a slight disclaimer - my suggestions are offered without merchantability or fitness for any purpose expressed or implied. I found the chapter on new plant paradigms very engaging - to the point I would immediately start to engineer them in my head. This would immediately lead me to some kind of difficulty or glitch that might appear in accomplishing the goal. This may caused my comment to imply, "that would be difficult", when what I'm really saying is, "This is where I would get stuck."

Computing and the Future HW 10 - University Accreditation in Machine Learning

Your name:_L. Van Warren_

1. For the technological topic of your choice, say what it is, give a present-day impact on individuals, and your opinion about whether it is good, bad, or whatever you think.

Machine Learning (ML) is my topic of choice. ML is having an impact on individuals at every stratum of society. Personal digital assistants like Siri, Alexa, Google Assistant, Cortana are enabling spoken interaction recognized by ML. The ensuing conversations are then facilitated by ML and queries and predictions are processed further by ML. This untethers computing from the mouse and keyboard, further facilitating mobility. My opinion is that this is good.

2. For the technological topic of 1 above, give five more present-day impacts on individuals. One of the five should relate to your career path. For each, give a good reason why the impact exists.

i. Change of Computational Paradigm: A disruptive change that breaks free from the imperative and applicative styles of algorithm execution, facilitating a more natural pattern recognition style of computation for everyone. Not since the advent of the personal computer, or the internet has a more significant change occurred. ML is a personal technology affecting individuals.

ii. Medicine: Skin Cancer is already more accurately diagnosed by machines than by experts. This will continue into other specialties of medicine, especially those that can be reduced to image or numerically based ones, like radiology, histology, pathology and clinical laboratory analysis. This will reduce medical costs and facilitate preventative medicine. Not getting cancer affects individuals.

iii. Shopping: ML has been affecting shopping for some time for online services like Amazon, Walmart and Target. Annotations like, "People who bought x, also bought y, and z" are produced by ML. Recommendations are similarly generated. These services affect individuals.

iv. Transportation: ML is currently part of a space race between companies like Uber, Lyft, Waymo and Google for creating driverless vehicles, and for the more mundane tasks like scheduling rides, matching riders to available vehicles and enabling real-time navigation and changes in the process. Getting where you are going is a personal, individual experience.

v. Entertainment: ML facilitates matching future programming to that which individuals have already consumed. This takes place on services like Netflix, the Apple Store, Spotify, Pandora and Amazon Prime. It is individuals that are entertained.


3. For each of the 6 impacts of questions 1 and 2, extend to past impacts and one possible future impact. Conciseness is acceptable...no need to write a book about this.

from Q1:

It is hard to comment on past impacts since ML is relatively new, having only existed in its present form since 2016. Because of this all my answers below with respect to past impacts will be somewhat attenuated. We can speak in terms of the technology not existing and the impact that NOT having it would have, or we can speak in terms of the impact that the technology has had, which we have already done. To address this position further we have to broaden our view to include those advances that have enabled ML to exist in its current form. I will list these advances in terms of changes of paradigms that have occurred in the hardware, software, languages, operating systems and human interface arenas.

from 2i:

For brevity I will give a few key examples from each area.

In computer science there have been several major computing changes of hardware paradigm:

• mechanical relays
• vacuum tubes
• transistors
• integrated circuits,
• VLSI
• microprocessors
• GPUs - first for graphics, later for ML
• TPUs

There have been major changes of software paradigm:

• machine language
• assembly language
• lexical analysis
• LL(*), LALR(1) parsers - used for Python
• recursive descent parsers

There have been major changes in computer languages:

• Fortran
• Lisp
• Algol
• Pascal
• C, C++
• Java/Javascript
• Python - the principle language of ML

There have been major changes in operating systems:

• OS360
• TOPS10
• VMS
• Unix - a common ML platform
• Windows - a common ML platform
• MacOS - a common ML platform

There have been major changes in user interface:

• Punched Cards and Printed Output
• The Paper TTY
• Calcomp Paper Plotters
• The Glass TTY
• The Mouse
• Frame Buffers
• Computer Graphics Technology and Scientific Visualization
• Bit-mapped/memory-mapped Display
• The Integrated Personal Computer
• The Smartphone - an ML Platform
• The Tablet - an ML Platform
• The Personal Digital Assistant - an ML Platform

It is worth noting that Unix has been in place since the late seventies. Machine Learning sits atop the current apex of hardware, software, languages, operating systems and user interface improvements. Computing has become inclusive of its own past and past impacts. Text processing and analysis tools developed in the late seventies remain viable today, albeit in improved form, running on faster hardware. ML will be the same way. A future impact will be the way ML changes the activity of programming itself, possibly to a conversational one. 


from 2ii:
Past impacts of ML to Medicine? We are just getting started.
A future impact is illustrated by the query: "Hey Google, build a database of NIH tuberculosis radiology and run a supervised learning session to answer which lobe of the lung is affected most in children between nine and eleven years of age."

from 2iii:
Past impacts of ML to Shopping? Again, we are just getting started. But the lines between past and future are starting to blur because older services will be remodeled and repurposed using the conversational interaction that ML provides. A future impact is illustrated by the interaction:
User: "Hey Alexa, fix dinner and deliver it."
Alexa: "What do you want me to fix?"
User: "The usual."
Alexa: "Okay, your pepperoni pizza will be delivered in 30 minutes."

from 2iv:
Past impacts of ML to Transportation? There have been a couple of significant accidents with driverless cars, causing vendors to be more cautions about releasing them to the public. Ralph Nader, in response to the Boeing Max 800 accidents that killed his grandniece, insists that we will have to keep humans in the loop rather than deifying machine learning and artificially intelligent machines.

A future impact is illustrated by the interaction:
User: "Hey Google, send a taxi para-drone and a passenger harness"
Alexa: "How heavy is your passenger?"
User: "100 kilos."
Alexa: "Okay, your drone will be waiting outside in five minutes."

from 2v:
Past impacts of ML to Entertainment? Too soon to tell. A future impact will be that ML and Augmented Reality will enable:

• The invisible wall between audience member and actor to blur.
• The line between movie and theatre will blur.
• The line between computer game and science fiction program will blur. 

4. For each of the 6 impacts, give a second alternative possible future impact on individuals.

We have a second possible future impact of

• ML on itself
• ML on Computation
• ML on Medicine
• ML on Shopping
• ML on Transportation
• ML on Entertainment.

from Q1: ML on itself
Unlike conventional programming, Machine Learning discovers the algorithm by being trained with training data. This algorithm discovery process will increase in sophistication and eventually ML will figure out how to discover ML algorithms. 


from 2i: ML on Computation
With the need for fewer programmers, ML could result lower employment rates for those with a computer science or programming background.


from 2ii: ML on Medicine
With routine diagnosis and treatment partially automated there will be a need for fewer doctors. One doctor will be able to do the work of many, and will only be needed in ambiguous or difficult cases. As in the computer science case ML could result lower employment rates for those with a medical degree. Just as the internet eliminated the middleman, Machine learning will eliminate the domain expert.

from 2iii:
With no need for brick and mortar stores, there will be less need for cashiers, sales clerks, stockroom personnel, and managers. Physical stores will be replaced by Virtual Stores like Amazon, WalMart, and Target. These in turn will utilize ML and robotic automation further reducing the need for human staff. The main job will be repairing the robots when they break, which will be done by replacing them. These robots will be manufactured on automated assembly lines, so only the most meta-level engineers and skilled repair staff will still have jobs. Even so, producers of products that are distributed via the Virtual stores will still be needed. They will include jewelry and sand sculptures made by people who live on the beach, wear shorts, play video games and smoke pot. 

from 2iv:
With driverless cars, buses, trains and aircraft, there will be no need for drivers, conductors or pilots. When these transportation systems fail, airbags will inflate and parachutes will deploy. Dazed passengers that survive the crashes will wander aimlessly, making smartphone calls and trying to find another way home.


from 2v:
With fewer jobs, the primary pastime will be entertainment. This will include binge-watching various television series, movies, and AR and VR theatrical productions and immersive video game experiences articulated above. With less reason to be concerned about quality control, many will spend most of their free hours using entertainment and recreational drugs.


5. For the technological topic of your choice, give a present-day impact on an organization, such as business, government or others, and your opinion.

Machine Learning has enabled Amazon to become the largest shopping service in the world and its owner, Jeff Bezos, to become the world's richest man.

6. Give 5 more present-day impacts on organizations. Of these 5 and the 1 you just discussed above, at least one impact should relate to business, one to government, and one to some other type of organization. For each of the 5, give at least one good reason.

i. The TensorFlow software for Machine Learning was open-sourced by Google and has made TensorFlow the defacto standard for ML development.


ii. ML is used by Facebook to perform automatic facial recognition for tagging images.


iii. ML is used by Netflix to optimize its revenue streams.


iv. ML is being used by the United States to create smart weapons.

v. ML is being used to predict stock market behavior by sentiment analysis.


7. For each of the 6 impacts of questions 5 and 6, extend to past impacts and one possible future impact.

from Q5:

As in the case for the individual, the impact of ML on businesses and governments is still in its infancy and it is too soon to tell. But there is a new battlefield opening up in cyberspace between competing nation states, vying for superiority in intelligence gathering, spying, intellectual property theft, and cyber attacks. Future wars will be fought in cyberspace, and spill over into the real world as a consequence.

from 6i:
The past impact of Google was in search, document retrieval, book and paper digitization. The future impact of Google will be in using these data sources as the fuel for ML training, testing, deployment and prediction in smart services formats as we are seeing with digital assistants.

from 6ii:
The past impact of Facebook was in bringing people together and letting them share experiences regardless of their geographic location. But they have used their collection of personal information to generate revenue and throw elections without concern for their constituency, which could lead to their future demise.

from 6iii:
The past impact of Netflix was bringing movies and television shows to the desktop, to the family room, eliminating the need to go to dangerous movie theaters, where the floors are stick, and shootings happen. The future of Netflix will continue this trend, making movie theaters a thing of the past, especially in an atmosphere of growing violence.

from 6iv:
The advent of smart weapons in the nuclear age will make wars, when the do occur, swift and final. Second and third world countries which do not possess or control significant computing resources will be unable to wage war in any but the most Luddite of fashion. The demise of ISIS is an example of the old style warfare meeting the age of the drone and intelligent system. In this case civil disobedience and domestic terrorism may become more commonplace with damage to computing infrastructure taking place as nation states and sovereign individuals engage in cyber warfare.

from 6v:
Stock market trading patterns will shift as algorithmic trading powered by machine learning causes fortunes to be exchanged on millisecond time frames.


8. For each of the 6 impacts, give a second alternative possible future impact on organizations.

from Q5:

With the reduction in employment, governments, organizations and businesses will be obligated to provide minimum basic incomes for those displaced from the workforce by Machine Learning. Educational institutions will have to convert to faster training formats to service the multiple careers that people will have as ML advances, obsoleting their previous career at each turn.

from 6i:
ML will reach beyond the informational and extend into home automation, robotics, smart appliances and services like water faucets that vend out water at a specific temperature and log when users wash their hands providing the alibi's and warnings of medical conditions like peripheral neuropathy that occurs in diabetes, detectable when a user continues to request increasing water temperatures because they can no longer feel the water on their hands.

from 6ii:
ML and blockchain will be combined to enable automatic real time voting on systems that protect users privacy. Services like Facebook at that exploited their user base will give way to more subtle exploitations and advertising that is more tailored to the user and more respectful of their privacy.

from 6iii:
Another impact of ML, in an era where personal entertainment is growing is personal exercise and workout regimes enabled by machine learning. Examples of these are virtual bicycle tours of the world where the user never has to leave their home to obtain a good workout.

from 6iv:
As governments engage in smart arms race, individuals will use computer viruses, and devices whose construction is facilitated by the abundance of information an anonymity of the blockchain to wage war against the state at a using machine learning. This will be a high tech evolution of old style guerilla war tactics.

from 6v:
Financial singularities resulting from stock market trading under the control of machine learning programs will cause unpredictable rises and fallings in the market. Naked short selling will occur when some companies are targeted by ML programs for automated takeover and gutting of their capital resources causing them to go out of business and cease operations in a single moment.


9. For the technological topic of your choice, give a present-day impact on society, and your opinion.

The impact of machine learning on transportation will eliminate the idea that individuals will own vehicles that must be parked 95% of the time. 

10. Give 5 more present-day impacts on society. For each of the 5, give at least one good reason.

i.
Fewer vehicles in circulation will mean car dealerships will be things of the past. 

ii.
Fewer vehicles in circulation will mean a need for fewer parking lots and fewer parking spaces.

iii.
Unused parking lots can be repurposed for new buildings, housing and recreational purposes. 

iv.
Machine learning will also result in fewer stores and the value of commercial real estate will plummet as fast tracts of existing buildings are repurposed.


v.
Instead of vehicles being customized to the image of their owner, they will become fewer in kind, fewer in color and less distinguishable by decoration.


11. For each of the 6 impacts of questions 9 and 10, extend to past impacts and one possible future impact on society.

from Q9:

The last time society went through an impact of magnitude comparable to Machine Learning is when the automobile was originally invented. Streets and highways had to exist before cars and delivery trucks could go into widespread circulation. Now the infrastructure has been built, but the nature of transportation is changing. The nature of the delivery of goods and services is changing. The 'Amazon Effect' of dumpsters filled with one-use cardboard boxes will give way to nearly 100% recycling and standardization of shipping containers.

from 10i:
Even though car dealerships will eliminate their showrooms, their repair shops will remain active. But the variety of cars and the number of cars they service will drop precipitously. There will be fewer cars and trucks, but those cars and trucks that remain will have many more miles on them.

from 10ii:
Fewer parking spaces will result in cities having less revenue from parking meters. There will be no need for meter people to give tickets and those people will not have jobs. There will be fewer cars towed away and impounded because individuals will not own them. Tow trucks will still exist for those vehicles that are driven non-stop 24/7/365.

from 10iii:
With more real estate available housing costs will drop and it will be possible to afford a housing in high density population locations more easily.


from 10iv:
Fewer stores will compound the affordability of houses for people at all income levels. Income levels may bifurcate further into rich vs. poor unless guaranteed basic incomes are mandated and implemented.



from 10v:
Fleets of every kind from taxi fleets like Uber and Lyft, to delivery truck fleets, to aircraft fleets will consolidate. There will be fewer vehicle types as the economies of scale impinge on the holders of large fleets who will unify and simplify their vehicle inventory to reap the economies of scale.

12. For each of the 6 impacts, give a second alternative possible future impact on society.

from Q9:

The impact of Machine Learning on society will be as far reaching as that which occurred when the automobile was invented. Social rites of passage, like learning to drive, owning a car, going on a date in a car will end. Going on a vacation in a personal car will give rise to new group and family destination activities facilitated by entertainment companies. Experience-based gifts will replace material-gifts, since material possessions will lose value in a highly virtualized economy. In their place will be virtual collections of objects in virtual cyberspaces. These virtual collections may be copied or protected by blockchain technologies ensuring their uniqueness.

from 10i:
With fewer domain experts in circulation the need to supply large numbers of them in the industrial, engineering, medical and scientific arts will decrease. With knowledge being instantly available via Machine Learning mediated conversations with homebots, the notion of going to school for four to eight years to obtain a professional degree will change.

from 10ii:
Just as stores will decrease in number, so will schools. More people, especially in a age of random shootings by the discontent, will elect to train their children in safe home environments. Since massive online courses taught by the best teachers will prevail, there will be need for fewer trained professors and teachers. They will share the same fate as other domain experts. More people will make jewelry and sand-sculptures. They will play music, dance on the beach and smoke pot from dawn till dusk.

from 10iii:
Those who have failed to adjust to the technical revolution facilitated by machine learning will have less to learn and less to do. There will become a class of knows and know-nots, replacing the haves and have-nots of the past. These classes will be populated by those who continue to invest in their personal education versus those who do not. Because of universal basic income everyone's survival will be assured. But the know-nots, lacking critical thinking skills, will become prey for political extremists, charlatans, religious cults, and schemes designed to extract from them even their basic minimum income.

from 10iv:
Know-nots, having been cheated out of the universal basic income, will also become have-nots. This will lead to crime and corruption in the inner city much as we see today, only with a more sophisticated high-tech flavor. Instead of gang members rolling in cars and committing drive-by shootings, they will hijack ubers and carry out their mischief using fleets of vehicles.

from 10v:
Transportation of people, packages and materiale will become hierarchical so as to exploit the economies of scale. Large modules will fragment into smaller modules for deployment on narrower roads and more specific arrival locations. Having accomplished their objective they will then recombine into larger units to complete the transportation cycle.