Computing and the Future HW 9 - Spoil Sports of the Prediction Game

Q1) Find videos on youtube about each of the spoilsports. Give

• the web addresses of the videos
• your commentary on them
• how good they are.

5/5 The Observer Effect

This video is entitled, "The Quantum Experiment That Broke Reality". It is found when searching for, "The Observer Effect". It a PBS-produced 13 minute piece that starts by reviewing the double slit experiment, but extends the kinds of particles that are used, from photons, to electrons, to buckyballs with 60 atoms each. It reviews the work of Niels Bohr and Werner Heisenberg. It captures the notion of the wave function more completely, in ordinary language, and speaks to how the wave function itself appears to examine, "every possible path", as Feynman often speaks of. It raises the question, "How does the wave function know where it should land so as to complete the interference pattern when delivered one particle at a time. The narrator provides "The Copenhagen Interpretation" that is is not until the position of the particle is detected (measured) that its location is decided (determined). Until that moment, the particle lives in a superposition of possible states that include every possibility. This measurement requires the presence of an Observer in the form of a measuring instrument which can have an effect on the outcome, as we saw in the Dr. Quantum Double Slit Experiment Video.

5/5 The Heisenberg Uncertainty Principle (HUP)

This video is entitled "Understanding the Uncertainty Principle with Quantum Fourier Series. It is also a PBS-produced 15 minute piece. I chose it because Fourier series was the first mathematical method used to codify Heisenberg's principle. The narrator develops an analogous uncertainty principle for sound waves and introduces the notion that momentum is a generalization of the notion of frequency. This affected me greatly.  This can be demonstrated beyond the scope of the video as follows: Say you want to know the momentum of a photon of a certain color, that is how much pressure the photon will exert when it reflects off of a mirror. To find the momentum of a photon you multiply its color (its frequency) by a constant. That constant is Planck's constant divided by the speed of light which is also a constant. The Born rule tells us the probability that we will find that the particle, the wave function we are looking for in a specific position. But in fact it only gives us a range of probabilities. 

 

I have been looking for a long time to represent all common calculations as interval arithmetic. That is, instead of adding two numbers we are uncertain of, we instead add two ranges or intervals that include the number, and account for our uncertainty. So instead of adding numbers of everything we add intervals in which the numbers are certain to be. Here's a quick example:

Let's say we want to add 2 and 4, but we aren't sure that two is exactly 2 or that four is exactly 4, but that each could be off by 1 in either direction. We would say that [1,3] + [3,5] is the actual situation we are trying to represent. Now in the case that both numbers were at the minimum of their possible values the answer would be 1 + 3 or just 4. In the case that both numbers were at the maximum of their possible values the answer would be 3 + 5 or just 8. We would then write the result as [4,8] and be certain that we were correct. To collapse this notation to ordinary numbers we would just average the extrema, 4 and 8, to obtain 6, which correctly answers the original problem. Now let's try multiplication of the two numbers in a similar way. We repeat the process above using multiplication everywhere addition was used before to obtain [3,15]. A problem pops up with multiplication in that we can no longer average the values a the endpoints of the interval to produced a correct, 'collapsed' calculation, because the multiplication of intervals (or errors) behaves differently than the summation of same. Computing the product turns out to be the min of all possible products on the left and the max of all possible products on the right or [min(3,5,9,15),max(3,5,9,15] which is just [3,15], but now collapsing does not work out to the average because the errors multiplied instead of added!

It is important to remember that HUP is a product rule stating that the product of our uncertainty in position and momentum must be greater than or equal to some constant.

We can use the delta 𝝙 character to imply differencing or σ character to imply the standard deviation. Let's try using interval arithmetic as an estimation method for the uncertainty principle. In that case we can restate the uncertainty principle as:

where x and p represent the position and momentum of the particle after some kind of measurement event. The 1 subscript implies 'before the event' and the 2 subscript implies 'after the event'. Now since photons have no mass we have to use de Broglie's equation for the momentum of a photon which multiplies it's frequency (color) by a composite constant. The composite constant is Planck's constant divided by the speed of light. Assuming k = h/c we write:

Referring to the frequency or color of light directly is a little clunky so we can use the wavelength λ instead which removes the need to use a composite constant. I find terahertz frequency numbers harder to remember. Remembering the color of light in nanometers is very easy - red is 800 nm and blue is 400 nm. In this case we have:

As we discovered above, interval products require we compute the interval:

If we do this for an example problem we notice that all the p values contain a factor of h which conveniently cancels out of both sides. This is easier than lugging h along due to its miniscule magnitude.

For our example problem let's assume that the wavelength, and therefore the momentum of the photon did not change at all. What uncertainty in the position of the photon would this confer on us? First we make lambda the same before and after the event:

Since lambda is a common factor, we cross multiply it to move it from the interval to the right-hand side:

At this moment, with no loss of generality, we can assume that x₁ is less than x₂, which enables us to resolve the max and min functions and obtain our final general purpose result that accounts for photon momentum even though photons are massless:

Now let's plug in some numbers and ask, what uncertainty in the position of the photon is conferred on us when we are exactly certain of the momentum? Now we can notice that both sides of this equation have units of meters. This allows us to express the result in the non-dimensional format:

This is a very exciting result! It tells us that no matter what the wavelength is, that the uncertainty in position is at least 1/4π, which is about 1/12, which is about 8 percent. In other words, we can know no better than within 8 percent the position of a photon by any measurement no matter how careful we are.

This provides a quick back-of-the-envelope method for classroom use.

This 8 percent number is quite important to resolve the following question I had:

I wanted to know relationship between the Uncertainty Principle and the Nyquist sampling theorem. The latter states. "We must sample a periodic signal at least twice as often as the highest frequency appearing in the signal", if we want to reproduce that signal. Harry Nyquist created this principle in the context of communication theory, but it reminds me very much of HUP and made me wonder if there isn't some fundamental connection between the two. But Nyquist is on the order of twice, or half, depending on how you slice it, while my HUP photon calculation is on the order of 8 percent, so that tends to disconnect them as fundamental ideas.

The Nyquist principle, which is also a great 'spoilsport' in its own right, explains why we get different results depending on how finely we sample the signal due to aliasing. You may be familiar with spatial aliasing as, "the jaggies" on old-style computer displays. Temporal aliasing occurs when we sample at less than the Nyquist rate in time resulting in propellers, wagon-wheels and helicopter rotors rotating backwards or even standing still. This suggests we can use the sampling rate to appear to travel in time by producing an alias that would resemble the actual phenomenon. Dr. Quantum's double-slit experiment video discusses how the presence of an observer causes a degeneration in the results, but I was curious as to whether or not this is a sampling rate, and therefore a Nyquist sampling rate issue. As we can now see, they don't appear to be related.

5/5 Quantum Tunneling

This video is entitled, "Is Quantum Tunneling Faster Than Light". It is found when searching for the phrase, "Quantum Tunneling". It is also a PBS-produced piece that is 11 minutes in length. It is in the same series as the video cited in The Observer Effect above. In this video the narrator lays out the fact that during quantum tunneling, a photon can appear to move faster than light when compared to an identical untunneling counterpart. But, this faster than light movement is confined to the uncertainty in force for that position and momentum. This means below the level of uncertainty in position and momentum ANYTHING can be happening, but above that, normal quantum rules apply... if you can call quantum weirdness normal.

5/5 The Butterfly Effect

The instructor was kind enough to let us view this video, which I had recently seen independently, in class. The most important line in the video is that, "miniscule disturbances neither increase or decrease the frequency of occurrences of various weather events [], the most they may do is modify the sequence in which these events occur." I made a passing reference to this critical, but little known aspect of the Butterfly Effect. This little-know and little-understood point was made clear in just 13 minutes of high quality computer graphic presentation.

4/5 External Perturbations 

For me the term 'External Perturbations' translates to 'Unwanted Experimental Noise'. In the electronics context there several sources of noise. These are covered in the CalTech video entitled,  "Physics of Shot Noise, Burst Noise, Flicker Noise" in a lecture given by Ali Hajimiri. He did not discuss Johnson-Nyquist thermal noise which was characterized in 1928. The level of mathematical aesthetic  was highly refined - something I've come to expect from CalTech content. I would have given this video five stars, but the sound quality was a bit off, and the lecture wasn't from prepared slides which would have shortened it somewhat, as in the PBS presentations. Hajimiri talks about the transit time of electrons and the intermittent arrival of charge being the source of shot noise. This reminds me of the intermittent change in forces that cause Brownian motion that we discussed in class. Wires and resistors do not manifest shot noise, but p-n junctions, capacitors and other gapped devices do. You are literally measuring quanta of charge in gapped devices from tunneling sorts of arguments. Burst or 'popcorn' noise comes from the trapping of electrons and their subsequent release due to crystal imperfections. Next Hajimiri discusses Flicker or 1/f noise that has entire conferences dedicated to its study. At SIGGRAPH in the eighties I attended a seminar on 1/f noise in the context of fractals by Richard Voss, a student of Benoit Mandelbrot, after whom the Mandelbrot fractals are named. It was fun to revisit this topic. It gets the name 1/f because of the log of its power spectral density curve rolls off with linearly with the log of the frequency. It is also called 'pink noise'. Hajimiri also talks about power laws: If we plot event count on the y axis and size of the event on the x axis, there are lots of 'noise' events in nature that follow this rule. For example there are many small magnitude earthquakes for every larger earthquake. So nature let's lots of small noise events happen for every big noise event that happens. Stock market changes also follow a 1/f noise curve. Hajimiri won the Feynman prize for teaching and has over 100 patents. A complete list of his lectures is here.

5/5 Existentialism

This video, Existentialism: Crash Course Philosophy #16, was 9 minutes long and was less formal than the previous PBS and CalTech videos. It was a commercial, produced ironically by PBS, for several existential points of view from philosophers beginning with Plato and Aristotle and their notion of 'Essence' defined as the thing that makes a thing what it is. A person's purpose derives from their Essence, as in, born to do a certain thing. This gives rise to the dogma of Essentialism. The narrator then ventures to Nietzsche and Nihilism, which is the dogma that life is ultimately meaningless. Then we get to John-Paul Sartre who returns us to Essentialism. This with a chicken vs. egg approach of Existence vs. Essence and it's our job to figure out what that one thing is, as Billy Crystal hears from Jack Palance in the movie, 'City Slickers'. He then leads us to theistic Existentialism, Kirkegaard and teleology, defined as the world was or was not created for a reason and the Absurd define as the search for Answers in an Answerless world. The Narrator then returns to Sartre and the fact that we are painfully free, so free that we must construct our own moral code in the absence of any standard code to live by since there is no absolute authority. This in turn gives rise to living authentically. The video wraps up by quoting the French philosopher and author Albert Camus who said, "The literal meaning of life is whatever you're doing that keeps you from killing yourself." This is my new favorite quote.

5/5 The Care Horizon

This video topic did not respond well to a search, so I used, "The Time Value of Money" instead since that derives directly from the lecture on that topic. I found a 3 minute video narrated by a fast talking economist. It's title was, "Time Value of Money - Macroeconomics 4.3". The narrator opens with a delay discounting question, "Would you rather have $100 now or $200 at some time in the future". He then breaks down exactly what he means by "at some time in the future" and provides interest rate equations for both the future value of the money, and the present value of the money compared to its value in the future. It was quick, mathematical and useful so I'm giving it five stars.

Q2) Discuss how each of the "spoilsports of prediction" applies or does not apply to your project topic.

My project has two parts, artificial neural networks (ANNs) and transcranial magnetic stimulation of real neural networks (rNNs) which I am simulating using an anatomically accurate model. I will answer each of the seven topics for both the ANNs and RNNs. Since both are neural, the answer will sometimes be the same.

The Observer Effect

The Observer Effect in ANNs is very interesting. The cost of documenting each of the millions of decisions a deep layer neural net is making is cost prohibitive. This fact leads to, "The Explainability problem in AI" which I have discussed at length in previous assignments. In short it is not currently possible for an ANN to explain itself and be computed in reasonable time.

The Observer Effect in rNNs can be seen with performers, actors, thespians and speakers. When they know the people in the audience they have different response than when the audience is anonymous. We also see the Observer Effect in candid camera situations where people become aware they are being recorded and behave differently. 

The Heisenberg Uncertainty Principle (HUP)

The manifestation of HUP in ANNs is less pronounced since it is a completely digital system. However ANN's do not give the same answer every time because the style of computing is inherently nondeterministic. This takes some getting used to when working with Machine Learning systems, especially if one has come from a deterministic computing background. For simple models it is possible to seed the random number generators such that they create reproducible results, but in general one can get significantly different answers from run to run with ANNs.

The manifestation of HUP in rNNs is a critical issue. In Transcranial Magnetic Stimulation, one cannot excite individual neurons because the certainty in position of the magnetic field is much larger than the neuron itself. Thus only tracts of neurons can be stimulated. In the case of my project I am trying to use moving permanent magnets instead of electromagnetic pulses being emmanated by TMS coils and capacitor discharges. My approach is similarly encumbered by the fact that the magnets are much larger than the neurons and also in motion. 

Quantum Tunneling

The effect of Quantum Tunneling in ANNs is that of being a facilitating principle. The Machine Learning codes are running on computers that actually use tunneling transistors to enable the hardware to work.

The effect of Quantum Tunneling in rNNs is by analogy. If a neuron is stimulated often enough, at high enough frequency, with sufficient potential, it fires according to its activation function. Tunneling and neural firing are both threshold phenomena. 

The Butterfly Effect

I translate the Butterfly Effect in the neural network case to ask whether small changes in neural state can have large differences in output. This is dependent on the architecture, topology and structure of the deep layer neural net, whether artificial or real. There are certainly ANNs whose architecture is such that the Butterfly Effect is in play, and similarly for rNNs operated by real people. But ANNs and rNNs can be such that small inputs do not cause large changes in outputs of the two systems. So in summary this spoilsport is architecture dependent. There are mellow people and mellow ANNs who don't fly off the handle, or change their computation at the slightest provocation. A good counterexample is driverless vehicle ANNs which must respond in real time to small changes in input, if that input feature represents a danger in the environment.

External Perturbations 

I translate the External Perturbations in the ANN case to ask whether the presence of environmental noise can affect the outcome of the experiment, or in the case of digital electronic case whether noise sources described above affect the outcome. The first comment is that the advent of the digital revolution immediately eliminated the noise that limited the complexity of analog computing systems. Given that the hardware was functioning properly, all decisions were mapped into the Boolean subspace of [0,1] with no intervening continuous maybe's from shot noise, popcorn noise, thermal noise or 1/f noise. Floating point number were then constructed from sequences of Boolean numbers preserving the reproducibility of the outcome. This simple representational decision gave rise to the entire digital revolution. Now we must ask if an earthquake or a power outage or a flood can affect a computer running an artificial neural network. Again this is a threshold phenom. Either the machine is running properly and we can count on reproducible results, or it is not, there is little in-between.

We can ask if External Perturbations affect the rNN case and the answer is certainly and in the analog sense, continuously. People are affected by noise and the TMS equipment can be affected by noise and external perturbations leading to comments like, "It worked better when the rotor was closer to the subject."  

Existentialism

It would seem the quest for meaning is one of the motivating factors of the Artificial Intelligence revolution to start with. We design system ANNs modeled after ourselves both to save labor and as a method of self-reflection.

With respect to rNNs we are the neural networks that are searching for meaning and having the existential crisis in the first place! It makes sense that we would want to probe our minds and ask, if, and to what extent, magnetic fields might be used to engage them.

The Care Horizon

The time value of ANNs can be seen in the application of Deep Learning to skin cancer detection. A 1 mm deep skin cancer lesion can be excised with no harm. A 4 mm deep skin cancer can spread cancer through the entire body and kill the patient. The time value of early detection in skin cancer is thus a matter of life and death.  Skin cancer and melanoma detection is one of the recent areas where great strides have been made. 

The time value of rNNs - how would we phrase that. Would we rather have a learned brain now, or be twice as learned in four years. This it seems is what education is all about. Will transcranial magnetic stimulation affect education? That remains to be seen. 

Q3) Take a favorite topic (your project topic is a good one). Discuss where it may be in 5 years, 10, 20, 50, 100, 200, 1,000, 10,000, and millions of years.

To make the process of answering this question more deterministic, more plausible and more interesting I used:

• Moore's Law for cost
• Gilder's Law for Bandwidth
• Metcalfe's Law for Network Benefit

For our purposes here these laws are:

• Moore's Law states every 1.5 years computers halve in price.
• Gilder's Law states that bandwidth doubles every 0.5 years.
• Metcalfe's Law states network benefit goes as the square of the users.

To populate the spreadsheet for a machine learning problem I contrived an example where there was a GPU-month of training time, which is typical. I used figures from Amazon Web Services for Training, Memory and Prediction Costs. I assumed the resulting system would receive a usage of 1000 predictions per month. The first observation from the simulation is that the 10,000 and one-million year event horizons are undecidable numerically. I used a current world population of 7.7 billion and an annual population growth of 7 percent. One interesting outcome is that the current hourly cost of training a machine learning system is about the same as that of a domain expert in the same discipline. This may bode poorly for domain experts who make their living consulting in the future!

One thing that these laws tell us is how much faster to expect the training system to run, but because these services are already being vended to us using concurrent platforms at Google, Amazon, Apple and Microsoft there seems to be little value in saying how fast running a query or prediction will be. The real cost is in training the model. If the model has already been trained, the answer is effectively instant. Training can take from hours to weeks depending on the complexity of the ANN and the amount of data in the training set. 

Finally, notice that none of the three laws include saturation to a maximum, or peaking followed by roll-off.  To model these effects with effective prognostication would be a time-intensive effort, with limited benefit, since we do not know the saturation or roll-off figures. If we were to attempt a more sophisticated model, we would need to know how many people the earth landmass and oceans can support. For the long term there would have to be an accounting of the sizeable colonies on the Moon, Mars, Mercury, Europa, and Enceladus as well, not to mention those articulated in the Asimov short story.

Q4) Read Asimov's "The Last Question" Critique and comment.

Asimov called this story, "One of my favorites", written in the year of my birth, 1956. In several iterations he goes about predicting large mainframe computers, server farms, networking, wireless communication, and the personal computing revolution. A question is asked at each generation of computer development is, "Can Entropy Be Reversed?". The answer from the automaton is the same at all incarnations but the last one, "THERE IS INSUFFICIENT DATA FOR A MEANINGFUL ANSWER."  

I found the story to be entertaining and a fundamental search for meaning. Since the version I listened to was read by Asimov himself it containing interesting verbal idiosyncrasies of his own upbringing that reminded me of the time I've spent with rabbi's and distinguished Jewish people including holocaust survivors. Subtle banter, argumentation and then the sounds of children playing punctuate the story and carry it along.

The story constitutes a prophetic piece of the future of computing with an accuracy even Nostradamus would envy. So I began to be interested in what Asimov got wrong in the story, as opposed to the amazement I had at what he got right. This is interesting to me since the story is highly predictive and there is no direct way Asimov could have known how things were going to turn out 62 years ago. 
One of the things he got wrong was the 'A' in the acronym AC which stood for Analog Computing. Computing went digital instead of analog. He spoke of the clicking of relays, which were digital, then observed that those relays were replaced by switching transistors, which hardly existed in 1956, then by subatomic particles. He got the tendency for rapid population growth correct and the tendency for humans to look for new quarters whenever this happened. 

Another thing he may have gotten wrong was interstellar travel being commonplace, since the speed of light still seems to place that firmly out of reach. But who knows? There's still time for wormholes...

Q5) Write up 250 words for the equivalent amount of work [on your project] explain specifically what you did.

This week was spent recovering, successfully, from a hardware failure that fried one of the boards that I was using to control the lighting of the rotor. This necessitated a revision the power supplies so that everything in the vTMS unit could be operated at 12-24 VDC. Of particular difficult was rebuilding the LED panels. This was necessary so the unit could operate at low (and safer) DC voltages instead of operating at 170 volts - which during a test blew a fuse and turned off all the lights. A load limiting resistor was installed after computing its optimal value so that the LEDs are not damaged by excessive current. Now the vTMS unit turns at variable speeds and the LED lighting panel behind the rotor brightens and dims according. This meant many hours soldering controls and configuring the enclosures that hold the rotors, the motors, the lighting panels, the variable speed control, the on-off switch, and the direction reversing switch. Extensive modeling of the all the parts was done to enable the clearances to be optimized. It is a tight fit to get everything in the enclosure to the point that final assembly is facilitated by a tongue depressor blade. Once assembled all the parts have adequate clearance. It would not have been possible to use off the shelf enclosures without modeling the components to determine their sizes and positions. The final wiring is the most difficult step and along with final assembly can best be described as building a ship in a bottle. Not shown here are the three controls at the back of the unit. A revised wiring diagram is also being developed. In addition to this work a formula for the gelatin brain has been developed and an experimental schedule to determine the optimum concentrations and electrical properties of the ingredients. 

• Prepare an outline for your paper (if you are doing a paper). You can use this outline later to help organize your report or other product, presentation, etc. If not doing a report, do something of equivalent effort instead and describe. 

The outline for my paper, is identical to the outline for my presentation which is presented below. Each slide will generate the verbiage to be included in my final report.

Q6) Provide an outline for your presentation:

Twenty Slides

Part One: TensorFlow Playground and Basis Functions for Prediction

• Slide 0: A Blazing Fast Introduction to Machine Learning
• Slide 1: The "Discovered Algorithm" Slide
• Slide 2: Hyperparameters
• Slide 3: Explainability
• Slide 4: Basis Functions: Explicit Functions: definition and example
• Slide 5: Basis Functions: Implicit Functions: definition and example
• Slide 6: Basis Functions:  Parametric Functions: definition and example
• Slide 7: Basis Functions: Iterated Functions: definition and example
• Slide 8: Basis Functions: Chaotic Functions: definition and example
• Slide 9: Do not ask a Cat AI what kind of Dog This Is
• Slide 10: The Existing Tensorflow Playground and a Measurement
• Slide 11: Modifications to Tensorflow Playground
• Slide 12: The Modified Tensorflow Playground and a Measurement

Part Two: Transcranial Magnetic Stimulation with Permanent Magnets

• Slide 0: The Question: Can we perceive a changing magnetic field?
• Slide 1: The Follow Up: Can Permanent Magnets Produce Perception?
• Slide 2: Rationale: Why Two Rotors
• Slide 3: The Caltech Video
• Slide 4: A Blazing Fast Introduction to TMS
• Slide 5: My first TMS experience
• Slide 6: My second TMS experience in the clinic.
• Slide 7: Building the vTMS† and vBrain†
• Slide 8: Demonstration of the vTMS†

Q7) 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. Discuss the pages, explaining what you agree with, disagree with, and how your views compare with those of other reviewers on Amazon or elsewhere.

Reviewed This Week: 

• Chapter 20 - The Teeming Cities of Mars
• Chapter 21 - Big Ice

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.

This document has become rather large, so I have developed a tool to ease converting it into a form compatible with the blog. There are now 320 review notes for the first 21 chapters.

Amazon Kindle reader, has some note-taking shortcomings. One is that the notes I make are written out from the Kindle reader without Chapter Headings or page numbers. Kindle instead uses the abstraction of Locations, which are invariant of which mobile or desktop device is being used to read the book. So the Chapter headings have to be preserved manually. Another shortcoming is that the output of the notes I make are in html which is different in style than the blogger html. There are also a number of punctuation errors in the html, entirely due to the crappy Kindle html translator that I correct using the new script. Below is a the snapshot of the Unix bash script that saves some labor, but not all, in making the Book Review changes compatible with Google Blogger. I historically avoid perl like the plague, but I was forced to use it because of a text processing issue called 'non-greedy matching' which I won't bore you with.

Computing and the Future HW 8 - TRIZ to STRIZ Method

Introduction

Before I answer the homework questions I want to briefly comment on two topics that came up in our previous class:

Boeing 737 Max Catastrophe
The first topic was the Boeing 737 Max catastrophe whose status has largely been addressed by this Seattle Times article. Our in-class analysis using the TRIZ method was very interesting also. 

My comment is this: When I first started thinking about the causes of the two accidents, first of the low-cost Indonesian Lion Air, then the Ethiopian event, I wondered if there was something about the location, the climate, or the ENVIRONMENT that the aircraft were being operated in that could have led to the problem. One example where environment is important is in the case of dissimilar metal corrosion. When two metals of different electrochemical oxidation reduction potential are used in a design, galvanic corrosion can lead to parts failure. A second example for turbomachinery, especially helicopters is sand and grit in the environment. I kept thinking over and over, "What makes the environment of these two crashes different that the typical US and Western European environments?" 

Well it turns out that Boeing CHARGED EXTRA for an additional attitude indicator, a switch and a panel light that enabled pilots to easily turn off the MCAS system when it started ratcheting them into an uncontrollable dive as described in the Seattle Times article.

So the difference was environmental all right, but it was the FINANCIAL ENVIRONMENT in which the aircraft were being operated that was different. I found this realization absolutely stunning - and sickening.

Speaking of Financials
The second comment I wanted to make was on the advent of PredictIt being a useable tool for predicting election outcomes. Prediction Markets are financial instruments that have a 'put your money where your mouth is' validation mechanism. This has gotten me to thinking about finances and cryptocurrencies. This week, Jack Dorsey, CEO of Square took an action that catalysed my thinking on this. The headline is: 'Shorting Banks' and here is my post:

In reality this post may be over-optimistic, but sovereign personal banking may become a long term trend.

Now on with the homework!


1a. TRIZ contains a 39x39 table for identifying things to improve about a particular technology. The 39 rows enumerate conflicts that an improvement tends to create. The 39 columns, which have the same labels as the rows, suggest solution strategies for each improvement/conflict pair.

• Propose three improvements (table rows) to a technology relevant to your project topic.
• Identify one or more conflicts (table columns) that each improvement will tend to create.
• Based on the cells of the table, suggest solution(s) to each improvement and conflict.

The technology I am going to suggest an improvement to is TRIZ itself. I am going to use the 40 TRIZ Principles to overcome limitations in the method. I will make three such improvements:

• Streamlining the Feature Vector by reducing it from length 39 to 20.
• Streamlining the Principle Vector, reducing it from length 40 to 23.
• Streamlining the TRIZ Matrix, reducing it from size 1521 to 400.

Then I will apply the improved technology which I called STRIZ for 'Streamlined TRIZ' to a technology, Machine Learning, which is relevant to my project.
In a way I am using TRIZ to improve TRIZ which is recursive. For the sake of completeness I will cite three worsening features of the current TRIZ, and then with the goal of improving them, find applicable cells in the TRIZ matrix. These three  cases are slightly contrived in that I actually improved the method by using the 40 TRIZ principles rather using than the specific trade-offs enumerated in the TRIZ matrix, but for the sake of using TRIZ to improve TRIZ, that is, for the sake of a recursive view of the problem and also for the sake of fulfilling the assignment requirements I will enumerate the three cases where the matrix could have been used. You can skip these three cases if you want to cut to the case.
Case 1:
Worsening Feature 36:

• Feature 36 of TRIZ is 'Device Complexity'.
  TRIZ is complex.

Improving Feature 33:

• Feature 33 of TRIZ is 'Ease of Operation'.
  We wish to ease TRIZ operation.

Solving Principle 17:

• Principle 17 'Another Dimension'We will reduce the dimensionality of
  TRIZ Features, Principles and Matrix

Case 2:

Worsening Feature 25:

• Feature 25 of TRIZ is 'Loss of Time'.
  TRIZ complexity makes it time intensive.

Improving Feature 33:

• Feature 33 of TRIZ is 'Ease of Operation'.
  We wish to speed, and therefore ease TRIZ operation.

Solving Principles 28, 10, 34:

• Principle 28 'Substitution'
• Principle 10 'Preliminary Action'
• Principle 34 'Discarding and Recovering'
  In these cases, logical substitution (as opposed to mechanical)
  is used to simplify TRIZ Features, Principles and Matrix.
  This simplifications constitute a 'Preliminary Action' done
  before the method is employed.
  In the process we discard one version of the method and
  recover another version of the method.

Case 3:

Worsening Feature 35:

• Feature 36 of TRIZ is 'Versatility/Adaptability'.
  TRIZ complexity reduces its versatility.

Improving Feature 38:

• Feature 38 of TRIZ is 'Extent of Automation'.
  We wish to automate TRIZ more fully.

Solving Principle 1 and 35:

• Principle 1 'Segmentation'
• Principle 35 'Parameter Changes'
• Segmentation, Merging and Parameter Changes are the Core of
  the TRIZ to STRIZ Transformation

TRIZ to STRIZ 
I want to make a few comments on the TRIZ method and suggest some simplifying transformations that are both semantic and mathematical in nature. I called the simplified TRIZ method STRIZ where the leading 'S' character implies both simplification and streamlining.
The fact that the rows and columns have the same labels indicates that what is an improvement in one context can be a conflict or liability in another. In software engineering, programmers commonly quip that,  "That's not a bug, it's a FEATURE", implying that a bug (worsening feature) in one context can be an improving Feature in another. 

In common engineering/design parlance the Feature space enumerates "trade-offs". Genrich Altshuller called them "technical contradictions". I admire Altshuller for attempting to make the invention process more deterministic. Said again, inspecting this set of phrases reveals design 'virtues' in one context are 'vices' in another. 'Features' is a very significant term in the Machine Learning - the topic I have chosen to apply TRIZ to. More on that after we streamline the method.

TRIZ Feature Vector Transformational Rules --> 39 Labels to 20 Labels

Here are the original 39 Feature Labels that TRIZ uses:

1           Weight of moving object
2              Weight of stationary
3           Length of moving object
4              Length of stationary
5             Area of moving object
6                Area of stationary
7           Volume of moving object
8              Volume of stationary
9                             Speed
10                            Force
11               Stress or pressure
12                            Shape
13          Stability of the object
14                         Strength
15        Durability of moving obj.
16    Durability of non moving obj.
17                      Temperature
18           Illumination intensity
19          Use of energy by moving
20      Use of energy by stationary
21                            Power
22                   Loss of Energy
23                Loss of substance
24              Loss of Information
25                     Loss of Time
26        Quantity of substance/the
27                      Reliability
28             Measurement accuracy
29          Manufacturing precision
30          Object-affected harmful
31         Object-generated harmful
32              Ease of manufacture
33                Ease of operation
34                   Ease of repair
35      Adaptability or versatility
36                Device complexity
37          Difficulty of detecting
38             Extent of automation
39                     Productivity

On disciplined examination the 39 labels can be transformed to 20. This gives us one to bind to each finger and toe! Here are the principles I used for the transformation:
a) Since these features can apply to objects (nouny things) OR processes (verby things) we can use the following substitution chain: Wherever we see the term 'Object' we substitute the term 'Object or Process'. Wherever we see the term 'Object of Process' we can substitute the term 'Entity'. Wherever we see the term 'Entity' we can delete it, with the understanding that it is there. This is similar to the idea used in tensor notation where repeated indices indicate summation, except we are using substitution and deletion. This removes superfluous uses of the word 'object/, which is explicit in 7 of the features and implied in the remaining 32. This transformation is more tersely stated:

Object --> Object or Process --> Entity --> (null)

b) We can eliminate the distinction as to whether an object is moving or stationary for brevity. If it is moving at a velocity of zero it is stationary. This fact when combined with the next transformation cuts the first eight Features to four.
c) We can include changes in the Feature along with the Feature. Mathematically we are lumping the Feature along with its derivatives in the same group.
d) We can denote the Features  of Force as being 'ON or BY' and Harm being 'TO or BY'. This collapses four Features to two.
e) We can merge concepts that have the same dimensions or units. For example stress, pressure and strength all have the same units of force divided by area. Invariably when we Stress an object or process we want to know if it has the Strength to survive it. So this merge makes physical sense via dimensional analysis.
f) Accuracy and Precision can be lumped, provided they are combined with the additional figures of merit, 'Bias' and 'Spread'.

After applying these transformations we obtain the streamlined list of twenty features:

A) 1&2&23&26) Mass and Changes in Mass
B) 3-8,41,42) Length, Area, Volume and Changes in Same
C)  40, 9,40) Position, Velocity, Acceleration
D)        10) Force (on or by)
E)     11&14) Stress or Strength
F)        12) Shape
G)        13) Stability
H)     15&16) Durability
I)        17) Temperature
J)     18-22) Energy Consumption/Production/Changes
K)     24&38) Information Change
L)        25) Time Loss
M)        27) Reliability
N)  28&29,43) Accuracy, Precision, Bias, Spread
O)     30&31) Harm (to or by)
P)        32) Manufacturability
Q)     33-35) Operability, Maintainability, Adaptability
R)        36) Complexity
S)        37) Detection Difficulty
T)        39) Productivity

When we merge table entries for simplification we simply merge their corresponding table entries. The transformation I have proposed reduces 1521 possible entries to 400. We also note that the table is symmetric, it is identical to its transpose. We further note that the principal diagonal is always empty, meaning the original table had a maximum of 1482 cells containing various problem-solving entries and the transformed table had 380 cells. This is a 3.9 fold reduction in complexity of the TRIZ matrix. Completing the transformation according to the rules listed above we have:

Streamlined TRIZ Features - Twenty STRIZ Features

1                    Mass
2              Dimensions
3               Positions
4                  Forces
5       Stress & Strength
6                   Shape
7               Stability
8              Durability
9             Temperature
10                 Energy
11            Information
12                   Time
13            Reliability
14    Accuracy, Precision
15                   Harm
16      Manufacturability
17            Operability
18             Complexity
19          Detectability
20           Productivity

TRIZ Principle Vector Transformation

For brevity I would prefer to stop here, but revision of the TRIZ Feature Vector is only the first part of a three-part streamlining process. For each entry in the Cartesian Product of the TRIZ Feature Vector there are one or more TRIZ Principles that can be invoked to solve the contradiction. There are 40 TRIZ Principles. Let us discover to what degree, they can be transformed and compacted. 

Definitions: A pronym is the original word, an antonym has the opposite meaning of the pronym. This definition is necessary because there is no single-word antonym of the actual word antonym. If this befuddles you as it did me, you can pretend I didn't say it.

Our transformational rules are as follows:

• When not present add antonym principle to pronym principle.
• Combine pronym and antonym versions of each principle to form a complementary pair of principles.
• When antonym and pronyms are already present combine them as complementary pairs.
• Purge or eliminate redundant Principles.

As before we enumerate the 40 Principles and prepare them.

The 40 Principles of TRIZ Annotated

 
1                                                                Segmentation
2                                           Taking Out / Separation / Removal
3                           Local Quality / Locality / Separation of Concerns
4                                                                   Asymmetry
5                                          Merging / Opposite of Segmentation
6                                      Universality / Combination of Concerns
7                                                   Nested Doll / Telescoping
8                                                      Anti-Weight / Add lift
9                                                     Preliminary Anti-Action
10          Preliminary Action / Like Preprocessing / Suggests Postprocessing
11                                             Beforehand cushioning / Backup
12                                           Equipotentiality / Counterweight
13                                        The Other Way Round / Invert Action
14                             Spheroidality / Curvature Instead of Linearity
15                                 Dynamics / Allow Object or Process to Move
16      Partial or Excessive Actions / Slightly Less or Slightly More of Same
17                                        Another Dimension / Add a Dimension
18                                                       Mechanical Vibration
19                                                            Periodic Action
20                                Continuity of Action / Operate continuously
21                                          Skipping / Operate Intermittently
22           Blessing in Disguise / Eliminate Harm by Amplifying or Combining
23                                      Feedback / Introduce or Modify Amount
24                       Intermediary / Use an intermediary object or process
25        Self-service / Make an object or process serve or perpetuate itself
26                                  Copying / Make lots of copies or simulate
27                 Cheap short-life objects / Disposability / Use Disposables
28      Mechanics substitution / Replace mechanical method with sensor method
29     Pneumatics and Hydraulics / Use gas and liquids instead of solid parts
30               Flexible Shells and Thin Films / Membranes instead of bricks
31                                      Porous materials / Introduce Porosity
32             Color changes / Change the color or transparency of an object 
33                        Homogeneity / Make objects out of the same material
34                                                  Discarding and Recovering
35         Param. Change / State Change, Conc., Temperature, Flexibility, etc
36                                                          Phase Transitions
37                          Thermal Expansion / Suggests Thermal Contraction 
38    Strong Oxidants   / Strong Reactivity / Conc. Variation / Param. Change
39     Inert Atmosphere / Weak   Reactivity / Conc. Variation / Param. Change
40                            Composite materials / Antonym of 33 Homogeneity

We note:
TRIZ 34 is already a complementary pair.
TRIZ 35 subsumes 36 as variants of parameter change/state change idea
TRIZ 35 and 38 are variants of the same idea

TRIZ to STRIZ Transformation

A)    1&2&5) Segmentation & Separation/Merging & Combining
B)      3&6) Separation of Concerns / Combination of Concerns
C)        4) Asymmetry / Symmetry
D)        7) Nested Doll / Telescoping
E)        8) Add or Subtract Weight or Lift
F)     9&10) Preprocess/Postprocess / Preliminary Anti-Action or Action
G)       11) Beforehand cushioning / Backup / Protect
H)       12) Equipotentiality / Gradient / Counterweight / Dead weight
I)       13) The Other Way Round / Invert Action or Order of Process
J)    14&30) Spheroidality / Curvature / Flexible Shells / Thin Films
K)       15) Dynamics / Allow Object or Process to Move or Enforce Stationarity
L)       16) Partial or Excessive Actions / Slightly Less or Slightly More
M)       17) Embed or Project / Add or Subtract a Dimension
N)    18&19) Mechanical Vibration / Periodic Action
O)    20&21) Operate continuously / Operate Intermittently
P)       22) Blessing in Disguise / Eliminate Harm by Amplifying or Combining 
Q)       23) Feedback / Introduce or Modify Amount
R)       24) Intermediary / Use an intermediary object or process
S)    25&26) Self-perpetuating / Copy, serve or perpetuate itself
T)    27&34) Increase/Decrease Lifetime 
                 Make Disposable or Reusable 
                 Discard or Recover
U)       28) Mechanics substitution / Replace mechanical method with sensor method
V) 29&31&32&
   35&36-38&
         39) Parameter Change / State, Color, Transparency, Concentration,
             Temperature, Porosity, Reactivity, Flexibility,
             Thermal Expansion, Contraction, etc,
             of Object, Process, or Environment.
W)    33&40) Homogeneity / Composite / Make out of the same or diff. material

The 23 Streamlined TRIZ Principles
Most of these 23 principles can be suffixed with the phrase,

'object, action, or process' therefore it is eliminated as superfluous. Consolidating these principles for compactness we obtain:

 
1                  Segmentation or Combination
2        Separation or Combination of Concerns
3                        Asymmetry or Symmetry
4               Recursive Packing or Unpacking
5               Add or Subtract Weight or Lift
6     Preprocess or Postprocess Action or Bias
7                   Cushion / Backup / Protect
8                     Add or Subtract Gradient
9                      Invert Order of Process
10      Add or Subtract Curvature or Thickness
11           Constrain or Unconstrain Movement
12                Titrate Actions or Processes
13                 Add or Subtract a Dimension
14             Add or Subtract Periodic Action
15      Operate Continuously or Intermittently
16      Reduce Harm by Amplifying or Combining
17                    Add or Subtract Feedback
18                Add or Subtract Intermediate
19                 Add or Subtract Duplication
20               Increase or Decrease Lifetime
21                         Method Substitution
22                            Parameter Change
23                    Homogeneity or Composite

TRIZ Matrix Transformation into STRIZ Matrix


Again for brevity I would prefer to stop here, but if one more step is taken, the entire TRIZ method is simplified. We are done with the conceptually hard part, all that is left is the horrific bookkeeping transformation necessary to transform the original TRIZ Matrix to the streamlined STRIZ Matrix. To do this we need some mapping vectors that map TRIZ Features into STRIZ Features, TRIZ Principles into STRIZ Principles. Then finally we can construct the STRIZ Matrix.

The original TRIZ Matrix had 1521 cells generated by the Cartesian product of the TRIZ Feature Vector. The TRIZ Matrix contained 4202 Principle references.

Construction of the STRIZ Matrix will be done in three steps:

• Lumping the TRIZ Principle References into the small STRIZ Matrix
• Translating the TRIZ Principle References to STRIZ Principle References
• Eliminating duplicate STRIZ Principle References
• Sorting the STRIZ Principles and Hyphenating Continuous Ranges

The resulting STRIZ Matrix has 400 cells generated by the Cartesian product of the STRIZ Feature Vector. The STRIZ Matrix contains 2347 Individual Principle References which when hyphenated into contiguous references is transformed to 1559 Principle References. This reduces the size of the TRIX matrix by nearly two thirds. Here is the STRIZ Matrix:

Three Proposed Improvements in Machine Learning
Besides using TRIZ to improve itself, I will discuss briefly a technology relevant to my project in Machine Learning. There are a couple of levels of abstraction that are very important to distinguish. We could improve Machine Learning in General, (MLG) or we could improve Machine Learning in Specific, that is a specific application (MLS).

If we were to improve the MLG case, we might:

• make development faster
• make it function with less loss of information,
• improve its accuracy and precision while reducing its bias
• make it easier to create, use, repair or adapt 
• reduce its complexity
• improve productivity with faster, better and cheaper neurons 
• reduce the harm of unexplainable algorithms
• ease development of ML applications

Harm can be done by employing algorithms whose chain of reasoning are not explainable, as in firing someone using a machine-learning program that pares extra employees off a roster.  One area that really needs some attention is the mapping of problems to algorithm in a rapid and efficient way, to wit, ease of development of ML apps.

If we were to improve the MLS case, we might:

• make execution faster
• change the topology of the neural network itself, that is, we might change the number of neurons and the way in which they are interconnected. 
• change the hyperparameters that determine how long the training session will take. These hyperparameters include the learning rate, the neuron type, the basis functions used to model certain features, the number of training sessions or epochs and so forth. 

Since we are asked to pick three proposed improvements, let's choose one from the MLG case and two from the MLS case.

TRIZ for the MLG Case: Faster Development and Harm Reduction


One of the things that makes development faster, as in a Classifier described below, is to train and test, that is train and use, similar neural nets for each output parameter we want to report . This means taking an ensemble of inputs and producing a single value label as output. Said in ML parlance: This means training a set of identical input features to output a single label in a classified learning situation. The only thing that is different is the type of label, everything else is the same. For this we apply the Streamlined TRIZ Principle 1 of segmentation and duplication. We segment by output parameter, and duplicate the networks, specializing each of them to output a specific parameter. This creates the disadvantage that we have all these copies to maintain. We go to the TRIZ table and note that we can consolidate those portions of the network that are the same (Streamlined Principle 1) and segment those portions of the network that are different. (Streamlined Principle 1)

For the MLG Case we choose to reduce the harm of unexplainable algorithms.
Because of the complexity of deep layer neural networks, there rapidly reaches a point where neural nets can effectively classify their inputs, but they cannot tell you how they have done so. This is Streamlined TRIZ Feature 17, COMPLEXITY. This problem is solved using Streamlined TRIZ Principle 17, FEEDBACK in the Tensorflow Playground by showing the input features that each neuron in each layer is processing using visualization of what each neuron in each layer is processing. I will point this out in my class project demonstration.

TRIZ for the MLS Case: Faster Execution

MLS Faster Execution

To speed execution MLS Case we will choose the hyperparameter optimization problem. For this situation we will invoke the Streamlined TRIZ Principle (STP) 19 of copying, self-duplication and self-perpetuation, as well as STP 12 parameter change, and titration. We will make identical versions of the network and run them with different values of learning rate, different numbers of training sessions and different train/test splits. The train/test split is how much of the original data is held separate from the training cases for evaluating or 'testing' how well the neural network functions.

1b. Pick a certain technology (existing or future) relevant to your project topic or any other topic.  Suggest how an implementation of it could "branch out" and do something else. For example, pencils are a technology that "branched out" to also have erasers, storage bins for extra lead, clips for attaching it to a pocket, and so on. []

The branch-out of Machine Learning is exemplified in a strawman proposal I just wrote for an 'Aircraft Classifier'. This tool takes as its input 'features', aircraft geometry, mass properties, power and outputs performance such as top speed, rate of climb and service ceiling. This is a supervised learning problem in that the outputs it gives are labels that correspond to  performance characteristics. Each performance type like range, top speed, rate of climb, service ceiling and so on will require their own neural network. The Classifier can then create interpolated designs that are more optimal than the existing aircraft it is trained on. Combining two such aircraft in a dogfight with a GAN, a Generative Adversarial Network, would allow a more optimal aircraft to be designed. What I'm really interested in is the applied optimal design, the almost real-time design evolution, that AI and ML can facilitate. This constitutes a major BRANCHING OUT that could affect the design of every industrial commodity.

2. For your project, write up 399 words and post to your blog, or do the equivalent in effort on something other than writing and explain briefly.

vTMS™ Progress I have collected most, if not all of the parts for my vTMS™ device and vBrain™ simulator. The vTMS™ work is of three kinds:

1. Mechanical Engineering
2. Electrical Engineering
3. Component Acceptance Testing

Mechanical Engineering Even with this prototype device the tolerances are extremely close - in some cases less than a millimeter makes a difference. For two of the difficult parts, first the rotor, and then the the lamp holder, I utilized 3D printing via ShapeWays. The costs are very reasonable, only eight dollars for the LED holder shown below. The tolerances here are around a hundredth of a millimeter. The lamp holder looks simple enough, but has to allow for cooling of the LED panel, clearance of the wires from the rotor and clamping with adequate structural strength.

I have used Rhino™, a 3D modeler, for  the design work. Here is a typical design image:

Which after some work, leads to a functioning assembly. Using a modeling program saves enormous amounts of time and money. This project would simply not be possible in this time frame without it.

In the process I have found that is is possible to 'supercharge' the permanent magnet (PM) assemblies by stacking them four deep on the rotor. Since PM's are weaker than the magnetic field generated by a traditional inductive TMS pulse I am literally 'stacking the deck' in favor of having an effect. When two rotor faces stick together, it takes many pounds of force to separate them. When the two rotors turn in proximity to each other they induce significant movement in each other. So some technique will be necessary in use to keep them from getting too close to the simulated brain. My rationale for using two rotors is that it is the interaction of the two fields that will allow for precise placement and increase the likelihood of induction of in-brain magnetic fields. Hair, skin, skull, fascia and the three meninges of the brain present a significant magnetic obstacle to a single rotor system. Using two rotors puts a changing field between them.

In addition to the rotor and motor assembly, there is a motor controller, an LED lamp driver, four controls and all the associated wiring. Fitting this unit into its protective housing was the most labor-intensive step so far. Literally millimeters count. You can also notice modeling shortcuts in these pictures. I do not model every thread on the screws or the bevels in the LED plate. This saves time.

Here is a shot of the controls, conveniently on the back. The silver knob powers up the unit which contains a power supply in the base. It turns the LEDs on, which backlight the rotor, and enable power to go to the controller. The red knob controls the speed of rotation with clockwise being faster. The green and red buttons are forward and reverse, respectively. They are momentary switches and must be depressed for the unit to operate. Once the units are operational and stable, the red/green buttons will be replaced with a double pole, double throw switch that leave the unit running without the need to keep holding the switch down. The remaining holes are for cooling the controller, LEDs and heat sink. A gentle breeze wafts through the unit. The motor uses about 3 watts of power. I have measured this using a power supply at 15 V which showed a current consumption of 200 mA. The LED panel is about 4 watts, so the total power consumption is 7 watts. Heating should not be a significant problem. The rotor itself generates a centrifugal flow of air.

Electrical Design

The electrical design is just as important as the mechanical design. It was roughed out on paper like this:

This was then transformed using eDraw™ into a clearer diagram. I find the additional rehearsals that come from codifying the drawing help to reduce the errors I make in assembly. This is especially true on a project that has never been done. Planning needs rehearsal to be successful. Here's the cleanup. The control circuitry is separate, omitted here for brevity.

An unlikely ensemble of tools is necessary for the surgery of assembly:

3. Grad students only: continue with the book you obtained. Read the next 20 pages. State what page numbers you have read. Explain what you agree with, disagree with, and how your views compare with those of other reviewers on Amazon or elsewhere. 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. In the Spring Break session for this question I read and reviewed five chapters, 15-19, of the book.