Computing and the Future 5 - Spacecraft Lifetime

First Impressions of an Article on Spacecraft Lifetime 

- image credit Boeing

The Space Review:
Moore's Law, Wright's Law and the countdown to exponential space

by Daniel Berleant, Venkat Kodali, Richard Segall, Hyacinthe Aboudja, and Michael Howell
Monday, January 7, 2019

In this article, Berleantz et. al. asks the question, "Is there a Moore's law that can be established around the proxy variable of Spacecraft Lifetime?" He goes about devising one using the precedents of Wright's Law for aircraft and Moore's law for integrated circuits.

Definitions

Moore's Law

In 1965, Gordon Moore noticed that the number of transistors per square inch on integrated circuits had doubled every year since their invention. Moore's law predicts that this trend will continue into the foreseeable future. ... The 18-month mark is the current definition of Moore's law.

Wright’s Law

Also called the Rule of Experience, was discovered by Theodore P. Wright and described in the paper, “Factors affecting the cost of airplanes” in the 1936 Journal of Aeronautical Sciences[1]. The simple form of the law is “we learn by doing” and the cost of each unit produced decreases as a function of the cumulative number of units produced.

Spacecraft Lifetime

What does it mean for a spacecraft to be alive? The structure (structs) of a spacecraft could be intact, but its communication (comm) systems could have failed due to radiation damage. Structs and comm could be intact but an instrument (inst) could have failed (Hubble).  Struct, comm, and inst could be intact but attitude control (gyro) could have failed. You get the idea. Within each of these categories are gating factors, such as:

• radiation budget
• cosmic ray energy
• collision energy
• number of revolutions
• component MTBF, e.g. capacitors

Key Idea: Moore's and Wright's Law's apply to the cost of making something per unit of performance, and don't speak to how long that manufactured article will last. The cost of something and how long it will last are, to first order, orthogonal attributes.

I say "first order" because of the implication that if something costs more, it may last longer, per the engineering adage, "Good, fast cheap. Pick two."

It might be useful to think in a statistical mechanics sort of way, using the notion of "mean free path" and compute the probability of collision.

Think of the ensemble spacecraft as a particle in a statistical gas and ask how long it will go before it collides with something that will damage it.

Colliding with a paint fleck in orbit can have the same impact as a slug fired from a handgun.

- go there

There's a lot of debris in orbit, so the density of the statistical gas varies significantly with altitude and orbital parameters.

- go there

We need to include those particles in our statistical gas whose kinetic energies are on the order of those required to produce structural or electronic damage. As such our gas consists of several families of "molecules" including cosmic rays, radiation fields and space debris.

The situation that drove the accuracy of Moore's law was that it was based on a
"printed" technology, the principle variable over time being feature size over a planar two-dimensional area. This gave printing technology the particular exponential property that led to Moore's observations of transistor density. The interdisciplinary 'noise' that arose came from changing substrates and logic gate voltages did not impact the predictions. Perhaps that is since printing does not care what 'color' the ink is, the ink-color being analogous to the substrate, such as DTL, TTL, MOS, CMOS, and GAN.

To assess the proxy variable  'spacecraft lifetime' consider the predecessor variable 'aircraft lifetime'.

Consider for example the B-52 Stratofortress making its maiden flight in April of 1952. No aerospace engineer in their right mind would think that a flock of these would still be flying 67 years later, but that is exactly the situation.

Enabling this spectacular lifetime has been continual ground maintenance and propulsion and electronic upgrades. The basic structural chassis of the B-52 has remained the same but comm, nav and warfare electronics have been changed out many times over with advances like those predicted by Moore’s law. In that sense, the B-52, excepting the airframe, bears little resemblance to the April, 1952 version. It is also worth noting that a B-52 can be landed and serviced. An exploratory spacecraft typically cannot. We will revisit this in a moment.

Getting back to our (cost per unit of performance) vs. lifetime idea however, notice that production of B-52's has stopped, so Moore's law transforms from a rapidly growing exponential to an unchanging constant, or even undefined given they aren't made anymore.

Consider the care with which spacecraft are assembled using special materials, tooling and processes. These include accounting for dramatic thermal changes from launch into the hot, cold, hot vacuum of space.

Consider the principal risks besides the degradation due to thermal cycling:

• risk of collision 
• risk of exposure to radiation
• risk of MTBF of finite electronic component lifetime (see Capacitor Lifetime)

None of these can be eliminated and each of them can bring a sudden death to a perfectly well-designed and properly functioning spacecraft. In these cases, everything is fine until it isn't and then there is very little one can do about it, especially considering the long travel times.

All of these are risks difficult to predict accurately and all can be complete showstoppers which leads me back to the mean free path model of spacecraft lifetime, rather that cost per unit of performance.

To see what such a curve looks like in the presence of noise I invite you to the mybinder example described in Computing and the Future 3 - Algorithms and Prediction. In this you can try various amounts of noise and various numbers of data points and degrees of polynomials. You could even plug in your own data since the Python notebook is open-sourced.

The idea to use the proxy variable spacecraft lifetime is interesting, but factors other than time may have more predictive value. I previously mentioned mean free path and radiation, both of which are temporally varying fields.

Let me propose the alternate proxy variables, spacecraft material & substrate.

There’s been a drive in civil aviation to use composite materials that consist of an epoxy resin matrix and fibrous materials such as graphite, fiberglass and boron. These materials have certain properties in a terrestrial environment where ambient pressure is available and other properties in the space environment where they outgas. Outgassing is rapid at first then decays according to some half-life characteristic of the material, its thermal, pressure, and photooxidative environment.

Spacecraft material is as important as any other property previously enumerated in determining its functional lifetime. This has, in gross anatomy terms, two components:

• spacecraft structural material
• spacecraft electronic materials and operating voltages

These can be quantified according to the MFP lifetime calculation given above. There is the need to harden these against collisions with either macro particles like paint flecks, or nanoparticles like fast protons and gamma rays which also have the ability to destroy them quickly.

So I assert that principal determinant of spacecraft lifetime is not the year that spacecraft was made but rather the materials that it was made of and their characteristic operating voltage. For example CMOS operating at 1.3 V switching voltages is more vulnerable to damage by static electricity than the 5V static tolerant TTL logic it replaced. It replaced TTL logic because of miniaturization and power consumption concerns. As we go to smaller and smaller, circuit feature size and spacecraft containing said circuits become more vulnerable to failure. A lower energy incident can disable the functioning circuitry and we know from muon collision that it’s difficult to shield against all kinds of insults, say with heavy lead or Bremsstrahlung emitting water-based shields.

Now let’s play this out in the context of ancient aircraft. The material from which WW1-era aircraft made of wood and covered with cotton and cellulose nitrate caused them to be vulnerable to both fire and photodegradation. Doped fabric has a much shorter lifetime than a corresponding aluminum-skin aircraft because aluminum is not as vulnerable to these environmental insults. Aluminum is still vulnerable to cracking, fatigue from vibration and thermal cycling however.

So here at an end of our "First Impression" we can connect Moore’s law with spacecraft lifetime. The drivers of Moore’s law has been reduction in feature size.

We can look at Moore’s law as not being a cost law or a speed law but really being a size law - that is driven by the characteristic feature size of the circuit.

As we decrease feature size there is MONSTER over the hill. The MONSTER is this:  We decrease spacecraft lifetime exponentially because it takes less of an radiation insult to disable it. We have in effect decreased the effective mean free path.

We might compensate by fielding spacecraft with technologies not so easily ablated by radiation or paint flecks - where the feature size of the computers that are installed on such spacecraft remain relatively large.

Alternately we may employ redundancy to achieve the same effect with the understanding that a single paint fleck or muon pushing explosively through 4 processors will still destroy the lot of them.

So now we only need two assumptions 

Spacecraft lifetime is proportional to the complexity of its electronic equipment.

Each successive spacecraft generation will last half as long as it’s predecessor because the feature size has gone by down by a factor of Moore's law

This leads us to the bizarre and humorous situation that in order for us to reliably field a spacecraft in successive Moore’s law generations, we must double the shielding surrounding it every 18 months and when we doubled the shielding we double the weight of the spacecraft and when we double the weight of the spacecraft we double the launch cost and in the limit we find ourselves with a spacecraft that can no longer be launched because it isn’t heavy enough to be reliable. This places us right back where we started - on the ground. If nothing else, this argument may provide a law for the most powerful computer that can be launched with a specified lifetime.

Evolutionarily successful satellites such as planets solve this problem by having two things:

• an atmosphere and 
• a magnetic field

Planetary evolution teaches us that, without a sustained magnetic field, one cannot continue to have an atmosphere! Ablation by the sun simply removes it, as we see on our moon, and on Mars, where there also happens to be no air, no food and until recently discovered, no water.

We might take a lesson from nature and equip our satellites with a magnetic field and an atmosphere so as to shield them from the harmful effects of radiation and material collision during the course of their journeys. (Which in the limit could be a good argument for staying home and taking care of the satellite we currently occupy, but I digress...)

This leads us to ask, "What combination of atmosphere and magnetic field yield a sustainable spacecraft/satellite?" This will determine the minimum launch mass.

The earth gets its magnetic field from nuclear energy, that fuels the hydrodynamic motion of a molten metal core.

From specific energy and specific power considerations, spacecraft would also have to use nuclear energy to generate a sustaining magnetic field. Note that unlike our earth, the atmosphere of a spacecraft could be charged particles held in place that would serve as a cushion for radiation and particulate impacts. This would enable the mean free path to be on our side.

The maximum lifetime of such spacecraft would then be determined by the half-life of the materials used for the radioisotope thermal generator or RTG, like Voyager 1 and 2 are using.

Because of material and orbital considerations this might lead us to mine comets and asteroids for their ionizable materials, for potential and kinetic energy.

Instead of mining those materials and returning them to earth we might create strapon packages that would 'hijack' comets/asteroids and redirect them to the destination of our choice using them as our material and energetic resources along the way.

Returning to the atmosphere discussion, our atmosphere is held in place by gravity at a scale height corresponding to the free gas available.

Articulating the obvious - it’s not practical for us to launch from earth objects that are large enough to have a significant gravitational field -  the energy costs of laws are prohibitive. Yet we know we must generate and retain both a magnetic field and an atmosphere of some significant depth so as to insulate ourselves both from radiation and from collisions.

This means that instead of relying on the gravitational force to retain an atmosphere we would utilize an electrostatic or charged envelope around the spacecraft. This makes sense since the electromagnetic forces are orders of magnitude more powerful than gravitational ones.

This charged atmosphere could be dense enough to serve as an ablative and protective shield to deflect incident cosmic radiation and ballistic insult to the spacecraft.

We could then make lifetime estimates based on the size of this envelope and cost estimates based on the mass of the generating equipment necessary to create and maintain it.  This makes a comet hijacking package interesting - a Genesis-style strap-on - as a feasible method of long duration space exploration.

Waxing more fanciful: If we face these constraints based on first principles other civilizations would experience similar constraints. Trying to perform space exploration this way leads us to ask the question if alien civilizations have in fact used a strapon packages to hijack comets and asteroids to navigate to various celestial outposts.

My feeling is that it is better to explore with photons, which travel rapidly and are massless, than with more massive, slower materials.

Since time is limited, let me finish with this conclusion: Aliens, including ourselves are best advised to hijack comets and asteroids with strap-on Genesis packages to explore other worlds in a material way. My justification for this bizarre statement is provided by the arguments above.

References:

Wright TP, (1936). “Factors affecting the costs of airplanes.” Journal of Aeronautical Sciences 10: 302-328

Researcher finds Moore's Law and Wright's Law best predict how tech improves: https://phys.org/news/2013-03-law-wright-tech.html

Wright's Law Edges Out Moore's Law in Predicting Technology Development:
https://goo.gl/HhNpjJ

Do your projects follow Wright’s Law?:
https://www.controleng.com/articles/do-your-projects-follow-wrights-law/

HTML Borders:
https://www.quackit.com/html/codes/html_borders.cfm

Moore's Law:
https://en.wikipedia.org/wiki/Moore%27s_law

Good, Fast, Cheap: You Can Only Pick Two!:
https://goo.gl/tbuwuX

Mean Free Path:
http://hyperphysics.phy-astr.gsu.edu/hbase/Kinetic/menfre.html#c2

Orbital Debris:https://goo.gl/xoDuMF

Scientists Design A Way To Clean Up Space Trash:
https://goo.gl/m8JTgS

The MOS Transistor:
http://www.cs.mun.ca/~paul/transistors/node1.html

The Silicon Engine Timeline:
https://www.computerhistory.org/siliconengine/timeline/

Boeing B-52 Stratofortress:
https://en.wikipedia.org/wiki/Boeing_B-52_Stratofortress

Capacitor Lifetime:
https://goo.gl/Ayned9

Aircraft Fabric Covering:
https://en.wikipedia.org/wiki/Aircraft_fabric_covering

Genesis Project: Star Trek - The Wrath of Khanhttps://goo.gl/GrRVdp

An Interstellar Tourist Barrels Through the Solar System
https://goo.gl/NoFPwQ

Computing and the Future 8 - Thoughts on Quantum Computing

Richard Feynman once said, "...nobody understands quantum mechanics" and "If you think you understand quantum mechanics, you don't understand quantum mechanics." So let me be clear, I don't understand quantum mechanics. But I am taking some stabs at how to compute with it and have collected a few constructs along the way. We will  start with this overview by Dr. Shohini Ghose, then we will do some backstory, code something up, and suggest some exciting things to look into.

Quantum Computing Explained in 10 Minutes

If you haven't seen this, stop whatever you are doing right now and watch it unless you are doing brain surgery or flying a 747. Click on the caption below:

- Via Ted Library

The Illuminating Facebook Question

As a matter of practice I posted the video link on my Facebook. After a time Seiichi Kirikami, a geometer and mechanical engineer from Ibaraki, Japan, asked a honest, simple, and incredibly stimulating question:

My edited response was as follows:

I think of it in interval arithmetic, a chunk of numberline, using the notation [x1,x2] for closed intervals that include their endpoints we have:

[0,1] + [0,1] ~= [0,2]

I place the ~ character to include the fact that if entangled addends could be unentangled, then we could invert the operation to find out what the addends were AFTER the operation was complete. This is impossible with conventional addition where the addends are eventually discarded producing closure in TIME, which is interesting. Recall traditional closure means that if we add any two integers, we are guaranteed to get another integer we can represent in the space (barring finite state machine overflow concerns we need not worry about here ). Of the four basic operations +,-,*,/, three are closed, but division by zero is not closed, since it has two possible values, that could not differ by a greater amount. Those two values are +∞ and -∞ depending on whether the denominator our division problem is approaching zero from the left or the right and switching infinitely fast (like an entangled particle fate, faster than light?) as we cross the event horizon from positive to negative x or vica versa.


In her lecture, Dr. Ghose is doing a quantum Boolean operation, which does not have the citizenship of addition in its space. She is doing quantum AND ∩, OR ∪ or NOT(¬). This involves:

• Two quantities in the binary AND(a,b) case
• Two quantities in the binary OR (a,b) case, and
• One quantity in the unary NOT(a) case.

In traditional computing we create addition using the notion of a carry.

Answering Seiichi's Question

Note to the reader: the word "right" is spelled "rite" below so that it has the same number of letters as "left". Apologies to the orthographically sensitive.

If you look at the definition of a qubit on wikipedia you get a Bloch Sphere where the value of the qubit in its superposition state is representable by two parameters that we can think of as latitude and a longitude.

Until the qubit is "read" or "measured" and its wave function collapses the values of latitude and longitude can be anywhere on the Bloch sphere. The symbol |ψ> symbol is in ket notation where the ψ suggests the wave function. When you look at Ghose's video she uses a color swatch where the "color" can range between pure yellow or pure blue or some mixture. In my Facebook answer I started by using an interval to show that the state of the qubit could take on a continuous range of values between zero and one, stand-in's for yellow and blue, but the {lat,lon} vector is what we should really be using. For now I think that this {lat,lon} vector is just a complex number A + Bi, or {A, B} if you prefer. The alert reader will notice there is an issue as to how we resolve the interval arithmetic version [leftX, riteX] with {a, b} vector version. It would look something like {[leftA, riteA],[leftB,riteB]}, but we know that this apparent four degrees of freedom collapses to two as explained in the wiki, leaving us with a single complex number with a real and imaginary component. Also these can be represented in ket notation as |Z> = A + Bi for the qubit case. For multiple qubits we have:

- Great Quantum Mechanic Notation Video

where each a_i, b_i, is itself a complex number.

In the quantum world of wavefunctions the value of a qubit is a complex number, which allows for the mysteries of entanglement; This is just like the mystery that two complex numbers can be added together to get a real number with no complex component or an imaginary number with no real component. The latter can be thought of as what happens when waves in a ripple tank interfere constructively or destructively.

Adding Two Integers Bitwise

Instead of going straight for the quantum juglar, let's review:

"How do we add two integers using bitwise operators?"

This takes us to a discussion on StackOverflow that answers the question for imperative coding styles. Two solutions are provided, the recursive version is the most elegant so we code it up in our online tool that lets the reader run the code. The operator names are changed to reflect our intention, where the leading q stands for quantum. We hope we can morph this code into a more realistic complex case. Run this code by clicking on the caption below:

- Run this code

Towards a More Complete Answer

A more complete answer can be obtained by repeating Seiichi's Question literally asking:

"How do we add 1 + 1 on a quantum computer."

Again StackOverflow has a well developed answer to this question.

Implementing an Adder on the IBM Quantum Computer

Your assignment is to embed [0,1]+[0,1] on the IBM quantum computer. 

Later develop the following:

• think big, not small
• the antenna issue and microwave circulators
• Nicolas Gisin entangled photon experiment
• lithium niobate downconversion
• triplet fusion upconversion - summary here,  full article here.
• the half adder
• the full adder
• von Neumann architectures
• dsp architectures
• quantum computing architectures
• Gyros are weird, cognitive impediments to progress

The last item in the list led to my proposal for a Warm Quantum Computer.

The year is 1943 for quantum computing, what happens next?

Computing and the Future 3 - Algorithms and Prediction

Underfitting and Overfitting the Future

An example of Python Jupyter Notebook running in the MyBinder environment is "Fit - Polynomial Under and Overfitting in Machine Learning", written by the author. Because the source code is present in the notebook it can be adapted to search for polynomial laws that predict trends such as those modeled by Berleant et.al. in "Moore’s Law, Wright’s Law and the Countdown to Exponential Space". MyBinder enables web publication of a Python Jupyter Notebooks. There is a little overhead (yes, waiting) to run these remotely, but it is not punitive compared to the utility obtained. The user can develop in the high-performance personal environment for high-speed, privacy and convenience,  then deploy the result in a public setting for review and general edification of the many.

MyBinder Deployed Notebooks

Taylor Series Expansion for Future Prediction

The Taylor Series approximation of a function is a mathematical way of forecasting the future behavior of a spatial or temporal curve using a polynomial and derivatives of the function. With each additional term, the approximation improves, and the expansion is best in a local neighborhood of the function. Here is an animated example I did for John Conway. In this example we are trying to approximate a cyclic future, represented by the red sine wave with polynomials of ever higher degree, starting with a green line, a blue cubic, a purple quintic and so forth. The more terms we take, the better our forecast is, but our horizon of foresight remains limited. Notice that if we had chosen to represent our future using cyclics like sines and cosines, our predictions could be perfect, provided we sampled at the Nyquist frequency or better.

Taylor Series introduces the notion of a "basis function". The world looks like the sum of the primitive functions that one uses to model it. In the above example we are trying to model or approximate a periodic function - the sine wave, with a polynomial, that is not periodic. This phenom appears in machine learning also. If you do not have a given feature (aka basis function) in the training set, that feature will not be properly recognized in the test set.

Identifying Fallacies in Machine Learning

Consider training an AI to recognize different breeds of dogs. Then show that same AI a cat. It will find the dog that is closest in "dog space" to the given cat, but it still won't be a cat. If the basis function one chooses does not mimic the property you want to detect or approximate, it is unlikely to be successful. This behavior can be seen in the TensorFlow Neural Network Playground mentioned previously. This is an important principle that helps us to cut through deceitful glamour, false-mystique and unrealistic expectations of machine learning. It is so fundamental we could place it in the list, "Fallacies of Machine Learning", filed under the header, "If all you have is a hammer, everything looks like a nail". See "Thirty-Two Common Fallacies Explained" written by the author.

In conclusion, we discover, "the basis function should have behaviors/ingredients such that their combination can approximate the complexity of the behavior of the composite system", be they cats, or sine waves. Basis functions occur in finite-element analysis of structures which asks "Does the bridge collapse?". They appear in computational geometry as rational B-Splines, rational because regular B-Splines cannot represent a circle. Periodic functions appear in Fourier Analysis, such as the wildly successful Discrete Fourier Transform (DFT), audio graphic equalizers and Wavelet Transforms, a class of discrete, discontinuous and periodic basis functions. The richness of the basis functions we choose strictly limit our accuracy in prediction, temporally (the future), or spatially (the space we operate in). 

Genetics Algorithms

Predicting the future can be made to look like an optimization problem where we search a space of parallel possibilities for the most likely candidate possibility.

Sometimes the space that we want to search for an optimal solution is too large to enumerate and evaluate every candidate collection of parameters. In this circumstance we can use grid sampling methods, either regularly or adaptively spaced, refining our estimates by sampling those regions which vary the most.

We can use Monte Carlo random methods and we can use Genetic Algorithms to "breed" ever better possible solutions. For sufficiently complex problems, none of these methods are guaranteed to produce the optimal solution. When sampling with grids, we are not guaranteed that our grid points will be on the optimal set although we do have guidelines, like the Nyquist sampling theorem that says if we sample at twice the rate of the highest frequency of a periodic waveform, then we can reproduce that waveform with arbitrary accuracy. If we sample at a coarser resolution than the highest frequency then we get unwanted "aliases" of our original signal. 

An example of spatial aliasing is the "jaggies" of a computer display:

An example of temporal (time) aliasing are propeller spin reversals when filmed with a motion picture camera:

But sometimes the future we are trying to predict is not periodic and "history is not repeating itself". But forget all that. I mentioned all this to tell you about genetic algorithms. These are explained here by Marek Obitko who developed one of the clearest platforms for demonstrating them. Unfortunately Java Applets no longer work in popular browsers due to the discontinuance of NPAPI support. A world of producers and consumers demonstration is here.

Approximation and Refinement of Prediction

Sometimes we want to make an initial guess, and then refine this guess. An intuitive model for this is Chaikin's algorithm:

In this case we have some expected approximation of the future represented by a control polygon with few vertices. As we refine the polygon by recursively chopping off corners we end up with a smoothly curve or surface.

Iterated Systems

These are my favorite, so much that I've written a book on them. They are truly "future-based" equations that only assume that the future evolves from the past according to some set of steps. Because of their similarity to fractals I like the chaotic nature of the representation. If we want to model a chaotic future we need an chaotic underlying basis function. 

When the coefficients of the iterated system have a component of "noise" or randomness we can simulate an envelope of possible futures. Take for example the prediction of the future of the landing spot of an arrow. Random factors such as the wind, temperature, humidity, precipitation and gravitational constant (which varies with elevation) can all affect the final outcome. My final project may draw from this area.

Virtualizations

There are some virtualizations that are so effective they have become working principles in science and engineering. An example of one these is the principle of virtual work which is used to derive strain energy relationships in structural mechanics to enable the prediction of whether bridges will collapse at some point in the future. The amazing thing about the principle of virtual work is that a load is put on the bridge and then the displacements of various points on the bridge are imagined, even though the bridge is not really moving at all. The degree of this virtual displacement is used to calculate the strain in each element of the bridge or structure. If in any member element the strain exceeds the strength of the material, that member fails, and the collapse can be predicted and avoided by strengthening just the weak member.

Another virtualization that is timely are complex numbers that occur in wave functions, such as acoustics and quantum computing. (Imagine a sound machine that simulates quantum superposition!) When two complex waves meet they each contain imaginary components that do not exist. Yet if they combine additively or multiplicatively they can produce real outcomes. This is spooky and interesting.

Other virtualizations include:

• Maxwell's Demon in thermodynamics
• Schrodinger's Cat in Quantum Mechanics
• Newtonian Principle of Force and Momentum

All of which can be used in computer simulations of complex system like the stock market, terrorism and cancer.

Robert H. Cannon Jr. of CalTech in his 1967  book, "Dynamics of Physical Systems" discusses the convolution integral in control theory which incorporates the past state of the system to continuously impact the current and future states. This book written at the height of the Apollo era was amazingly ahead of its time in codifying control system analysis techniques using Laplace transforms, a complex number transform similar to the Fourier transform.  Kalman filtering can be applied to complex systems where the state of the system is changing rapidly. 

The Stop Button Problem

The Stop Button Problem in Artificial General Intelligence (AGI) is a fascinating study in what happens when what the Robot wants is different from what the Person wants. The video, The Stop Button Problem,  by Rob Miles describes the problem in detail.

The I, Robot Problems mentioned in the ICF course April 2013 based on Isaac Asimovs, "Three Laws of Robotics" discuss also.

Miles proposes proposes a solution to this problem using Cooperative Inverse Reinforcement Learning. 

The take home message is, "Make sure that humans and robots want the same thing or there will be trouble."

Sentiment Analysis

Clive Humby, a UK mathematician has said that, "Data is the new oil". Andrew Ng, a leading ML researcher makes the statement that "AI is like electricity", compounding this information-as-{utility, power, energy} metaphor. I have used the phrase, "Hot and Cold Running Knowledge" to describe the situation we currently find ourselves in.

- From DreamGrow

Social networks like Twitter, Facebook, Instagram, are fertile fields for harvesting user sentiments. User sentiment affect purchasing decisions, voting decision, and market prices for commodities such as stocks, bonds and futures.

Machine Learning is being increasingly used to scrape these social networks to determine sentiments in a kind of superpredictive Delphi method.

Handy's Law in Geotagging

Mentioned in the course notes for ICF January 2014, "Handy's Law" states, "pixels/dollar doubles yearly". Consider Nokia's new five camera design.

The question in my mind is this a tip of the hat to superfluous excess like the fin race of the fifties, a tech/fashion bubble of yesteryear - or does it represent a true increase in utility?

Computational Medicine

This is a separate essay.

Computing and the Future 2 - Computational Medicine

Introduction

I am developing this line of reasoning for a course I plan to take: Information Computing and the Future.

Computational Medicine is an area that is socially, politically and technically fascinating.

It is socially fascinating because, with the advent of successful machine learning, it holds the promise to democratize access to medical care.

It is politically fascinating because there are entrenched interests making large amounts of money from the status quo. These interests include health insurance companies, hospitals and care providers. The term "care providers" is a catch all for doctors, generalists and specialists, nurses, RN's and LPN's and support staff such as radiation technicians, respiratory therapists, physical therapists, lab technicians and so forth.

It is technically fascinating because everyone is in need of competent health care and to the extent that some portions of diagnosis and treatment can be automated, more people can receive timely and effective treatment.

I will focus on the computational aspects since they hold much promise for progress and are more tractable than the social and political areas.

Computational Medicine in the Small

The turn of the millennium produced the first nearly complete sequence of the human genome, which is computationally a base-four digital Turing Tape whose length is two instances of a 3.234 Giga base pairs, one from each parent. These codes are replicated 37 trillion times in the 250+ cell types of the body. There has been substantial recent progress in gene sequencing, but a gap remains between the code of the genes (genotype), and the invisible circuits that regulate gene expression (phenotype).

Understanding the underlying genetic components of disease will continue to be great step forward in guiding accurate diagnosis and treatment.

Computational Medicine in the Large

Machine learning has been applied with great effect to cancer detection at the macroscopic level of breast cancer radiology inspection and skin cancer (melanoma) screening. For a season a schism emerged between those who are domain experts in the biological sciences and those who are so trained in the computational sciences. As Machine Learning continues to outpace expensive cancer specialists, there may be a "circling of the wagons" by those who have held an economic monopoly in this diagnostic area. They can become conflicted in their duty to heal the patient and amass large profits for themselves and their institutions.

Computational Medicine in the Huge

We can zoom our computational lens out to include populations of people, taking an "epidemiological" point of view to the national or worldwide level.

On January 2, 2019, a list of the drugs approved by the FDA in 2018 was released. Some of these drugs are "orphan drugs", that is drugs that treat conditions that are relatively rare in the world population. There is less economic incentive to manufacture and research these drugs than those for more common conditions such as cancer, HIV, cystic fibrosis, malaria and river-blindness. However the emergent theme in most of these new drugs is their astronomical pricing, making them unavailable to those, who in many cases, most need them. Here are just 3 out of 59 entries from the list above, two of which cost more than $250,000 wholesale per year:

One of the drugs in this list - Galafold, for fatty buildup disease - costs $315,000 per year, yet could be synthesized by someone with high school lab skill and a chemistry set!

This pharmacological injustice can lead to the social bifurcation of "haves and have-nots" - preconditions that fulment unrest, conflict and sometimes all out war.

But here I want to focus on a pattern of computational interaction that has a more positive end, and that could ultimately democratize access to diagnosis and treatment - all facilitated by information processing in the future.

Daisy Wheel Diagnostics™

Preamble

What follows below is more autobiographical than I want or like. I am attempting to reconstruct a line of thinking, a chain of reasoning that led to my current perspective, and enlightens what will come next. Apologies for the first-person perspective.

Introduction

When a family member of mine developed cancer I felt that it was important to understand this complex disease from a comprehensive point of view. Typically we are conditioned to look at "single-factor" causes of diseases that are in fact multifactor in nature. The first thing I did was to start trying to draw cause and effect graphs between the genes that are implicated in cancer, since visualization of that which cannot be seen has been a source of breakthroughs in clinical medicine, both with radiology, the microscope and clinical lab spectroscopy.

Some Connections to the Her2Neu Receptor Gene

After that I took genetics, biochemistry and molecular biology and wrote a summary treatise on the various factors that enable cancer to develop and treatment approaches.

Four Categories of Carcinogenesis

In broad strokes, here is a pattern of inquiry that I have developed over time, out of habit, first from seven years working in clinical laboratories, and later with five family members who have had cancer, or died of it, and two who have had mental illness. I have drawn the blood and taken EKG's on thousands of people. I have run hematology, clinical chemistry, and bacteriology tests on these same people and produced reports that were provided to attending physicians that determined treatment. I have attended appointments with surgical oncologists, radiation oncologists, and hematological oncologists (the mnemonic in cancer is "slash-burn-poison" for radiotherapy, chemotherapy and surgery respectively). That is the cancer front.

Mental illness is more of a black box with respect to the clinical laboratory because we do not as of yet have a way of sampling the concentration of intrasynaptic neurotransmitter levels along the neural tracts in the living brain. Nonetheless behavior and thought-patterns themselves can be diagnostic, which creates huge opportunities for machine learning diagnostics.

To challenge the black-box definition: Let me conclude this introduction with an observation, useful in the definition and treatment of mental illness:

"If a patient is successfully treated with a certain drug known to be specific for their symptoms, then in all likelihood, they have the disease associated with that set of symptoms." This is not circular, it is rather "substance-oriented".The constellation of drugs administered to a successfully treated patient, constitute a definition of their specific condition. The inference being made is that the successful suite of substances that restores neurotransmitter concentrations and brain chemistry to normal levels serves as a label for the condition from which the person suffers.

In computational terms, these treatments constitute an alternate name for their condition, to wit, "The condition successfully treated by XYZ in amounts xyz." So the patient has the condition XYZxyz. This makes sense if we consider the underlying biochemical nature of mental illness at the small molecule level as being that of chords of specific receptors being up and down regulated in certain patterns at certain times. There are larger aggregations of neural tract organization that are obviously also important, but my sense is that these are more significant in aphasia and specific disabilities that are separately discerned from conditions such as bipolar disorder, obsessive-compulsive disorder, schizophrenia and so forth. End of introduction.

Patterns of Human vs. Computational Inquiry

Over time I have developed some habits of inquiry, due to my mother, a medical technologist and my dad, a software engineer, who taught me how to assess whether a given care provider "was good". Answering the question, "but are they good" was an unspoken goal that attempted to assess their competence, depth of education and instinctive ability to accurately diagnose and treat various illness. Whenever I am engaged with a medical care provider, I am trying to make an accurate estimation of their abilities, since in the end, it can spell life or death. That is a purely human activity.

Over the years, part of this process has become more computational in nature as I attempted to ask the most actionable set of questions during an appointment. These questions create an atmosphere of seriousness that most competent care providers enjoy. The advent of Google has amplified the ability to prepare to an extreme degree and can positively impact the continuing education of the care providers as well. Patient-provider education is an two-way, continuing and iterative process.

Design Patterns of Computationally-Assisted Inquiry:

• Drug-driven
• Disease-driven
• Cell-driven
• Test-driven
• Physiology-driven

Let's define each case:

Drug-Driven (or pharmacology driven):

In this scheme, the name of a candidate drug typically prescribed for treating a condition is used to find:

• Chemical structure, 
• Indications for use
• Contraindications
• Mode of action
• Side effects
• Toxicity / L:D

Drug-driven strategies use variants in baseline structures to optimize treatments and minimize side effects. This optimization is man-machine process.

The Physician's Desk Reference canonizes this information for commonly prescribed drugs. For example, digoxin, derived from the Foxglove plant is indicated for congestive heart failure and improves pumping efficiency, but its toxic level is very close to its therapeutic level making it somewhat risky to use.

There are intellectual rights issues that emerge, but machine learning can address these by enabling knowledge utilization without distribution of the original content.

Imagine you are in a pharmacy where all known drugs are present. Given you, your genes, and your current state, what is the optimal combination of these substances and the dosing schedule that most benefit both short and long term health and quality of life?

Disease Driven (or illness driven):

In this approach:

• A complaint is registered
• A history is taken
• Vital signs are measured
• Patient's symptoms are recorded
• Diagnostic tests are ordered and results recorded

These are combined in a complex composite record which is represented as the patient's chart which includes text, handwriting, hand-annotated diagrams, 

These records accumulate as a stack with each successive visit. eCharting has been taking hold for the past few years, but a remarkable amount of patient information lies fallow in discarded, obsolete patient records in cold-storage. It is essential that organizations like Google digitize such patients charts before they are lost to perpetuity. This would involve scanning treatments and outcomes with the same scale and enthusiasm that the world's libraries have enjoyed in recovering old books. Thank you Google! This would create large training sets for machine learning and further of codification of medical knowledge in a machine-learning context. HIPAA laws and frivolous copyright lawsuits have obstructed this Nobel-prize worth activity, perhaps due to concerns of litigation-sensitive record custodians.

Cell-Driven (or genetics driven):

Cell-Driven strategies include:

• Cell population counting in hematology
• Differential cell identification (diff)
• Flow cytometry in cancer diagnosis
• Genome sequencing

Cell population counting includes red cell, white cell, platelets and differential cell identification as part of the complete blood count (CBC and Diff). Differential cell identification can be done manually or by flow cytometry where white cells are sorted into types including lymphocytes, monocytes, neutrophils, eosinophils and basophils.

Modern and future approaches include characterizing the

• Receptors (cell logic gates) expressed and their mutations
• Cytokines (chemical signals) that are driving their differentiation
• Metabolic products

Consider the gene-product as drug treatment point of view. If we knew the exact amount of over or under expression of genes and their cognate gene products, these could be compensated for by a custom cocktail of appropriate pharmacological agents. Small molecule agents can be ingested via the digestive tract, while protein products must often be administered intravenously since the protein structures are degraded when in contact with the digestive tract enzymes and low pH acidic conditions.

Test-Driven (clinical lab and radiology driven):

Test-Driven strategies include Clinical:

• Chemistry for Kidney, Liver, Heart and Organ System Function
• Bacteriology for Infections Disease
• Histology for Determination of Abnormal Cell Types
• Ultrasound in 2, 3 and 4 dimensions
• X-Rays
• PET scans (Positron-Emission Tomography)
• MRI/fMRI (functional Magnetic Resonance Imagery)
• CAT scans (Computer-Aided Tomography)

Physiology Driven (or illness driven):

Higher levels of organization than just the cell can be players in disease process.  Organs operate in synchrony like the members of an orchestra, some autonomously and some with directed signaling. The sympathetic vs. parasympathetic nervous system are example of this. Endocrine disorders can manifest at relatively large distances from the source of the problem. Diabetes is a complex disease with multiple factors operating at multiple scales including genetics and environment.

With these design patterns in place, to borrow a phrase from software engineering, we should think about how they might be combined into a diagnostic and treatment system that could reduce the cost of healthcare help the most people possible.

Now recall the Daisy Wheel printers of the past. They achieved a speedup factor of two simply by not starting at the beginning of the next line to be printed. Since the mechanical movement of the print head was the most expensive step, the even lines were printed left to right, while the odd lines were printed right to left, which required only the minimal computation of reversing the order of the characters to be printed on the odd lines. Does this metaphor fit with the application of knowledge derived from each of the medical processes described above?