TRIZ

Developing Creative Imagination in a structured way

Have you wondered how it is possible to develop imagination in general and creative imagination in particular. After all, it seems like the state of the things is that some people have it and others don’t. But don’t be so fast to think so. It turns out that it is possible to train your imagination in a similar way as how you can gain muscles by exercising in a structured way.

To save you time and effort imaging such a structured and systematic approach to developing creative imagination you can turn to a method that was developed by Genrikh Altshuller the creator of the Theory Of Inventive Problem Solving also known in its Russian acronym form as TRIZ.

One constituent part of TRIZ is Development of Creative Imagination. It consists of a number of methods and imaginative techniques (operators) that can help you develop creative imagination in engaging and joyful manner.

In this post I’d like to mention some of the techniques or imaginative operators that can be helpful in developing creative imagination. These operators were introduced by sci-fi writer Pavel Amnuel.

A complete list of operators comes below. I call them operators in the mathematical sense, since they act on an object, idea etc. and transform them in one way or the other. As you may notice, first operator in the list is Inverse.

  • Do the “opposite” – Inversion.
    (The following methods form pairs in which one element is the opposite of the other—i.e., using the “do the opposite” operator or duality.)
  • Increase – Decrease
  • Acceleration – Deceleration
  • Addition – Removal (or Insertion – Extraction)
  • Generalization – Delimitation (or Universal – Defined)
  • Quantization – Continuity (for process or action)
  • Segmentation – Unification (for a property or object)
  • Make Dynamic – Static
  • Turn Artificial – Natural
  • A method to change immutable things (Change what is usually considered unchangeable).

Next, I’ll provide visual examples of each operator by referencing sci-fi movies that incorporated them.

Examples of the operators in sci-fi movies

Supersize me

The movies below are an example of Increase – Decrease operator where kids were shrunk and then a kid was enlarged. And what’s funny going from an idea of one movie to the other involved simply using an Inversion operator.

Most probably first literary reference of this operator in fiction was: Gulliver’s Travels by Jonathan Swift (1726)

  • Honey, I Shrunk the Kids (1989),

  • Honey, I Blew Up the Kid (1992)

Superman

The movie below is an example of Acceleration and Addition operators when a number of regular human abilities were increased and placed in a single person.

Superman movie from 1978.

Superman was originally created by DC Comics (1938).

Superman’s famous arsenal of powers has included flight, super-strength, super-speed, vision powers (including x-ray, heat-emitting, telescopic, infra-red, and microscopic vision), super-hearing, and super-breath.

Intelligence Enhancement

The movie below is an example of Addition – Removal operator when super-intelligence was added to a person that didn’t have it originally.

The Lawnmower Man (1992).

A short story Flowers for Algernon by Daniel Keyes (1959) is based on a similar idea.

The Invisible Man

The image below is an example of the Insertion – Extraction operator where a property of being visible was extracted from a body of a person.

Book: The Invisible Man by H.G. Wells (1897)


Time Travel

The movie below is an example of the Inversion and Discrete – Continuous operator when a continuous time made discrete and traversable back and forth.

  • Film: Back to the Future (1985)
  • The idea originally came from the book: The Time Machine by H.G. Wells (1895)

Anti-gravity

The idea below is based on the Method to change immutable things (Change what is usually considered unchangeable).

The concept was first introduced formally as “Cavorite” in H. G. Wells’ The First Men in the Moon (1901).

Capitan’s Log

The idea below is based on the Segmentation-Unification operator when an object is segmented into atoms and then assembled into a whole again.

  • Teleportation is the transfer of matter from one point to another, more or less instantaneously.
  • The word “teleportation” was coined in 1939 by American writer Charles Fort to describe the strange disappearances and appearances of anomalies, which he suggested may be connected.
  • Achieved by physicists in 1997.

Solve everyday issues with GenAI assistant

Image taken from https://milkyway2.com/amnuel/Rtv.htm

In this post I’d like to provide a short example of how chatbots, in particular Windows Copilot, can help you address issues related to web browsing, programming, reverse engineering, intelligent internet searching (aka semantic searching) etc.

The issue

In this case I wanted to check what sci-fi writer Pavel Amnuel had on his website. Even though that website looks dated and it is, the information it contains is still relevant to this day. There you can find his essays on hard science fiction, a “A Course of Lectures on TRIZ and the Development of Creative Imagination (PTB)” and more.

That course on Development of Creative Imagination consists of thirteen parts, and it turns out that the 11th one has incorrect encoding and looks like this at the time of writing

ÐÒ – ÝÒÎ Î×ÅÍÜ ÏÐÎÑÒÎ!

Ï.Àìíóýëü

Êóðñ ëåêöèé ïî ðàçâèòèþ òâîð÷åñêîãî âîîáðàæåíèÿ è òåîðèè ðåøåíèÿ èçîáðåòàòåëüñêèõ çàäà÷ äëÿ íà÷èíàþùèõ.
(Ïðîäîëæåíèå 11)


 òåêñòå èñïîëüçîâàíû íåêîòîðûå çàäà÷è ïî ÒÐÈÇ è ÐÒ èç êíèã è ñòàòåé Ã.Ñ.Àëüòøóëëåðà, Á.Ë.Çëîòèíà, À.Â.Çóñìàí è äðóãèõ àâòîðîâ.
 

ÝÒÈ ÔÀÍÒÀÑÒÈ×ÅÑÊÈÅ ÎÒÊÐÛÒÈß

Ìû ãîâîðèëè îá “îòêðûòèÿõ”, êîòîðûå äåëàåò ñàìà ïðèðîäà. Íî è â ÷åëîâå÷åñêîì îáùåñòâå íå îäíè ëèøü ðàáîòíèêè íàóêè ñîâåðøàþò îòêðûòèÿ. Íå íóæíî çàáûâàòü î êîíêóðåíòàõ – ïèñàòåëÿõ-ôàíòàñòàõ. Òîíêîñòÿìè íàó÷íûõ çíàíèé îíè âëàäåþò äàëåêî íå âñåãäà, íî ïðàâèëàìè ôàíòàçèðîâàíèÿ ïîëüçóþòñÿ êóäà ëó÷øå ó÷åíûõ.

How to fix it with GenAI?

So, what I did I’ve posted a part of it in Windows Copilot and asked about the encoding issue. It came up with a correct explanation right away.

prompt: fix encoding of this text …

response:
The text you’ve provided appears to be Cyrillic text misinterpreted as Latin characters due to incorrect character encoding. Most likely, it was originally written in Russian using Windows-1251 encoding, but then read or displayed using Windows-1252 (Latin-1) or UTF-8 without proper conversion.

🔍 Correct Encoding: Windows-1251 (Cyrillic)

When decoded properly, the text reads as:
РТВ — ЭТО ОЧЕНЬ ПРОСТО!
Г. Альтшуллер
Курс лекций по развитию творческого воображения и теории решения изобретательских задач для начинающих.
(Proverb 11)

В тексте есть некоторые задачи по ТРИЗ из РТВ, из книг и статей Г.С. Альтшуллера, Б.Л. Злотина, А.В.В. Асмана и других авторов.

🛠 How to fix encoding issues

If you encounter similar garbled text:

  1. Identify the original encoding — in this case, Windows-1251.
  2. Use a text editor or converter that allows you to change encoding (e.g., Notepad++, Sublime Text, iconv).
  3. Convert from Windows-1251 to UTF-8 to make it readable across modern systems.

Would you like me to help you convert a file or more text like this?

I’ve tried a couple of approaches, again, using Copilot’s assistance. In the end what I’ve ended up doing is saving the page as html file and updating the head element with the following meta tag below which provided correct Cyrillic encoding instead of the Latin one.

<meta charset=”windows-1251″>

This is how the properly encoded version looks like


I’ve also notified the author about this issue, and hopefully, by the time you are reading this post it will be fixed already.

Bonus

One more thing, if you’ve noticed the link to Pavel Amnuel’s website opens that page in English. This is no coincidence. The page is originally in Russian, but Copilot helped me to discover that Google Translate allows to translate a web page into a specific language and get a link to that translated page. How neat is that?


prompt: I mean I need a link that I can share after it was translated
response:
Ah, got it! You want a sharable link to the translated version of the page. Here’s how to do that using Google Translate:

✅ Create a sharable translated link

Use this format:
https://translate.google.com/translate?hl=en&sl=ru&u=https://milkyway2.com/amnuel/Rtv.htm
This link:

  • Can be shared with anyone — they’ll see the English version of the Russian page
  • Translates the page from Russian (sl=ru) to English (hl=en)
  • Automatically opens the translated version in Google Translate

By the way, while on that website you can right click on the mouse button and translate any of the articles into English. Nowadays, translations are of a very high quality.
Also, I saw that when you navigate to a translated page link, other linked pages are translated automatically!

Summary

Here you have it Windows Copilot or any other similar tool can do wonders if you ask it to help.

Long time no see

Recently, I’ve read the Remembrance of Earth’s Past trilogy by Chinese author Cixin Liu and was very much impressed by it. This in turn awakened my fascination with hard science fiction and development of creative imagination. What I mean by creative imagination development is a technique that was promoted by Genrikh Altshuller who was inventor, sci-fi writer and the creator of The Theory of Inventive problem Solving (TRIZ). To provide some context that creative imagination development technique was conceived by Altshuller with the help of his students, such Pavel Amnuel, who himself is a prolific si-fi writer and astrophysicist.

Since I am interested in topic of guided inventions which Genrikh Altshuller was written about I intend to write a number of post related to how hard sci-fi ideas can be purposefully engineered, using the tools of creative imagination development technique.

Stay tuned.

Real examples of inventions. Inventions are all around us.

IPv6 network packet: format specification by Kaitai Struct

Some definitions

This post is another one in the series of posts about Inventions and Theory of Inventive Problem Solving (abbreviated in Russian as TRIZ). First, I’d like to call it Inventiveness Theory for short and I’ll use it interchangeably with TRIZ. Because I find it very strange that the Russian acronym ТРИЗ was translated into English as TRIZ and used throughout. Also the Theory of Inventive Problem Solving sounds very cryptic and confusing to an English speaker to say the least.

Why do I write these posts?

The main urge to right these posts is to share the excitement I have about how Inventiveness Theory can help ordinary people to fell like they have tools to be creative in ways that they could hardly imagine, since inventions and inventors are covered with the mythology of divine inspirations and thousands of trial and error attempts before being able to come up with an invention. Inventiveness Theory shows that it’s not exactly the case and its tools and methodology can direct you to invention using algorithmic approach.

Inventiveness is in every engineering field

In the previous post I provided examples of how Inventiveness Theory uses standard solutions to problems that have similar structure. In this post I’d like to show inventiveness is used in every human endeavor. For example, it’s difficult to think of any progress in engineering, physics, mathematics, chemistry without engineers and scientist constantly resolving contradictions and this way making inventions. If you recall Inventiveness Theory defines invention as a resolution of a contradiction that the problem presents.

When Genrikh Altshuller and Rafael Shapiro first wrote about TRIZ in 1956 article1 most of the examples they provided for the applications of this approach were from mechanical engineering. Later Altshuller and his students worked on more advanced version of Inventiveness Theory which was applied to electrical engineering, chemistry and other fields. Only in recent decades TRIZ was applied to electronics, software engineering and other disciplines.

Since, I have an experience in software development and software testing I’d like to provide most of my examples from these fields. But, I’ll also provide examples from electronics, physics and mathematics. It turns out there could be no science or engineering as we know it without creativity.

Computer Science

Hardware

If we look at Computer Science history we’ll see that it evolved from invention to invention. First, there were mechanical calculators, then came electro-mechanical ones using relays, then electronic computers using lamps. Then with an advent of the semiconductors computers were made with hundreds of transistors later to be replaced with microchips having billions of transistors in one single microcircuit.

Software

Software too had an interesting evolution moving from machine language programming to invention of compilers that allowed a more abstract approach to programming to high level programming languages like Java, Python etc. that allowed programmers to reason almost in a human language while righting programs.

Each of these evolutions wouldn’t be possible without a chain of inventions that scientist and engineers made along the way. For example, let’s take Data Structures and Algorithms topics that any programmer learns to some degree. Sorting algorithms like bubble sort or merge sort are an example of inventions when a problem of sorting data is resolved by various creatives ways, like swapping as in bubble sort or divide and conquer as in merge sort approaches.

What is interesting is that in TRIZ there are a number of ways to resolve a physical contradiction2 which are

  • In space
  • In time
  • In structure
  • By condition

And in Computer Science computational complexity is also measured in space by memory storage requirements and in time it takes for an algorithm to run.

Real example

One concrete example of a contradiction in software engineering is how to be able to update software without the need to rebuild it which requires extensive resources and procedures once it is deployed in production. The contradiction here is that software should have certain parameters to function, but there is a need for these parameters to be changed when required. This particular problem was resolved in structure by extracting parameter values into a dedicated configuration file. This file is loaded by the main program on start up and can be even reloaded on the fly. This approach allows to update the parameters as required without a need to invest additional development time and rebuilding the software.

References

  1. Altshuller, Genrikh, Shapiro, Rafael. “On Psychology of Inventive Creativity”. Questions of Psychology, no. 6, 1956, p. 37-49.
  2. Petrov, Vadimir. TRIZ Basics: Theory of Inventive Problem Solving. Self publishing, Kindle edition 2019, p. 368.

The same trick used all over again. Using standard solutions.

In this post I continue to talk about how the methods from the Theory of Inventive Problem Solving (abbreviated as TRIZ in Russian) can be used by anyone to solve day to day challenges.

This time, again we’ll look at the problem I had in my house and how it was resolved using inventive approach.

Inventive situation

The door lock handle that you see at the top of this post is a typical one that is used across North America, particularly, in Canada. The side of the lock that has a handle resides inside the house. The main issue I had with it was that it was very difficult if not impossible to understand what position handle was in. Is it locked or unlocked, especially in evenings when the lighting conditions are poor. Just look at the image below and tell me whether you can see the handle at all?

As you saw the handle blends with the circular base it attached to and is hardly visible if at all.

As in the previous post where there issue was to find who had stolen a tire valve cap the contradiction is that a cap and in this case a handle has the same color as the base of the lock and is poorly discernable to understand what the sate of the lock is.

You possibly guessed that as in the previous case we possibly can play with the shape of the handle, its color or something else. Again, Ideal Final Result in this inventive situation is for the handle to notify by itself that it’s locked or unlocked. And as you, probably, guessed correctly we can use the same solution as before, namely, we can color it in a white color with wite-out.

And this is what I did at first as can be seen in the image below

This wasn’t a bad idea, but I thought I can do even better, especially at night, since the white color doesn’t glow in the dark. But a luminous tape does. So that was what I did, I used such a tape. When the lights are on it absorbs light and later it emits it and this way the tape glows. This allows you to see at a glance whether the door is locked or unlocked as can be seen below

As you’ve noticed the solution to this problem looks very much like the one before. And actually, in TRIZ there are typical problems and non-typical problems. When you see that a problem at hand is a typical problem it means that there are already existing solutions that you can use out of the box to solve it. In TRIZ one such toolbox is Standard Inventive Solutions also known as Standards. This can be seen in the diagram taken from Vladimir Petrov’s book TRIZ Basics.

Petrov, Vladimir. TRIZ Basics: Theory of Inventive Problem Solving, 2018

Ideal Final Result. Or how to solve it?

What is it all about?

As I mentioned in the previous post I’d like to share with you how anyone can be an inventor and solve everyday problems from simpler to not so using the methodology from the Theory Of Inventive Problem Solving (aka TRIZ in Russian).

Today we will look at a real problem that I faced while living in an apartment building in Israel.

Inventive problem

That apartment building had a public storage where tenants could store their bicycles and other stuff. So it was natural for me to store my own bike there as well. But it happened that someone stole the black valve cap on one of the tires of my bike.

This is how the valve cap I am talking about looks like

How to solve it?

Well, how would you solve this issue? I can tell you right away that at that time there were no cameras installed in the the storage room, and installing one would be prohibitive. It was possible not to do anything as well as a solution.

So let’s try to approach this problem using tools from TRIZ. As I mentioned before that’s first identify a contradiction that happens in this situation. I want to have my valve cap in place without people ‘borrowing’ it. Then more specifically, we may say that we want the cap to be distinguishable if it is stolen, and ideally not to be stolen in the first place. This is what called in TRIZ as administrative contradiction.

The issue lies in the fact that the valve cap is black on any of the bikes and it is indistinguishable when stolen. So the technical contradiction as it’s known in TRIZ, means that the cup isn’t distinguishable when it should be clearly distinguishable.

Ideal Final Result (IFR)

Now, it’s time to introduce one more term, which is an Ideal Final Result. It will help us to focus the contradiction and to resolve it. Ideal Final Result means that we are interested in such a state when the problem that exists resolves by itself, or it does not require any solution at all. When we think about the valve cap issue this way the solution becomes crystal clear.

How about making the valve cap to notify us by itself that it was stolen? One way to achieve it is to have a valve cap that has a different structure, like shape or color. Well, indeed changing the color of the cap was what I did. I took a BIC wite-out and simply colored the cap in white color! Yep. That solved the issue, since even if the cap was stolen I’d see it right away on another bike.

This approach solved the issue and also made the valve easier to spot when I needed to inflate the tire.

Well, thank you for reading until the end and stay tuned for the next blog post in the series.

Take care.

Inventions are everywhere. You just need to look carefully.

From idea to invention by an algorithm

I think I’ve exhausted the nutrition and fasting topics, so I decided to move on to something else like creativity and inventions. You’ve heard it right, inventions. I’ve already posted a number of posts on this subject before. It happened that around 2004 I stumbled upon an article by Pavel Amnuel about how to come up with sci-fi ideas using an algorithm and since then I was hooked. Through Pavel Amnuel I learned about Genrikh Artshuller a soviet sci-fi writer and an inventor of the Theory of Inventive Problem Solving (aka TRIZ in Russian). Later I met with Pavel Amnuel who was a sci-fi writer and astrophysicist himself. Reading articles and books at altshuller.ru website I came alone Searing Mind short sci-fi story that Altshuler wrote. So I decided to translate it into Hebrew which I did together with Israela Beker. Through this work I came to know Vladimir Petrov who was an inventor and an engineer and also Altshuller’s close student as Pavel Amnuel was.

Everyone can be an inventor

Since then, I am back and forth with regard to TRIZ having read a number of books that Genrikh Altshuller wrote and trying to use his approach in a real life. So I thought to myself that I could write a series of posts where I’d describe how it was possible to use TRIZ methods in solving every day’s problems. How it is possible to approach various issues that each one of us face in everyday life. As one author wrote all that we see around us that was artificially made was once a one’s person idea. And it’s correct. Anything from houses, furniture, cars, computers didn’t exist and had to be invented. Actually, almost everything that we deal with on a daily basis which is a non-leaving matter was created by humans.

What I intend to write?

So there you have it a post that is dedicated to looking at a problem and how it was solved using TRIZ methodology. By the way what I refer to as a problem is actually defined as a contradiction in TRIZ. Contradiction lies at the heart of TRIZ, since its machinery is useful only when contradiction found and clearly reviewed to be later resolved. By the way, invention defined in TRIZ as a resolution of a contradiction. So if there was no contradiction there was no invention either by TRIZ definition. This makes it much more easier to decern among patents what is actual genuine invention in comparison to being a clone or a modification of an existing invention.

Next post will be such a post as described above. Till then take care.

It’s a kind of magic. How kids can have fun with math.

source

The title of this post can seem strange to you. What mathematics has to do with magic? In my opinion, it depends on what feelings you took from math lessons at school, college or university. There are people who were frightened by math or bored by it. But there were also a lucky few who were able to spot something beautiful about math on their own or thanks to a good teacher. There’s another approach when math lessons cannot do the trick for you. Genrich Altshuller didn’t call it magic, but an encounter with a miracle. In the book How to become a genius. Life strategy of a creative person by Altshuller and Vertkin they mentioned that creative people at an early age encountered a miracle that heavily influenced their trajectory in life. If we take mathematics as an example, showing a kid that mathematics isn’t a boring, but actually interesting field to participate in can be such a decisive encounter with a miracle. And it seems to me the best way to appreciate the beauty of mathematics is by actually doing it.

To organize such an encounter for my daughter, I suggested her to create a YouTube Kids channel about mathematics for kids where she can upload videos on mathematics that kids can understand and relate too. She agreed and with a little help from me she was able to create two videos already. One about square numbers, that as their name suggest, have a shape of a square. Another one about a visual way of depicting the addition of two numbers that third graders are tasked to do at school. Let’s take for example, 111 + 37. It turns out that before kids start using column method addition it’s difficult for them to find the right answer. Using binary trees can visualize the composition of the two numbers and help a kid in summing these numbers together.

One interesting aspect of creating such short video clips is that it encourages a parent and a kid to work together to be ready to present the topic clearly in a way that a kid can understand what the video is about. Also, teaching something is one of the best ways to understand it yourself. One additional thing to mention is that the videos in the channel can cover topics that are not explained at school at all or taught only in upper grades, which provides a kid with an advantage of an early exposure to advanced and interesting mathematical topics.

This way of introducing kids to mathematics has a promise of removing boredom and rut repetition of solving similar exercises and shows kids that math has more to it than how it’s usually taught at school.

Unboxing inventions and innovations

Photo by Kelli McClintock on Unsplash

It seems like there is hardly a person who didn’t hear the phrase “Thinking outside of the box”. As Wikipedia entry says it’s “a metaphor that means to think differently, unconventionally, or from a new perspective.” While it sounds good in theory, it is unclear what one should do to think unconventionally, differently, creatively etc. Only demanding from someone to think outside of the box, doesn’t provide clear guidance on how to achieve this goal.

The same issue happens in education, when a student is taught any subject that requires thinking beyond what was taught in a lesson or a lecture. There are people who can do better than others in such situations and we tend to label them as creative, smart and sometimes genius. But the psychological research into what makes experts experts, for example done by Anders K. Ericsson et al, shows that this has to do more with the way an expert practiced, and not the innate cognitive abilities.

So what makes us creative and can it be taught and learned? The short answer is yes and the rest of this post will try to justify this answer. The question of creative thinking is relevant in most fields of daily life where problems arise and when there is no obvious way of how to solve them. Here we go into realm of innovation and invention. There are many definitions of these two terms, so let me quote one from Merriam-Webster on the difference between invention and innovation

What is the difference between innovation and invention?
The words innovation and invention overlap semantically but are really quite distinct.

Invention can refer to a type of musical composition, a falsehood, a discovery, or any product of the imagination. The sense of invention most likely to be confused with innovation is “a device, contrivance, or process originated after study and experiment,” usually something which has not previously been in existence.

Innovation, for its part, can refer to something new or to a change made to an existing product, idea, or field. One might say that the first telephone was an invention, the first cellular telephone either an invention or an innovation, and the first smartphone an innovation.

Chuck Swoboda, in his The Innovator’s Spirit book also provides detentions for an innovation and an invention that will be discussed in this post and they are

An invention, by definition, is something new—something that’s never been seen before. An innovation, on the other hand, especially a disruptive one, is something new that also creates enormous value by addressing an important problem.

While I do not have any objection to his definition of an innovation, I don’t agree with the definition of an invention. Saying that invention “is something new that’s never been seen before” is too vague a definition to be practical. It takes a quick look into submitted patents to see that there are lots of similar, if not outright identical patents issued for inventions. Which means the definition of invention being something never seen before fails to capture this. Also by the same token invention “being something new” fails too.

But it turns out there is quite precise definition, that exits since 1956, of a technical invention, which was provided by Genrich Altshuller and Rafael Schapiro in a paper About the Psychology of Inventive Creativity (available in Russian) published in Psychology Issues, No. 6, 1956. – p. 37-49. In the paper they mentioned that as a technical system evolves there could arise contradictory requirements between parts of the system. For example, lots of people use mobile phones to browse the internet. To be able to comfortably see the content on the screen of the phone, the screen should be as big as possible, but this requirement clashes (contradicts) with the size of the mobile phone, which should be small enough to be able to hold it comfortably in a hand or carry it in a pocket.

Altshuller and Shapiro defined the invention as a resolution of the contradictory requirements between parts of the system, without having to trade off requirements to achieve the solution. This definition of invention allows to talk precisely about what can be thought as invention and what can’t. Generally speaking, contradictory requirements can be resolved in space, time or structure. For example, returning to the mobile phone example, to resolve the contradiction in structure of the phone, between the size of the screen and the size of the phone there is a functionality that was introduced in mobile phones that allows to screencast the video and audio from a phone to a TV screen using Wi-Fi radio signal. YouTube application on Android phones supports this functionality.

Altshuller wrote a number of books on the subject of creative thinking, particularly books that developed the Theory of Inventive Problem Solving (abbreviated as TRIZ in Russian). In these books the ideas about a contradiction, an invention and an algorithmic approach (ARIZ) to how to invent by solving contradictions in technical problems are elaborated. To name just a few books in chronological order, written by Altshuller

  • How to learn to invent (“Как научиться изобретать”), 1961
  • Algorithm of Invention(“АЛГОРИТМ изобретения”), 1969
  • Creativity as an Exact Science: Theory of Inventive Problem Solving (“ТВОРЧЕСТВО как точная наука: Теория решения изобретательских задач”), 1979

What is important to mention about the books is that they contain systematic, detailed and step by step explanations of how to invent using an algorithm. Lots of examples and exercises for self-study included in them. The books by Altshuller somewhat resemble in their content and in a way of presenting the material books written by George Polya.

Polya being a productive mathematician was also interested in how to convey his ideas in a way that could be easily understood by other people. To this end he wrote a number of books directed to pupils, students, teachers and general audience.

For example, his book How To Solve it first published in 1945 is a step by step instruction set on how to approach mathematical problems in a systematic way, using heuristics that mathematicians accumulated doing math for thousands of years. It very much resembles to me the structure and approach taken in Altshuller’s How to learn to event. Later, Polya wrote two additional books on how mathematicians think and how they arrive to mathematical theories. Each of the books consist of two volumes and they are

What is interesting to mention is that the books written by Polya and Altshuller more than fifty years ago contained very insightful ideas and heuristics to tackle math and inventive problems. But today it’s still difficult to find a widespread adoption of these ideas in education, industry or elsewhere. For example, The Princeton Companion to Applied Mathematics book from 2015 mentions only a rudimentary number of math Tricks and Techniques in the chapter I, Introduction to Applied Mathematics, on pages 39-40, out of 1031 pages.

As well as the general ideas and principles described in
this article, applied mathematicians have at their disposal
their own bags of tricks and techniques, which
they bring into play when experience suggests they
might be useful. Some will work only on very specific
problems. Others might be nonrigorous but able to give
useful insight. George Pólya is quoted as saying, “A
trick used three times becomes a standard technique.”
Here are a few examples of tricks and techniques that
prove useful on many different occasions, along with a
very simple example in each case.

– Use symmetry…
– Add and subtract a term, or multiply and divide by a term….
– Consider special cases…
– Transform the problem…
– Proof by contradiction…
– Going into the complex plane…

As a summary, if you are curious whether it’s possible to learn how to be more creative, inventive or, in general, approach problems in a systematic way, then check the books by Genrich Altshuller and George Polya. They may provide you with just the tools that you were looking for, but didn’t know where to find.

Systematic Approach To Applications Of Deep Learning

Hidden potential

The interest in Deep Learning research and applications is as hot as never before. A countless number of new research papers can be found at arXiv.org almost every day. Those papers provide us with descriptions of novel ways Artificial Neural Networks can be applied to various fields of our daily life. What is fascinating in Deep Learning is the fact that neural networks seem like universally capable to be applied to various kinds of problems that previously were tackled with a tailored approach. Moreover, each day there is an article or blog post that tells us about even more exotic ways of applying Deep Learning. The problem with those articles, blog posts and even books is that they do not provide systematic treatment of neural networks applications. At least, so far I haven’t seen this was done and if you know about such attempts please let me know.

Sate-of-the-art

While searching for materials for this post I’ve found a number of articles that summarize Deep Learning applications. Here comes a number of quotes from those articles with related links.

1. The first post called 8 Inspirational Applications of Deep Learning by Jason Brownlee is from Machine Learning  Mastery blog.

Here’s the list:

  1. Colorization of Black and White Images.
  2. Adding Sounds To Silent Movies.
  3. Automatic Machine Translation.
  4. Object Classification in Photographs.
  5. Automatic Handwriting Generation.
  6. Character Text Generation.
  7. Image Caption Generation.
  8. Automatic Game Playing.

As it can be seen these applications can be concisely described by the sensory modalities Artificial Intelligence research was initially applied to which are Audio, Visual and Spatial modalities. 

2. This one is called Deep Learning Use Cases and is taken from a site dedicated to Deeplearning4j machine learning library for Java.

3. Next one is called Deep Learning Applications in Science and Engineering by John Murphy. This article describes similar applications of Deep Learning as previous ones but also provides more exotic applications, such as Scientific Experiment Design, High Energy Physics and Drug Discovery.

4. In addition I want to mention The Next Wave of Deep Learning Applications post which
 is full of most exotic applications that maybe you haven’t heard about them before. To name a few there are Weather Forecasting and Event Detection, Neural Networks for Brain Cancer Detection applications.

5. The last one is a question about Deep Learning applications in Quora that  has a number of helpful answers.

What is lacking in all those resources is the systematic approach that not only describes current applications of Deep Learning but which is also capable to predict possible future applications.

Combinations Matrix

I propose to use Combinations Matrix that all current Deep Learning applications can be deduced from and it also has  built in merit of hinting for future applications.

This matrix has in its rows and columns various types of sensory modalities organized in such a way that pairs of options of those modalities such as speech – image recognition etc. can be found in it. When a pair is found it may be interpreted in the terms of a current or possible future applications of Deep Learning.

Prediction example

If we look at row 4 and column B will find there ‘Speech recognizer -> Speech generator’ pair from Audio modality which can be interpreted as language to language translation application, such as Google translate. Moreover, if we choose row 6 and column D will find there ‘Image recognizer -> Image generator’ which is exactly the idea behind the Deep Convolutional Inverse Graphics Network paper at arXiv.org.

 It can be seen that this matrix has following number of possible pairs  = 12 * (12 – 1) = 132.  In general case pairs = N*(N – 1).

If we want to think about a novel application it is possible systematically go over the matrix and look for it or pick a random pair, such as row 4 and column H which is ‘Image recognizer -> Natural language generator’. It may be  an application that lip reads a person talking in front of a mobile phone camera and generating text to be sent to another application. This application is useful when there is a noisy environment on the background (idea comes from here).

Notice that this matrix is composed for the sake of an example and it may be organized in other ways that may produce another combinations for possible applications of Deep Learning. Moreover this matrix may be multi-dimensional to take into account tuples of various parameters.

Morphological Matrix

Additional way to try to predict applications of Deep Learning is to use morphological matrix method developed by Fritz Zwicky, Swiss-national astrophysicist based at the California Institute of Technology. By the way, this method has been successfully used to predict the existence of neutron stars. The good explanation of what is morphological matrix and its applications may be found at Swedish Morphological Society. For our purposes it is sufficient to know that this matrix can be composed in such a way that the first row has various sensory modalities such as audio, visual, touch etc. and the rest of the rows provides possible options for those modalities. The screenshot will help to clarify this.

Prediction example

Now that we have a morphological matrix it is possible to think of Deep Learning applications as a set of options of modalities. For example, if we take speech recognition, image recognition and natural language generation options form the table we can get an essence of the following novel Deep Learning application such as lip reading described in the Lip Reading Sentences in the Wild paper submitted to arXiv.org in November, 16th, 2016.

Notice that this matrix can be composed in other ways to produce different sets of possible applications for Deep Learning.

Conclusion

As it was shown in this post it is possible and effective to systematically look for Deep Learning applications in particular and Machine Learning in general by means of Combinations and Morphological matrices.

  

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