Real examples of inventions. Inventions are all around us.

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

Altshuller, Genrikh, Shapiro, Rafael. “On Psychology of Inventive Creativity”. Questions of Psychology, no. 6, 1956, p. 37-49.
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.

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

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

Mathematics and Plausible Reasoning, 1954
- Vol. I. Induction and Analogy In Mathematics
- Vol. II. Patterns of Plausible Inference
Mathematical Discovery : On understanding, learning, and teaching problem solving, Vol. I and II,1961

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

From here

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:

Colorization of Black and White Images.

Adding Sounds To Silent Movies.

Automatic Machine Translation.

Object Classification in Photographs.

Automatic Handwriting Generation.

Character Text Generation.

Image Caption Generation.

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.

Source: Deeplearning4j site

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.

Combinations matrix

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.

Morphological matrix

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.

כדור הבדולח של דמיון

(קטעים מתוך חוברת לימוד למהנדסים וממציאים)

תרגום: אנדרי צ’רמסקוי, 2007. באישור המחבר.

פרק ראשון. אלגוריתם רעיונות חדשים

נפנה לניתוח רעיונות הקשורים לתחזיות בספרות מדע בדיוני. רעיוניות כאלה מוצאים לעיתים קרובות ביצירות ששייכות לענף מדעי-טכנולוגי בספרות (חלקית זה נכון גם לאוטופיות, לאלו, בהן המחבר לא רק יוצר מודל חברתי של החברה העתידנית, אלא גם מנסה לחזות את הישגיה המדעיים והטכנולוגיים).

התפקיד התחזיתי של מדע בדיוני נעוץ בייצור רעיונות חדשים, אשר פותרים כל מיני בעיות מדעיות וטכנולוגיות, נוסף על כך, גם רעיונות כאלה אשר עדיין לא נהגו על ידי מדע וטכנולוגיה בני זמנינו ולא הפכו לנושא של מחקר מדעי ופיתוחים טכנולוגיים מואצים. אולם, האם סופר מדע בדיוני יכול לחזות פתרון בעיות אשר עוד לא הפכו לנושא מחקר מדעי? התשובה לשאלה הזו יכולות להוות תחזיות מדעיות וטכנולוגיות של ז’ול ורן, הרברט ג’ורג’ ולס, אלקסנדר בילאייב. רעיונות חיזוי מוצלחים היו גם אצל סופרי מדע בדיוני אחרים, למשל, אצל אודוייבסקי (דרכים נעות מעצמן), בגדנוב (מנועים גרעיניים, בתי חרושת אוטומטיים).

אפשר לומר, בוודאי, שרעיונות סופרי מדע בדיוני (הלאה אני אקצר את השם כסמ”בים) מתגשמים רק במקרה. הנה מה שכתב, לדוגמה, פיסיקאי נודע בלוחינצב: “מספר מילים על תפקיד שמשחקים סמ”בים. לעניות דעתי, רב רובו של תחזיותם פשוט שגוי. למרות זאת, הם יוצרים מודלים, אשר יכולים להשפיע ובאמת משפיעים על בני אדם, אשר עוסקים במדע.” הייתכן, שרב תחזיות של סמ”בים הם רק מיקריות פשוטה? בכל שנה יוצאים לאור מאות ספרים חדשים, שבהם נאמר המון שטויות על העתיד. ובאמת, יכול לקרות מצב שבערמת השחט הענקית הזו תימצא המחט.

הרעיון הזה, מוצדק, אבל… לא נכון. רק בימינו מוציאים לאור בכל שנה מאות ספרים חדשים, רק שלא בכל מקום, אלא רק בארה”ב. לפני שלושים או שמונים שנים זרם ספרות מדע בדיוני היה הרבה יותר חלש, ולעומת זאת תחזיות נכונות, למרבה ההפתעה, היו רבות יותר! ז’ול ורן תיאר מדינה עתידנית ברומן “חמש מאות מיליונים של ביגומה”. עיר שטלשטדט עם מנהיגה חצי המשוגע, אשר היה דומה בו זמנית להיטלר, סטלין וסדאם חוסיין. הניבוי של הסמ”ב הגדול היה מדוייק אף בפרטים. במקביל לז’ול ורן, גם קרל מרקס כתב על מדינה עתידנית. מסתבר, שסמ”ב צרפתי הצליח ברומנים שלו כנביא הרבה יותר טוב מאשר מרקס בעבודותיו המדעיות. תשעים וחמישה אחוזים של בדיות ז’ול ורן התגשמו, בהיפוך מוחלט לתחזיות של מחבר ה”הון” אשר אפילו אחוז אחד מהן לא התגשם. יתרה מזו, היו שוכחים את מרקס כבר מזמן, לולא לנין שהשטלת בשם מרקס על השלטון ברוסיה.

מדוע עכשיו כמות היצירות של מדע בדיוני הלכה וקטנה? סדר העדיפויות השתנה. עד לפני עשרים וחמש שנים סמ”בים אהבו לכתוב על עתיד המדע והטכנולוגיה, באופנה היה מדע בדיוני טהור. לאחר מכן, פנטזיה הגיעה להחליפו שבה אין אזכור למדע, ובה שולטים בעולם קלפים קסומים, וחוקים נחקקים לא בידי המשפטנים אלא על ידי הקוסמים. כשאין רומנים על מדע עתידני, אין גם תחזיות. צריך לקחת בחשבון, שלעתים קרובות חושבים לתחזיות מדע בדיוני דברים שהם אינם בכלל תחיזות. יתרה מזאת, קורה שחלק מכריע של רעיונות אשר נהגו על ידי מדענים תוך כדי מחקר או על ידי ממציאים תוך כדי פתרון בעייה טכנית גם כן שגויות (הרי עדיין בשימוש כולם שיטת ניסוי וטעיה!). לתחזיות הסמ”בים נחזור בהמשך. בואו נפנה לרעיונות המדענים.

הקשיחות וההנמקה הנראות לעין של היפוטזות, לעתים קרובות מכריחות אותנו לשכוח שרב רובן תיעלם מבלי להשאיר אחריהן עקבות. רק רעיונות והיפוטזות שהן בנות קיימא שורדות (כמו במדע בדיוני!). שיטת ניסוי וטעיה, שהיא ברירת מחדל במחקר מדעי, דורשת התבוננות ברעיונות כלשהם, אשר מתוכם רק אחד מתגלה כנכון ונשמר לעתיד. תחזית, אשר הורכבה על פי כל כללי העתידנות המודרנית, גם כן ברב המקרים מתגלה כמוטעת לאותו רגע שאליו הורכבה, אלא אם כן מעדכנים אותה בהתחשב ברקע תחזיתי משתנה. תחזית היא דינמית, היא משתנה יחד עם נסיבית החיים, על מנת להתגלות כנכונה בעתיד.

יצירת מדע בדיוני היא סטטית. היא נכתבה והוצאה לאור. הרעיון הנהגה של המחבר מקובע ולא משתנה. דינמיות של התחזית נוצרת רק במקרה, כאשר סמ”ב אחר שמתחשב במצב חדש במדע ובטכנולןכיה, לוקח ומשנה את הרעיון. יצירת מדע בדיוני החדשה מקבעת את התחזית בנקודה חדשה. אולם הקורא לעתים קרובות לא מתחשב ברציפות התחזיות שכזו, שמקרבת אותן לדינמיות תחזיות אשר נעשו לפי חוקי העתידנות. הקורא בוחן את היצירה הראשונה מסוגה עם רעיון מסויים, ומסיק שסמ”ב טעה.

כמובן, הקורא צודק. אבל אז, גם במדע צריך להתחשב בטיוטות ראשוניות של תיאוריות חדשות, אשר גם כן ברב המקרים היו שגויות. ישנו דבר נוסף. יצירת מדע בדיוני עם ניבוי שגוי, אם היא כתובה היטב, אם היא ספרות אמיתית, תרגש קורא לאורך זמן ותשמש למבקרים דוגמה לכך, כיצד טועים הסמ”בים. לעומת זאת, רעיון מדעי או טכנולוגי שגוי קיים לא יותר מרגע עד שמחליפו רעיון אשר קרוב יותר לאמת או לפתרון טכנולוגי נכון. לכן יוצא כך, שטעויות מדענים ומהנדסים “מתפוגגים” עם הזמן, לעומתן טעויות הסמ”בים זכות לחיים ארוכים. דרך אגב, סופרים, העובדים בתת-ענף של מדע בדיוני טכנולוגי, כלומר אלה שבכוונה מהנדסים הישגים מדעים וטכנולוגיים אפשריים, טועים לעתים רחוקות. ניזכר באותן תחזיות של ז’ול ורן (כאשר מתוכן התבררו כנכונות 80%), בילאייב (התגשמו כמעט כולן), הרברט ג’ורג’ ולס, הרנסבק, בגדנוב, גנריך אלטוב (אלטשולר) ואחרים.

מסקרנות תחזיות, הכלולות במדע הבדיוני המודרני: מחלום מרחיק לכת על תנועה במימד אפס (nil-teleportation) עד לרעיונו של גנריך אלטוב שנמצא בגבול פרוייקט הנדסי, על חומר פרומגנטי דינמי (a dynamic ferro-material). מדע בדיוני בהישגיו הגדולים ביותר באמת מסוגל לחזות בהצלחה רעיונות מדעים וטכנולגיים עתידיים.

כמה שזה לא פרדוקסלי, סמ”בים טועים לעתים קרובות יותר, כאשר משתמשים ביצירותיהם ברעיונות מדעיים וטכנולוגיים אמיתיים של זמנם. הנה דוגמה. בשנת 1946 אסטרונומים טרם ידעו שכוכבים ניטרוניים קיימים, עד גילוי הפולסרים נשארו יותר מ-20 שנים. אולם כבר חלפו 12 שנים אחרי פירסום העבודה של באדה וצוויקי, אשר בה נאמר שכוכבים ניטרוניים חייבים להיווצר כתוצאה מהתפוצצויות של סופרנובות. וחלפו 8 שנים אחרי פירסום העבודה של אופנהיימר וולקוב, אשר המבנה הפנימי של כוכבים אלה תואר בה. הדעה הרווחת הייתה שכל הכוכבים בסופו של דבר הופכים לננסים לבנים. בדיוק בשנת 1946 ראה אור סיפורו של ליינסטר “מגע ראשון” על מפגש חללית מכדה”א עם חללית של חוצונים, הטסה ממעמקיה של גלקסייה. המפגש מתקיים בערפלית העקרב קרוב לכוכבה המרכזי. על פי תפיסות מדעיות של אותה עת זה היה ננס לבן. על פי הנתונים המודרניים שברשותינו זהו כוכב ניטרוני. סמ”ב השתמש בדעה רווחת בסיפור וטעה. הטעות של דור שלם של אסטרונומים מזמן נשכחה, הסיפור “מגע ראשון” עדיין נקרא ומוכנס לאוספים.

דוגמה אחרת היא חיים על מאדים. אחרי שסקיאפרלי “גילה” תעלות על מאדים ולוול יחס יצירתן לתושבי המאדים, הנטייה היתה להשליך בצורה אנלוגית ישירות בין עולם החי והצומח של מאדים לכדה”א. במסגרת הרעיון הזה הוסברו שינוים עונתיים של כיסויי קטבים, צורתן של תעלות, צבע היבשות וכו’. יתרה מזאת, נוצרה אסטרובוטניקה, אשר מדען סובייטי טיחוב פיתח אותה.

במסגרת הרעיון הזה עבדו גם סמ”בים, החל מויליאם ס. בורוז. הרעון נראה כנושא פרי גם כצורה אומנותית (ניזכר ב”אאליטה” של א. טולסטוי!). מכושפים על ידי צורתיות של התעלות, סמ”בים לא ראו צורך בחיזוי צורת חיים אחרת, שונה משלנו על מאדים. התעלות נתפסו כראיה מדעית אותה אי אפשר לעקוף. האשליה הוסרה ישר אחרי הטיסות הראשונות של חלליות. מאות יצירות על אנשי מאדים מייד התיישנו והפכו לשמש כתזכורת נצחית על טעויות.

כמובן, אפילו מענף התחזיתי של מדע בדיוני אי אפשר לדרוש תחזיות של תגליות מדיות עתידיות (על אף שישנן דוגמאות כאלו במדע בדיוני כלל עולמי). קודם כל, סמ”בים חוקרים יעדים, אשר ניצבים לפני חברה, עורכים ניסוים מחשבתיים, מנתחים אפשרויות השגה של מטרות שהוצבו ותוצאות אפשריות. הקורא מצידו (כולל גם ממציא או מדען) מקבל לעתים קרובות לא רמז עבה, אלא לומד לחפש דרכים לא טריוויאליות לפתרון בעיות מדעיות וטכנולוגיות.

מדע בדיוני עם התכונה הזו שלו נתפס כשטח אש מחשבתי, שבו נבדקים להשרדותם לא רעיונות מסורתיים, אלא רעיונות “מטורפים” לעתים, היפוטזות וקונספטים של מדע וטכנולוגיה. שטח האש הזה מהווה הזדמנות נדירה לדמיין בבירור תוצאות חברתיות, פסיכולוגיות ואטיות של רעיונות חדשים. ניבוים מדע בדיוניים מאפשרים להבין תכופות יותר, כיצד תשפיע נטייה כזו או אחרת של התפתחות רעיון מדעי-טכנולוגי על חיי בני אדם, מאשר תחזית מדעית-טכנולוגית. הניבוים מאפשרים למשוך תשומת לב החברה לתוצאות חיוביות ושליליות אפשריות.

באיזה אופן אם כך, נוצר רעיון מדעי-טכנולוגי חדש? ברב המקרים כתוצאה של הארה “פשוטה” בעקבות הירהורי המחבר בבעייה כזו או אחרת. אולם, ניתוח רעיונות מדעים-טכנולוגיים קיימים מאפשר למצוא סידרת כללים ושיטות הנדסת רעיונות מדע בדיוניים. אנחנו עוברים לתיאור עקרונות הנדסה שכזאת.

מייד צריך להדגיש: שליטה בשיטות הללו כלל לא מחליפה תהליך מחשבתי, לא הופכת אותו לאוטומטי. הדרך עדיין ארוכה עד ליוצר אוטומטי של רעיונות, כמו גם עד למכונת ההמצאות. העיקר מה שנותן לימוד שיטות של הנדסת רעיונות הוא ייצור השקפת עולם מקורית. כמובן, צריך להכיר מדע וטכנולוגיה, פילוסופיה ופסיכולוגיה. מוטב להכיר גם במדע בדיוני ובתולדותיו.

Creativity explained

Hello everybody,

In summary this post is about creativity and whether or not it can be developed. That`s define ‘creativity’ as an ability to propose novel ideas, ways of doing things, new approaches to tackling everyday issues.

It is natural to ask whether creativity can be developed or is it an inborn gift that is present or not. To spare debates let’s assume that creativity though inborn to some extent can be developed by exercise and practice. This is a topic for research on its own but everyday experiences corroborate our assumption.

Here comes the question:

Have it happened to you to feel the feeling of being not the silliest person in the world but at times when there was a need to create some novel idea, image it was pretty hard to do it? Good example of this is drawing exercise when there is a need to draw on free topic. It may be somewhat painful experience since good ideas do not seem to come out.

I felt this too until in about 2005 I came across very interesting article on hard science-fiction and how it is written by Pavel Amnuel a science-fiction writer and physicist. His approach to science-fiction was unconventional. He wrote about scientific ideas as a core for any serious Sci-Fi writing. The article talked about levels in Sci-Fi ideas and methods of developing creative thinking by reading Sci-Fi and analyzing underlying ideas and trying to develop them if possible. Amnuel also mentioned well known Soviet Sci-Fi writer Genrikh Altov who had influenced him a lot and was his friend and teacher. It is thanks to Altov influences that Amnuel started to work on a Manual for developing creative imagination.

Well. What hard science-fiction has to do with creativity you may ask? And the answer is that there is a direct link between them. As you may testify yourself Sci-Fi has an ability to expand and develop our imagination by letting us imagine worlds that are non-existent. But to gain the most of it this process may be guided instead of been spontaneous during reading.

There are a number of well known methods or approaches to developing creativity if you will. Certainly you’ve already heard about brainstorming the method that facilitates generation of ideas by group of people. This method was popularized by Alex Faickney Osborn in the 1963 book Applied Imagination. About the same time period Synectics came to scene. Its three assumptions according to its inventor William J.J. Gordon are

The creative process can be described and taught;
Invention processes in arts and sciences are analogous and are driven by the same “psychic” processes;
Individual and group creativity are analogous. (taken from Wikipedia)

Edvard De Bono‘s method of hats that described in his book Six Thinking Hats is worth mentioning too. It is about thinking of problems while wearing different hats that symbolize different mindsets.

But let’s return to the beginning of the post and mentioned earlier creativity development by elaborating on science fiction ideas. I personally find this approach most interesting and engaging.

OK. What literature we may find on the subject. It happened that Genrikh Altshuller (pen-name Altov) in addition to being a Sci-Fi writer was also an inventor of a methodology or Theory of inventive problems solving (TRIZ) or ТРИЗ in Russian. This methodology description deserves post on its own but for now it is sufficient to know that Development of creative imagination is an integral part of it.

If this information is of interest to you, you may find additional information in following books. Some of them in Russian, English and Hebrew.

This one is for free and is a bit of self-promotion

Translation of Altov’s hard Sci-Fi Scorching Mind novel into Hebrew by Andrei Cheremskoy and Israela Becker.

And following are books that you may find as a very good reading at least

Books in English by Genrikh Altshuller at Amazon.
Hard Sci-Fi books in Russian by Pavel Amnuel.
Altshuller’s And Suddenly the Inventor Appeared book translation into Hebrew by Yehuda Stupniker.

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ScienceAppliedForGood

with Andrey Cheremskoy

TRIZ