Category Archives: Creativity

Curiosity Drives Creativity & Innovation

Source: IdeaToValue website, Jun 2017

the world’s greatest innovators are passionately curious and even nosy or annoying.

They approach situations and problems from an open, childlike, mind unconfined by rigidity or preconceived notions.

Fueled by curiosity, they ask crazy questions.

Their expertise grows as they actualize their curiosity by developing a love of learning. Their curiosity impulse and prior knowledge alert them to invisible gaps or details others miss, fueling even more questioning. Their curiosity drives them to become persistent. Their wide interests and curiosity enable them to apply ideas across divergent fields, improving upon the ideas of others,

Their wide interests and curiosity enable them to apply ideas across divergent fields, improving upon the ideas of others, synthesizing ideas, and discovering patterns from disparate fields to generate new ideas. Curiosity reveals new options even at dead ends and inspires a sense of purpose and meaning. Continuously rewarded and renewed curiosity becomes a lifelong passion.

How to nurture the curious attitude?

  1. Find and remove what gets in the way of your curious mind
  2. Never be too shy to ask questions, and ask questions even when you think you know everything you need to know. 
  3. Become more a interesting person and live a more interesting life by reconnecting with your inner child, sense of wonder, and mindset 
  4. Turn away from the familiar, and open your mind to new ideas, interests,experiences, and adventures 
  5. Dig deeper and understand the context, origin, and history of things
  6. Forge deep and quality relationships by showing your sincere and genuine interests in people around you, across all levels
  7. Build your own lab with full of experimental tools as your sandbox to tinker or try out new things; enjoy mistakes and failures 
  8. Finally, work with inquisitive minds, rather than just qualified and experienced people

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Claude Shannon on Creativity

Source: The Creativity Post, Aug 2017

A very small percentage of the population produces the greatest proportion of the important ideas.

This is akin to an idea presented by an English mathematician, Turing, that the human brain is something like a piece of uranium. The human brain, if it is below the critical lap and you shoot one neutron into it, additional more would be produced by impact. It leads to an extremely explosive of the issue, increase the size of the uranium.

Turing says this is something like ideas in the human brain. There are some people if you shoot one idea into the brain, you will get a half an idea out. There are other people who are beyond this point at which they produce two ideas for each idea sent in.

Those are the people beyond the knee of the curve. I don’t want to sound egotistical here, I don’t think that I am beyond the knee of this curve and I don’t know anyone who is. I do know some people that were. I think, for example, that anyone will agree that Isaac Newton would be well on the top of this curve. When you think that at the age of 25 he had produced enough science, physics and mathematics to make 10 or 20 men famous — he produced binomial theorem, differential and integral calculus, laws of gravitation, laws of motion, decomposition of white light, and so on.

Now, what is it that shoots one up to this part of the curve? What are the basic requirements? I think we could set down three things that are fairly necessary for scientific research or for any sort of inventing or mathematics or physics or anything along that line. I don’t think a person can get along without any one of these three.

The first one is obvious — training and experience. You don’t expect a lawyer, however bright he may be, to give you a new theory of physics these days or mathematics or engineering.

The second thing is a certain amount of intelligence or talent. In other words, you have to have an IQ that is fairly high to do good research work. I don’t think that there is any good engineer or scientist that can get along on an IQ of 100, which is the average for human beings. In other words, he has to have an IQ higher than that. Everyone in this room is considerably above that. This, we might say, is a matter of environment; intelligence is a matter of heredity.

Those two I don’t think are sufficient. I think there is a third constituent here, a third component which is the one that makes an Einstein or an Isaac Newton. For want of a better word, we will call it motivation.

In other words, you have to have some kind of a drive, some kind of a desire to find out the answer, a desire to find out what makes things tick. If you don’t have that, you may have all the training and intelligence in the world, you don’t have questions and you won’t just find answers.

This is a hard thing to put your finger on. It is a matter of temperament probably; that is, a matter of probably early training, early childhood experiences, whether you will motivate in the direction of scientific research. I think that at a superficial level, it is blended use of several things.

This is not any attempt at a deep analysis at all, but my feeling is that a good scientist has a great deal of what we can call curiosity. I won’t go any deeper into it than that. He wants to know the answers. He’s just curious how things tick and he wants to know the answers to questions; and if he sees thinks, he wants to raise questions and he wants to know the answers to those.

another thing I’d put down here is the pleasure in seeing net results or methods of arriving at results needed, designs of engineers, equipment, and so on. I get a big bang myself out of providing a theorem. If I’ve been trying to prove a mathematical theorem for a week or so and I finally find the solution, I get a big bang out of it. 

The first one that I might speak of is the idea of simplification. Suppose that you are given a problem to solve, I don’t care what kind of a problem — a machine to design, or a physical theory to develop, or a mathematical theorem to prove, or something of that kind — probably a very powerful approach to this is to attempt to eliminate everything from the problem except the essentials; that is, cut it down to size.

Almost every problem that you come across is befuddled with all kinds of extraneous data of one sort or another; and if you can bring this problem down into the main issues, you can see more clearly what you’re trying to do and perhaps find a solution. Now, in so doing, you may have stripped away the problem that you’re after. You may have simplified it to a point that it doesn’t even resemble the problem that you started with; but very often if you can solve this simple problem, you can add refinements to the solution of this until you get back to the solution of the one you started with.

A very similar device is seeking similar known problems. I think I could illustrate this schematically in this way. You have a problem P here and there is a solution S which you do not know yet perhaps over here. If you have experience in the field represented, that you are working in, you may perhaps know of a somewhat similar problem, call it P’, which has already been solved and which has a solution, S’, all you need to do — all you may have to do is find the analogy from P’ here to P and the same analogy from S’ to S in order to get back to the solution of the given problem.

This is the reason why experience in a field is so important that if you are experienced in a field, you will know thousands of problems that have been solved. Your mental matrix will be filled with P’s and S’s unconnected here and you can find one which is tolerably close to the P that you are trying to solve and go over to the corresponding S’ in order to go back to the S you’re after. It seems to be much easier to make two small jumps than the one big jump in any kind of mental thinking.

Another approach for a given problem is to try to restate it in just as many different forms as you can. Change the words. Change the viewpoint. Look at it from every possible angle. After you’ve done that, you can try to look at it from several angles at the same time and perhaps you can get an insight into the real basic issues of the problem, so that you can correlate the important factors and come out with the solution.

It’s difficult really to do this, but it is important that you do. If you don’t, it is very easy to get into ruts of mental thinking. You start with a problem here and you go around a circle here and if you could only get over to this point, perhaps you would see your way clear; but you can’t break loose from certain mental blocks which are holding you in certain ways of looking at a problem. That is the reason why very frequently someone who is quite green to a problem will sometimes come in and look at it and find the solution like that, while you have been laboring for months over it. You’ve got set into some ruts here of mental thinking and someone else comes in and sees it from a fresh viewpoint.

Another mental gimmick for aid in research work, I think, is the idea of generalization. This is very powerful in mathematical research. The typical mathematical theory developed in the following way to prove a very isolated, special result, particular theorem — someone always will come along and start generalization it. He will leave it where it was in two dimensions before he will do it in N dimensions; or if it was in some kind of algebra, he will work in a general algebraic field; if it was in the field of real numbers, he will change it to a general algebraic field or something of that sort.

This is actually quite easy to do if you only remember to do it. If the minute you’ve found an answer to something, the next thing to do is to ask yourself if you can generalize this anymore — can I make the same, make a broader statement which includes more — there, I think, in terms of engineering, the same thing should be kept in mind. As you see, if somebody comes along with a clever way of doing something, one should ask oneself “Can I apply the same principle in more general ways? Can I use this same clever idea represented here to solve a larger class of problems? Is there any place else that I can use this particular thing?”

Next one I might mention is the idea of structural analysis of a problem. Suppose you have your problem here and a solution here. You may have two big a jump to take. What you can try to do is to break down that jump into a large number of small jumps.

If this were a set of mathematical axioms and this was a theorem or conclusion that you were trying to prove, it might be too much for me try to prove this thing in one fell swoop. But perhaps I can visualize a number of subsidiary theorems or propositions such that if I could prove those, in turn I would eventually arrive at this solution.

In other words, I set up some path through this domain with a set of subsidiary solutions, 1, 2, 3, 4, and so on, and attempt to prove this on the basis of that and then this one the basis of these which I have proved until eventually I arrive at the path S.

Many proofs in mathematics have been actually found by extremely roundabout processes. A man starts to prove this theorem and he finds that he wanders all over the map. He starts off and prove a good many results which don’t seem to be leading anywhere and then eventually ends up by the back door on the solution of the given problem; and very often when that’s done, when you’ve found your solution, it may be very easy to simplify; that is, to see at one stage that you may have short-cutted across here and you could see that you might have short-cutted across there.

The same thing is true in design work. If you can design a way of doing something which is obviously clumsy and cumbersome, uses too much equipment; but after you’ve really got something you can get a grip on, something you can hang on to, you can start cutting out components and seeing some parts were really superfluous. You really didn’t need them in the first place.

Now one other thing I would like to bring out which I run across quite frequently in mathematical work is the idea of inversion of the problem. You are trying to obtain the solution S on the basis of the premises P and then you can’t do it.

Well, turn the problem over supposing that S were the given proposition, the given axioms, or the given numbers in the problem and what you are trying to obtain is P. Just imagine that that was the case. Then you will find that it is relatively easy to solve the problem in that direction. You find a fairly direct route. If so, it’s often possible to invent it in small batches.

In other words, you’ve got a path marked out here — there you got relays you sent this way. You can see how to invert these things in small stages and perhaps three or four only difficult steps in the proof.

Be Curious

Source: The Creativity Post, Jul 2017

  1. find and remove what gets in the way of your curious mind
  2. never be too shy to ask questions, and ask questions even when you think you know everything you need to know. Carefully and intentionally frame questions
  3. become a more interesting person and live a more interesting life by reconnecting with your inner child, sense of wonder, and catechumen’s mindset
  4. turn away from the familiar, and open your mind to new ideas, interests, experiences, and adventures
  5. dig deeper and understand the context, origin, and history of things
  6. forge deep and quality relationships by showing your sincere and genuine interests in people around you, across all levels
  7. build your own lab full of experimental tools as your sandbox to tinker or try out new things; enjoy mistakes and failures
  8. work with people with inquisitive minds, rather than just qualified and experienced people

Questions That Prompt Creativity

Source: The Creativity Post, Jul 2017

One of the most powerful forces for tapping and honing your creativity is inquiry. It isn’t the question, however, that makes inquiry so potent. It’s the mindset, the way in which you look at the world around you and the thinking that accompanies it.

That’s where the power lies. The clout comes forth when you make that mindset active – probing, being curious, and pursuing those things that make your head turn. It’s an important distinction. When we neglect that distinction we forget that questions are simply tools. Questions aid the probing and direct curious exploration. But once the questions are answered, the inquiry remains – at least for the person who understands that creativity is a uniquely human capacity in need of perpetual use and shaping.

As part of a multi-year creativity study, I had the unique opportunity to speak in depth with nearly seventy MacArthur Fellows. While unique in a number of ways, the Fellowship stands apart as the only award given explicitly for creativity. In the press it’s more popularly referred to as the “Genius Award”, a two-edged distinction to be sure, but one that gives a proper nod to a group of people who know a little something about creativity in actual practice. When I spoke with each of these creative practitioners I asked them this:

Two common elements made all of their questions powerful.

First, each question logically fit into what that person was passionate about and practiced. At times this common trait of “fit with passion” astounded, such as the novelist whose ongoing question was, “What is it about the ‘lie’ of fiction that we seem to need or at least be attracted to?” or the choreographer who couldn’t stop pursuing the question, “Why is danger profound?” Astounding or not, the key was that the question did it’s job, stimulating deeper curiosity and fueling the pursuit of passion in their actual professional practice.

The second feature that made these questions powerful was that each question was also “perpetual“. There is no single or final answer, for example, to the civil rights lawyer whose question was, “How can I change the way we think about this?” In a similar way, how could the photojournalist answer for all time, “How can I weave the light into the dark?” All of these deeply creative souls had some lingering question rolling around in their head (sometimes consciously, often not) that had no end, only the promise of ongoing possibility. It’s the very kind of question that ensures inquiry remains the focal point.

Such questions exist for us all. No one can tell us what ours is. But the odds are better than not that there is a question driving you regardless of how closely you may take note of it right now. What the question means to you, how you phrase or apply it, even the question itself is meant to have a strong element of fluidity. What matters is the habit of asking, and when we allow a question to drive us to keep doing that, our odds of being creative rise dramatically. 

Creativity Surpasses Automation

Source: MIT, Sep 2017

NPR: What do you see as the sector of the workforce that is least likely to change or least likely to disappear?

BRYNJOLFSSON: Well, there are three big categories that machines are really bad at. They’ve made tremendous advances, but they’re bad at first off doing creative work. Whether you’re an entrepreneur, or a scientist, or a novelist, I think you’re in pretty good shape doing that long-range creativity. 

There’s probably no better time in history to be somebody with some real creative insights. And then the technology helps you leverage that to millions, or billions of people. People who can combine some creativity with an understanding of the digital world are especially well-positioned.

Bongard Problems Require Creativity

Source: Quanta magazine, Jun 2017

Bongard problems go to the heart of scientific discovery: They give you two sets of six related figures, the left-hand set consisting of examples that satisfy some unknown rule, and the right-hand set having items that break the rule. Your task, like that of a scientist faced with messy data from nature, is to figure out the rule.

The visual nature of these problems reveals a great deal about human perception and cognition, which Foundalis describes nicely on his site. The primitive spatial functions required to reason about these problems, such as the ability to detect shapes in different sizes and orientations, are relatively hard to program into computer algorithms, but our visual system, honed by evolution, accomplishes them automatically.

Can you solve these three Bongard problems and discover the hidden laws in this toy universe?

Problem 1

 
 

Courtesy of Harry Foundalis. Bongard Problem 44 (Designer: M.M. Bongard)

Courtesy of Harry Foundalis. Bongard Problem 44 (Designer: M.M. Bongard)

Problem 2

 
 

Courtesy of Harry Foundalis. Bongard Problem 110 (Designer: Douglas Hofstadter)

Courtesy of Harry Foundalis. Bongard Problem 110 (Designer: Douglas Hofstadter)

Problem 3

Courtesy of Harry Foundalis. Bongard Problem 230 (Designer: Joseph A.L. Insana)

Courtesy of Harry Foundalis. Bongard Problem 230 (Designer: Joseph A.L. Insana)

Bongard problems and code-breaking games like Mastermind require the use of inductive reasoning. This generally means going from the particular to the general (as enshrined in the mnemonic PIG, for particular-inductive-general). In contrast, most mathematical problem solving relies on deductive methods, in which you reason from a known general rule to a particular conclusion.

Whereas the conclusions from deductive reasoning follow entirely from the initial assumptions, inductive rules, and therefore scientific conclusions, are basically well-supported guesses that fit existing data well. The problem is that many possible rules can fit the data

Are Ideas Getting Harder to Find?

Source: Forbes, Dec 2016
Research paper: https://web.stanford.edu/~chadj/IdeaPF.pdf

An interesting paper on the idea that the productivity of researchers is falling. That is, we have to devote ever more effort to developing new technologies and this spells, in the end, doom for economic growth. The point being that advancing technology is what allows productivity growth, it is productivity growth which enables economic growth. Thus, if we have to devote ever more resources to finding and developing those new technologies then we’ll end up running out of resources to do so and thus economic growth will fail to happen.

what is actually being measured, when they look at Moore’s Law, is the exploitation of an already known idea. Not the finding of new and different ideas.

What we do have here is proof that established firms are having to put greater effort into chip advancements over time.