Lab Notebook: How to explain Silicon Valley to a Martian.

Explaining Silicon Valley to humans is quite satisfying but can be very hard. John Kelley and I spend an entire academic quarter on this task and we manage to cover only the basics. Human students differ significantly in their cognitive, motivational, and emotional backgrounds. Some of them are locals, who know a lot about hi-tech businesses; some are newcomers, who want to understand better the place they happen to be visiting. Some are interested in personal stories of inventors and entrepreneurs; some are eager to learn the core principles behind SV-style innovations. The list of student interests and motivations could go on and on, but inevitably, our explanations of how Silicon Valley works are subjective.

On the other hand, to a Martian, all human individuals, how they live and do business, look pretty much the same. To explain Silicon Valley to a Martian, we would have to rely on objective truths, not our human emotions and motivations. Therefore, universal mathematical principles would be a logical place to start.

In the 1930s, a human mathematician, Alan Turing, came up with an abstract model for an automatic machine that can execute computational instructions. According to the model, the abstract machine is fed with an infinite tape with symbols on it. The machine can move the tape and manipulate symbols: read, write, or erase them. The state of the machine and its follow-up manipulations depend on its earlier interaction with the symbols. A Universal Turing Machine can simulate any Turing Machine with any symbolic input.

Silicon Valley is a place on Earth, where people know how to make physical machines that can perform operations of a Universal Turing Machine. More importantly, they know how to make such machines run exponentially better over time. That is, humans in Silicon Valley can double computational speed of the machines every 1.5 Earth years. We humans call this technology pattern "The Moore's Law." It held true for the last 50 years, making our computations roughly 1,000,000,000,000,000 faster in the process.

In Silicon Valley, humans also learned how to grow data storage capacity even faster than computations. We humans call this "The Kryder's Law." To top it off, in Silicon Valley humans learned how to connect all kinds of exponentially better performing computational and memory devices with exponentially faster networks. We humans sometimes call this pattern the "Nielsen's Law." The interaction of these three laws made Silicon Valley a computational technology powerhouse. To simplify our conversation, I will call this amazing, exponentially growing technological capacity "Silicon Valley Machine 1" or "Machine 1."

Examples of Machine 1 are: the Integrated Circuit, the magnetic hard drive, the Ethernet, the Internet, the Google Spanner, etc.

Note that the Machine 1 by itself is meaningless to humans. Although it can shuffle symbols back and forth exponentially faster, somebody needs to figure out how to make this symbol shuffling useful. Fortunately for humans in Silicon Valley, mathematicians back in the 1930s formulated another principle — the Church-Turing thesis. Based on the thesis, we can explain to an ordinary Martian that any computable logic, no matter how long or complicated, can be executed by the Universal Turing Machine, implemented either in hardware or software or both. Therefore, if somebody (let's call him Steve Jobs) comes up with an idea for a logically consistent, useful computing device or application, it is guaranteed to work. Guess what, we say to a curious Martian, smart humans in Silicon Valley figured out a way to regularly come up with useful applications. To simplify our conversation, I will call this ingenuous creative capability "Silicon Valley Machine 2" or "Machine 2."

Examples of Machine 2: computer gaming, the spreadsheet, digital movies, the world-wide web, the iPhone, Google Search, social networking, etc.

Over the last 50 years, the folks in Silicon Valley learned how to hook up the ingenuous Machine 2 to the exponentially growing Machine 1. As a result, products, services, and experiences produced in Silicon Valley are becoming — year after year — exponentially faster and more useful to other humans. This is it. The success of Silicon Valley is based on fundamental mathematical truths as well as our ability to make abstract concepts objectively real and subjectively useful to other humans.

tags: innovation, book, machine1, machine2, silicon valley, 

Lab Notebook: Jobs' "A team" vs creative malcontents (black sheep)

Steve Jobs famously insisted on working only with an "A team" at Apple. Remarkably, an experience from his other company, Pixar, shows that a group of malcontents, i.e. employees who have something to prove, can be as creative. Here's a quote from a Brad Bird's interview about his work on The Incredibles and Ratatouille:

So I said, “Give us the black sheep. I want artists who are frustrated. I want the ones who have another way of doing things that nobody’s listening to. Give us all the guys who are probably headed out the door.” A lot of them were malcontents because they saw different ways of doing things, but there was little opportunity to try them, since the established way was working very, very well.

We gave the black sheep a chance to prove their theories, and we changed the way a number of things are done here. For less money per minute than was spent on the previous film, Finding Nemo, we did a movie that had three times the number of sets and had everything that was hard to do.

I would say that involved people make for better innovation. Passionate involvement can make you happy, sometimes, and miserable other times. You want people to be involved and engaged. Involved people can be quiet, loud, or anything in-between—what they have in common is a restless, probing nature: “I want to get to the problem.

One of the "secrets" of Silicon Valley is that malcontents from one company can form highly successful startups. (Adobe, 3Com, SGI, Netscape, Seagate, Palm, etc.)

tags: innovation, book, example, creativity

Lab Notebook: Silicon Valley and the Law of Diminishing Marginal Utility

Silicon Valley seems to defy one of the fundamental laws of economics: The Law of Diminishing Marginal Utility. The law, according to an investopedia article (need to find a textbook reference), says:

A law of economics stating that as a person increases consumption of a product - while keeping consumption of other products constant - there is a decline in the marginal utility that person derives from consuming each additional unit of that product.

They illustrate the law with a short video about a hungry girl with a pizza. The first slice of pizza tastes better than anything before.

The second slice of pizza still tastes good, but by the fifth piece, the girl is already full and she hates the fact that she has to eat the entire thing.

Let's try to apply this law to a major Silicon Valley innovation: web-based email. We skip the Hotmail vs Rocketmail controversy and go directly to the Yahoo vs Google email rivalry. In the early 2000s, Yahoo mail was an undisputed leader in this web services domain. The company was giving users 10 or 20 MB storage for free and charged for extra. Then came Google and offered at least 1GB of free storage with its brand new gMail. According to the law of diminishing marginal utility, this move would be "unlawful." That is, why offer people a lot more if we know from economics that usefulness declines with quantity of goods consumed?

Paradoxically, the more storage gmail offered, the more useful its service proved to be to the users, myself included. Millions flocked to gmail, abandoning their yahoo mailboxes.

In general, exponential growth in processing power, storage, and bandwidth (what I call "Machine 1") that Silicon Companies have been driving for the last 50 years makes users happier, defying the economics textbook wisdom taken at face value.

tags: machine1, economics, 10X, innovation, book, google, yahoo

Lab Notebook: Why money technology works

5 goats = 1 cow
12 eggs = 4 loafs of bread
1 gun = 2 horses
....
1 goat = 20 loafs of bread
1 horse = 3 goats
...

All these barter equations carry enormous amount of information about goods involved in the exchange and rules to calculate their "aboutness," including expectations about the future (weather, hunger, wisdom of the ruling king, etc.) The more goods, services, and experiences (GSE) are available, the more information is necessary to make calculations and decisions whether to transfer the GSEs into one's future.
When instead of the barter equations we use money, we compress huge amounts of GSE aboutness into a single number. That is, from an innovation perspective, money and markets are compression mechanisms. The compress payloads, so that we can increase the GSE space exponentially.

Thousands years ago, Spartans used heavy metal bars to represent value. You couldn't carry it; only mark up one's ownership of a "slice" of iron.
Today, we use hard drives of financial institutions to accomplish the same task. Except, using computer technology and market mechanisms, we managed to compress our expectations about the past, present, and future of a myriad of GSEs in a bunch of zeros and ones.

5 goats = 0000010000
12 eggs = 0000001010
1 gun = 0001010010
...

The amazing aspect of this mechanism is that money and markets allow us to decompress a single number into all kinds of GSEs - pure magic.
tags: invention, innovation, money, technology, book, tool, control, deontic, payload, aboutness

Lab Notebook: an illustration to Machines 1 & 2 concepts

Two Silicon Valley companies merge, citing the need to build computing capabilities (Machine 1 is a bottleneck for Machine 2):

Rosati [the CEO of the merged company] added, “If we are to become the workplace for the world, we need to make massive investments in technology, massive investments in data science, massive investments in predictable business outcomes. Fundamentally, we have a shot at building a business on the scale of Amazon or LinkedIn or iTunes.” (ref: Gannes, Liz. oDesk and Elance Merge to Create One Big Freelancer Company. All Things D, December 18, 2013.)

I can use this example in explaining Machines 1&2 in the Greatest Innovations of Silicon Valley course/book.

tags: tgisv, book, example, machine1, machine2

Social Networking is the new TV, only much better

A study of new mothers shows that social networking has a strong positive effect on their well-being:

On average, mothers were 27 years old (SD = 5.15) and infants were 7.90 months old (SD = 5.21). All mothers had access to the Internet in their home. New mothers spent approximately 3 hours on the computer each day, with most of this time spent on the Internet. Findings suggested that frequency of blogging predicted feelings of connection to extended family and friends which then predicted perceptions of social support. This in turn predicted maternal well-being, as measured by marital satisfaction, couple conflict, parenting stress, and depression. In sum, blogging may improve new mothers’ well-being, as they feel more connected to the world outside their home through the Internet.

Source: Brandon T. McDaniel • Sarah M. Coyne • Erin K. Holmes. New Mothers and Media Use: Associations Between Blogging, Social Networking, and Maternal Well-Being. DOI 10.1007/s10995-011-0918-2
 

Another study of random 79 undegrads shows that:

In Experiment 2, those who focused on their Facebook page scored significantly higher in general self-esteem, but not narcissism, than a control group.

Across both experiments we found consistent evidence that narcissists reported having more ‘‘friends’’ on the SNSs. Partici- pants with higher NPI scores reported having more friends and more page views on MySpace and reported having more friends on Facebook.  

 The paper also notes that the new generation of students is quite different from the general population

 A recent Pew Research Center survey found that, com- pared to 20 other countries, rates of SNSs usage in America were among the highest (Pew Research Center, 2011). Among Americans, 80% of respondents aged 18–29 used SNSs (compared to 62% of those aged 30–49 and 26% of those 50 and older) and that 61% of users had college degrees, indicating that SNSs users are dis- proportionately young, educated adults. 

Looks like SNSs provide opportunities to improve social well-being by starring in one's own show.

tags: network, social, information, graph, research, science, book, facebook

Creativity Quote of the Day: Invention vs Engineering

In 1905, Dr. Hammond V. Hayes took over AT&T's engineering department. Here's a quote from his 1906 letter where he describes his approach to innovation:

Every effort in the Department is being executed toward perfecting the engineering methods; no one is employed who as an inventor is capable of originating new apparatus of novel design. In consequence of this it will be necessary in many cases to depend on the acquisition of inventions of outside men.

Today, many leading technology companies follow this recipe for success by acquiring promising startups, rather than developing their own original ideas.


Source: Leonard S. Reich. Industrial Research and the Pursuit of Corporate Security: The Early Years of Bell Labs. The Business History Review, Vol. 54, No. 4, Business History and the History of Technology (Winter, 1980), pp. 504-529
http://www.jstor.org/stable/3114217

tags: creativity, invention, innovation, book, business

Scalable Innovation: Figures for Section I (pages 3-59).

Today, I continue posting figures from our new book Scalable Innovation: A Guide for Inventors, Entrepreneurs, and IP Professionals. In my previous post I uploaded and explained figures from the Introduction and Prologue. Now we continue with Section I, where we introduce a system model that explains existing inventions, technologies, and patents. We also show how to use the model for developing new ideas.


Chapter 1. We start with Invention, a great children poem by Shel Silverstein. For copyright reasons the publisher is not allowed to print the poem in our book, but you can find it on the web.

Figure 1.1 illustrates the problem encountered by the inventor.

FIGURE 1.1    “The cord ain’t long enough.”

Chapter 2. In this chapter we show (in 3-D!) how to map our system model on the Invention and discover missing elements.

FIGURE 2.1    Invention as a system concept, mapped onto its physical implementation.

FIGURE 2.2    The system model.

FIGURE 2.3    A working invention with all the system elements present.

To further explain the system model, we follow up with a number of examples, starting with Edison's electricity distribution system. Why Edison? Because many people believe he is the greatest innovator of all time without really understanding what he actually invented.

FIGURE 2.4 The diagram is courtesy the Lemelson–MIT Program. (From Lance Whitney, "Edison tops Jobs as world’s greatest innovator," c|net, January 26, 2012)

We show that Edison's real breakthrough was the new, scalable parallel electric grid, not the light bulb. The picture below shows grid design "before" (a) and "after" (b) Edison.

FIGURE 2.6 Before: (a) In the old electric grid the voltage decreased with distance away from the electricity generator, causing the bulbs to glow less brightly, or requiring the use of thicker (and thus more expensive) wires. After: (b) Edison introduces a compensating line (ground return) that allows the use of high voltages (which reduced the amount of expensive copper wiring needed), and at the same time permits all light bulbs to continue operating, unaffected by any one burning out, for example, and also allowing for additional generators or lamp arrays to be connected more easily.

In our second example we apply the system model to Steve Job's system and show how it goes far beyond the iPhone.

FIGURE 2.8    Mapping Steve Jobs’ system in 3-D

FIGURE 2.9 A 2-D diagram of the implementation layer. Element positions correspond to their system level functionality.

Chapter 3. We use the model analyze and understand patents.

FIGURE 3.1 Guiding Plasmon Signal, US Patent 7,542,633.

FIGURE 3.2. Zooming in on a specific system element. Control subsystem within a system.

Chapter 4. We consider the paradox of system interfaces and how successful solutions enable rapid growth.


In the beginning of the 20th century, GE developed an ingenious brand marketing campaign to promote its light bulbs, positioning Edison as a celebrity inventor (The greatest innovator of all time!).

FIGURE 4.1 Edison’s light bulb: the Sun’s only rival. Pictures courtesy of The Smithsonian Institution. (From Carl Sulzberger, A bright and profitable idea: Four decades of Mazda incandescent lamps, Power & Energy 4, 3 (2006): 78.)

Few people know that Edison's longest lasting invention is the standard screw-in light bulb socket (a system interface between the grid and lighting device).

FIGURE 4.2    Edison screw-in socket, US Patent 438,310.

Another example of a long-lasting system interface:

FIGURE 4.4    The QWERTY keyboard. C. L. Sholes’ typewriter US Patent 207,559.

Chapter 5. Here we introduce the concept of system Control Points.

FIGURE 5.1 A system diagram with Control Points and interfaces.

(To be continued...)

Scalable Innovation: Figures for Foreword and Prologue.

Our book Scalable Innovation: A Guide for Inventors, Entrepreneurs, and IP Professionals is now available for the general public, e.g. on Amazon. All figures in the text are printed in gray scale, which is not always optimal for our color originals. For those of you who enjoy reading the book, Max Shtein and I (Eugene Shteyn) would like to provide all figures in color, as originally intended (click to enlarge, if necessary).

Today, we upload figures for the introductory chapters of the book.

 

Foreword 

The figure below illustrates diminishing innovation-related growth. We should learn how to innovate better to address the problem!

Figure F.1 Growth in real GDP per capita, 1300-2100, with actual and hipothetical paths.
Source: Robert J. Gordon. Is U.S. economic growth over? Faltering innovation confronts the six headwinds. 2012. NBER Working Paper 18315. [5]

Prologue

This is how a typical trade-off diagram looks like: an improvement in one aspect makes another worse.

FIGURE 0.1    Shipment ␣exibility versus fuel ef␣ciency trade-off diagram. Cargo shipping by sea is more fuel ef␣cient than by airplane, train, or truck, but the shipment may take longer to arrive, and possible destinations may be limited to major ports.

Often, people think about innovation as a one-dimensional process: Old vs New.

FIGURE 0.2  The general view of old versus new that conflates the concepts/meanings of the terms invention and innovation.

By contrast, we want to emphasize a more realistic — seesaw-like — nature of the process.

FIGURE 0.3. A more realistic, two-dimensional depiction of the difference (and path) between invention and innovation.

We see innovation as a space where new things or ways of getting them done become possible. From this perspective, companies choose to enter innovation spaces at different points.

FIGURE 0.4. A company entry point into an innovation space. Apple and Facebook entered their respective innovation spaces closer to the invention point than Microsoft and Google+.

The process of innovation requires contributions from different people, either as individuals or members of the community. Eventually, breakthrough ideas become routine. You can see some of origins for this diagram in my earlier blog posts.

FIGURE 0.5 (a, b) Diagrams illustrating in graphical form the main conclusions of a recent study by Greg Linden et al., Innovation and Job Creation in a Global Economy. The Case of Apple iPod, 2011, http://pcic.merage.uci.edu/papers/2009/ InnovationAndJobCreation.pdf. To earn good money, one must become involved in non-routine processes in generating new products or services. Routine jobs pay little and are the target of relentless outsourcing, off-shoring, and, ultimately, automation. (For a stark illustration, see a related article by The Week editorial staff, “A Day in the Life of a Warehouse Wage Slave,” http://theweek.com/article/index/228096/a- day-in-the-life-of-a-warehouse-wage-slave and the recent purchase by Amazon of a warehouse robotics system.)

We believe creativity is a process that requires the right match between skills and challenges.

FIGURE 0.6 An illustration of the concept of Flow proposed by Mihaly Csikszentmihalyi. (Wikipedia. http://en.wikipedia.org/wiki/␣le:challenge_vs_skill. svg; Source: Mihaly Csikszentmihalyi. Finding Flow: The Psychology of Engagement with Everyday Life. New York: Basic Books. 1997, p. 31.)

( To be continued...)

Quote of the Day: Model-Dependent Realism

 Stephen Hawking (and Leonard Mlodinow) write:

we will adopt a view that we will call model-dependent realism: the idea that a physical theory or world picture is a model (generally of a mathematical nature) and a set of rules that connect the elements of the model to observations. (The Grand Design).

In Scalable Innovation, Max and I introduce a system model (my Stanford students know it as 5-element analysis) and multiple case studies that connect the model to observation. Part I of the book briefly introduces the model and shows how to make the connections. Part II discusses methods for navigating system levels and their connections to reality. Finally, Part III shows the system dynamics: how it evolves along the S-curve and its correspondence to observations.

tags: book, quote, model, five element analysis,

A simple test to discover your inner mathematician.

A new study from the John Hopkins University shows that performance on Approximate Number System (ANS) test is a good predictor for people's math abilities at school. ANS is not a uniquely human trait; it is also present in other mammals including rats. Click on the link ( leading to a NYT page) to test your own primordial number sense. You'll see something like this:

The teacher effect: bad performance on a problem-solving task.

Sheena Iyengar, professor at Columbia Business School, talks about an experiment conducted with Anglo– and Japanese-American children in San Francisco. All kids were asked to solve several anagrams, the only difference being how they chose their task.

The first group got to choose their own anagram set and the marker to write the answers. The second group was told by the teacher, Ms Smith, which anagrams to work on and what marker to use. The third group was told that it was their mother who recommended the anagram set and the marker for writing. Behind the scenes, the experimenters ensured that in all three conditions the kids were involved in the same activity.

The children's performance turned out to be very different. Once clear difference was that both Anglo- and Japanese-American children performed the worst in the teacher condition (the left columns on the chart above). The other one was the contrast in the mother condition: Anglo–American kids performed much worse than Japanese kids (the right columns).

Looks like giving people a choice, even when the choice itself is meaningless, improves their performance, but as the rest of the talk shows, not happiness.

tags: education, control, psychology, performance, book,  video, information

How to become an inventor in 5 easy steps.

Popular mechanics has an article on how to become an inventor. Here's an outline:

Intro: Have a metaphorical light bulb go in your head.
Step 1: Cultivate an Idea.
Step 2: Build a Prototype.
Step 3: File a Patent.
Step 4: Test the Market.
Step 5: Sell it or Make it.

The article is a typical media combination of fact and fiction. For example, there's no evidence whatsoever that the proverbial light bulb went off in Edison's head when he came up with his real lightbulb design. Some people experience the Aha moment, some don't. There's no relationship between the intensity of personal experience and quality of an inventive idea.

Nevertheless, a certain patent-related statistics I found illuminating. One out of three patent applications is granted as a patent, with the number of commercially viable patents ranging from 0.2 to 5 percent. In other words, the most optimistic estimate of the overall system efficiency would be 5/3≈1.7%, which is comparable to the efficiency, or more accurately, the inefficiency of Newcomen's steam engine created in 1714.

The good news is the invention revolution is yet to come!

tags: psychology, media, invention, method, patents,

Back to Edison: the war of currents revisited.

Google is helping the transition from industrial to information-age power infrastructure:

Google is investing $280 million in SolarCity, a company that leases out solar panels to home owners.
Google said it was a move to increase the amount of “distributed solar power.” That means that the power from solar panels is generated on the roofs of homes and is used by those homes instead of having to travel through a power grid. That can help reduce some of the strain on power grids during peak usage hours, when homes are drawing more electricity for air conditioning or, in the future, electric car charging.

This transition will begin in earnest when efficient energy storage becomes available to home owners and/or neighborhoods. Today, there's no practical way to store solar or wind power for future use on a small scale. Once this problem is solved, the current power distribution system, which wastes from 30 to 50 percent of transmitted energy, will be out of the picture, at least in the residential market.

Then, since the vast majority of information gadgets we'll be using consume low-power DC current, the next step will be creation of an in-home low-power DC network to feed them (rather than using AC/DC converters for everything, from PC to TV to car). This will probably take another 10 to 20 years.

tags: distribution, storage, energy, book, system, evolution, payload, s-curve, synthesis

Evolution of perceptable information: Solid -> Liquid -> ... Gas?

I'm finishing up The Social Animal, by David Brooks. One phrase caught my attention because it illustrates current transition from documents-centric computer interfaces to stream-centric ones:

The unconscious treats information like a fluid, not a solid. (p.239).

With twitter, the humanity's unconscious found a way to satisfy its need for ceaseless stream of background chatter: crowd-wisdom, crowd-sourcing, crowd-everything.

I wonder, what would be the next step in this direction.

tags: innovation, payload, quote, book, youtube, system, evolution

Invention of the day: bitter medicine no more.

To fight a nascent headache, I took two capsules of Acetominophen, commonly known by its brand name Tylenol. What a remarkable invention, I thought. Not the medicine itself, but the way it is packaged. The two-part capsule is ubiquitous. It easily defeats the old common wisdom about the necessity of swallowing a bitter pill to beat whatever ails you. I wish I could invent something like that.

To my utter astonishment, I learned that medicine capsules were invented more than 150 years ago. Since everybody on the web seems to quote Wikipedia on it (with a reference to volume 7 of Encyclopedia of Pharmaceutical Technology which few read), I'm doing it too:

James Murdock of London patented the two-piece telescoping gelatin capsule in 1847. The capsules are made in two parts by dipping metal rods in the gelling agent solution. The capsules are supplied as closed units to the pharmaceutical manufacturer. Before use, the two halves are separated, the capsule is filled with powder or more normally spheroids made by the process of spheronization (either by placing a compressed slug of powder into one half of the capsule, or by filling one half of the capsule with loose powder) and the other half of the capsule is pressed on.

Though new medicine capsule inventions, according to codingfutures.com, happen almost every day, the 150-year old idea is still alive and well.

tags: invention, problem, book, payload, packaging,  health, solution

Creativity metaphor: opening a door.

Psychologist Mihaly Csikszentmihalyi (pronounced: chick sent me high), the author of the Flow theory, talks in a TED lecture about creativity.



Among other things mentions a poet's description of a pre-breakthrough moment:

It's like opening a door that's floating in the middle of nowhere and all you have to do is go and turn the hand and open it and let yourself sink into it. You can't particularly force yourself through it. You just have to foat. If there's any gravitational pull, it's from the outside world trying to keep you back from the door.

He also mentions that Einstein described a similar experience.

I can probably design a creativity exercise along these lines to invoke the "open door" effect explicitly. It would be much better than asking students or invention workshop participants to think outside of the box.

tags: creativity, course, psychology, stanford, book

How to Invent: Reverse Brainstorming (part 4). Problem selection.

The second half of Reverse Brainstorming is Problem Selection.  By the end of the session, we have to select problems that are worth solving, i.e. the problems that are worth our investment of time, money, and effort. Everything else (like 95%) is noise.

Here's a couple of diagrams illustrating the outcome of the session:

As you can see, numbered problems from the list created during the first part are first assigned Value and Timing, then sorted accordingly. Since the Value dimension invokes many questions from participants, I'll have to explain it in a separate post.

On the other hand, Timing is understood intuitively. Nevertheless, it is still important to make sure everybody has the same frame of reference. For some people, like a startup product organization, short-term means six months; for others, like a nanotech research group, short-term means six years. To avoid confusion, both short-(ST) and long-term (LT) terms have to be defined before problem evaluation, e.g. ST = 1year, LT = 5years.

One important consideration to keep in mind, though. You, as the session moderator, have to have a more sophisticated understanding of timing because when it comes to invention most simple intuitive notions, even the familiar ones like timing, tend to be wrong.

Let me explain. Time measured in calendar years reflects periods of rotation of the Earth around the Sun: 1 year - one rotation, 5 years - five rotations. Unless your group is dealing with agricultural products or services, which depend on Sun patterns, the Earth's rotation has almost no connection with invention and innovation. I say almost, because we do have major purchasing seasons, e.g. Christmas, Chinese New Year, Back to School, etc. Nevertheless, real timing has nothing to do with the rotation of the Earth. Rather, orthogonal to our everyday thinking, innovation timing has to do with technology or business constraints. That is, short-term means we are working inside a set of constraints, while long-term means we've managed to break through at least some of them.

If you have time during the session, it might be a good idea to convey to the group the concept of timing as constraint-related, rather than calendar-related. Often, it gets people thinking outside of the box their intuitive mindset and leads to a better understanding of Value and problems that prevent us from getting to it.

tags: reverse brainstorming,  reverse brainstorm, book, course, stanford, constraints, time, high value.

Previous posts on Reverse Brainstorming Howto:
1. How to Invent: Reverse Brainstorming.
2. It may look like this.
3. Concept Diagram: Reverse vs Traditional Brainstorming

Incentives vs Creativity

In a TED talk, Daniel Pink says that on creative tasks direct incentives, e.g. "the faster you solve the problem, the more money you get", make people perform worse, not better.



On one hand, we know, since Stanford psychologist Robert B.Zajonc's experiments on cockroaches, rats, chickens, and humans, that pressure and incentives improve a creature's performance on standard tasks. Cockroaches ran faster in familiar mazes when compete with other cockroaches; chickens peck faster at food when other chickens are around, etc. When the problem is unfamiliar, for example, a cockroach is given a new maze, the mere presence of other cockroaches increases the time the cockroach needs to solve the maze. To summarize, pressure brings out standard responses, usually, based on training and/or experience.

On the other hand, complete freedom of action doesn't necessarily brings creative results. Though Pink cites Google's 20% free time rule - it's often said that googlers are free to spend 20% of their work time on whatever they want - there's very little evidence that the rule works. My contacts within and around the company say that, due to lack of results, the program has been either completely scrapped or significantly reduced in scope. Besides, we all, even the creative types, face deadlines - there are papers to be written, inventions to be made, business deals signed, etc. Often, it's easy to mistake abundance of procrastination for lack of creativity.

Thus, we have a "creativity" dilemma:
- pro: We should use incentives and deadlines because they make people work harder to deliver on their tasks
- contra: W should not use incentives and deadlines because they bring up standard non-creative solutions.

Though I don't have a full solution to this problem, I believe at its crux lies the confusion between creativity and spontaneity. To me, there's seems to be an implicit assumption that creativity cannot be taught. Bot nobody really knows what creativity is. [Some, like psychologists R.E.Nisbett and M.Csikszentmihalyi, even say that creativity as a human trait does not exit at all.] Therefore, to solve the dilemma we have to question the "can't-be-taught" assumption", especially, in the area of technological creativity and problem-solving. In my personal experience, I was taught to be an inventor, and I either taught or helped other people to learn how to solve problems creatively. Some of it was spontaneous, but most of the time we relied on methods rather than luck.

tags: creativity, course, book, youtube, psychology

How to Invent: Reverse Brainstorming (part 3). concept diagram.

This diagram (from the electronic version of my upcoming book) shows the conceptual difference between Reverse Brainstorming (circled in red on the left) and Standard Brainstorming (in blue).

Traditional (standard) brainstorming starts with the assumption that the problem has already been selected. It's explicitly recommended that for a brainstorming session to be effective there should be a single well-defined problem for participants eventually to solve (by generating lots of ideas).

The trouble with this approach is that if a wrong problem is selected all ideas turn out to be ... well, not good at all. This happens not because the participants are not creative enough, but because a wrong approach is used for problem definition, which is a common occurrence in uncertain situations.

Reverse Brainstorming addresses this issue by making people starting earlier in the thought process, making sure the right problem is identified for solving.

tags: reverse brainstorm, brainstorming,  book, method,  course

Previous posts on Reverse Brainstorming Howto:
1. How to Invent: Reverse Brainstorming.
2. It may look like this