Category Archives: Creativity

What Evolution Teaches Us About Creativity

Source: Kirkus Reviews, Jun 2019

Most readers associate evolution with Darwinian natural selection, but Wagner points out its limited creative capacity.

In natural selection, a better adapted organism produces more offspring. This preserves good traits and discards bad ones until it reaches a peak of fitness. This process works perfectly in an “adaptive landscape” with a single peak, but it fails when there are many—and higher—peaks.

Conquering the highest—true creativity—requires descending into a valley and trying again. Natural selection never chooses the worse over the better, so it can’t descend.

Wagner devotes most of his book to the 20th-century discovery of the sources of true biological creativity: genetic drift, recombination, and other processes that inject diversity into the evolutionary process.

His final section on human creativity contains less hard science but plenty of imagination. The human parallel with natural selection is laissez faire competition, which is efficient but equally intolerant of trial and error.

Far more productive are systems that don’t penalize failure but encourage play, experimentation, dreaming, and diverse points of view.

In this vein, American schools fare poorly, but Asian schools are worse.

Related Resources:

https://www.santafe.edu/news-center/news/book-review-life-finds-way-andreas-wagner

“Exploratory play,” remarks Wagner, “is about creating a diversity of experiences or ideas, only some of which will eventually lead somewhere and be successful.”

Failure is key to success, Wagner insists, and it should be embraced as a necessary part of the creative process. “If we are honest with ourselves, we understand that we are failing more often than we are succeeding, and that is a very Darwinian concept,” he says. “Even very successful scientists have a lot of failures.”

https://inquisitivebiologist.wordpress.com/2019/07/17/book-review-life-finds-a-way-what-evolution-teaches-us-about-creativity/

as Life Finds a Way shows, not all solutions are equally good. To evolve from a suboptimal solution to a superior one usually involves several steps through intermediary solutions that are even worse, something that natural selection acts against. So how does evolution overcome such obstacles?

What if a population ends up on a suboptimal peak? From the image you can see that, unless you can do it in a single step, you cannot just descend one peak, move through a valley, and up the other peak. Natural selection will eliminate those individuals who “try”

how does nature get off suboptimal peaks? Biological traits are ultimately coded for by DNA and as biologists know, life has other options to change DNA than single mutations such as genetic drift and recombination.

The former is the chance disappearance of certain genes when all individuals carrying it die, something that is statistically much more likely in small populations.

The latter is the wholesale exchange of chromosome regions during meiosis, the cell divisions that creates sperm and egg cells. Drift is dangerous and can push whole populations away from fitness peaks and into extinction (this is why conservation biologists are so concerned about habitat fragmentation).

Wagner likens recombination to nothing less than teleportation; it allows offspring to take large leaps to a completely different part of an adaptive landscape.

Most combinations will be nonsensical, but many will not. Interestingly, Wagner’s computational work suggests that the number of viable genes or proteins encoded by these possibilities is vast. There are many possible solutions to a problem. So many, in fact, that they form networks. Wagner called it a hidden architecture that accelerates life’s ability to innovate.

Here too, finding better solutions sometimes requires big leaps, which can be brought about by play, daydreaming, or other means.

Elastic Thinking

Source: FS, Nov 2019

some suggestions for how to develop elastic thinking:

  • Cultivate a “beginner’s mind” by questioning situations as if you have no experience in them.
  • Introduce discord by pursuing relationships and ideas that challenge your beliefs.
  • Recognize the value of diversity.
  • Generate lots of ideas and don’t be bothered that most of them will be bad.
  • Develop a positive mood.
  • Relax when you see yourself becoming overly analytical.

Designing an Infographic

Source: FastCoDesign, Jun 2012

  1. GATHERING DATA
  2. READING EVERYTHING
  3. FINDING THE NARRATIVE
  4. IDENTIFYING PROBLEMS
  5. CREATING A HIERARCHY
  6. BUILDING A WIREFRAME
  7. CHOOSING A FORMAT
  8. DETERMINING A VISUAL APPROACH
  9. REFINEMENT AND TESTING
  10. RELEASING IT INTO THE WORLD

Examples:

Human Creativity

Source: Fast Company, Apr 2019

there’s one crucial area where neural networks do not outperform humans: creativity.

Oleinik’s analysis is further evidence that AI will likely only replace repetitive tasks that humans aren’t particularly skilled at to begin with.

why are neural nets so bad at being creative? Neural networks are machine learning algorithms composed of layers of calculations that excel at ingesting vast amounts of data and finding every pattern within them. They fundamentally rely on statistical regression–which means that while they’re good at identifying patterns, they fail miserably to anticipate when a pattern will change, let alone connect one pattern to an unrelated pattern, a crucial ingredient in creativity.

“Scholars in science and technology studies consider the capacity to trace linkages between heterogeneous and previously unconnected elements as a distinctive human social activity,” Oleinik writes. Unfortunately, creativity would be impossible without radical predictions, something regression analysis will never be able to do.

Second, because all patterns appear to be meaningful to an algorithm based purely on how prevalent they are in the data, neural networks fail to distinguish between which patterns are meaningful and which aren’t–an additional foundational element of creativity. Computers may come up with novel ideas, but they may not be valuable ideas because value is a collective agreement, dictated by groups of people.

Finally, because neural networks do not understand, let alone incorporate, outside context, they are unable to make adjustments based on social norms and interactions beyond the realm of their specific purpose and data set. In other words, they lack social intelligence, which is important for creativity since, “innovations are often embedded in social connections and relationships,” Oleinik says.

“Creativity is hardly possible without one’s capacity to think metaphorically, to coordinate proactively and to make predictions that go beyond simple extrapolation,” Oleinik argues.

that doesn’t mean that neural nets aren’t excellent mimickers of creativity. “In the words of a sociologist,” Oleinik writes, “a robot powered by neural networks may be a good [a]ctor, i.e. someone who closely follows the script, but not a [s]ubject, i.e. someone who meaningfully changes and rewrites the imposed rules.”

For instance, a neural net would be excellent at studying all of Picasso’s paintings and producing a new work that copies the famed artist’s style. In fact, many contemporary artists have played with neural networks in exactly this way, creating new portraits that look like they could have been painted by an old master but are in fact computer-generated.

But what a neural net may never be able to do is look at Picasso’s paintings and respond to them in a way that meaningfully adds to the artistic conversation by generating new patterns. The neural net itself can never be in dialogue with the artistic past without a human there to give it intent–it is only a shallow imitator, devoid of true meaning. As prominent AI artist Mario Klingemann pointed out when his first AI artwork was up for auction, he is the artist, not the computer.

Ultimately, neural nets are not designed for creativity. Instead, they are designed for a world with clean, precise data. Oleinik points out that in a neural net’s ideal world, you remove data’s messiness–messiness that often comes from the unpredictability of human creativity. Take, for example, the optimal situation in which to create self-driving cars: roads where everybody, be they human or machine, follows the rules to a T, where there is no randomness whatsoever and everything is entirely predictable.

Coders

Source: Marginal Revolution, Mar 2019

The list of small-person or one-person innovators is long…[long list follows]…

The reason so few people can have such an outsize impact, Andreessen argues, is that when you’re creating a weird new prototype of an app, the mental castle building is most efficiently done inside one or two isolated brains. 

The 10X productivity comes from being in the zone and staying there and from having a remarkable ability to visualize a complex architecture. 

“If they’re physical capable of staying awake, they can get really far,” he says.  “The limits are awake time.  It takes you two hours to get the whole thing loaded into your head, and then you get like 10 or 12 or 14 hours where you can function at that level.”  The 10Xers he has known also tend to be “systems thinkers,” insatiably curious about every part of the technology stack, from the way currents flow in computer processors to the latency of touchscreen button presses.  “It’s some combination of curiosity, drive, and the need to understand.  They find it intolerable if they don’t understand some part of how the system works.”

Rushkoff: Platform Cooperatives to “create value for one another”

Source: P2PFoundation, Feb 2019

We want a meaningful way to create value for one another.

How can the digital economy reward people instead of extracting their value?
The fast answer: platform cooperatives. Give workers and ownership stake.

The digital economy can distribute wealth if people own the means of production.

Chart Mistakes

Source:The Economist, Mar 2019

Mistake: Truncating the scale

Mistake: Forcing a relationship by cherry-picking scales

Mistake: Choosing the wrong visualisation method

Mistake: Taking the “mind-stretch” a little too far

Mistake: Confusing use of colour

Mistake: Including too much detail

Mistake: Lots of data, not enough space