Category Archives: Creativity

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:

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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

Random Concentrated Breakthroughs

Source: Marginal Revolution, Feb 2019

For a big breakthrough in some area to come, many different favorable inputs had to come together.  So the Florentine Renaissance required the discovery of the right artistic materials at the right time (e.g., good tempera, then oil paint), prosperity in Florence, guilds and nobles interested in competing for status with artistic commissions, relative freedom of expression, and so on.

2. To some extent, but not completely, the arrival of those varied inputs is random.  Big breakthroughs are thus hard to predict and also hard to control.

3. A breakthrough in one area increases the likelihood that further breakthroughs will come in closely related areas.  So if the coming together of the symphony orchestra leads to the work of Mozart and Haydn, that in turn becomes an inspiration and eases the path for later breakthroughs in music, not just Mahler but also The Beatles, compared to say how much it might ease future breakthroughs for painting.

4. Some breakthroughs are very very good for economic growth, such as the Industrial Revolution.  But most breakthroughs do not in any direct way boost gdp very much.  The Axial age led to the creation of significant religions and intellectual traditions, but the (complex) effects on gdp are mostly lagged and were certainly hard to see at the time.

Smaller Teams Disrupt; Larger Teams Develop

Source: HBR, Feb 2019

while large teams do indeed advance and develop science, small teams are critical for disrupting it—a finding with broad implications for science and innovation.

high-impact discoveries and inventions today rarely emerge from a solo scientist, but rather from complex networks of innovators working together in larger, more diverse, increasingly complex teams. This trend reflects an important conclusion that has become a simple prescription: when it comes to teaming, bigger is better.

larger teams are not optimized for discovery or invention. For example, large teams are more likely to have coordination and communication issues—getting everyone on board for an unconventional hypothesis or method, or changing direction to follow a new lead, will prove challenging. Large teams can also be risk-averse, as they demand an ongoing stream of success to “pay the bills.” As such, large teams—like large business organizations—tend to focus on sure bets with more established markets. By contrast, small teams—like small ventures–with more to gain and less to lose, are more likely to undertake new, untested opportunities.

whereas large teams tended to develop and further existing ideas and designs, their smaller counterparts tended to disrupt current ways of thinking with new ideas, inventions, and opportunities.

large teams excel at solving problems, but it is small teams that are more likely to come up with new problems for their more sizable counterparts to solve. Work by large teams tends to build on more recent, popular ideas, while small teams reach further into the past, finding inspiration in more obscure prior ideas and possibilities. Large teams, like large movie studios, more likely generate sequels than new narratives. We found that as team size grows from 1 to 50 members, the associated level of disruption drops precipitously.

both types of teams are essential for the long-term vitality of innovation: while small teams can drive disruption and innovation, larger teams can pick up the ball and engage in greater development of a given area, as part of a virtuous cycle.