Category Archives: Creativity

Curious Children

Source: MindShift, Jun 2018

Despite the centrality of curiosity to all scientific endeavors, there’s a relative dearth of studies on the subject itself. Fortunately, scientists such as Jirout and others are actively unraveling this concept and, in the process, making a convincing case that we can and should teach young minds to embrace their inquisitive nature.

Prachi Shah, an associate professor of pediatrics at the University of Michigan, published findings from a study of 6,200 children and found that elevated curiosity was linked to higher math and literacy skills among kindergarteners. That effect remained strong even when researchers compared kids with similar levels of “effortful control,” or the ability to concentrate and pay attention. Even more surprising, she discovered that students from impoverished backgrounds with a strong thirst for knowledge performed as well as those from affluent homes.

neuroscience is starting to explain curiosity’s power. When we’re hungry for answers, our brain activity changes in ways that help us retain new information. For one, the curious mind engages processes and brain regions associated with anticipating a reward. We want to learn more because the answers are satisfying. In addition, the hippocampus, a memory hub, ramps up activity, preparing to store information. The more we want to know an answer, research suggests, the more memorable it becomes.

Related Reading: <useful reading>

“The Psychology and Neuroscience of Curiosity” ScienceDirect, Nov 2017


Music to Match Images

Source: The Verge, May 2018

a new project by Japanese researchers takes advantage of this imaginative potential and combines it with AI to magical effect. The resulting web app — “Imaginary Soundscape” — uses machine learning to match any picture you upload with a suitable audio pairing.

Upload an Japanese woodcut of fishing boats, for example, and the system offers waves and water sounds; load an abstract painting of nightingales, and you’re given a garden soundscape of wind chimes and birds. Often the results are exactly what you’d expect, but more interesting is when the system picks up on elements in the picture you might not immediately have thought of (like pairing Megatron with tractor sounds), or that make no sense at all (like this painting of handsmatched with sounds from a live sports game).

Scientific Paper –> Dynamic Medium

Source: The Atlantic, Apr 2018

… the basic means of communicating scientific results hasn’t changed for 400 years. Papers may be posted online, but they’re still text and pictures on a page.

The Watts-Strogatz paper described its key findings the way most papers do, with text, pictures, and mathematical symbols. And like most papers, these findings were still hard to swallow, despite the lucid prose. The hardest parts were the ones that described procedures or algorithms, because these required the reader to “play computer” in their head, as Victor put it, that is, to strain to maintain a fragile mental picture of what was happening with each step of the algorithm.

Victor’s redesign interleaved the explanatory text with little interactive diagrams that illustrated each step. In his version, you could see the algorithm at work on an example. You could even control it yourself.

the whole problem of scientific communication in a nutshell: Scientific results today are as often as not found with the help of computers. That’s because the ideas are complex, dynamic, hard to grab ahold of in your mind’s eye. 

… to create an inflection point in the enterprise of science itself. 

In the mid-1600s, Gottfried Leibniz devised a notation for integrals and derivatives (the familiar ∫ and dx/dt) that made difficult ideas in calculus almost mechanical. Leibniz developed the sense that a similar notation applied more broadly could create an “algebra of thought.” Since then, logicians and linguists have lusted after a universal language that would eliminate ambiguity and turn complex problem-solving of all kinds into a kind of calculus.

 As practitioners in those fields become more literate with computation, Wolfram argues, they’ll vastly expand the range of what’s discoverable. The Mathematica notebook could be an accelerant for science because it could spawn a new kind of thinking.

To write a paper in a Mathematica notebook is to reveal your results and methods at the same time; the published paper and the work that begot it. Which shouldn’t just make it easier for readers to understand what you did—it should make it easier for them to replicate it (or not).

With millions of scientists worldwide producing incremental contributions, the only way to have those contributions add up to something significant is if others can reliably build on them. “That’s what having science presented as computational essays can achieve,” Wolfram said.

Pérez admired the way that Mathematica notebooks encouraged an exploratory style. “You would sketch something out—because that’s how you reason about a problem, that’s how you understand a problem.” Computational notebooks, he said, “bring that idea of live narrative out … You can think through the process, and you’re effectively using the computer, if you will, as a computational partner, and as a thinking partner.”

A federated effort, while more chaotic, might also be more robust—and the only way to win the trust of the scientific community.

It’ll be some time before computational notebooks replace PDFs in scientific journals, because that would mean changing the incentive structure of science itself. Until journals require scientists to submit notebooks, and until sharing your work and your data becomes the way to earn prestige, or funding, people will likely just keep doing what they’re doing.

When you improve the praxis of science, the dream is that you’ll improve its products, too. Leibniz’s notation, by making it easier to do calculus, expanded the space of what it was possible to think.

Einstein Thinks in 4 Dimensions

Source: Forbes, Dec 2016

Consider a famous shape, the Klein bottle, which is a bottle that loops into itself to create a shape with no inside and outside. You can obtain it by gluing together two Möbius strips along their edge.


Sadly, we can’t quite fit a Klein bottle in our 3-dimensional space, since it is not supposed to actually cut through itself. Like the ribbon above, the critical junction where it folds back in is meant to allow the two tubes to be completely separated.

But that’s quite easy to achieve using color, right? Here’s an example:

This uses the exact same approach as we had with the ribbon. The tube shifts into a different area in color space as it loops back in, and by the time it passes itself it is already very separated (green vs white). The Klein bottle sits very comfortably in 4-dimensional space, without any nasty self intersections.

Multi-Dimensional Thinking

Source: The Creativity Post, Mar 2018

Claude Shannon published his seminal paper A Mathematical Theory of Communication, establishing the thereafter held fact that everything–all information, in all of its forms–can be broken into 1’s and 0’s

What Shannon’s work highlights is the flexibility of abstraction. If before tools were designed based on the information they were carrying, the new binary norm lets us ignore that and know that whatever we want to say–no matter how long, in what language and whether it carries sense–could be communicated in bits.

Through the joint work of Shannon and Reenskaug we have written a status quo of extreme abstraction and rigid efficiency. The meaning of information is irrelevant, all data can be reduced to 1’s and 0’s.


Symbiosis @ the MIT Media Lab

Source: MIT Press, Feb 2018

Doug Engelbart envisioned that the computer would be a tool for intellectual and artistic creativity; now, our devices are designed less around creation, and more around consumption.

Garry wasn’t surprised when a human grandmaster with a weak laptop could beat a world-class supercomputer. But what stunned Garry was who won at the end of the tournament — not a human grandmaster with a powerful computer, but rather, a team of two amateur humans and three weak computers! The three computers were running three different chess-playing AIs, and when they disagreed on the next move, the humans “coached” the computers to investigate those moves further.

As Garry <Kasparov> put it: “Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.”

When you create a Human+AI team, the hard part isn’t the “AI”. It isn’t even the “Human”.

It’s the “+”.

AIs are best at choosing answers. Humans are best at choosing questions.

the human chooses the questions, in the form of setting goals and constraints — while the AI generates answers, usually showing multiple possibilities at once, and in real-time to the humans’ questions. But it’s not just a one-way conversation: the human can then respond to the AI’s answers, by asking deeper questions, picking and combining answers, and guiding the AI using human intuition.

a mix of intuition and logic that surpasses either one alone.

Since the design of Human+AI systems is such a new field — in fact, it’s pretty generous to call it a “field”, it’s more like a small patch of grass — there are lots of unsolved problems, like:

  1. What kind of questions should a human ask? In all the above examples, the question is usually “what possible solutions fit these goals & constraints?”
  2. How should humans and AIs communicate? You don’t have to use words, or even code; the painting example has the human and AI communicate through pictures!
  3. How can multiple humans or multiple AIs work together? All the above examples had just one human working with one AI, but the winner of the 2005 Centaur Chess tournament had two humans and three AIs — how can this scale to dozens, thousands, even millions of people and/or machines?

AIs choose answers. Humans choose questions.

Mother Nature’s most under-appreciated trick: symbiosis.

It’s an Ancient Greek word that means: “living together.”

Symbiosis shows us you can have fruitful collaborations even if you have different skills, or different goals, or are even different species. Symbiosis shows us that the world often isn’t zero-sum — it doesn’t have to be humans versus AI, or humans versus centaurs, or humans versus other humans. Symbiosis is two individuals succeeding together not despite, but because of, their differences. Symbiosis is the “+”.

Lovelace’s Creativity Test

Source:  Motherboard, Jul 2014

The Lovelace Test is designed to be more rigorous, testing for true machine cognition. It was designed in the early 2000s by Bringsjord and a team of computer scientists that included David Ferrucci, who later went on to develop Jeopardy-winning Watsoncomputer for IBM. They named it after Ada Lovelace, often described as the world’s first computer programmer.

The Lovelace Test removes the potential for manipulation on the part of the program or its designers and tests for genuine autonomous intelligence—human-like creativity and origination—instead of simply manipulating syntax.

An artificial agent, designed by a human, passes the test only if it originates a “program” that it was not engineered to produce. The outputting of the new program—it could be an idea, a novel, a piece of music, anything—can’t be a hardware fluke, and it must be the result of processes the artificial agent can reproduce. Now here’s the kicker: The agent’s designers must not be able to explain how their original code led to this new program.

In short, to pass the Lovelace Test a computer has to create something original, all by itself.

Even the most advanced self-learning neural network can only perform tasks that are first mathematized and turned into code. So far, essentially human functions like creativity, empathy and shared understanding—what is known as social cognition—have proved resistant to mathematical formalization.

Related Resource:
“The Lovelace 2.0 Test of Artificial Creativity and Intelligence”, Oct 2014