Notes

Systems, Creativity

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Versions of Complexity

I had the pleasure of co-teaching Complexity by Design at Parsons SDM last semester and engaging with wonderful thinkers and institutes in this space. 

It is becoming increasingly clear that complex thinking (definition to come later) is a core part of modern life. This was discussed in different circles before we all got into this state of unknowingness. 

I have been working with a very acute definition of complexity, but given that the field is emerging (no pun intended), I wanted to linger a moment on its positionality.


I have been working with 2.5 versions of complexity: a partial list of links and resources to follow.

V1: Scientific

  • Santa Fe Institute, and their Complexity Explorers Group on FB

  • New England Complex Systems Institute

  • MIT Center for Collective Intelligence

  • Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms...

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80/20 Aesthetics

The Market for Creativity, AI, and Spreadsheets

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Source

I am revisiting some thoughts on creativity and AI. In 2014/2015, I extensively researched AI, language, and system thinking (including an academic collaboration in computational linguistics and a TEDx talk on the topic).

Since then, I have shifted my writing to the psychology of creativity (going further into the augmentation side of the AI discourse) and now work as an executive creativity coach, running workshops on asking writing questions and developing ideas at the speed of clarity.

I am sharing some thoughts that might be useful for those looking to understand our moment and the space of possibilities.

Structure

A machine will better do anything structured better than a human.

A fixed relationship between variables is, in essence, a model and will continue to point in the direction of a decision.

It is up to a...

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Common Dominator

When designing for universality we design for a common dominator. Something we all do the same, a feature we all share (sit when we drive, walk on 2 legs). Reality then reminds us that universality is futile, we’re not all the same, and that if you look close enough the entire world is made of edge cases.

Once we get more personal with our constitutes (users, friends) it is easy to see that we’re all unique. In some way designing for a common dominator is like writing a script for a date beforehand, with a bunch of a if-then forks in your text.

Design for agency is like going to therapy. It is much more meta, and speaks to the forces of decision making and self fulfillment we all share.

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Mass Produced Software

Mass produced software (MPS) is one that put the emphasis on the cranks and bolts of a large machines, that is hard to start and even harder to stop. It is about anomaly detection, about the reduction of individualism. About robots and humans that stand in lines, about hips of data perfectly pile up against a jig.

MPS is the opposite of the human sense making process. It is senseless. Like Soylent. It is purely utilitarian, and it waits for humans to make sense of it in the production side (brand) and consumption (meaning).

Context leads to more context. Radically contextual software (RCS) does not view context as a tangent from the assembly line, but the shortest path to meaning (and value).

RCS computes on the user end (edge computing), does not require a remote server by default, and is keen to think of itself as a single instance of utility (rather than a client to a mothership...

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Machine to Human, Instead of Human to Machine

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We could generalize software solutions as humans trying to find their way to a server. Through an iPhone app, a website, or a kiosk. The data is store remotely, the utility is hiding behind a propriety door (gatekeeper), the human is the one seeking that door, with a key (account, authentication), and behind that gated space the user’s data (& the system’s value) sits.

What can do to keep data user bound, with machines coming to meet the user where they are, with no data retention and paid for services?

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Goals and Directions

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Arts and Science, ‘19

I have been running a series of salons for the last few years (together with XXIX). Guests included business minds, scientist and philosophers. Following a bit of reflection I decided to refocus the series from science (subject matter exploration), to a more meta place. Current tagline is: A Salon Series for Practitioners with a Purpose.

As I am planing next year, I would love for members of this list to suggest possible guests (or nominate themselves).

↳ https://goo.gl/forms/plMSUEOZuAz7xMmj2

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Machines That Think Think Like Machines

“Machines that think think like machines. That fact may disappoint those who look forward, with dread or longing, to a robot uprising. For most of us, it’s reassuring. Our thinking machines aren’t about to leap beyond us intellectually, much less turn us into their servants or pets. They’re going to continue to do the bidding of their human programmers.

Much of the power of artificial intelligence stems from its very mindlessness. Immune to the vagaries and biases that attend conscious thought, computers can perform their lightning-quick calculations without distraction or fatigue, doubt or emotion. The coldness of their thinking complements the heat of our own.”

Nicholas G. Carr, from “What To Think About Machines that Think”, edited by John Brockman

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Lower Case

Intentionally not capitalizing could be a powerful statement. It is an act of building something new, free from any Cannon, any top town values.

Our new design needn’t match The Design. It stands on its own, seeking an honest and generous fit. It does not need a certificate. Our design need not a diploma, nor to be an expert (remember that robots are the biggest narrow experts).

It is Taylorism, Six Sigma, and the Standard that got us used to make Best, fast. But with constantly changing thinking schemes and self driving assembly lines who even has the time to capitalize?

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Thoughtfulness

Being absorbed in or involved in thought is incredibly valuable. Firstly because it is innately human (machines can’t think [not for themselves at least]), and because it is good for innovation (assembly lines don’t think either).

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