Artificial intelligence will alter almost every industry in the coming year. Improving data technologies, faster computing, and a more receptive public will give way to new system models and tools we cannot yet imagine.

We have some things to do to clear the foggy mist over the current hype.

How do machines work?

Since the ENIAC (1946) invention, we have been programming machines to perform different tasks.

Before that point, tools like hammers or cranes could only do one thing. When a multipurpose machine was made available, we could suddenly write instructions for a machine to follow.

It was magical and must have felt like we got a new collaborator in the office – one is far more efficient and never complains.

That awe and our innate psychological biases to assign human-like attributes to machines (think of the last time you spoke to your car) inspired our early dreams of...

Part 1

Models, Views, Controllers

Model View Controller is a server architecture model invented at Xerox Parc in the late 70s. Initially, a system to implement graphic user interfaces for the desktop machine; it is now the core of practically anything we refer to as a digital product.

“The essential purpose of MVC is to bridge the gap between the human user’s mental model and the digital model that exists in the computer. The ideal MVC solution supports the user illusion of seeing and manipulating the domain information directly. The structure is useful if the user needs to see the same model element simultaneously in different contexts and/or from different viewpoints. The figure below illustrates the idea.”

— Trygve Reenskaug

Origin of MVC

More concretely, the architecture illustrates the connection between intricate data sets and the intent of bridging the way humans and...

An ongoing list of tools I often find myself sharing with people.

To Do’s

• Asana.com (cloud)
• Things (desktop)

Writing

• IA Writer (desktop)
• Dropbox Paper (cloud)

Blogging

• Blot.im

Email

• Buttondown

Blogging + Email

• Substack
• Write.as

Moodboarding/images

• Anything.io

Homepage

• persona.co

Contacts

• Clay.earth

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...

The Market for Creativity, AI, and Spreadsheets

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...

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.

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...

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?