As humans, we store information and ideas in complex 3 dimensional structures. Accounting for nuances, weight of logic and auxiliary topics. When we need to use an idea we slice through that 3d spatial matrix and render a flat idea, into a sentence for example, or a decision.

The person on the other side then unpacks what we said into their own mental map, and on we go.

As we started using machines for tele–comm we reaped all of the advantages, with relatively small loss of meaning.

Ironically the deeper we are in the algorithmic age the more loss in digital signal we should be expecting. By asking machines to extract meaning (make decisions for example) we’re relinquishing understanding, and set the machines to fail.

We must not confuse a transistor for a translator.

We are now asking a binary machine (Shannon, 1948) to unpack levels of knowing, in a deeply qualitative state. We...

Imagine I installed a bucket of paint on top of a ladder, with a canvas underneath. I carefully balance the bucket of paint, and measure the amount of paint in it. I connect a small string to the handle of the bucket, and once all components were ready I pull the string. The bucket falls, the paint spills and results in a beautiful composition, with compelling accents and negative spaces.

Who made that art? Was it me or gravity?

With that in mind – what is generative art, and how flawed is the statement “I made an ‘AI’ make X”?

Probably as hyperbolic as saying that I made gravity make a painting.

I am currently reading Changing on the Job (leading up to Simple Habits for Complex Times)

constructive–developmental theories are centered on the particular meaning-making of each individual rather than on age or phase of life. They are constructive because they are concerned with the way each person creates her world by living it (rather than believing, as some theories do, that the world is outside us and there is some kind of objective truth to be discovered). They are developmental because they are concerned with the way that construction changes over time to become more complex and multifaceted.

Unlike the age/phase theories, constructive–developmental theories do not assume that years lived or life stages completed necessarily mean anything developmental at all. There are a wide variety of constructive–developmental theories—all with broad similarities in their orientation to...

Because machines operate in average, what they serve might work for people close enough to that average.
So when going to an automated doctor for a headache, or a designer bot making you a logo, if you’re close enough to the median taste (or unable to articulate your preference) such services shall seem good, and affordable.

However once you start asking follow up questions to that diagnosis, or inquire about a typeface choice – a machine (by today’s definition and under its current architecture) will be unable to help.

But most of us seek to understand the service or product we are paying for. We want to make sure we’re making the right choice with our money, and want to be able to address future situations without having to call the ‘shop’ again every time.

Numbers will need to be studied, but I argue that this median offering will only catch a small portion of the market, and that...

Experiences write mental models for change, inspiration and empathy. We write our (and our client’s) stories into experiences, design experiential environments and use them in social interactions to foster deeper connections.

Yet, to be human means that we can never really understand what another person is experiencing.
Our inner world is complex and multi–dimensional. Our cognition is made of consciousness and unconscious forces, some logical and some behavioral. We all share the same world, clearly - but our perception of that world could be very different.

In his 1974 essay ‘What is it Like to be a Bat?’, the philosopher Thomas Nagel claims that we have no way of knowing what a bat experience is like (or that we can’t grasp bat-ness) because our mental world is filled with human experiences.

Our own experience provides the basic material for our imagination, whose range is...

Algorithms only look for averages.That is due to the absolute and never discussed fact that machines operate in averages.

When Spotify recommends you music is because it averages your taste and deems some songs to be proximity ‘x’ to that average taste. Same with Netflix, Amazon and Google ads.

The brain (and subsequently humans) operate very differently.

We don’t calculate what we don’t need do. We simple do things, cutting through an array of signals, numbers and symbols (consider the mathematical complexity of catching a baseball).

We’re not rational, we’re behavioral.

We take longer to make spreadsheet calculations: analyzing insurance policy’s true risk score, or 1000 steps of permutation into a Go game.

However we normally know what to do, in an almost innate way. That is partly why trying to universalize the human expense into a general purpose machine is impossible.

...

AI will is a thru–technology, and not a destination in and of itself.

Job >> AI >> New Job

We all heard that AI will change everything about out jobs, if we’re lucky enough to have one once the robot becomes intelligence.

In the last couple of posts I wrote about organizational design and self development. Those charges will be thru–AI and not by AI.
In other words statistical computation will not make us more liminal thinkers, but the act of wrapping our head around these new tool will.

It will leave us more enlightened, and more articulate.

Part of the reason that this message is not on everyone’s mind is that we’re not used to thinking about ourselves.

We tend to think about what we make, our output. Playing mantras like ‘Talk is cheap’ (tell that a robot who can make anything you want, but can’t think).

In a world of technological abundance thinking will exponentially...

It does not matter if you’re a carpenter, agile software coach, a CEO or a scientist. We all get paid to think. That is our commodity, which with time should increase in value.

Now there are 2 ways of going about bringing the way you think to the ‘market of ideas’.

The first one is to hide it behind what you make (a carpenter, brings their chair to a furniture market), or you can speak to it (normally in brand language, say Knoll in the furniture example).

The same logic applies to the world of IP, and intangibles.

We can monetize methodologies (Design Thinking for example), draw letters, or code machine learning algorithms.

If we get traction in the market, it means that customers are responding to the way we think – so we can raise the price, and charge a premium.

To be in control of the situation means that we are able to peel the layers from what we do, to who we are...

For all of the talk of liminal thinking and self reflection a lot of this written here is about technology, and innovation. Using tools in new ways, and building concoctions of tools not used before.

To get more specific, I see this as a triangulation of (1) pushing the tools quantitively, (2) pushing the humans qualitatively and (3) conversing both on the system level.

When all we do is push on the quant side we propagate biases, miss on the benefits of diversity and suffer the industrialist side effects of brute force computation. Also pushing ethnography, to not only read humans, but also write.

Bridging coaching, organizational design and ethnography, with innovation, computer science and agile development — all wrapped in a system design and complexity science envelope.

In a way design (especially for interactive mediums) is authoritative, or at least guiding.

Humans, users display properties of emergence when using such systems. As users we might come up with new ideas on how to use a tool, and write new interaction paradigms (with other users, and the system itself).

As users, we don’t operate in an if–then programming logic.

This is supposedly a wicked problem, because the permutations of what people might want to do are infinitely larger than what we think they would.

But I do want to offer a different perspective, which might be useful.

To do that I am going to borrow terminology from the world of agent based modeling (ABM).

In ABM we model a system, its agents, and the interaction between each of those. So rather than funneling the entire system to one unified outcome, we articulate and program our opinion about what an agent (a node in...