Playtime (Jacques Tati, 1967) is as timely now as it was in 1967 – and should be given space to both laugh and be critical about culture, design and technology.
Primary Time, 1974 U-matic fim transferred to DVD Edition 2/3
Lovely short from Nora Bateson.
I recommend checking out her other videos as well.
I always enjoy reading No Mercy / No Malice, but this one is particularly great.
Thanks Emma for highlighting.
Rana Foroohar published an excellent op-ed on the need to move from linear to complex thinking, in the face of apparent interconnectedness and unknown unknown world.
Linear systems and baseline reversion to equilibrium is generally assumed. And efficiency rather than resiliency is encouraged.
If the economics profession is going to help solve the world’s biggest problems — from pandemics and climate change to deglobalisation and inequality — economists must stop tweaking the edges of their models and think outside the box.
Brian Arthur’s (Santa Fe Institute, PARC) ideas on complexity economics are very relevant to this line of thinking. For example, his paper on Complexity economics: a different framework for economic thought as well as some of some SFI’s other published content on COVID.
Thank you so much Samuel, for offering to translate the 10 principles (part of my practice manifesto) of generous design to Spanish.
Beastie Boys movie on Apple TV is a great story about dumb kids turning to smart men.
It is a story of change, relentless pursuit of the self, and a lot of growth.
when MCA passed away in 2012, I asked a group of friends to design a line from a Beastie Boys on a solid background.
Below are some sample —
During these days of zoom fatigue, sometimes it is good to go through clutter, tidy up. I discovered some gems sifting through records and recorded this short mix of psych/rock and jazz
I will be remiss if I didn’t mention this classic meditation
The future of design is meta. And may not be designed by designers.
In a world of abundant technology, the bottleneck is the ability to read between the lines, to exercise meta creativity. Technology is becoming cheaper by the day. Big tech is not only creating a plethora of digital products, but also setting the stage for everyone else to do the same. This is what an exponential world means. When the barrier to produce a highly complicated algorithm is removed, smart machines abound.
Employees at Facebook now have access to a graphic interface that allows anyone in the company to touch and manipulate highly advanced machine-learning models. The more layers we build on top of these technologies, the more accessible they become, and the faster we will approach this age of abundance, and also scarcity. Because this abundance—like any other—negotiates with scarcity. And this scarcity is one of creativity.
Economies of scale seek exponential growth, making it impossible for the traditional creative to compete with large and increasingly autonomous systems. Code systems will optimize themselves, and deliver endlessly efficient solutions. What consultants in TATA call “a context of one” brings with it a world where the sheer act of finishing a thought will make it a done reality.
With endless firepower, however, we need to know where to aim.
In order for AI to truly augment and not replace the human practitioner must too grow. If you follow instructions, those instructions can be passed onto a machine that will do so better. If our creativity is dormant, we will be automated. An organic human robot is inferior to a silicon one, because he is less efficient. When you’re unable to explain why you get out of bed in the morning, go to work, or make the decisions people ask you to make—you are losing to the machine. Efficiency will wash you away if you lack the emotional and social intelligence to open up, and to reflect on questions of agency and the powers of your practice.
Efficiency operates on lines of the known. It delivers endless incremental, structured improvements. Meta thinking , on the other hand, is fundamentally unstructured and unknown, and therefore adaptive. It is the intuitive and spontaneous moment when reading between the lines, it is the articulation of invisible patterns, the meaning-making from the unknown. Machines can’t optimize the unknown.
A meta thinker will not be automated because she can articulate the how and why of her practice. An intentional practitioner spends time in their head. They are able to identify opportunities for conviction, and avoid those that will overfit them in someone else’s plan.
If you ever want to know if someone is intentional in their work, simply ask them what are they interested in. If they respond with what they do for a living (their job) then you can assume that they’re an intention-less cog, and like all cogs will be eaten by the efficiency machine.
The efficiency machine is made out of AI, the status quo, and human cogs. It is not interested in change, but rather more efficient sameness. It averages humans and flattens contexts. It segments the human condition. Before conditioned to be cogs, humans always sought change, growth, and fulfillment. We navigate ambiguous situations, we make sense of unexpectedness, and we operate in liminality.
A meta thinker will be rejected by the efficiency machine, as it has no use to it. The machine only seeks those who are able to stand in line, and fit in. But our practitioner, the one able to articulate his practice, need not fit in, because his agency is internally motivated. And that is what a practice is: an exploration, a journey. An intentional pursuit of interests, gradually moving towards articulating, and always seeking allies. It is not pulled back by the inability to fit in, but driven by it. It negotiates with, but never dictated by, external guidance.
This exploration by the meta thinker is deeply personal but always connected to other deep thinkers. This is the true meaning of an exponential world: an explosion of taxonomies of meaning. Intentional practitioners connecting to those they seek. Liminal ideas in conversation with each other, the remix of intersecting fields, books, and backgrounds. It is our individual and collective ability to generate value from incomplete puzzles.
Knowledge is diminishing in value in this new world we’re building. Do you know who knows everything? The efficiency machine. You could print all of the machine knows: like my friend Paul Soulellis does in his project The Library of The Printed Web (currently part of MoMA’s collection). The websites he prints, the printing process itself, and the paper are all cheap and accessible. Paul actually makes the full files available to download. But the taxonomy Paul built is invincible; it is a generative engine of meaning. This act of human creativity is squarely outside the reach of machines, which are only capable of brute force, number-crunching pursuits.
The meta creative looks at the abundance of facts, images, and pages, to then places them in a new taxonomy, a new context, and by doing so generates new meaning, inviting others to engage with it.
The meta creative is not interested in production, because production is free. The meta creative’s deliverables are finished thoughts. Sound arguments, provocatively put together, generative and generous.
In a future of abundance we should look for what’s scarce, We should seek other creative, generous humans who are able to articulate what they’re interested in.
Now ask yourself: what am I interested in? Who are those that I seek? Articulate and reflect on these questions. These are the cornerstones of your practice, and will return dividends long after the robot takes your job.
Originally published as part of The Book of Beautiful Business
A beautiful thread on Audio Science Review
As you might know – I write about AI, creativity, complexity, and even make some music. Years ago, maybe 10, I used to have a blog: a general catch all, Wordpress site which was a log of interestingness. I had all kind of fun playing with sidebar widgets (I even created one), and of course constantly failed at keeping my tags and categories organized.
It was a loosely structured set of content, with no particular end but to be published. This new publication is one such thing; a blog, which hopefully I can nurture and develop for years to come.
You can expect links to talks, ideas, musings, books, music and culture.
As a first such share this is a mix I recorded last week.
Santa Fe Institute released a set of short essays on questions which arise from Covid–19, below are some highlights and links.
I learnt about this through this great conversation between David Krakauer and Michael Garfield on the Complexity podcast.
But there is a flip side to this entanglement of complex systems: transmission, unlike the complexity of genetics, and social systems, economies, and ecosystems, can be relatively easily understood, and, by extension, controlled.
We use our understanding of the common factor of transmission to our advantage: continue to mobilize the largest information-transmission network the world has ever seen – our technologies of communication — to enable the collective action needed to eliminate the transmission of the virus. Strategic isolation is our anti-viral flash-anti-mob.
Here, scientists face a clear tradeoff. Wide ranges are much more likely to be correct, but can offer limited guidance to policymakers. Narrow ranges facilitate political decision-making, but are more likely to be wrong. Thus, when scientists decide how to report results to policymakers, they have to balance the need for action-guiding advice against the risk of their advice being wrong. These are value-laden decisions that cannot be outsourced to policymakers. Thus, as politicians continue to call on the expertise of scientists in order to respond to the current pandemic, scientists must embrace the fact that they are being asked to make ethical decisions.
So what’s a complexity scientist to do? In our research group in Leipzig, we believe we can establish general statistical regularities using simplifying assumptions and procedures that can compensate for data fluctuations.
So what is the effect of group size on the transmission rates of infectious disease? This question raises many secondary questions. How long does one stay within a group — perhaps two hours at a ball game, but all day in kids’ classrooms — and how does that interact with group size? How thoroughly within a group does transmission occur? Surely somebody in the bleachers cannot directly infect someone in a box seat above home plate. And what about whether the group is indoors or outdoors; what about wind and humidity?
Our world mostly works. When you’re leaving the airplane, don’t think, follow: good design nudges you all the way to the taxi. The architect Christopher Alexander built a life’s work on showing how something as simple as the design of a home’s window seat has, over centuries, adjusted to a delicate balance of physical, psychological, and social needs. In equilibrium, good systems get you by on instinct.
Like the hiker who brought a can of espresso beans, however, many of us are now noticing how much of day-to-day mind-life has been cooked, not left raw. By choice, or by necessity, we’re forced to think about things we’ve usually left to the environment. As I asked a friend who teaches philosophy: have you ever done this much thinking before?
I have had the pleasure of co–teaching Complexity by Design at Parsons SDM last semester, and engage with wonderful thinkers and institutes in this space.
It is becoming increasingly clear that complexity 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.
other topics include: chaos, fractals, bio-mimicry, modeling, netLOGO
V 2.5: self leading
A lot of the leadership advice, individuation, Jungian ideas of synchronicity, and adjacent thinking on signifiers and semiotics are all very much complexity friendly.
Mostly because they accept the behaviorist nature of our world (the noise in your head is different than the noise in mine).
My working list of axioms around complex systems is:
interconnected over rules design: they are not designable
in fact: emergence (‘it just happens…’ as one student informally articulated) is the opposite of design
A system is as complex as we need it to be: we can exercise reduction if the situation allows, and seek extra details (context) when the solution slides off the problem
Complex systems are open ended
hence a machine can never be truly intelligent by the way (I recommend Marcus’ book for those interested in that point)
Complicated systems–like a car, computer program or the highway system–are an elaborate stacking of known constructs.
We can model the difference between complex and complicated as the difference between designing a highway system or designing less accidents.
p.s. I am sure I left links out, please comment with ideas and suggestions - I would love to add to this list.
Visual blogging used to be the highest form of blogging. Older internet surfers could reminisce on the blackhole-ness of the now-defunct ffffound!. As an invite only service, being able to post images–and not only browse–was just about as cool as a job at Apple in the late 00’s.
In 2009 Andreas Philstrom (@suprb) started Dropular which brought together a small and committed group of designers to build a competing repo of beautiful graphics, letters and images (I even developed a small Wordpress plugin for it).
This was all very much influenced by the endless hacking potential of RSS feeds, and in the center of this xml rhizome was of course Google Reader.
I will be remiss if I won’t mention the app I built with Dai Hovey (@14lox), which essentially let you read blogs just by looking at pictures, essentially scraping images: http://work.byed.it/App-By-Edit (the irony of the fact that I am now a writer is not lost on me)
We tried to later develop this for Tumblr, but various API changes made this too difficult to maintain: http://work.byed.it/Viewr
Instagram is somewhat of an anomaly but an elephant I can’t avoid. In IG images are soliciting likes, in an effort supported by the platform, which is channeling the flow of ad dollars. To some extent Dropular (as an example) was more of an informal editorial effort, than a social network.
At the core of it, the ‘visual like’ is a very visceral moment for the designer’s work (and image consumption). It needn’t a classification (like Are.na), tagging (like Pinterest) nor social scoring (like IG).
I hope that now that Wordpress owns Tumblr it could be that place.
This was a long winded to say that I am giving my very old Tumblr some attention, maybe you want to follow along
p.s. I suspect that the decline in this form of web use is related to autonomous image making, aggregation and copyright monitoring.
I had the pleasure of moderating a panel for AIA NY in December.
You can watch the recording of evening through this link.
In my introduction I spoke of 3 interesting trends I am seeing in AI design and innovation:
1- Abundance and Scarcity
We have more technology than ever before, in a more affordable way, but with every abundance comes scarcity, what is ours?
As we have more technology, we have less of something else. Abundance always negotiates with scarcity. More machine based decisions mean less human involvement, more stats mean less decision on the fly, more standardization mean less thoughtfulness.
Scarcity is generally a great place to look for differentiation, and more so in the groundswell of AI abundance.
Innovation lives in the space between scarcity and harmful abundance
In the inner ring people are unable to exercise agency, outside the other innovation is unchecked and damaged the environment, or ethics.
In between we let electrons bounce. Where do current innovations sit, and who is left moderating and making sure we’re in the safe zone?
3 - The knowledge shift
What is known? What can be known?
What does the past tell us about the future? and where does sense-making fit within this frame?
I suspect that linguistics and epistemology is going to make its way into design and innovation as AI is going to become more prevalent in the decision economy.
This will be a good place to remind that I am slowly organizing (and looking for more people for) a group to write an open–source report on designing for autonomous systems.
Than you to AIA NY committee for the invitation, and to the panelists for their thoughts and presentation:
Daniel Pittman, Partner (Strategy & Innovation), TAD Associates
Will Shapiro, Co-Founder and CEO, Topos Inc.
Andreas Hoffbauer, Founder and Director, Atelier Kultur
Melissa Marsh, Assoc. AIA, Founder & Executive Director, PLASTARC and Senior Managing Director - Occupant Experience, Savills