Tags: cybernetics



Sunday, June 18th, 2023

The man who tried to redeem the world with logic - Big Think

The fascinating—and tragic—story of Walter Pitts and Walter McCulloch whose lives and work intersected with Norbert Wiener and John von Neumann:

Thanks to their work, there was a moment in history when neuroscience, psychiatry, computer science, mathematical logic, and artificial intelligence were all one thing, following an idea first glimpsed by Leibniz—that man, machine, number, and mind all use information as a universal currency. What appeared on the surface to be very different ingredients of the world—hunks of metal, lumps of gray matter, scratches of ink on a page—were profoundly interchangeable.

Tuesday, March 14th, 2023


The last talk at the last dConstruct was by local clever clogs Anil Seth. It was called Your Brain Hallucinates Your Conscious Reality. It’s well worth a listen.

Anil covers a lot of the same ground in his excellent book, Being You. He describes a model of consciousness that inverts our intuitive understanding.

We tend to think of our day-to-day reality in a fairly mechanical cybernetic manner; we receive inputs through our senses and then make decisions about reality informed by those inputs.

As another former dConstruct speaker, Adam Buxton, puts it in his interview with Anil, it feels like that old Beano cartoon, the Numskulls, with little decision-making homonculi inside our head.

But Anil posits that it works the other way around. We make a best guess of what the current state of reality is, and then we receive inputs from our senses, and then we adjust our model accordingly. There’s still a feedback loop, but cause and effect are flipped. First we predict or guess what’s happening, then we receive information. Rinse and repeat.

The book goes further and applies this to our very sense of self. We make a best guess of our sense of self and then adjust that model constantly based on our experiences.

There’s a natural tendency for us to balk at this proposition because it doesn’t seem rational. The rational model would be to make informed calculations based on available data …like computers do.

Maybe that’s what sets us apart from computers. Computers can make decisions based on data. But we can make guesses.

Enter machine learning and large language models. Now, for the first time, it appears that computers can make guesses.

The guess-making is not at all like what our brains do—large language models require enormous amounts of inputs before they can make a single guess—but still, this should be the breakthrough to be shouted from the rooftops: we’ve taught machines how to guess!

And yet. Almost every breathless press release touting some revitalised service that uses AI talks instead about accuracy. It would be far more honest to tout the really exceptional new feature: imagination.

Using AI, we will guess who should get a mortgage.

Using AI, we will guess who should get hired.

Using AI, we will guess who should get a strict prison sentence.

Reframed like that, it’s easy to see why technologists want to bury the lede.

Alas, this means that large language models are being put to use for exactly the wrong kind of scenarios.

(This, by the way, is also true of immersive “virtual reality” environments. Instead of trying to accurately recreate real-world places like meeting rooms, we should be leaning into the hallucinatory power of a technology that can generate dream-like situations where the pleasure comes from relinquishing control.)

Take search engines. They’re based entirely on trust and accuracy. Introducing a chatbot that confidentally conflates truth and fiction doesn’t bode well for the long-term reputation of that service.

But what if this is an interface problem?

Currently facts and guesses are presented with equal confidence, hence the accurate descriptions of the outputs as bullshit or mansplaining as a service.

What if the more fanciful guesses were marked as such?

As it is, there’s a “temperature” control that can be adjusted when generating these outputs; the more the dial is cranked, the further the outputs will stray from the safest predictions. What if that could be reflected in the output?

I don’t know what that would look like. It could be typographic—some markers to indicate which bits should be taken with pinches of salt. Or it could be through content design—phrases like “Perhaps…”, “Maybe…” or “It’s possible but unlikely that…”

I’m sure you’ve seen the outputs when people request that ChatGPT write their biography. Perfectly accurate statements are generated side-by-side with complete fabrications. This reinforces our scepticism of these tools. But imagine how differently the fabrications would read if they were preceded by some simple caveats.

A little bit of programmed humility could go a long way.

Right now, these chatbots are attempting to appear seamless. If 80% or 90% of their output is accurate, then blustering through the other 10% or 20% should be fine, right? But I think the experience for the end user would be immensely more empowering if these chatbots were designed seamfully. Expose the wires. Show the workings-out.

Mind you, that only works if there is some way to distinguish between fact and fabrication. If there’s no way to tell how much guessing is happening, then that’s a major problem. If you can’t tell me whether something is 50% true or 75% true or 25% true, then the only rational response is to treat the entire output as suspect.

I think there’s a fundamental misunderstanding behind the design of these chatbots that goes all the way back to the Turing test. There’s this idea that the way to make a chatbot believable and trustworthy is to make it appear human, attempting to hide the gears of the machine. But the real way to gain trust is through honesty.

I want a machine to tell me when it’s guessing. That won’t make me trust it less. Quite the opposite.

After all, to guess is human.

Tuesday, July 20th, 2021

Dancing With Systems - The Donella Meadows Project

We can’t control systems or figure them out. But we can dance with them!

  1. Get the beat.
  2. Listen to the wisdom of the system.
  3. Expose your mental models to the open air.
  4. Stay humble. Stay a learner.
  5. Honor and protect information.
  6. Locate responsibility in the system.
  7. Make feedback policies for feedback systems.
  8. Pay attention to what is important, not just what is quantifiable.
  9. Go for the good of the whole.
  10. Expand time horizons.
  11. Expand thought horizons.
  12. Expand the boundary of caring.
  13. Celebrate complexity.
  14. Hold fast to the goal of goodness.

Monday, January 6th, 2020

w/e 2020-01-05 (Phil Gyford’s website)

While being driven around England it struck me that humans are currently like the filling in a sandwich between one slice of machine — the satnav — and another — the car. Before the invention of sandwiches the vehicle was simply a slice of machine with a human topping. But now it’s a sandwich, and the two machine slices are slowly squeezing out the human filling and will eventually be stuck directly together with nothing but a thin layer of API butter. Then the human will be a superfluous thing, perhaps a little gherkin on the side of the plate.

Sunday, April 28th, 2019

Norbert Wiener’s Human Use of Human Beings is more relevant than ever.

What would Wiener think of the current human use of human beings? He would be amazed by the power of computers and the internet. He would be happy that the early neural nets in which he played a role have spawned powerful deep-learning systems that exhibit the perceptual ability he demanded of them—although he might not be impressed that one of the most prominent examples of such computerized Gestalt is the ability to recognize photos of kittens on the World Wide Web.

Sunday, June 12th, 2016

Man-Computer Symbiosis

J. C. R. Licklider’s seminal 1960 paper. I’ve added it to this list of reading material.

The title should, of course, read “Person-Computer Symbiosis.”

Saturday, March 5th, 2016

Journal of Design and Science

A new publication from MIT. It deliberately avoids the jargon that’s often part and parcel of peer-reviewed papers, and all of the articles are published under a Creative Commons attribution licence.

The first issue is dedicated to Marvin Minsky and features these superb articles, all of which are independently excellent but together form an even greater whole…

Design and Science by Joi Ito:

When the cybernetics movement began, the focus of science and engineering was on things like guiding a ballistic missile or controlling the temperature in an office. These problems were squarely in the man-made domain and were simple enough to apply the traditional divide-and-conquer method of scientific inquiry.

Science and engineering today, however, is focused on things like synthetic biology or artificial intelligence, where the problems are massively complex. These problems exceed our ability to stay within the domain of the artificial, and make it nearly impossible for us to divide them into existing disciplines.

Age of Entanglement by Neri Oxman:

This essay proposes a map for four domains of creative exploration—Science, Engineering, Design and Art—in an attempt to represent the antidisciplinary hypothesis: that knowledge can no longer be ascribed to, or produced within, disciplinary boundaries, but is entirely entangled.

Design as Participation by Kevin Slavin:

The designers of complex adaptive systems are not strictly designing systems themselves. They are hinting those systems towards anticipated outcomes, from an array of existing interrelated systems. These are designers that do not understand themselves to be in the center of the system. Rather, they understand themselves to be participants, shaping the systems that interact with other forces, ideas, events and other designers. This essay is an exploration of what it means to participate.

The Enlightenment is Dead, Long Live the Entanglement by Danny Hillis:

As our technological and institutional creations have become more complex, our relationship to them has changed. We now relate to them as we once related to nature. Instead of being masters of our creations, we have learned to bargain with them, cajoling and guiding them in the general direction of our goals. We have built our own jungle, and it has a life of its own.

Friday, July 24th, 2015

Meet Walter Pitts, the Homeless Genius Who Revolutionized Artificial Intelligence

The fascinating story of logic, learning, and the origins of electronic computing. Russell, Shannon, Turing, Wiener, Von Neumann …they’re all in there, woven around the tragic figure of Walter Pitts.

It is a sad and beautiful world.

Thanks to their work, there was a moment in history when neuroscience, psychiatry, computer science, mathematical logic, and artificial intelligence were all one thing, following an idea first glimpsed by Leibniz—that man, machine, number, and mind all use information as a universal currency. What appeared on the surface to be very different ingredients of the world—hunks of metal, lumps of gray matter, scratches of ink on a page—were profoundly interchangeable.

Monday, July 14th, 2014

The Eccentric Genius Whose Time May Have Finally Come (Again) - Doug Hill - The Atlantic

A profile of Norbert Wiener, and how his star was eclipsed by Claude Shannon.

Monday, July 8th, 2013

The Hut Where the Internet Began by Alexis C. Madrigal in The Atlantic

A wonderful article looking at the influence that Vannevar Bush’s seminal article As We May Think had on the young Douglas Engelbart.

Monday, September 19th, 2011

I, Interface

’s , though currently fictional, are an excellent set of design principles:

  1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
  2. A robot must obey any orders given to it by human beings, except where such orders would conflict with the First Law.
  3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

One could easily imagine a similar set of laws being applied to field of user experience and interface design:

  1. An interface may not injure a user or, through inaction, allow a user to come to harm.
  2. An interface must obey any orders given to it by users, except where such orders would conflict with the First Law.
  3. An interface must protect its own existence as long as such protection does not conflict with the First or Second Law.

Okay, that last one’s a bit of a stretch but you get the idea.

In his later works Asimov added the zeroth law that supersedes the initial three laws:

  1. A robot may not harm humanity, or, by inaction, allow humanity to come to harm.

I think that this can also apply to user experience and interface design.

Take the password anti-pattern (please!). On the level of an individual site, it could be considered a benefit to the current user, allowing them to quickly and easily hand over lots of information about their contacts. But taken on the wider level, it teaches people that it’s okay to hand over their email password to third-party sites. The net result of reinforcing that behaviour is definitely not good for the web as a whole.

I’m proposing a zeroth law of user experience that goes beyond the existing paradigm of user-centred design:

  1. An interface may not harm the web, or, by inaction, allow the web to come to harm.

Wednesday, September 17th, 2008

Ethan Hein | Player1Ready

Judging from the research information collected on Delicious, Flickr and Last.fm, this book proposal—tying together informatics, music and games—could blossom into a great read.