50 Years Later, We’re Still Living in the Xerox Alto’s World - IEEE Spectrum
A profile of the Xerox Alto and the people behind it.
A profile of the Xerox Alto and the people behind it.
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.
But a machine for writing isn’t the same as a machine that writes for you. A machine for viewing photos isn’t the same thing as a machine that travels in your stead. A machine for sketching isn’t the same thing as a machine that designs. I love doing these things and doing them more efficiently. But I have no desire to have them done for me. It’s a key distinction: Do not automate the work you are engaged in, only the materials.
I don’t think I agree with Don Knuth’s argument here from a 2014 lecture, but I do like how he sets out his table:
Why do I, as a scientist, get so much out of reading the history of science? Let me count the ways:
- To understand the process of discovery—not so much what was discovered, but how it was discovered.
- To understand the process of failure.
- To celebrate the contributions of many cultures.
- Telling historical stories is the best way to teach.
- To learn how to cope with life.
- To become more familiar with the world, and to know how science fits into the overall history of mankind.
I need to seek out this documentary, Top Secret Rosies: The Female Computers of World War II.
It would pair nicely with another film, The Eniac Programmers Project
In 1990, the science fiction writer Douglas Adams produced a “fantasy documentary” for the BBC called Hyperland. It’s a magnificent paleo-futuristic artifact, rich in sideways predictions about the technologies of tomorrow.
I remember coming across a repeating loop of this documentary playing in a dusty corner of a Smithsonian museum in Washington DC. Douglas Adams wasn’t credited but I recognised his voice.
Hyperland aired on the BBC a full year before the World Wide Web. It is a prophecy waylaid in time: the technology it predicts is not the Web. It’s what William Gibson might call a “stub,” evidence of a dead node in the timeline, a three-point turn where history took a pause and backed out before heading elsewhere.
Here, Claire L. Evans uses Adams’s documentary as an opening to dive into the history of hypertext starting with Bush’s Memex, Nelson’s Xanadu and Engelbart’s oNLine System. But then she describes some lesser-known hypertext systems…
In 1985, the students at Brown who encountered Intermedia had never seen anything like it before in their lives. The system laid a world of information at their fingertips, saved them hours at the library, and helped them work through tangles of thought.
Claire L. Evans on computational slime molds and other forms of unconvential computing that look beyond silicon:
In moments of technological frustration, it helps to remember that a computer is basically a rock. That is its fundamental witchcraft, or ours: for all its processing power, the device that runs your life is just a complex arrangement of minerals animated by electricity and language. Smart rocks.
Portrait of the genius as a young man.
It is fortifying to remember that the very idea of artificial intelligence was conceived by one of the more unquantifiably original minds of the twentieth century. It is hard to imagine a computer being able to do what Alan Turing did.
This is a great piece! It starts with a look back at some of the great minds of the nineteenth century: Herschel, Darwin, Babbage and Lovelace. Then it brings us, via JCR Licklider, to the present state of the web before looking ahead to what the future might bring.
So what will the life of an interface designer be like in the year 2120? or 2121 even? A nice round 300 years after Babbage first had the idea of calculations being executed by steam.
I think there are some missteps along the way (I certainly don’t think that inline styles—AKA CSS in JS—are necessarily a move forwards) but I love the idea of applying chaos engineering to web design:
Think of every characteristic of an interface you depend on to not ‘fail’ for your design to ‘work.’ Now imagine if these services were randomly ‘failing’ constantly during your design process. How might we design differently? How would our workflows and priorities change?
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.
PIctures of computers (of the human and machine varieties).
An ongoing timeline of computer technology in the form of blog posts by Sinclair Target (that’s a person, not a timeslipping transatlantic company merger).
This strikes me as a sensible way of thinking about machine learning: it’s like when we got relational databases—suddenly we could do more, quicker, and easier …but it doesn’t require us to treat the technology like it’s magic.
An important parallel here is that though relational databases had economy of scale effects, there were limited network or ‘winner takes all’ effects. The database being used by company A doesn’t get better if company B buys the same database software from the same vendor: Safeway’s database doesn’t get better if Caterpillar buys the same one. Much the same actually applies to machine learning: machine learning is all about data, but data is highly specific to particular applications. More handwriting data will make a handwriting recognizer better, and more gas turbine data will make a system that predicts failures in gas turbines better, but the one doesn’t help with the other. Data isn’t fungible.
If only all documentation was as great as this old manual for the ZX Spectrum that Remy uncovered:
The manual is an instruction book on how to program the Spectrum. It’s a full book, with detailed directions and information on how the machine works, how the programming language works, includes human readable sentences explaining logic and even goes so far as touching on what hex values perform which assembly functions.
When we talk about things being “inspiring”, it’s rarely in regards to computer manuals. But, damn, if this isn’t inspiring!
This book stirs a passion inside of me that tells me that I can make something new from an existing thing. It reminds me of the 80s Lego boxes: unlike today’s Lego, the back of a Lego box would include pictures of creations that you could make with your Lego set. It didn’t include any instructions to do so, but it always made me think to myself: “I can make something more with these bricks”.
The transcript of a terrific talk on the humane use of technology.
Instead of using technology to replace people, we should use it to augment ourselves to do things that were previously impossible, to help us make our lives better. That is the sweet spot of our technology. We have to accept human behaviour the way it is, not the way we would wish it to be.
This 1993 article by Mark Weiser is relevant to our world today.
Take intelligent agents. The idea, as near as I can tell, is that the ideal computer should be like a human being, only more obedient. Anything so insidiously appealing should immediately give pause. Why should a computer be anything like a human being? Are airplanes like birds, typewriters like pens, alphabets like mouths, cars like horses? Are human interactions so free of trouble, misunderstanding, and ambiguity that they represent a desirable computer interface goal? Further, it takes a lot of time and attention to build and maintain a smoothly running team of people, even a pair of people. A computer I need to talk to, give commands to, or have a relationship with (much less be intimate with), is a computer that is too much the center of attention.
The title is pure clickbait, and the moral panic early in this article repeats the Toyota myth, but then it settles down into a fascinating examination of abstractions in programming. On the one hand, there’s the problem of the not enough abstraction: having to write in code is such a computer-centric way of building things. On the other hand, our world is filled with dangerously abstracted systems:
When your tires are flat, you look at your tires, they are flat. When your software is broken, you look at your software, you see nothing.
So that’s a big problem.
Bret Victor, John Resig and Margaret Hamilton are featured. Doug Engelbart and J.C.R. Licklider aren’t mentioned but their spirits loom large.
Anecdotes about the development of Apple’s original Macintosh, and the people who made it.
Like a real-life Halt And Catch Fire.
The Long Now Foundation has been posting some great stuff on their blog lately. The latest is a look at orreries, clocks, and computers throughout history …and into the future.
A fascinating bit of technological archeology tracing some of the oldest still-running software, from a COBOL program at the Pentagon to the firmware on the Voyager probes.