This is absolutely wonderful!
There’s deep dives and then there’s Marcin’s deeeeeeep dives. Sit back and enjoy this wholesome detective work, all beautifully presented with lovely interactive elements.
This is what the web is for!
The mathematics behind the halting problem is interesting enough, but what’s really fascinating is the community that coalesced. A republic of numbers.
Writing, both code and prose, for me, is both an end product and an end in itself. I don’t want to automate away the things that give me joy.
And that is something that I’m more and more aware of as I get older – sources of joy. It’s good to diversify them, to keep track of them, because it’s way too easy to run out. Or to end up with just one, and then lose it.
The thing about luddites is that they make good punchlines, but they were all people.
I feel like there’s a connection here between what Kevin Kelly is describing and what I wrote about guessing (though I think he might be conflating consciousness with intelligence).
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.
Picture someone tediously going through a spreadsheet that someone else has filled in by hand and finding yet another error.
“I wish to God these calculations had been executed by steam!” they cry.
The year was 1821 and technically the spreadsheet was a book of logarithmic tables. The frustrated cry came from Charles Babbage, who channeled his frustration into a scheme to create the world’s first computer.
His difference engine didn’t work out. Neither did his analytical engine. He’d spend his later years taking his frustrations out on street musicians, which—as a former busker myself—earns him a hairy eyeball from me.
But we’ve all been there, right? Some tedious task that feels soul-destroying in its monotony. Surely this is exactly what machines should be doing?
I have a hunch that this is where machine learning and large language models might turn out to be most useful. Not in creating breathtaking works of creativity, but in menial tasks that nobody enjoys.
Someone was telling me earlier today about how they took a bunch of haphazard notes in a client meeting. When the meeting was done, they needed to organise those notes into a coherent summary. Boring! But ChatGPT handled it just fine.
I don’t think that use-case is going to appear on the cover of Wired magazine anytime soon but it might be a truer glimpse of the future than any of the breathless claims being eagerly bandied about in Silicon Valley.
You know the way we no longer remember phone numbers, because, well, why would we now that we have machines to remember them for us? I’d be quite happy if machines did that for the annoying little repetitive tasks that nobody enjoys.
I’ll give you an example based on my own experience.
Regular expressions are my kryptonite. I’m rubbish at them. Any time I have to figure one out, the knowledge seeps out of my brain before long. I think that’s because I kind of resent having to internalise that knowledge. It doesn’t feel like something a human should have to know. “I wish to God these regular expressions had been calculated by steam!”
Now I can get a chatbot with a large language model to write the regular expression for me. I still need to describe what I want, so I need to write the instructions clearly. But all the gobbledygook that I’m writing for a machine now gets written by a machine. That seems fair.
Mind you, I wouldn’t blindly trust the output. I’d take that regular expression and run it through a chatbot, maybe a different chatbot running on a different large language model. “Explain what this regular expression does,” would be my prompt. If my input into the first chatbot matches the output of the second, I’d have some confidence in using the regular expression.
A friend of mine told me about using a large language model to help write SQL statements. He described his database structure to the chatbot, and then described what he wanted to select.
Again, I wouldn’t use that output without checking it first. But again, I might use another chatbot to do that checking. “Explain what this SQL statement does.”
Playing chatbots off against each other like this is kinda how machine learning works under the hood: generative adverserial networks.
Of course, the task of having to validate the output of a chatbot by checking it with another chatbot could get quite tedious. “I wish to God these large language model outputs had been validated by steam!”
Sounds like a job for machines.
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.
A history of buttons …and the moral panic and outrage that accompanies them.
By looking at the subtexts behind complaints about buttons, whether historically or in the present moment, it becomes clear that manufacturers, designers and users alike must pay attention to why buttons persistently engender critiques. Such negativity tends to involve one of three primary themes: fears over deskilling; frustration about lack of user agency/control; or anger due to perceptions of unequal power relations.
This is a rather beautiful piece of writing by Tom (especially the William Gibson bit at the end). This got me right in the feels:
Web 2.0 really, truly, is over. The public APIs, feeds to be consumed in a platform of your choice, services that had value beyond their own walls, mashups that merged content and services into new things… have all been replaced with heavyweight websites to ensure a consistent, single experience, no out-of-context content, and maximising the views of advertising. That’s it: back to single-serving websites for single-serving use cases.
A shame. A thing I had always loved about the internet was its juxtapositions, the way it supported so many use-cases all at once. At its heart, a fundamental one: it was a medium which you could both read and write to. From that flow others: it’s not only work and play that coexisted on it, but the real and the fictional; the useful and the useless; the human and the machine.
An online museum of sounds—the recordings of analogue machines.
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.
Prompted by his time at Clearleft’s AI gathering in Juvet, Chris has been delving deep into the stories we tell about artificial intelligence …and what stories are missing.
And here we are at the eponymous answer to the question that I first asked at Juvet around 7 months ago: What stories aren’t we telling ourselves about AI?
There was a time, circa 2009, when no home design story could do without a reference to Mad Men. There is a time, circa 2018, when no personal tech story should do without a Black Mirror reference.
Black Mirror Home. It’s all fun and games until the screaming starts.
When these products go haywire—as they inevitably do—the Black Mirror tweets won’t seem so funny, just as Mad Men curdled, eventually, from ha-ha how far we’ve come to, oh-no we haven’t come far enough.
James talks about automation and understanding.
Just because a technology – whether it’s autonomous vehicles, satellite communications, or the internet – has been captured by capital and turned against the populace, doesn’t mean it does not retain a seed of utopian possibility.
The transcript of Josh’s fantastic talk on machine learning, voice, data, APIs, and all the other tools of algorithmic design:
The design and presentation of data is just as important as the underlying algorithm. Algorithmic interfaces are a huge part of our future, and getting their design right is critical—and very, very hard to do.
Josh put together ten design principles for conceiving, designing, and managing data-driven products. I’ve added them to my collection.
- Favor accuracy over speed
- Allow for ambiguity
- Add human judgment
- Advocate sunshine
- Embrace multiple systems
- Make it easy to contribute (accurate) data
- Root out bias and bad assumptions
- Give people control over their data
- Be loyal to the user
- Take responsibility
I wrote a little something recently about some inspiring projects that people are working on. Like Matt’s Machine Supply project. There’s a physical side to that project—a tweeting book-vending machine in London—but there’s also the newsletter, 3 Books Weekly.
I was honoured to be asked by Matt to contribute three book recommendations. That newsletter went out last week. Here’s what I said…
The Victorian Internet by Tom Standage
A book about the history of telegraphy might not sound like the most riveting read, but The Victorian Internet is both fascinating and entertaining. Techno-utopianism, moral panic, entirely new ways of working, and a world that has been utterly transformed: the parallels between the telegraph and the internet are laid bare. In fact, this book made me realise that while the internet has been a great accelerator, the telegraph was one of the few instances where a technology could truly be described as “disruptive.”
Ancillary Justice: 1 (Imperial Radch) by Ann Leckie
After I finished reading the final Iain M. Banks novel I was craving more galaxy-spanning space opera. The premise of Ancillary Justice with its description of “ship minds” led me to believe that this could be picking up the baton from the Culture series. It isn’t. This is an entirely different civilisation, one where song-collecting and tea ceremonies have as much value as weapons and spacecraft. Ancillary Justice probes at the deepest questions of identity, both cultural and personal. As well as being beautifully written, it’s also a rollicking good revenge thriller.
The City & The City by China Miéville
China Miéville’s books are hit-and-miss for me, but this one is a direct hit. The central premise of this noir-ish tale defies easy description, so I won’t even try. In fact, one of the great pleasures of this book is to feel the way your mind is subtly contorted by the author to accept a conceit that should be completely unacceptable. Usually when a book is described as “mind-altering” it’s a way of saying it has drug-like properties, but The City & The City is mind-altering in an entirely different and wholly unique way. If Borges and Calvino teamed up to find The Maltese Falcon, the result would be something like this.
When I sent off my recommendations, I told Matt:
Oh man, it was so hard to narrow this down! So many books I wanted to mention: Station 11, The Peripheral, The Gone-Away World, Glasshouse, Foucault’s Pendulum, Oryx and Crake, The Wind-up Girl …this was so much tougher than I thought it was going to be.
And Matt said:
Tell you what — if you’d be up for writing recommendations for another 3 books, from those ones you mentioned, I’d love to feature those in the machine!
Done!
Station Eleven by Emily St. John Mandel
Station Eleven made think about the purpose of art and culture. If art, as Brian Eno describes it, is “everything that you don’t have to do”, what happens to art when the civilisational chips are down? There are plenty of post-pandemic stories of societal collapse. But there’s something about this one that sets it apart. It doesn’t assume that humanity will inevitably revert to an existence that is nasty, brutish and short. It’s also a beautifully-written book. The opening chapter completely sucker-punched me.
On the face of it, this appears to be another post-Singularity romp in a post-scarcity society. It is, but it’s also a damning critique of gamification. Imagine the Stanford prison experiment if it were run by godlike experimenters. Stross’s Accelerando remains the definitive description of an unfolding Singularity, but Glasshouse is the one that has stayed with me.
The Gone-Away World by Nick Harkaway
This isn’t an easy book to describe, but it’s a very easy book to enjoy. A delightful tale of a terrifying apocalypse, The Gone-Away World has plenty of laughs to balance out the existential dread. Try not to fall in love with the charming childhood world of the narrator—you know it can’t last. But we’ll always have mimes and ninjas.
I must admit, it’s a really lovely feeling to get notified on Twitter when someone buys one of the recommended books.
I just vended The City & The City, by China Miéville http://machine.supply/books/genmon/167
— Machine Supply (@MachineSupply) March 30, 2016
I just vended The Victorian Internet, by Tom Standage http://machine.supply/books/genmon/165
— Machine Supply (@MachineSupply) March 31, 2016
A profile of Norbert Wiener, and how his star was eclipsed by Claude Shannon.
Have you tried turning it off and on again?
This is a terrific write up of this year’s dConstruct, tying together all the emergent themes.
A really terrific piece by George Dyson taking a suitably long-zoom look at information warfare and the Entscheidungsproblem, tracing the lineage of PRISM from the Corona project of the Cold War.
What we have now is the crude equivalent of snatching snippets of film from the sky, in 1960, compared to the panopticon that was to come. The United States has established a coordinated system that links suspect individuals (only foreigners, of course, but that definition becomes fuzzy at times) to dangerous ideas, and, if the links and suspicions are strong enough, our drone fleet, deployed ever more widely, is authorized to execute a strike. This is only a primitive first step toward something else. Why kill possibly dangerous individuals (and the inevitable innocent bystanders) when it will soon become technically irresistible to exterminate the dangerous ideas themselves?
The proposed solution? That we abandon secrecy and conduct our information warfare in the open.
In which twelve drawings of historical drawing machines are drawn by a computer numerical controlled machine.