We don’t store words or the rules that tell us how to manipulate them. We don’t create representations of visual stimuli, store them in a short-term memory buffer, and then transfer the representation into a long-term memory device. We don’t retrieve information or images or words from memory registers. Computers do all of these things, but organisms do not.
Tammy takes a deep dive into our brains to examine the psychology of web performance. It opens with this:
If you don’t consider time a crucial usability factor, you’re missing a fundamental aspect of the user experience.
I wish that more UX designers understood that!
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.
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.
This explains rubber ducking.
Speaking out loud is not only a medium of communication, but a technology of thinking: it encourages the formation and processing of thoughts.
I left the office one evening a few weeks back, and while I was walking up the street, James Box cycled past, waving a hearty good evening to me. I didn’t see him at first. I was in a state of maximum distraction. For one thing, there was someone walking down the street with a magnificent Irish wolfhound. If that weren’t enough to dominate my brain, I also had headphones in my ears through which I was listening to an audio version of a TED talk by Donald Hoffman called Do we really see reality as it is?
It’s fascinating—if mind-bending—stuff. It sounds like the kind of thing that’s used to justify Deepak Chopra style adventures in la-la land, but Hoffman is deliberately taking a rigorous approach. He knows his claims are outrageous, but he welcomes all attempts to falsify his hypotheses.
I’m not noticing this just from a short TED talk. It’s been one of those strange examples of synchronicity where his work has been popping up on my radar multiple times. There’s an article in Quanta magazine that was also republished in The Atlantic. And there’s a really good interview on the You Are Not So Smart podcast that I huffduffed a while back.
But the most unexpected place that Hoffman popped up was when I was diving down a SETI (or METI) rabbit hole. There I was reading about the Cosmic Call project and Lincos when I came across this article: Why ‘Arrival’ Is Wrong About the Possibility of Talking with Space Aliens, with its subtitle “Human efforts to communicate with extraterrestrials are doomed to failure, expert says.” The expert in question pulling apart the numbers in the Drake equation turned out to be none other than Donald Hoffmann.
A few years ago, at a SETI Institute conference on interstellar communication, Hoffman appeared on the bill after a presentation by radio astronomer Frank Drake, who pioneered the search for alien civilizations in 1960. Drake showed the audience dozens of images that had been launched into space aboard NASA’s Voyager probes in the 1970s. Each picture was carefully chosen to be clearly and easily understood by other intelligent beings, he told the crowd.
After Drake spoke, Hoffman took the stage and “politely explained how every one of the images would be infinitely ambiguous to extraterrestrials,” he recalls.
I’m sure he’s quite right. But let’s face it, the Voyager golden record was never really about communicating with an alien intelligence …it was about how we present ourself.
I like this skewering of the cult of so-called-neuroscience; the self-help book equivalent of eye-tracking.
A pitch-perfect parody of people that peeve.
A look at our inbuilt confirmation biases.
An excellent rebuttal by Steven Pinker to Nicholas Carr's usual trolling.
It turns out that the brain is a scale-free small-world network in a state of self-organised criticality. Just like the internet.
A detailed look at the troubled history of George Lakoff, the father of conceptual metaphor.
There appears to be a form of synesthesia where people "hear" motion. Watch this video (repeatedly) to test your own sensory perception.
I saw Steven Pinker give a talk recently and he spent a fair amount of time talking about swearing. He has written up that part of the talk into an article for the New Republic.
A good, if somewhat dispiriting, overview of Artificial Intelligence. (There's some nice typesetting on this page)