It doesn’t bother me much that bleeding-edge ML technology sometimes gets things wrong. It bothers me a lot when it gives no warnings, cites no sources, and provides no confidence interval.
Yes! Like I said:
Expose the wires. Show the workings-out.
I hope to make something that could only exist because I made it. Something that is the one thing that it is. Not an average sentence. Not a visual approximation of other people’s work. Not a stolen concept that boils lakes and uses more electricity than anything in my household.
This is insightful:
AI and automation is often promoted as a way of handling complexity. But handling complexity isn’t the same as reducing it.
In fact, by getting better at handling complexity we’re increasing our tolerance for it. And if we become more tolerant of it we’re likely to see it grow, not shrink.
From that perspective, large language models are over-engineered bandaids. They might appear helpful at the surface-level but they’re never going to help tackle the underlying root causes.
AI image generation is essentially a truncated exercise in taste; a product of knowing which inputs and keywords to feed the image-mashup machine, and the eye to identify which outputs contain any semblance of artistry. All that is to say: AI itself can’t generate good taste for you.
Don’t get me wrong, there are some features under the mislabeled bracket of AI that have made a huge impact and improvement to my process. Audio transcription has been an absolute game-changer to research analysis, reimbursing me hours of time to focus on the deep thinking work. This is a perfect example of a problem seeking a solution, not the other way around. The latest wave of features feel a lot like because we can rather than we should, because.
GPT-4 is impressive, but a layperson can’t wield it the way a programmer can. I still feel secure in my profession. In fact, I feel somewhat more secure than before. As software gets easier to make, it’ll proliferate; programmers will be tasked with its design, its configuration, and its maintenance. And though I’ve always found the fiddly parts of programming the most calming, and the most essential, I’m not especially good at them. I’ve failed many classic coding interview tests of the kind you find at Big Tech companies. The thing I’m relatively good at is knowing what’s worth building, what users like, how to communicate both technically and humanely. A friend of mine has called this A.I. moment “the revenge of the so-so programmer.” As coding per se begins to matter less, maybe softer skills will shine.
Voigt-Kampff.
Thinking about priorities at UX Brighton.
Writing and reading.
A presentation at An Event Apart Chicago 2019.
We’ve taught machines to hallucinate so let’s be honest about their hallucinations.