Consumer AI
In the AX era, the focus of design is no longer on creating static flows for the user to follow, but on designing the agent’s goals, capabilities, and personality to act on the user’s behalf.
The experience becomes “liquid,” shaped in real time by the interaction between user, agent, and the digital service ecosystem.
The experience becomes “liquid,” shaped in real time by the interaction between user, agent, and the digital service ecosystem.
The End of the User Interface?
“No more UI: once superintelligence arrives in 2030, there will be no UI design, since users will be using their agents instead of interacting directly with websites or traditional software.” — Jakob Nielsen
The End of the User Interface?
RS: It’s a response to a suite of trends, and it really has to do with the evolution of the internet over the last ten years or so. It’d be easy to say it’s all because algorithmic crud is being shoveled at us by recommendation engines, and a lot of it is that. But there’s also a lot of beautiful and interesting stuff out there—there’s just so... See more
Quit Optimizing for Algorithms. Make Something Weird.
We’ve seen this happen so many times. AI programs master the perfect New Yorker short story format, and then we all decide the format is busted, and we’re onto the next thing. We know this about art, right? First, you have to master the basic technical skills. Credit where it’s due, AI technologies have ascended that slope, and you can get... See more
Quit Optimizing for Algorithms. Make Something Weird.
RS: Absolutely. They always talk about a model scoring 99.7% on a test. Well, how about a joke test? I confidently presume the answer is currently no, and I think it stays no for a long time, even at their most capable. This isn’t a moralistic “go humans” judgment; it’s the technical reality. They operate on a probability distribution of data. I... See more
Quit Optimizing for Algorithms. Make Something Weird.
Training a model like GPT-3 reportedly costs over $4 million in computational resources alone. The process requires thousands of specialized processors running continuously for months, consuming enough electricity to power hundreds of homes for a year. These models train on datasets containing hundreds of billions of words, essentially reading more... See more
How Fine-Tuning Transforms Generic AI Models into Specialists
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