We hoped for a bicycle for the mind; we got a Lazy Boy recliner for the mind.
Nicky Case on how Douglas Engelbart’s vision for human-computer augmentation has taken a turn from creation to consumption.
When you create a Human+AI team, the hard part isn’t the “AI”. It isn’t even the “Human”.
It’s the “+”.
I love the small web, the clean web. I hate tech bloat.
And LLMs are the ultimate bloat.
So much truth in one story:
They built a machine to gentrify the English language.
They have built a machine that weaponizes mediocrity and sells it as perfection.
They are strip-mining your confidence to sell you back a synthetic version of it.
AI has the Jeopardy Phenomenon too.
If you use it to generate code that is outside your expertise, you are likely to think it’s all well and good, especially if it seems to work at first pop. But if you’re intimately familiar with the technology or the code around the code it’s generating, there is a good chance you’ll be like hey! that’s not quite right!
Not just code. I’m astounded by the cognitive dissonance displayed by people who say “I asked an LLM about {topic I’m familiar with}, and here’s all the things it got wrong” who then proceed to say “It was really useful when I asked an LLM for advice on {topic I’m not familiar with, hence why I’m asking an LLM for advice}.”
Like, if you know that the results are super dodgy for your own area of expertise, why would you think they’d be any better for, I don’t know, restaurant recommendations in a city you’ve never been to?
I am interested in art—we are interested in art, in any and all of its forms—because humans made it. That’s the very thing that makes it interesting; the who, the how, and especially the why.
The existence of the work itself is only part of the point, and materializing an image out of thin air misses the point of art, in very much the same way that putting a football into a Waymo to drive it up and down the street for a few hours would be entirely missing the point of sports.
A great interview with Ted Chiang:
Predicting the most likely next word is different from having correct information about the world, which is why LLMs are not a reliable way to get the answers to questions, and I don’t think there is good evidence to suggest that they will become reliable. Over the past couple of years, there have been some papers published suggesting that training LLMs on more data and throwing more processing power at the problem provides diminishing returns in terms of performance. They can get better at reproducing patterns found online, but they don’t become capable of actual reasoning; it seems that the problem is fundamental to their architecture. And you can bolt tools onto the side of an LLM, like giving it a calculator it can use when you ask it a math problem, or giving it access to a search engine when you want up-to-date information, but putting reliable tools under the control of an unreliable program is not enough to make the controlling program reliable. I think we will need a different approach if we want a truly reliable question answerer.
Most obviously, aliveness is what generally feels absent from the written and visual outputs of ChatGPT and its ilk, even when they’re otherwise of high quality. I’m not claiming I couldn’t be fooled into thinking AI writing or art was made by a human (I’m sure I already have been); but that when I realise something’s AI, either because it’s blindingly obvious or when I find out, it no longer feels so alive to me. And that this change in my feelings about it isn’t irrelevant: that it means something.
More subtly, it feels like our own aliveness is what’s at stake when we’re urged to get better at prompting LLMs to provide the most useful responses. Maybe that’s a necessary modern skill; but still, the fact is that we’re being asked to think less like ourselves and more like our tools.
Language matters.
A large language model is as neutral as an AK-47.
Brian Eno on prototyping and fidelity.
Seminal technology.
Game of Thrones spoilers ahoy.