You can call me AI

I’ve mentioned before that I’m not a fan of initialisms and acronyms. They can be exclusionary.

It bothers me doubly when everyone is talking about AI.

First of all, the term is so vague as to be meaningless. Sometimes—though rarely—AI refers to general artificial intelligence. Sometimes AI refers to machine learning. Sometimes AI refers to large language models. Sometimes AI refers to a series of if/else statements. That’s quite a spectrum of meaning.

Secondly, there’s the assumption that everyone understands the abbreviation. I guess that’s generally a safe assumption, but sometimes AI could refer to something other than artificial intelligence.

In countries with plenty of pastoral agriculture, if someone works in AI, it usually means they’re going from farm to farm either extracting or injecting animal semen. AI stands for artificial insemination.

I think that abbreviation might work better for the kind of things currently described as using AI.

We were discussing this hot topic at work recently. Is AI coming for our jobs? The consensus was maybe, but only the parts of our jobs that we’re more than happy to have automated. Like summarising some some findings. Or perhaps as a kind of lorem ipsum generator. Or for just getting the ball rolling with a design direction. As Terence puts it:

Midjourney is great for a first draft. If, like me, you struggle to give shape to your ideas then it is nothing short of magic. It gets you through the first 90% of the hard work. It’s then up to you to refine things.

That’s pretty much the conclusion we came to in our discussion at Clearleft. There’s no way that we’d use this technology to generate outputs for clients, but we certainly might use it to generate inputs. It’s like how we’d do a quick round of sketching to get a bunch of different ideas out into the open. Terence is spot on when he says:

Midjourney lets me quickly be wrong in an interesting direction.

To put it another way, using a large language model could be a way of artificially injecting some seeds of ideas. Artificial insemination.

So now when I hear people talk about using AI to create images or articles, I don’t get frustrated. Instead I think, “Using artificial insemination to create images or articles? Yes, that sounds about right.”

“AI is inevitable” is bullshit · Eric Eggert

LLMs are useful when you need a compromise between fast and good. You will never get a good outcome fast.

I’m afraid we are settling into a status of good enough when using “AI,” which is especially hurtful for accessibility.

The Majority AI View - Anil Dash

Technologies like LLMs have utility, but the absurd way they’ve been over-hyped, the fact they’re being forced on everyone, and the insistence on ignoring the many valid critiques about them make it very difficult to focus on legitimate uses where they might add value.

Where’s the AI design renaissance?

I’ve had some incredibly productive moments with AI design tools. But I’ve had at least as many slogs, where I can’t get it to do some basic thing I should’ve done myself 45 minutes ago.

My hunch: vibe coding is a lot like stock-picking – everyone’s always blabbing about their big wins. Ask what their annual rate of return is above the S&P, and it’s a quieter conversation 🤫

This, in my opinion, is how we end up with a firehose of AI hype, and yet zero signs of a software renaissance. As Mike Judge points out, the following graphs are flat: (a) new app store releases, (b) new domain names registered, (c) new Github repositories.

The Programmer Identity Crisis ❈ Simon Højberg ❈ Principal Frontend Engineer

I prefer my tools to help me with repetitive tasks (and there are many of those in programming), understanding codebases, and authoring correct programs. I take offense at products that are designed to think for me. To remove the agency of my own understanding of the software I produce, and to cut connections with my coworkers. Even if LLMs lived up to the hype, we would still stand to lose all of that and our craft.

Life Is More Than an Engineering Problem | Los Angeles Review of Books

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.