Tags: learning

I went to the UX Brighton conference yesterday.

The quality of the presentations was really good this year, probably the best yet. Usually there are one or two stand-out speakers (like Tom Kerwin last year), but this year, the standard felt very high to me.

But…

The theme of the conference was UX and “AI”, and I’ve never been more disappointed by what wasn’t said at a conference.

Not a single speaker addressed where the training data for current large language models comes from (it comes from scraping other people’s copyrighted creative works).

Not a single speaker addressed the energy requirements for current large language models (the requirements are absolutely mahoosive—not just for the training, but for each and every query).

My charitable reading of the situation yesterday was that every speaker assumed that someone else would cover those issues.

The less charitable reading is that this was a deliberate decision.

Whenever the issue of ethics came up, it was only ever in relation to how we might use these tools: considering user needs, being transparent, all that good stuff. But never once did the question arise of whether it’s ethical to even use these tools.

In fact, the message was often the opposite: words like “responsibility” and “duty” came up, but only in the admonition that UX designers have a responsibility and duty to use these tools! And if that carrot didn’t work, there’s always the stick of scaring you into using these tools for fear of being left behind and having a machine replace you.

I was left feeling somewhat depressed about the deliberately narrow focus. Maggie’s talk was the only one that dealt with any externalities, looking at how the firehose of slop is blasting away at society. But again, the focus was only ever on how these tools are used or abused; nobody addressed the possibility of deliberately choosing not to use them.

If audience members weren’t yet using generative tools in their daily work, the assumption was that they were lagging behind and it was only a matter of time before they’d get on board the hype train. There was no room for the idea that someone might examine the roots of these tools and make a conscious choice not to fund their development.

There’s a quote by Finnish architect Eliel Saarinen that UX designers like repeating:

Always design a thing by considering it in its next larger context. A chair in a room, a room in a house, a house in an environment, an environment in a city plan.

But none of the speakers at UX Brighton chose to examine the larger context of the tools they were encouraging us to use.

One speaker told us “Be curious!”, but clearly that curiosity should not extend to the foundations of the tools themselves. Ignore what’s behind the curtain. Instead look at all the cool stuff we can do now. Don’t worry about the fact that everything you do with these tools is built on a bedrock of exploitation and environmental harm. We should instead blithely build a new generation of user interfaces on the burial ground of human culture.

Whenever I get into a discussion about these issues, it always seems to come back ’round to whether these tools are actually any good or not. People point to the genuinely useful tasks they can accomplish. But that’s not my issue. There are absolutely smart and efficient ways to use large language models—in some situations, it’s like suddenly having a superpower. But as Molly White puts it:

The benefits, though extant, seem to pale in comparison to the costs.

There are no ethical uses of current large language models.

And if you believe that the ethical issues will somehow be ironed out in future iterations, then that’s all the more reason to stop using the current crop of exploitative large language models.

Anyway, like I said, all the talks at UX Brighton were very good. But I just wish just one of them had addressed the underlying questions that any good UX designer should ask: “Where did this data come from? What are the second-order effects of deploying this technology?”

Having a talk on those topics would’ve been nice, but I would’ve settled for having five minutes of one talk, or even one minute. But there was nothing.

There’s one possible explanation for this glaring absence that’s quite depressing to consider. It may be that these topics weren’t covered because there’s an assumption that everybody already knows about them, and frankly, doesn’t care.

To use an outdated movie reference, imagine a raving Charlton Heston shouting that “Soylent Green is people!”, only to be met with indifference. “Everyone knows Soylent Green is people. So what?”

HTML for People

This is excellent! A free web book (it’s a book! it’s a website!) that teaches you how to make a website from scratch:

I feel strongly that anyone should be able to make a website with HTML if they want. This book will teach you how to do just that. It doesn’t require any previous experience making websites or coding. I will cover everything you need to know to get started in an approachable and friendly way.

👏

This seems to be the attitude of many of my fellow nerds—designers and developers—when presented with tools based on large language models that produce dubious outputs based on the unethical harvesting of other people’s work and requiring staggering amounts of energy to run:

This is the future! I need to start using these tools now, even if they’re flawed, because otherwise I’ll be left behind. They’ll only get better. It’s inevitable.

Whereas this seems to be the attitude of those same designers and developers when faced with stable browser features that can be safely used today without frameworks or libraries:

I’m sceptical.

Report: Thinking about using AI? - Green Web Foundation

A solid detailed in-depth report.

The sheer amount of resources needed to support the current and forecast demand from AI is colossal and unprecedented.

You should go to conferences - localghost

Obviously I’m biased, but I very much agree with Sophie.

A short note on AI – Me, Robin

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.

First Impressions of the Pixel 9 Pro | Whatever

At this point, it really does seem like “AI” is “bullshit you don’t need or is done better in other ways, but we’ve just spent literally billions on this so we really need you to use it, even though it’s nowhere as good as what we were already doing,” and everything else is just unsexy functionality that makes what you do marginally easier or better. I’m sorry we live in a world where enshittification is being marketed as The Hot And Sexy Thing, but just because we’re in that world, doesn’t mean you have to accept it.

I’ve noticed a really strange justification from people when I ask them about their use of generative tools that use large language models (colloquially and inaccurately labelled as artificial intelligence).

I’ll point out that the training data requires the wholesale harvesting of creative works without compensation. I’ll also point out the ludicrously profligate energy use required not just for the training, but for the subsequent queries.

And here’s the thing: people will acknowledge those harms but they will justify their actions by saying “these things will get better!”

First of all, there’s no evidence to back that up.

If anything, as the well gets poisoned by their own outputs, large language models may well end up eating their own slop and getting their own version of mad cow disease. So this might be as good as they’re ever going to get.

And when it comes to energy usage, all the signals from NVIDIA, OpenAI, and others are that power usage is going to increase, not decrease.

But secondly, what the hell kind of logic is that?

It’s like saying “It’s okay for me to drive my gas-guzzling SUV now, because in the future I’ll be driving an electric vehicle.”

The logic is completely backwards! If large language models are going to improve their ethical shortcomings (which is debatable, but let’s be generous), then that’s all the more reason to avoid using the current crop of egregiously damaging tools.

You don’t get companies to change their behaviour by rewarding them for it. If you really want better behaviour from the purveyors of generative tools, you should be boycotting the current offerings.

I suspect that most people know full well that the “they’ll get better!” defence doesn’t hold water. But you can convince yourself of anything when everyone around is telling you that this is the future baby, and you’d better get on board or you’ll be left behind.

Baldur reminds us that this is how people talked about asbestos:

Every time you had an industry campaign against an asbestos ban, they used the same rhetoric. They focused on the potential benefits – cheaper spare parts for cars, cheaper water purification – and doing so implicitly assumed that deaths and destroyed lives, were a low price to pay.

This is the same strategy that’s being used by those who today talk about finding productive uses for generative models without even so much as gesturing towards mitigating or preventing the societal or environmental harms.

It reminds me of the classic Ursula Le Guin short story, The Ones Who Walk Away from Omelas that depicts:

…the utopian city of Omelas, whose prosperity depends on the perpetual misery of a single child.

Once citizens are old enough to know the truth, most, though initially shocked and disgusted, ultimately acquiesce to this one injustice that secures the happiness of the rest of the city.

It turns out that most people will blithely accept injustice and suffering not for a utopia, but just for some bland hallucinated slop.

Don’t get me wrong: I’m not saying large language models aren’t without their uses. I love seeing what Simon and Matt are doing when it comes to coding. And large language models can be great for transforming content from one format to another, like transcribing speech into text. But the balance sheet just doesn’t add up.

As Molly White put it: AI isn’t useless. But is it worth it?:

Even as someone who has used them and found them helpful, it’s remarkable to see the gap between what they can do and what their promoters promise they will someday be able to do. The benefits, though extant, seem to pale in comparison to the costs.

Why “AI” projects fail

“AI” is heralded (by those who claim it to replace workers as well as those that argue for it as a mere tool) as a thing to drop into your workflows to create whatever gains promised. It’s magic in the literal sense. You learn a few spells/prompts and your problems go poof. But that was already bullshit when we talked about introducing other digital tools into our workflows.

And we’ve been doing this for decades now, with every new technology we spend a lot of money to get a lot of bloody noses for way too little outcome. Because we keep not looking at actual, real problems in front of us – that the people affected by them probably can tell you at least a significant part of the solution to. No we want a magic tool to make the problem disappear. Which is a significantly different thing than solving it.

Does AI benefit the world? – Chelsea Troy

Our ethical struggle with generative models derives in part from the fact that we…sort of can’t have them ethically, right now, to be honest. We have known how to build models like this for a long time, but we did not have the necessary volume of parseable data available until recently—and even then, to get it, companies have to plunder the internet. Sitting around and waiting for consent from all the parties that wrote on the internet over the past thirty years probably didn’t even cross Sam Altman’s mind.

On the environmental front, fans of generative model technology insist that eventually we’ll possess sufficiently efficient compute power to train and run these models without the massive carbon footprint. That is not the case at the moment, and we don’t have a concrete timeline for it. Again, wait around for a thing we don’t have yet doesn’t appeal to investors or executives.

Why A.I. Isn’t Going to Make Art | The New Yorker

Using ChatGPT to complete assignments is like bringing a forklift into the weight room; you will never improve your cognitive fitness that way.

Another great piece by Ted Chiang!

The companies promoting generative-A.I. programs claim that they will unleash creativity. In essence, they are saying that art can be all inspiration and no perspiration—but these things cannot be easily separated. I’m not saying that art has to involve tedium. What I’m saying is that art requires making choices at every scale; the countless small-scale choices made during implementation are just as important to the final product as the few large-scale choices made during the conception.

This bit reminded me of Simon’s rule:

Let me offer another generalization: any writing that deserves your attention as a reader is the result of effort expended by the person who wrote it. Effort during the writing process doesn’t guarantee the end product is worth reading, but worthwhile work cannot be made without it. The type of attention you pay when reading a personal e-mail is different from the type you pay when reading a business report, but in both cases it is only warranted when the writer put some thought into it.

Simon also makes an appearance here:

The programmer Simon Willison has described the training for large language models as “money laundering for copyrighted data,” which I find a useful way to think about the appeal of generative-A.I. programs: they let you engage in something like plagiarism, but there’s no guilt associated with it because it’s not clear even to you that you’re copying.

I could quote the whole thing, but I’ll stop with this one:

The task that generative A.I. has been most successful at is lowering our expectations, both of the things we read and of ourselves when we write anything for others to read. It is a fundamentally dehumanizing technology because it treats us as less than what we are: creators and apprehenders of meaning. It reduces the amount of intention in the world.

s19e01: Do Reply; Use plain language, and tell the truth

Very good writing advice from Dan:

Use plain language. Tell the truth.

Related:

The reason why LLM text for me is bad is that it’s insipid, which is not a plain language word to use, but the secret is to use words like that tactically and sparingly to great effect.

They don’t write plainly because most of the text they’ve been trained on isn’t plain and clear. I’d argue that most of the text that’s ever existed isn’t plain and clear anyway.

Slop is Good • furbo.org

Trust.

Aboard Newsletter: Why So Bad, AI Ads?

The human desire to connect with others is very profound, and the desire of technology companies to interject themselves even more into that desire—either by communicating on behalf of humans, or by pretending to be human—works in the opposite direction. These technologies don’t seem to be encouraging connection as much as commoditizing it.

Pop Culture

Despite all of this hype, all of this media attention, all of this incredible investment, the supposed “innovations” don’t even seem capable of replacing the jobs that they’re meant to — not that I think they should, just that I’m tired of being told that this future is inevitable.

The reality is that generative AI isn’t good at replacing jobs, but commoditizing distinct acts of labor, and, in the process, the early creative jobs that help people build portfolios to advance in their industries.

One of the fundamental misunderstandings of the bosses replacing these workers with generative AI is that you are not just asking for a thing, but outsourcing the risk and responsibility.

Generative AI costs far too much, isn’t getting cheaper, uses too much power, and doesn’t do enough to justify its existence.

AI and Asbestos: the offset and trade-off models for large-scale risks are inherently harmful – Baldur Bjarnason

Every time you had an industry campaign against an asbestos ban, they used the same rhetoric. They focused on the potential benefits – cheaper spare parts for cars, cheaper water purification – and doing so implicitly assumed that deaths and destroyed lives, were a low price to pay.

This is the same strategy that’s being used by those who today talk about finding productive uses for generative models without even so much as gesturing towards mitigating or preventing the societal or environmental harms.

Looking back on ten years of codebar Brighton, I’m remembering how much I got out of being a coach.

Something that I realised very quickly is that there is no one-size-fits-all approach to coaching. Every student is different so every session should adapt to that.

Broadly speaking I saw two kinds of students: those that wanted to get results on screen as soon as possible without worrying about the specifics, and those who wanted to know why something was happening and how it worked. In the first instance, you get to a result as quickly as possible and then try to work backwards to figure out what’s going on. In the second instance, you build up the groundwork of knowledge and then apply it to get results.

Both are equally valid approaches. The only “wrong” approach as a coach is to try to apply one method to someone who’d rather learn the other way.

Personally, I always enjoyed the groundwork-laying of the second approach. But it comes with challenges. Because the results aren’t yet visible, you have to do extra work to convey why the theory matters. As a coach, you need to express infectious enthusiasm.

Think about the best teachers you had in school. I’m betting they displayed infectious enthusiasm for the subject matter.

The other evergreen piece of advice is to show, don’t tell. Or at the very least, intersperse your telling with plenty of showing.

Bret Viktor demonstrates this when he demonstrates scientific communication as sequential art:

This page presents a scientific paper that has been redesigned as a sequence of illustrations with captions. This comic-like format, with tightly-coupled pictures and prose, allows the author to depict and describe simultaneously — show and tell.

It works remarkably well. I remember how well it worked when Google first launched their Chrome web browser. They released a 40 page comic book illustrated by Scott McCloud. There is no way I would’ve read a document that long about how browser engines work, but I read that comic cover to cover.

This visual introduction to machine learning is another great example of simultaneous showing and telling.

So showing augments telling. But interactivity can augment showing.

Here are some great examples of interactive explainers:

• Cameras and Lenses by Bartosz Ciechanowski,
• An Interactive Guide to CSS Grid by Josh W Comeau,
• CSS :has() Interactive Guide by Ahmad Shadeed,
• Pizza Exchange Rate by Nathan Yau, and
• just about everything by Nicky Case.

Lea describes what can happen when too much theory comes before practice:

Observing my daughter’s second ever piano lesson today made me realize how this principle extends to education and most other kinds of knowledge transfer (writing, presentations, etc.). Her (generally wonderful) teacher spent 40 minutes teaching her notation, longer and shorter notes, practicing drawing clefs, etc. Despite his playful demeanor and her general interest in the subject, she was clearly distracted by the end of it.

It’s easy to dismiss this as a 5 year old’s short attention span, but I could tell what was going on: she did not understand why these were useful, nor how they connect to her end goal, which is to play music.

The codebar website has some excellent advice for coaches, like:

• Do not take over the keyboard! This can be off-putting and scary.
• Encourage the students to type and not copy paste.
• Explain that there are no bad questions.
• Explain to students that it’s OK to make mistakes.
• Assume that anyone you’re teaching has no knowledge but infinite intelligence.

Notice how so much of the advice focuses on getting the students to do things, rather than have them passively sit and absorb what the coach has to say.

Lea also gives some great advice:

1. Always explain why something is useful. Yes, even when it’s obvious to you.
2. Minimize the amount of knowledge you convey before the next opportunity to practice it. For non-interactive forms of knowledge transfer (e.g. a book), this may mean showing an example, whereas for interactive ones it could mean giving the student a small exercise or task.
3. Prefer explaining in context rather than explaining upfront.

It’s interesting that Lea highlights the advantage of interactive media like websites over inert media like books. The canonical fictional example of an interactive explainer is the Young Lady’s Illustrated Primer in Neal Stephenson’s novel The Diamond Age. Andy Matuschak describes its appeal:

When it wants to introduce a conceptual topic, it begins with concrete hands-on projects: Turing machines, microeconomics, and mitosis are presented through binary-coding iron chains, the cipher’s market, and Nell’s carrot garden. Then the Primer introduces extra explanation just-in-time, as necessary.

That’s not how learning usually works in these domains. Abstract topics often demand that we start with some necessary theoretical background; only then can we deeply engage with examples and applications. With the Primer, though, Nell consistently begins each concept by exploring concrete instances with real meaning to her. Then, once she’s built a personal connection and some intuition, she moves into abstraction, developing a fuller theoretical grasp through the Primer’s embedded books.

(Andy goes on to warn of the dangers of copying the Primer too closely. Its tricks verge on gamification, and its ultimate purpose isn’t purely to educate. There’s a cautionary tale there about the power dynamics in any teacher/student relationship.)

There’s kind of a priority of constituencies when it comes to teaching:

Consider interactivity over showing over telling.

Thinking back on all the talks I’ve given, I start to wonder if I’ve been doing too much telling and showing, but not nearly enough interacting.

Then again, I think that talks aren’t quite the same as hands-on workshops. I think of giving a talk as being more like a documentarian. You need to craft a compelling narrative, and illustrate what you’re saying as much as possible, but it’s not necessarily the right arena for interactivity.

That’s partly a matter of scale. It’s hard to be interactive with every person in a large audience. Marcin managed to do it but that’s very much the exception.

Workshops are a different matter though. When I’m recruiting hosts for UX London workshops I always encourage them to be as hands-on as possible. A workshop should not be an extended talk. There should be more exercises than talking. And wherever possible those exercises should be tactile, ideally not sitting in front of a computer.

My own approach to workshops has changed over the years. I used to prepare a book’s worth of material to have on hand, either as one giant slide deck or multiple decks. But I began to realise that the best workshops are the ones where the attendees guide the flow, not me.

So now I show up to a full-day workshop with no slides. But I’m not unprepared. I’ve got decades of experience (and links) to apply during the course of the day. It’s just that instead of trying to anticipate which bits of knowledge I’m going to need to convey, I apply them in a just-in-time manner as and when they’re needed. It’s kind of scary, but as long as there’s a whiteboard to hand, or some other way to illustrate what I’m telling, it works out great.

Declare your AIndependence: block AI bots, scrapers and crawlers with a single click

This is a great move from Cloudflare. I may start using their service.

I went to codebar Brighton yesterday evening. I hadn’t been in quite a while, but this was a special occasion: a celebration of codebar Brighton’s tenth anniversary!

The Brighton chapter of codebar was the second one ever, founded six months after the initial London chapter. There are now 33 chapters all around the world.

Clearleft played host to that first ever codebar in Brighton. We had already been hosting local meetups like Async in our downstairs event space, so we were up for it when Rosa, Dot, and Ryan asked about having codebar happen there.

In fact, the first three Brighton codebars were all at 68 Middle Street. Then other places agreed to play host and it moved to a rota system, with the Clearleft HQ as just one of the many Brighton venues.

With ten years of perspective, it’s quite amazing to see how many people went from learning to code in the evenings, to getting jobs in web development, and becoming codebar coaches themselves. It’s a really wonderful community.

Over the years the baton of organising codebar has been passed on to a succession of fantastic people. These people are my heroes.

It worked out well for Clearleft too. Thanks to codebar, we hired Charlotte. Later we hired Cassie. And it was thanks to codebar that I first met Amber.

Codebar Brighton has been very, very good to me. Here’s to the next ten years!

New Web Development. Or, why Copilots and chatbots are particularly bad for modern web dev – Baldur Bjarnason

The paradigm shift that web development is entering hinges on the fact that while React was a key enabler of the Single-Page-App and Component era of the web, in practice it normally tends to result in extremely poor products. Built-in browser APIs are now much more capable than they were when React was first invented.