Just last week I came across an example of what Ethan describes here: accessibility (in a pattern library) left to automatic checks rather than human experience.
Rachel follows up on my recent post about CSS grid in old IE with her thoughts.
As Jeremy notes, the usefulness of a tool like Autoprefixer is diminishing, which is a good thing. It is becoming far easier to code in a way that supports all browsers, where support means usable in an appropriate way for the technology the user has in front of them. Embrace that, and be glad for the fact that we can reduce complexity based on the increasing interoperability of CSS in our browsers.
A near-future sci-fi short by Hannu Rajaniemi that’s right on the zeitgest money.
The app in her AR glasses showed the car icon crawling along the winding forest road. In a few minutes, it would reach the sharp right turn where the road met the lake. The turn was marked by a road sign she had carefully defaced the previous day, with tiny dabs of white paint. Nearly invisible to a human, they nevertheless fooled image recognition nets into classifying the sign as a tree.
An even-handed assessment of the benefits and dangers of machine learning.
Almost every technological innovation over the last 300 years has had side effects which actually increase the number of opportunities for employment. The general trend is that the easier something is to do, the more demand there is for it.
Cameron looks at the historical effects of automation and applies that to design systems. The future he sees is one of increased design democratisation and participation.
This is actually something that designers have been championing for decades – inclusive design at all levels of the company, and an increase in design thinking at all stages of product development. Now that we finally have a chance of achieving that it’s not a time to be scared. It’s a time to be celebrated.
If you’re looking for an accessible standalone autocomplete script, this one from GDS looks very good (similar to Lea’s awesomplete).
During the Industrial Revolution, as new machines were invented to increase output, business owners often dreamed of an entirely automated workforce—of a factory without workers. I assume their workers had different dreams.
Ethan thinks through the ethical implications of increasing automation and efficiency über alles:
I can’t stop thinking about how much automation has changed our industry already. And I know the rate of automation is only going to accelerate from here.
At the very least, maybe it’s worth asking ourselves what might happen next.
A smart look back at historical examples of regulation and what we can learn from them today, by Justine Leblanc:
- Railways in the UK: Public interest as a trigger for regulation
- Engineering in Canada: Accountability as a trigger for regulation
- The automotive industry in the USA: Public outrage as a trigger for regulation
This is a really good use-case for cancelling fetch requests: making API calls while autocompleting in search.
There was a time, circa 2009, when no home design story could do without a reference to Mad Men. There is a time, circa 2018, when no personal tech story should do without a Black Mirror reference.
Black Mirror Home. It’s all fun and games until the screaming starts.
When these products go haywire—as they inevitably do—the Black Mirror tweets won’t seem so funny, just as Mad Men curdled, eventually, from ha-ha how far we’ve come to, oh-no we haven’t come far enough.
For any single scenario you can name it’ll be easier to create a process for it than build a culture that handles it automatically. But each process is a tiny cut away from the freedom that you want your team to enjoy.
There’s this idea that our homes — and our lives, and our workflows, and everything, really — should be micromanaged and accessed through technology, but, like many new experiments, this kind of technological advance has little actual real-world benefit. Like many new experiments, smart home technology is a perceived convenience masked as a wild hair — it’s advancement because we can, not because we need to.
A lyrical assessment of the current state of home automation.
Things are getting really smart on their own, but they’re still struggling to interact as a community — the promise of a smart home falling short because our appliances can’t draft a cohesive constitution. What’s more, we ourselves are struggling to modulate our reaction to these gadgets. We’re getting excited about automated lights and pretending the future has already come.
A thoroughly entertaining talk by Andy looking at the past, present, and future of robots, AI, and automation.
I still find the landscape of build tools completely overwhelming, but I found this distinction to be a useful way of categorising the different kinds of build tools:
Build tools do two things:
- Install things
- Do things
So bower, npm and yarn install things, whereas grunt, gulp, and webpack do things.
Training a neural network to do front-end development.
I didn’t understand any of this.
James talks about automation and understanding.
Just because a technology – whether it’s autonomous vehicles, satellite communications, or the internet – has been captured by capital and turned against the populace, doesn’t mean it does not retain a seed of utopian possibility.
A deep dive into the CSS declaration that Jen told me she wants on a T-shirt.
I’m a Google Manufacturing Robot and I Believe Humans Are Biologically Unfit to Have Jobs in Tech - McSweeney’s Internet Tendency
Normally a McSweeney’s piece elicits a wry chuckle, but this one had me in stitches.
Humans are also far more likely to “literally cannot right now.” I have never met an automaton that literally could not, though I have met some that theoretically would not and hypothetically might want to stop.
The transcript of Josh’s fantastic talk on machine learning, voice, data, APIs, and all the other tools of algorithmic design:
The design and presentation of data is just as important as the underlying algorithm. Algorithmic interfaces are a huge part of our future, and getting their design right is critical—and very, very hard to do.
Josh put together ten design principles for conceiving, designing, and managing data-driven products. I’ve added them to my collection.
- Favor accuracy over speed
- Allow for ambiguity
- Add human judgment
- Advocate sunshine
- Embrace multiple systems
- Make it easy to contribute (accurate) data
- Root out bias and bad assumptions
- Give people control over their data
- Be loyal to the user
- Take responsibility