Link tags: ias

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Artificial intelligence: who owns the future? - ethical.net

Whether consciously or not, AI manufacturers have decided to prioritise plausibility over accuracy. It means AI systems are impressive, but in a world plagued by conspiracy and disinformation this decision only deepens the problem.

What The Last of Us, Snowpiercer and ‘climate fiction’ get wrong - BBC Culture

I not only worry that “cli-fi” might not be an effective form of environmental expression – I have come to believe that the genre might be actively dangerous, stunting our cultural ability to imagine a future worth living in or fighting for.

Envisioning Our Shared Storm with Andrew Dana Hudson - Long Now

This observation feels spot-on to me:

The shift that I noticed, totally anecdotally, is literary writers are starting to write more dystopian climate futures and science fiction writers are starting to write about climate solutions.

Dystopias Now | Commune

These days I tend to think of dystopias as being fashionable, perhaps lazy, maybe even complacent, because one pleasure of reading them is cozying into the feeling that however bad our present moment is, it’s nowhere near as bad as the ones these poor characters are suffering through.

Kim Stanley Robinson on dystopias and utopias.

The energy flows on this planet, and humanity’s current technological expertise, are together such that it’s physically possible for us to construct a worldwide civilization—meaning a political order—that provides adequate food, water, shelter, clothing, education, and health care for all eight billion humans, while also protecting the livelihood of all the remaining mammals, birds, reptiles, insects, plants, and other life-forms that we share and co-create this biosphere with. Obviously there are complications, but these are just complications. They are not physical limitations we can’t overcome. So, granting the complications and difficulties, the task at hand is to imagine ways forward to that better place.

React Bias

Dev perception.

The juxtaposition of The HTTP Archive’s analysis and The State of JS 2020 Survey results suggest that a disproportionately small—yet exceedingly vocal minority—of white male developers advocate strongly for React, and by extension, a development experience that favors thick client/thin server architectures which are given to poor performance in adverse conditions. Such conditions are less likely to be experienced by white male developers themselves, therefore reaffirming and reflecting their own biases in their work.

Coded Bias Official Trailer on Vimeo

Coded Bias follows MIT Media Lab researcher Joy Buolamwini’s startling discovery that many facial recognition technologies fail more often on darker-skinned faces, and delves into an investigation of widespread bias in artificial intelligence.

What’s Happening? Or: How to name a disaster - Elvia Wilk - Bookforum Magazine

It went unnamed by Doris Lessing and Cormac McCarthy. William Gibson called it The Jackpot:

On the one hand, naming the crisis allows one to apprehend it, grasp it, fight back against it. On the other hand, no word can fully encompass it, and any term is necessarily a reduction—the essence of “it” or “change” is not any singular instance but rather their constancy.

Memoirs Of A Survivor, The Peripheral, Parable Of The Sower, New York 2140, The Road, Children Of Men, Station Eleven, Severance, The Rapture, Ridley Walker:

Fiction can portray ecologies, timescales, catastrophes, and forms of violence that may be otherwise invisible, or more to the point, unnameable. We will never grasp the pandemic in its entirety, just like we will never see the microbe responsible for it with the naked eye. But we can try to articulate how it has changed us—is changing us.

Ted Chiang Explains the Disaster Novel We All Suddenly Live In - Electric Literature

Ted Chiang’s hot takes are like his short stories—punchy, powerful, and thought-provoking.

Artificial Intelligence: Threat or Menace? - Charlie’s Diary

I am not a believer in the AI singularity — the rapture of the nerds — that is, in the possibility of building a brain-in-a-box that will self-improve its own capabilities until it outstrips our ability to keep up. What CS professor and fellow SF author Vernor Vinge described as “the last invention humans will ever need to make”. But I do think we’re going to keep building more and more complicated, systems that are opaque rather than transparent, and that launder our unspoken prejudices and encode them in our social environment. As our widely-deployed neural processors get more powerful, the decisions they take will become harder and harder to question or oppose. And that’s the real threat of AI — not killer robots, but “computer says no” without recourse to appeal.

Science Fiction Doesn’t Have to Be Dystopian | The New Yorker

Ted Chiang has new collection out‽ Why did nobody tell me‽

Okay, well, technically this is Joyce Carol Oates telling me. In any case …woo-hoo!!!

Untold History of AI - IEEE Spectrum

A terrific six-part series of short articles looking at the people behind the history of Artificial Intelligence, from Babbage to Turing to JCR Licklider.

  1. When Charles Babbage Played Chess With the Original Mechanical Turk
  2. Invisible Women Programmed America’s First Electronic Computer
  3. Why Alan Turing Wanted AI Agents to Make Mistakes
  4. The DARPA Dreamer Who Aimed for Cyborg Intelligence
  5. Algorithmic Bias Was Born in the 1980s
  6. How Amazon’s Mechanical Turkers Got Squeezed Inside the Machine

The history of AI is often told as the story of machines getting smarter over time. What’s lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies.

Programming as translation – Increment: Internationalization

Programming lessons from Umberto Eco and Emily Wilson.

Converting the analog into the digital requires discretization, leaving things out. What we filter out—or what we focus on—depends on our biases. How do conventional translators handle issues of bias? What can programmers learn from them?

Why Data Is Never Raw - The New Atlantis

Raw data is both an oxymoron and a bad idea; to the contrary, data should be cooked with care.

Big ol’ Ball o’ JavaScript | Brad Frost

Backend logic? JavaScript. Styles? We do that in JavaScript now. Markup? JavaScript. Anything else? JavaScript.

Historically, different languages suggested different roles. “This language does style.” “This language does structure.” But now it’s “This JavaScript does style.” “This JavaScript does structure.” “This JavaScript does database queries.”

Reluctant Gatekeeping: The Problem With Full Stack | HeydonWorks

The value you want form a CSS expert is their CSS, not their JavaScript, so it’s absurd to make JavaScript a requirement.

Absolutely spot on! And it cuts both ways:

Put CSS in JS and anyone who wishes to write CSS now has to know JavaScript. Not just JavaScript, but —most likely—the specific ‘flavor’ of JavaScript called React. That’s gatekeeping, first of all, but the worst part is the JavaScript aficionado didn’t want CSS on their plate in the first place.

How to build a simple Camera component - Frontend News #4

A step-by-step guide to wrapping up a self-contained bit of functionality (a camera, in this case) into a web component.

Mind you, it would be nice if there were some thought given to fallbacks, like say:

<simple-camera>
<input type="file" accept="image/*">
</simple-camera>

Should computers serve humans, or should humans serve computers? | Read the Tea Leaves

Between the utopian and dystopian, which vision of the future seems more likely to you? Which vision seems more true to how we currently live with technology, in the form of our smartphones and social media apps?

[Essay] Known Unknowns | New Dark Age by James Bridle | Harper’s Magazine

A terrific cautionary look at the history of machine learning and artificial intelligence from the new laugh-a-minute book by James.

Artificial Intelligence for more human interfaces | Christian Heilmann

An even-handed assessment of the benefits and dangers of machine learning.