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Errors in machine learning systems are an interesting phenomenon. Obscuring the role of AI as a shiny tool of capital, the phenomenon is usually studied with the aim to overcome those errors. This text aims for another direction by digging into one particular error, the phenomenon of “adversarials”.

Fun fact: The more you tip ChatGPT, the longer the responses are. Who would have thought that AIs love money as much as humans do... - The baseline prompt…

The story of AI images isn’t just about robots, but also about the people behind these strange, futuristic visuals.

In January 2021, we launched ‘A New AI Lexicon:’ a call for contributions to generate alternate narratives, positionalities, and understandings to the better known and widely circulated ways of talking about AI.

The world is racing to develop ever more sophisticated large language models while a small language model unfurls itself in my home.

Maybe more people are writing about the real and potential problems of technology today than ever before. That is mostly a good thing. The…

A Stanford professor’s dismantling of Silicon Valley’s intellectual origins, by Adrian Daub. Published by FSG Originals & Logic.

Amid the coverage of Sam Altman returning to the helm of OpenAI, women are being written out of the future of AI

As a critical researcher of AI myself and experience as entrepreneur I know how hard it is to navigate the trade offs between surviving in the market and being…

EDITED BY THAO PHAN, JAKE GOLDENFEIN, DECLAN KUCH & MONIQUE MANN

Redaction poetry transformation of Marc Andreessen's hypercapitalist anti-regulation anti-ethics growth-obsessed Techno-Optimist Manifesto

To understand the divide between AI boosters and doomers, one must unpack their common origins in a bundle of ideologies known as TESCREAL.

A collection of the best technical, social, and economic arguments

On artist Zach Blas's wide-ranging practice that scrutinizes the relationship between digital technologies and the cultures and politics that animate them.Za...

Generative AI is just Big Data 2.0.

The Aestheticization of Algorithmic Ideologies

The long read: Artificial intelligence in its current form is based on the wholesale appropriation of existing culture, and the notion that it is actually intelligent could be actively dangerous

Artificial Intelligence struggles to created perfect hands. The reasons behind this are both technical and biological.

Humanity is never fully in control of its creations. This lesson from Mary Shelley has remained relevant for over 200 years.

Generative AI systems act like "stochastic parrots," using statistical models to guess word orders and pixel placements. That's incompatible with a free press that commands its own words.

Prompt-based image generators seem unhackable. But a committed glitch artist can still instigate interventions.

The much-hyped AI tools of the future are being built by a globally dispersed army of data workers.

Are algorithms biased? Rodrigo Ochigame traces newfound interest in this question back to the early attempts to formalize notions of fair decision making.