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Machine Bias explore the unconscious bias built into AI systems and map potential strategies for a more inclusive tech landscape.

With this write-up, I want to outline my current thinking around large language models like OpenAI's chatGPT.

Co-Designing with AI in Sight - Volume 2

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OpenAI’s chatbot offers paraphrases, whereas Google offers quotes. Which do we prefer?

List of notable films and videos using AI generative models like Stable Diffusion and Midjourney .

Stephen Wolfram explores the broader picture of what's going on inside ChatGPT and why it produces meaningful text. Discusses models, training neural nets, embeddings, tokens, transformers, language syntax.

bleeding edge is a feed of noteworthy developments in AI.

Authors are getting a hand from machine learning tools — and some of them think it’s the future of writing.

When we announced AlphaFold 2 last December, it was hailed as a solution to the 50-year old protein folding problem. Last week, we published the scientific paper and source code explaining how we created this highly innovative system, and today we’re sharing high-quality predictions for the shape of every single protein in the human body, as well as for the proteins of 20 additional organisms that scientists rely on for their research.

A visual history of artificial neural networks from 1943 to 2020

What if a major issue with AI stock images is the abundant use of the color blue in the background? Instead of promoting access and forms of agonism in the debate about AI, the blue has the opposite consequence of “putting them to sleep,” developing forms of resignation in the general public.… Continue reading Nel blu dipinto di blu; or the “anaesthetics” of stock images of AI →

The black box narrative was originally intended as a ‘wake-up call’ to direct our attention towards certain risks of algorithmic automation. But it also threatens to obscure our view of the people behind algorithmic systems and their value judgements.… Continue reading From Black Box to Algorithmic Veil: Why the image of the black box is harmful to the regulation of AI →

The AI-generated rapper's record deal has been terminated.

Proving you're a human on a web flooded with generative AI content

Antler presents the first comprehensive map of the Generative AI landscape available to everyone.

The BBC Research and Development team asked hundreds of children this question as part of their Get Curious event at the Manchester Science Festival.

Silicon Valley utopians imagine AI solutions to ecological crisis, while being oblivious to the real material and ecological harms their fantasies wreak. Jessica Silva was killed by the Mexican National Guard while protesting against water diversions to the United States. In 2020, long drought

2022-23 CAST Visiting Artist Andreas Refsgaard engages the MIT community in the ethics and play of creative coding.

We are at a critical turning point. In order to innovate and thrive in a rapidly changing global environment, new norms are needed.

An article by Pau Garcia — CEO of Domestic Data Streamers

My new book: Big Data. Big Design: Why Designers Should Care about A.I. . Order here . What does this topic have to do with you? Designers stand at the verge of a great professional opportunity: artificial intelligence. This technology enables computers to study the world and make predictions using unstructured data. We can speak to machines—and machines can…

Experiential AI is proposed as a new research agenda in which artists and scientists come together to dispel the mystery of algorithms and make their mechanisms vividly apparent. It addresses the challenge of finding novel ways of opening up the field of artificial intelligence to greater transparency and collaboration between human and machine. The hypothesis is that art can mediate between computer code and human comprehension to overcome the limitations of explanations in and for AI systems. Artists can make the boundaries of systems visible and offer novel ways to make the reasoning of AI transparent and decipherable. Beyond this, artistic practice can explore new configurations of humans and algorithms, mapping the terrain of inter-agencies between people and machines. This helps to viscerally understand the complex causal chains in environments with AI components, including questions about what data to collect or who to collect it about, how the algorithms are chosen, commissioned and configured or how humans are conditioned by their participation in algorithmic processes.