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Documentation to facilitate communication between dataset creators and consumers.

There has been increased attention toward the datasets that are used to train and build AI technologies from the computer science and social science research co

Supporting transnational worker organizing should be at the center of the fight for “ethical AI.”

While AI can speed up the writing process, it doesn’t optimize quality — and it endangers our sense of connection to ourselves and others.

Amid a pair of Hollywood strikes that have found screenwriters and actors questioning the rise of artificial intelligence, studios and streaming companies are bulking up on AI staff.

When we have the power to ask for anything, what will we choose?

The countdown has begun for The PublicSpaces Conference: For a Collective Internet, set to take place on June 27 and 28. Emma Yedema, editor and producer at PublicSpaces, sat down with Abdo Hassan for an interview.

Part 1 # Models, Views, Controllers # Model View Controller is a server architecture model invented at Xerox Parc in the late 70s. Initially, a system to implement graphic user interfaces for the desktop machine; it is now the core of practically...

To further the understanding of training data, the Knowing Machines Project developed SeeSet, an investigative tool for examining the training datasets for AI. Here you will find nine essays from individual members of our team. Each one uses SeeSet to explore a key AI dataset and its role in the construction of 'ground truth.'

This anthology was curated from stories of living with data and AI in/from the majority world, narrated at a storytelling workshop in October 2021 organized by Data & Society Research Institute.

We present EKILA; a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance – determining the generative model and training data responsible for an AI-generated image. Furthermore, EKILA extends the non-fungible token (NFT) ecosystem to introduce a tokenized representation for rights, enabling a triangular relationship between the asset’s Ownership, Rights, and Attribution (ORA). Leveraging the ORA relationship enables creators to express agency over training consent and, through our attribution model, to receive apportioned credit, including royalty payments for the use of their assets in GenAI.

Symbolic language can help us grasp the nature and power of what is coming

Nora N. Khan assembles a cast of luminaries to consider the far-reaching implications of AI and computational culture. –$40

Co-authored with Grace Carney A few months ago Fred kicked off a conversation about what the “native” applications of AI technology will be. What are the

Conversation around generative AI tends to focus on whether its development is open or closed. It's more responsible to envision releases along a gradient.

🔬 Research Summary by Shreyasha Paudel, a Ph.D. student at the University of Toronto with an interdisciplinary research focus that combines Human-Computer Interaction with critical theories from…

Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images. In this paper we consider what the future might hold. What will happen to GPT-{n} once LLMs contribute much of the language found online? We find that use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear. We refer to this effect as Model Collapse and show that it can occur in Variational Autoencoders, Gaussian Mixture Models and LLMs. We build theoretical intuition behind the phenomenon and portray its ubiquity amongst all learned generative models. We demonstrate that it has to be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of content generated by LLMs in data crawled from the Internet.

How many humans does it take to make tech seem human? Millions.

Read and download Artificial Intelligence and Post-Capitalism by Thanasis Apostolakoudis on OA.mg