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Nieuwe Instituut’s research team proposes Tool Sheds as the focus for this year’s open call for fellows

Do you have the ambition as an artist or designer to further develop your technical skills? Do you like to work in a technical workshop and help other artists? Would you like to do your own...

Products of Place visualises a speculative near-future where an AI-powered design process generates products with materials that are locally abundant and specific to place.

As part of my graduation project at the HKU in the field of interaction design, I focused on exploring ways to enhance the user experience when interacting w...

Since launching our grantmaking in 2018, we are proud to have supported the strategic litigation activities of around 50 brilliant organisations and individuals in about 80 different cases working to advance digital rights in Europe. We’ve funded successful litigation that has helped end the use of predictive policing risk-scoring tools,…

Free workshop: (Ab)using AI as an artist [in English] The onset of publicly available AI-generation for images and text can be a source of existential stress for artists. Virtually any style can be copied now. Complex, intricate compositions are just a prompt away. Making things that appear to be art no longer requires years of study and practice. The uncanny valley has been bridged and we have arrived as pioneers at a new digital frontier. Here, well-trained intelligent beings have dedicated their existence to devising a facsimile of human imagination and creativity. To do so, they make use of the billions of examples we have digitized and uploaded ourselves over the last few decades. We constructed artificial intelligence to assist us in our daily lives, to work more effectively and to reduce (manual) labor. As with anything digital, the goal is to create less friction and easier transactions. Now, the robots are asked to make art while the markets seem more overheated than ever. Once again, automation has led to more work, not more free time. Still, they are dependent on human input. What if these new technologies were (ab)used in a way that makes your life as an artist a lot easier? If students can prompt an AI to do their homework, why wouldn’t there be similar uses for this technology for your life as an artist? On Thursday the 13th of July artists Dakota Havard and Sjef van Beers will give you some insight into the latest developments in the relationship between machine learning and autonomous art. In a workshop format they will introduce you to the available tools and will help you make the robots do the dirty work for you. From writing application text to conjuring up whole exhibitions, the potential for removing a lot of the bureaucratic hurdles involved with being an artist is enormous. The workshop (Ab)using AI as an artist is organised by Singular-Art and Dakota Havard in close collaboration with Lindenberg Kunst en Technologie, platform for art and technology. At the time of this event Dakota will have his first solo at Singular-Art (Waalkade 72), FEAIR, which opens on the 8th of July and runs until the 20th of August. This workshop is in English. You can join for free. Sign up with an email to info@oddstream.nl.

AI powered surveys, the new way to do user research.

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.

Open Humans empowers people with their personal data. From genomes to GPS: you can explore data analyses, do citizen science, and donate data to research.

A list of microgrant programs for your good ideas. Contribute to nayafia/microgrants development by creating an account on GitHub.

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.

Students in RISD's Digital + Media graduate program make research-driven, multimedia work informed by art, science and technology. Learn more at RISD.edu.

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

Leverage hallucinations from Large Language Models (LLMs) for novelty-driven explorations. - GitHub - DivergentAI/dreamGPT: Leverage hallucinations from Large Language Models (LLMs) for novelty-dri...

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…

About Us Encord is a fast-growing startup building an active learning platform for computer vision AI applications. Our mission is to enable companies to unlock the power of AI. We have raised $20M from top investors including CRV, Y Combinator Continuity, the Harvard Management Company, top industry executives, and other leading Bay Area investors. Started by ex-computer scientists, physicists, and quants, we felt first hand how the lack of tools to prepare quality training data was impeding the progress of building practical AI. AI feels to us like what the early days of computing or the internet must have felt like, where the potential of the technology is clear, but the tools and processes surrounding it are terrible. We have devised a unique methodology for automating the tasks related to preparing quality training data, in effect turning the training data problem into a data science problem. Role and Responsibilities We’re looking for a product designer to accelerate our eff

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.

Hi, I'm Pi. I'm your personal AI, designed to be supportive, smart, and there for you anytime. Ask me for advice, for answers, or let's talk about whatever's on your mind.

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