A group of EU policymakers proposes three risk categories for AI applications. AI has long existed in everyday life. It goes mostly unnoticed by users, hidden in the software they use on their smartphone, in their search engine, or in their autonomous household vacuum cleaner. But, how do we deal with AI decisions and content? Or with the data that (has) to be collected and processed when using AI software?

A collaborative effort has led to the creation of a new open source AI language model that anyone can use democratizing access to AI. "Unlike other, more famous large language models such as OpenAI's GPT-3 and Google's LaMDA, BLOOM (which stands for BigScience Large Open-science Open-access Multilingual Language Model) is designed to be as transparent as possible, with researchers sharing details about the data it was trained on, the challenges in its development, and the way they evaluated its performance. OpenAI and Google have not shared their code or made their models available to the public, and external researchers have very little understanding of how these models are trained. BLOOM was created over the last year by over 1,000 volunteer researchers in a project called BigScience, which was coordinated by AI startup Hugging Face using funding from the French government. It officially launched on July 12. The researchers hope developing an open-access LLM that performs as well as other leading models will lead to long-lasting changes in the culture of AI development and help democratize access to cutting-edge AI technology for researchers around the world. The model's ease of access is its biggest selling point. Now that it's live, anyone can download it and tinker with it free of charge on Hugging Face's website. Users can pick from a selection of languages and then type in requests for BLOOM to do tasks like writing recipes or poems, translating or summarizing texts, or writing programming code. AI developers can use the model as a foundation to build their own applications."

New research from Columbia university suggests that the safeguards that prevent image synthesis models such as DALL-E 2, Imagen and Parti from being able to output damaging or controversial imagery are susceptible to a kind of adversarial attack that involves'made up' words. The author has developed two approaches that can potentially override the content moderation measures in an image synthesis system, and has found that they are remarkably robust even across different architectures, indicating that the weakness is more than just systemic, and may key on some of the most fundamental principle of text-to-image synthesis. The first, and the stronger of the two, is called macaronic prompting. The term'macaronic' originally refers to a mixture of multiple languages, as found in Esperanto or Unwinese. Perhaps the most culturally-diffused example would be Urdu-English, a type of'code mixing' common in Pakistan, which quite freely mixes English nouns and Urdu suffixes.

OpenAI is a non-profit artificial intelligence research company founded in 2015, which aims to promote and develop artificial intelligence systems. In the founders' words, it can benefit humanity as a whole, where the primary motivation is to prevent the misuse of these systems from harming human beings. They have one of the most powerful neural networks for language processing in the market called GPT-3. This company is the creator of DALL·E 2, an amazingly complex system that works with two neural networks: a text encoder and an image encoder, which are trained on a large collection of text-image pairs to form a model that can generate images of future requests. In general aspects, it would work in a similar way to how the human brain does when it comes to remembering, just as the mind is capable of imagining situations through images, this system is in charge of finding the relationship between an image and a text that describes it.

Last week OpenAI moved DALL-E 2, the image generation tool, into Beta (the company hopes to expand its current user base to 1 million) while granting users the "the right to reprint, sell, and merchandise" images they generate with DALL-E. This is useful for users who wish to use the generated images for commercial purposes, like making illustrations for children's books. Other openly available AI image generation models face similar problems. Also, it's not clear if OpenAI violated any IP laws for just training on these Internet images and then commercializing their model. While the UK is exploring allowing commercial use of models trained on public but trademarked data, the U.S. may not follow suit.

Using the DALL·E artificial intelligence tool to create images of what a nuclear-heated spa might look like, inspired by Iceland's Blue Lagoon.

As if the internet wasn't already bizarre enough, the deluge of weird AI art created by image generators such as DALL·E 2, MidJourney and Craiyon is making things even stranger. From crossbred cartoon characters to surreal food, apocalyptic selfies muppet fashion shows and – erm – people with tennis balls for heads, DALL·E 2 and others seem to be able to create any weird AI art you can describe in their prompt boxes – with varying degrees of success. Of course DALL·E 2 and other AI art generators don't think of these outlandish things themselves and don't'know' what they're helping users to create. They run text prompts through the databases of millions of images and captions that they've learned. This means the results are only as weird as users' own imaginations. There are plenty of existential concerns about where this could all be going and what it means for human artists, but AI won't be taking over the world and turning us into slaves yet.

This week's list of top data news highlights covers July 30, 2022 to August 5, 2022 and includes articles on practicing complex surgeries with virtual reality and using an AI system to create an advertising campaign. India's National Tiger Conservation Authority (NCTA) has used over 26,000 cameras to capture over 24 million images of tigers around the country. Conservationists are using an AI system to identify tigers found in the images and quantify the total tiger population in the country. The NCTA plans to use an AI system to map patrol routes throughout sanctuaries to better monitor tigers next. Researchers at the University of New Orleans, Louisiana Department of Environmental Quality, and Jefferson Parish Department of Environmental Affairs have used a supercomputer to simulate the diffusion and dispersion of chemical compounds that can deodorize a landfill.

DALL·E 2 is OpenAI's awesome piece of technology that can create original, realistic images and art from a text description. It's able to combine concepts, attributes, and styles. For an aphantasic, AI enthusiast like myself this innovation is awesome. It's nearly addicting to play around with and it's impressive to see how far these models have come. The example above is one of the many awesome images I've generated with DALLE 2 over the past few days.

Apple shows its latest AI system GAUDI. It can generate 3D indoor scenes and is the foundation for a new generation of generative AI based on NeRFs. So-called neural rendering brings artificial intelligence to computer graphics: AI researchers at Nvidia, for example, are showing how 3D objects are created from photos, and Google is relying on Neural Radiance Fields (NeRFs) for Immersive View or developing NeRFs for rendering people. So far, NeRFs are mainly used as a kind of neural storage medium for 3D models and 3D scenes, which can then be rendered from different camera perspectives. This is how the frequently shown camera movements through a room or around an object are created.