ELON MUSK, not content with helming recent purchase Twitter alongside SpaceX and his other long-standing firms, has announced an artificial intelligence start-up called xAI. People have speculated that it might be an attempt to challenge OpenAI's ChatGPT, an AI-powered chatbot that has grown to 100 million monthly users in the blink of an eye. But a veil of mystery hangs over the venture, whose goal is "to understand the true nature of the universe". Musk isn't averse to grandiose statements and marketing bluff – a SpaceX mission to Mars is seemingly always just on the horizon – but a …

The tool, called PhotoGuard, works like a protective shield by altering photos in tiny ways that are invisible to the human eye but prevent them from being manipulated. If someone tries to use an editing app based on a generative AI model such as Stable Diffusion to manipulate an image that has been "immunized" by PhotoGuard, the result will look unrealistic or warped. Right now, "anyone can take our image, modify it however they want, put us in very bad-looking situations, and blackmail us," says Hadi Salman, a PhD researcher at MIT who contributed to the research. It was presented at the International Conference on Machine Learning this week. PhotoGuard is "an attempt to solve the problem of our images being manipulated maliciously by these models," says Salman.

Facing calls to put guardrails on artificial intelligence development, a group of tech companies including Alphabet Inc.'s Google and OpenAI Inc. are creating an industry body to ensure that AI models are safe. The effort, also backed by AI startup Anthropic and Microsoft Corp., aims to consolidate the expertise of member companies and create benchmarks for the industry, according to a statement Wednesday. The group, known as the Frontier Model Forum, said it welcomed participation from other organizations working on large-scale machine-learning platforms. The fast proliferation of generative AI tools such as OpenAI's ChatGPT, which can create text, photos and even video based on simple prompts, has put pressure on tech giants to tread carefully. The companies involved in the Frontier Model Forum have already agreed to put safeguards in place -- at the urging of the White House -- before Congress potentially passes binding regulations.

In machine learning, generative modeling aims to learn to generate new data statistically similar to the training data distribution. In this paper, we survey learning generative models under limited data, few shots and zero shot, referred to as Generative Modeling under Data Constraint (GM-DC). This is an important topic when data acquisition is challenging, e.g. healthcare applications. We discuss background, challenges, and propose two taxonomies: one on GM-DC tasks and another on GM-DC approaches. Importantly, we study interactions between different GM-DC tasks and approaches. Furthermore, we highlight research gaps, research trends, and potential avenues for future exploration. Project website: https://gmdc-survey.github.io.

The emergence of generative pre-trained models has facilitated the synthesis of high-quality text, but it has also posed challenges in identifying factual errors in the generated text. In particular: (1) A wider range of tasks now face an increasing risk of containing factual errors when handled by generative models. (2) Generated texts tend to be lengthy and lack a clearly defined granularity for individual facts. (3) There is a scarcity of explicit evidence available during the process of fact checking. With the above challenges in mind, in this paper, we propose FacTool, a task and domain agnostic framework for detecting factual errors of texts generated by large language models (e.g., ChatGPT). Experiments on four different tasks (knowledge-based QA, code generation, mathematical reasoning, and scientific literature review) show the efficacy of the proposed method. We release the code of FacTool associated with ChatGPT plugin interface at https://github.com/GAIR-NLP/factool .

Worldcoin, the token of the crypto project co-founded by OpenAI Chief Executive Officer Sam Altman, rallied on its first day of trading on Monday as investors piled into the hype around artificial intelligence. Worldcoin jumped to as high as $3.58 from the initial price of $1.70 before falling back to $2.52 as of 11:12 a.m. in London, data compiled by CoinMarketCap data showed. By then, roughly $145 million worth of the token had been traded, after exchanges like Binance listed it. Worldcoin, Altman's eyeball-scanning crypto project which officially launched on Monday, uses a small device called an "orb" to scan people's eyeballs in order to generate a a unique digital identity. That identity, or World ID, grants its holder "proof of personhood" in the Worldcoin parlance.

The AI summer is well and truly upon us. Whether we call this period the peak of the "hype cycle" or simply the moment the curve goes vertical will only be obvious in hindsight, but the cadence of big news in the field has gone from weekly to almost daily. Let's catch up with what the biggest players in AI – Meta, Microsoft, Apple and OpenAI – are doing. Always one to keep its cards close to its chest, don't expect to hear of many R&D breakthroughs from Cupertino. Even the AI work that has made it into shipping products is hidden rather than shouted from the rooftops, with the company talking about "machine learning" and "transformers" at its annual worldwide developer conference (WWDC) last month, but conspicuously steering clear of saying "AI".

OpenAI's ChatGPT seems ubiquitous, but open source versions of instruction-tuned text generators are gaining the upper hand. In just 6 months, at least 15 serious alternatives have emerged, all of which have at least one important advantage over ChatGPT: they are a lot more transparent. Insight into training data and algorithms is key for responsible use of generative AI, a team of linguists and language technology researchers at Radboud University claim. The researchers have mapped this rapidly evolving landscape in a paper and a live-updated website. This shows there are many working alternative "open source" text generators, but also that openness comes in degrees and that many models inherit legal restrictions.

Advances in computational design and manufacturing (CDaM) have already permeated and transformed numerous industries, including aerospace, architecture, electronics, dental, and digital media, among others. Nevertheless, the full potential of the CDaM workflow is still limited by a number of barriers, such as the extensive domainspecific knowledge that is often required to use CDaM software packages or integrate CDaM solutions into existing workflows. Generative AI tools such as Large Language Models (LLMs) have the potential to remove these barriers, by expediting the CDaM process and providing an intuitive, unified, and user-friendly interface that connects each stage of the pipeline. However, to date, generative AI and LLMs have predominantly been applied to non-engineering domains. In this study, we show how these tools can also be used to develop new design and manufacturing workflows.

For an artist or a graphic designer, the spatial layout of a scene is a critical design choice. However, existing text-to-image diffusion models provide limited support for incorporating spatial information. This paper introduces Composite Diffusion as a means for artists to generate high-quality images by composing from the sub-scenes. The artists can specify the arrangement of these sub-scenes through a flexible free-form segment layout. They can describe the content of each sub-scene primarily using natural text and additionally by utilizing reference images or control inputs such as line art, scribbles, human pose, canny edges, and more. We provide a comprehensive and modular method for Composite Diffusion that enables alternative ways of generating, composing, and harmonizing sub-scenes. Further, we wish to evaluate the composite image for effectiveness in both image quality and achieving the artist's intent. We argue that existing image quality metrics lack a holistic evaluation of image composites. To address this, we propose novel quality criteria especially relevant to composite generation. We believe that our approach provides an intuitive method of art creation. Through extensive user surveys, quantitative and qualitative analysis, we show how it achieves greater spatial, semantic, and creative control over image generation. In addition, our methods do not need to retrain or modify the architecture of the base diffusion models and can work in a plug-and-play manner with the fine-tuned models.