Google Research introduced Generative Infinite-Vocabulary Transformers (GIVT), pioneering real-valued vector sequences for AI. This approach aims to address limitations in existing transformer models for image generation by using real-valued vectors instead of discrete tokens and exploring various sampling methods. The paper’s authors highlight GIVT’s performance and emphasize their reliance on standard deep learning techniques.

A new approach to creating mesmerizing optical illusions has emerged, eschewing assumptions about human perception by using a text-to-image diffusion model. This method generates multi-view illusions, including visual anagrams, polymorphic jigsaws, and even three to four view illusions. By sidestepping traditional assumptions, it offers a fresh and accessible tool for crafting captivating visual transformations.

The evolving landscape of AI demands a shift towards human-centric design. Don Norman emphasizes aligning AI with human instincts, while ‘Design Fiction’ helps project future usages. Scientific advancements by organizations like DeepMind and Nvidia set the groundwork, and disruptive AI usages inspired by science fiction can enhance everyday lives. Collaboration between designers and AI experts…

MIT Generative AI Week featured a flagship full-day symposium and four subject-specific symposia, aiming to foster dialogue about generative artificial intelligence technologies. The events included panels, roundtable discussions, and keynote speeches, covering topics such as AI and education, health, creativity, and commerce. The week concluded with a screening of the documentary “Another Body,” followed by…

Cognitive science studies suggest typicality is vital for category knowledge, affecting human judgment. Machine learning methods offer assurance in predictions, but considering atypicality alongside confidence improves accuracy and uncertainty quantification. Recalibration techniques with atypicality-aware measures elevate performance across subgroups. Atypicality should be integrated into models for enhanced reliability in AI.

Meta AI has introduced “Relightable Gaussian Codec Avatars,” a revolutionary method for achieving high-fidelity relighting of dynamic 3D head avatars. The approach relies on a 3D Gaussian geometry model and a learnable radiance transfer appearance model to capture sub-millimeter details and enable real-time relighting. This innovation elevates the realism and interactivity of avatar animation, marking…

Brain organoids, lab-grown mini-brains created from human stem cells, have been integrated with computers to achieve speech recognition. This innovative “Brainoware” system, described in a study in Nature Electronics, represents a shift from traditional AI using silicon chips. Despite challenges, its potential for creating energy-efficient AI hardware with human brain-like functionality is evident.

A University of Warwick study unveils an AI system, X-Raydar, trained on 2.8 million chest X-rays, demonstrating comparable accuracy to doctors in diagnosing 94% of conditions. It highlights potential for efficient diagnosis, particularly in addressing radiologist shortages. X-Raydar has been open-sourced to foster further advancements in AI medical technology.

This paper introduces LiDAR, a metric designed to measure the quality of representations in Joint Embedding (JE) architectures, addressing the challenge of evaluating learned representations. JE architectures have potential for transferable data representations, but evaluating them without access to a task and dataset is difficult. LiDAR aims to facilitate efficient and reliable evaluation.