MIT CSAIL and FAIR Meta have introduced Representation-Conditioned Image Generation (RCG) framework, pioneering high-quality image generation without human annotations. This self-supervised approach leverages Representation Diffusion Model and pre-trained encoders to achieve state-of-the-art results in class-unconditional and conditional image generation, promising significant advancements in self-supervised image synthesis.

Notus, a new language model, builds on Zephyr’s success by fine-tuning data curation, prioritizing high-quality data from UltraFeedback and emphasizing user preference alignment. Implementing a meticulous curation process, Notus aims to elevate language model performance by reiterating response generation and AI ranking stages. These efforts have resulted in competitive performance and a commitment to open-source…

A team from Columbia University and Google has introduced ‘ReconFusion,’ an artificial intelligence method for achieving high-quality 3D reconstructions from a limited number of images. It effectively addresses challenges such as artifacts and catastrophic failures in reconstruction, providing robustness even with sparse input views. This advancement holds promise for various applications.

Axel Springer, a major German publishing house, has announced the closure of its news outlet, Upday, which will be relaunched as an AI-driven trend news generator, marking a significant shift from traditional journalism to AI-led content creation. This move signals the company’s commitment to exploring AI’s potential in journalism, despite resulting job cuts.

Researchers from FNii CUHKSZ and SSE CUHKSZ have introduced MVHumanNet, a vast dataset for multi-view human action sequences with comprehensive annotations, such as human masks, camera parameters, 2D and 3D key points, SMPL/SMPLX parameters, and textual descriptions. MVHumanNet is poised to drive innovation in large-scale 3D human-centric tasks like action recognition, human NeRF reconstruction, and…

Google researchers developed a new fine-tuning strategy, called chain-of-thought (CoT), to improve language models’ performance in generating correct answers. The CoT technique aims to maximize the accuracy of responses, surpassing other methods like STaR and prompt-tuning. The study also introduces a control-variate technique and outlines future research directions for further advancements.

Apple recently released MLX, a machine learning framework designed for Apple silicon. Inspired by existing frameworks, it offers a user-friendly design, Python and C++ APIs, composable function transformations, and lazy computations. MLX supports multiple devices, high-level packages like mlx.optimizers and mlx.nn, and has various applications, aiming to simplify complex model building and democratize machine learning.

Google’s demo video of its new model Gemini was impressive, but it fell short of the marketing hype. The video showcased interactions that were actually based on detailed text prompts and still images, not live demonstrations. Google’s claims about Gemini’s capabilities raise questions about AI innovation and future developments compared to existing models like GPT-4.

Deep learning’s wide-ranging applications, including robotics, face challenges due to its reliance on pre-existing data. PyPose, developed on the PyTorch framework, introduces a novel approach blending deep learning with physics-based optimization. This versatile toolkit aids in building and testing various robotic tools efficiently, enhancing performance and adaptability in challenging tasks. Researchers emphasize its revolutionary impact…

Researchers from multiple universities and NVIDIA have developed Dolphins, a vision-language model for autonomous vehicles. Dolphins excel in providing driving instructions by combining language reasoning with visual understanding, exhibiting human-like features such as rapid learning and interpretability. The model addresses challenges in achieving full autonomy in vehicular systems and emphasizes the importance of computational efficiency.