The (Long) Tail Wags the Dog: The Unforeseen Consequences of AI’s Personalized Art

Recent advancements in machine learning focus on diffusion models (DMs), offering powerful tools for modeling complex data distributions and generating realistic samples in various domains. However, the theoretical understanding of DMs needs improvement. Researchers at ENS…

Practical Solutions for Image Generation with DiT-MoE Efficiently Scaling Diffusion Models Diffusion models can efficiently handle denoising tasks, turning random noise into target data distribution. However, training and running these models can be costly due to…

Practical Solutions for Running Large Language Models on Commodity Hardware Deploying advanced machine learning models on resource-constrained devices like edge devices, mobile platforms, or low-power hardware has been challenging due to the computational and memory resources…

Vision-Language Models: Practical Solutions and Value Evolution of Vision-Language Models Vision-language models have evolved significantly, with two distinct generations. The first generation expanded on large-scale classification pretraining, while the second generation unified captioning and question-answering tasks.…

Challenges with Large Language Models (LLMs) Static Knowledge Base: LLMs often provide outdated information because their knowledge is fixed. Inaccuracy and Fabrication: They can create incorrect or fabricated responses, leading to confusion. Enhancing Accuracy with RAG…

Veriff is an identity verification platform partner for organizations in various industries. They use advanced technology, including AI-powered automation and human feedback, to verify user identities. Veriff standardized their model deployment workflow using Amazon SageMaker, reducing…

Researchers from MIT, Meta, and Codec Avatars Lab introduced PlatoNeRF, an innovative method for single-view 3D reconstruction using lidar and neural radiance fields. By leveraging time-of-flight data, PlatoNeRF overcomes limitations of prior methods, enabling reconstruction of…

Speech recognition technology continually seeks advancements in algorithm and models for improved accuracy and efficiency across languages and dialects. Carnegie Mellon University and Honda Research Institute Japan introduce OWSM v3.1, leveraging the E-Branchformer architecture to achieve…

AI Solutions for Drug Discovery and Structural Biology Addressing Challenges with MISATO In the field of AI technology, the drug discovery community faces challenges in creating precise models for drug design. MISATO, developed by leading research…

Overview of Fugatto Fugatto is an innovative AI model introduced by NVIDIA that enhances audio creation by generating and manipulating music, voices, and sounds. With 2.5 billion parameters, it combines text prompts with advanced audio synthesis,…

Researchers from various universities developed SANeRF-HQ, improving 3D segmentation using the SAM and NeRF techniques. Unlike previous NeRF-based methods, SANeRF-HQ offers greater accuracy, flexibility, and consistency in complex environments and has shown superior performance in evaluations,…

Understanding Multi-Modal Data Exploration Researchers are working on systems that can explore different types of data together, like text, images, and videos. This is especially important in fields like healthcare, where doctors need to look at…

MIT researchers have developed a new approach, called StableRep, for training self-supervised methods using synthetic images generated by text-to-image models. By treating multiple images from the same text prompt as positive examples for each other, StableRep…

Meet &AI: An AI-Powered Platform that Streamlines Patent Due Diligence Picture this: a legal firm tasked with assessing the validity of a patent or patent claims. This is a common challenge for patent attorneys, involving extensive…

An autonomous AI system rapidly learned and successfully executed Nobel Prize-winning chemical reactions, a process completed in just minutes with no errors on its first try. The development marks the first instance of non-organic intelligence planning,…

The paper, presented at the NeurIPS 2023 ICBINB workshop, examines the use of pre-trained language models in text-to-image auto-regressive generation, finding them of limited utility and providing a twofold analysis related to cross-modality tokens.