AI

  How can missing portions of a 3D capture be effectively completed? This research paper from Google Research and UC Berkeley introduces “NeRFiller,” a novel approach for 3D inpainting, which addresses the challenge of reconstructing incomplete 3D scenes or objects often missing due to reconstruction failures or lack of observations. This approach allows precise and…

  The problem of video understanding and generation scenarios has been addressed by researchers of Tencent AI Lab and The University of Sydney by presenting GPT4Video. This unified multi-model framework supports LLMs with the capability of both video understanding and generation. GPT4Video developed an instruction-following-based approach integrated with the stable diffusion generative model, which effectively…

  Large Language Models (LLMs), like GPT, PaLM, LLaMA, etc., have attracted much interest because of their incredible capabilities. Their ability to utilize the strength of Natural Language Processing, Generation, and Understanding by generating content, answering questions, summarizing text, and so on have made LLMs the talk of the town in the last few months….

  A group of researchers from various institutions, including the University of Pittsburgh, Weill Cornell Medicine, Telemedicine & Advanced Technology Research Center, Uniformed Services University, Brooke Army Medical Center, and University of Pittsburgh Medical Center have examined the ethical principles of generative AI in healthcare, particularly focusing on transparency, bias modeling, and ethical decision-making concerns….

  Multimodal pre-training advancements address diverse tasks, exemplified by models like LXMERT, UNITER, VinVL, Oscar, VilBert, and VLP. Models such as FLAN-T5, Vicuna, LLaVA, and more enhance instruction-following capabilities. Others like Flamingo, OpenFlamingo, Otter, and MetaVL explore in-context learning. While benchmarks like VQA focus on perception, MMMU stands out by demanding expert-level knowledge and deliberate…

  Google DeepMind’s researchers have developed SODA, an AI model that addresses the problem of encoding images into efficient latent representations. With SODA, seamless transitions between images and semantic attributes are made possible, allowing for interpolation and morphing across various image categories. Diffusion models have revolutionized visual synthesis, excelling in diverse tasks like image, video,…

  Perplexity, an innovative AI startup, has introduced a solution to transform information retrieval systems. This launch introduces two new large language models (LLMs), pplx-7b-online and pplx-70b-online, which mark the pioneering foray into publicly accessible online LLMs via an API. Unlike traditional offline LLMs like Claude 2, these models leverage live internet data, enabling real-time,…

  Researchers from Google Research and UIUC propose ZipLoRA, which addresses the issue of limited control over personalized creations in text-to-image diffusion models by introducing a new method that merges independently trained style and subject Linearly Recurrent Attentions (LoRAs). It allows for greater control and efficacy in generating any matter. The study emphasizes the importance…

  The soaring capabilities of language models in real-world applications are often hindered by the intricate challenges associated with their large-scale training using conventional methods like standard backpropagation. Google DeepMind’s latest breakthrough, DiLoCo (Distributed Low-Communication), sets a new precedent in language model optimization. In the paper “DiLoCo: Distributed Low-Communication Training of Language Models,” the research…

  EPFL researchers, in collaboration with Apple, have introduced a new approach to speculative sampling called Parallel Speculative Sampling (PaSS). This new approach allows for the drafting of multiple tokens simultaneously using a single model, combining the benefits of auto-regressive generation and speculative sampling. The PaSS method was evaluated on text and code completion tasks,…