CybAI news

Generative AI has the potential to change business operations across a vast range of sectors. In terms of securing business growth, the GenAI revolution cannot be understated.  With projections suggesting that the global generative AI market size is set to reach $967.65 billion by 2032, representing a CAGR of 39.6%, it's clear that the artificial intelligence boom will be a major disruptive force throughout business operations worldwide.  In addition to this, McKinsey data suggests that the impact of generative AI on productivity has the potential to add trillions of dollars to the global economy. For businesses eager to scale up their operations…

arXiv:2408.03360v1 Announce Type: new Abstract: Dataset Distillation aims to compress a large dataset into a significantly more compact, synthetic one without compromising the performance of the trained models. To achieve this, existing methods use the agent model to extract information from the target dataset and embed it into the distilled dataset. Consequently, the quality of extracted and embedded information determines the quality of the distilled dataset. In this work, we find that existing methods introduce misaligned information in both information extraction and embedding stages. To alleviate this, we propose Prioritize Alignment in Dataset Distillation (PAD), which aligns information from the…

arXiv:2408.03356v1 Announce Type: new Abstract: Differentiable volumetric rendering-based methods made significant progress in novel view synthesis. On one hand, innovative methods have replaced the Neural Radiance Fields (NeRF) network with locally parameterized structures, enabling high-quality renderings in a reasonable time. On the other hand, approaches have used differentiable splatting instead of NeRF's ray casting to optimize radiance fields rapidly using Gaussian kernels, allowing for fine adaptation to the scene. However, differentiable ray casting of irregularly spaced kernels has been scarcely explored, while splatting, despite enabling fast rendering times, is susceptible to clearly visible artifacts. Our work closes this gap by…

arXiv:2408.03505v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have extended the success of large language models (LLMs) to multiple data types, such as image, text and audio, achieving significant performance in various domains, including multimodal translation, visual question answering and content generation. Nonetheless, existing systems are inefficient to train MLLMs due to substantial GPU bubbles caused by the heterogeneous modality models and complex data dependencies in 3D parallelism. This paper proposes Optimus, a distributed MLLM training system that reduces end-to-end MLLM training time. Optimus is based on our principled analysis that scheduling the encoder computation within the LLM…