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This newsletter will discuss some cool AI research papers and AI tools. Happy learning!

➡️ Researchers from the University of Washington and Duke University Introduce Punica: An Artificial Intelligence System to Serve Multiple LoRA Models in a Shared GPU Cluster

How can multiple Low-rank adaptation (LoRA) models be efficiently served on a shared GPU cluster? "Punica" addresses this issue with an innovative CUDA kernel design enabling the batching of GPU operations for diverse LoRA models. This unique approach allows a single GPU to maintain only one copy of the base pre-trained model while serving multiple LoRA models, greatly improving GPU efficiency in terms of memory and computational resources. Punica's scheduler effectively manages multi-tenant LoRA workloads in a shared GPU environment. In comparative evaluations, Punica showcases a 12-fold increase in throughput for serving multiple LoRA models over existing state-of-the-art LLM serving systems, with a minimal addition of just 2ms latency per token.

➡️  Meet JARVIS-1: Open-World Multi-Task Agents with Memory-Augmented Multimodal Language Models

How can agents achieve human-like planning and control in an open-world environment with infinite tasks? This paper introduces "JARVIS-1", a sophisticated agent designed for the Minecraft universe, capable of handling multimodal observations (visual and textual instructions) and executing complex tasks. Built on pre-trained multimodal language models, JARVIS-1 converts inputs into actionable plans, guiding goal-conditioned controllers. A key innovation is its multimodal memory, blending pre-trained knowledge with game experiences for enhanced planning. In tests, JARVIS-1 performed exceptionally in over 200 Minecraft tasks, showing a significant improvement in long-horizon tasks like the diamond pickaxe challenge. Its ability to self-improve through lifelong learning demonstrates potential for greater general intelligence and autonomy.

➡️  Researchers from SJTU China Introduce TransLO: A Window-Based Masked Point Transformer Framework for Large-Scale LiDAR Odometry

How can transformer architecture, successful in 2D vision tasks, be adapted for 3D vision, especially with the challenges posed by point clouds? This paper introduces TransLO, a novel approach that processes large-scale point clouds efficiently by projecting them onto a 2D surface and utilizing a local transformer with linear complexity. The key components of TransLO include a Window-based Masked transformer with Self Attention (WMSA) for long-range dependency capture, and a Masked Cross-Frame Attention (MCFA) for frame association and pose estimation. A binary mask is proposed to address the sparsity of point clouds. Notably, TransLO is the first transformer-based LiDAR odometry network. It outperforms existing learning-based methods and even bests LOAM in most evaluations on the KITTI odometry dataset, achieving an average rotation RMSE of 0.500°/100m and a translation RMSE of 0.993%.

➡️ This AI Paper Introduces LLaVA-Plus: A General-Purpose Multimodal Assistant that Expands the Capabilities of Large Multimodal Models

How can a multimodal assistant be enhanced to better interact with and fulfill real-world tasks? "LLaVA-Plus" addresses this by expanding the capabilities of large multimodal models. It houses a skill repository containing various pre-trained vision and vision-language models, activating the appropriate tools in response to user inputs. Trained on multimodal instruction-following data, LLaVA-Plus adeptly handles tasks involving visual understanding, generation, external knowledge retrieval, and compositions. Empirical evidence shows that LLaVA-Plus not only surpasses its predecessor, LLaVA, in existing capabilities but also introduces new functionalities. A key innovation is its ability to ground image queries directly, maintaining active engagement throughout human-AI interactions, which significantly enhances tool use and opens up novel scenarios.

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