ChatGPT does medicine
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Welcome to the latest edition of AI Insights, your portal to the fascinating world of artificial intelligence. In this edition, we bring you 5 papers carefully chosen by our CTO and AI Researcher, Vishwas Mruthyunjaya. These papers are your ticket to the most cutting-edge breakthroughs, innovative methodologies, and visionary ideas currently steering the course of AI.
Open source toolkit for RAG, fine-tuning, and model serving ( 🔗 Check the GitHub Repo)
To bridge the gap between general Large Language Models (LLMs) and domain-specific data, introducing the Tiger toolkit. This open-source resource, comprising TigerRag, TigerTune, TigerDA, and TigerArmor, empowers developers to create tailored AI models and language applications, ushering in a new era of precision and customization in the AI landscape.
Tiger Toolkit: Offers a suite of open-source tools, including TigerRag, TigerTune, TigerDA, and TigerArmor, empowering developers to create AI models aligned with their unique needs.
Precision AI: Facilitates customization of AI systems, enabling alignment with specific intellectual property and safety requirements, ushering in an era of precision in AI applications.
Closing the Gap: Aims to bridge the chasm between LLMs and domain-specific data, fostering a new phase in language modeling tailored to organizations' distinct demands.
Shaping the Future: Positions Tiger toolkit as a pivotal player in shaping the next era of language modeling and AI customization for diverse domains.
ChatGPT-Powered Hierarchical Comparisons for Image Classification (🔗 Read the Paper)
In addressing the zero-shot open-vocabulary challenge in image classification, a new framework emerges. Leveraging pretrained vision-language models such as CLIP and the knowledge from large language models like ChatGPT, this approach tackles biases in classification. It introduces a novel image classification framework via hierarchical comparisons, offering an intuitive, effective, and explainable solution.
Hierarchical Comparisons: The framework uses large language models to create hierarchical class groupings, allowing for more precise image classification.
Effective and Explainable: This approach not only improves accuracy but also provides transparent results.
Resolving Biases: By addressing biases in classification, it reduces the problem of similar descriptions for related but distinct classes.
Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans (🔗 Read the Paper)
This work addresses the challenges of diffusion-based planning in long-horizon, sparse-reward tasks. While diffusion models show promise in generating trajectories, they may not always produce feasible plans, which is a limitation in safety-critical applications.
Plan Refinement: The proposed approach focuses on refining unreliable plans generated by diffusion models, making them more dependable.
Restoration Gap Metric: A novel metric, the "restoration gap," is introduced to evaluate individual plans. This metric helps assess plan quality.
Guidance for Refinement: The gap predictor estimates the restoration gap, providing guidance for refining diffusion-based plans.
Attribution Map Regularizer: An additional technique is presented to prevent adversarial refining guidance, ensuring the further refinement of infeasible plans.
Demonstrated Effectiveness: The approach's effectiveness is demonstrated on various benchmarks requiring long-horizon planning, particularly in offline control settings. It also offers explainability through attribution maps, aiding in understanding plan generation.
Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V (🔗 Read the Paper)
This paper presents a critical evaluation of the multimodal large language model GPT-4 with Vision (GPT-4V) in the context of Visual Question Answering (VQA) tasks. The study explores GPT-4V's capabilities in answering questions alongside images, focusing on medical datasets from various modalities and objects of interest.
Multimodal Proficiency: The study assesses GPT-4V's ability to handle questions paired with images across diverse medical modalities such as Microscopy, Dermoscopy, X-ray, and more.
Comprehensive Medical Inquiries: The datasets encompass a wide range of medical questions, including sixteen distinct question types, creating a comprehensive evaluation scenario.
Textual Prompts: Experiments use carefully designed textual prompts to guide GPT-4V in combining visual and textual information.
Unreliable for Diagnostics: The accuracy score highlights that the current version of GPT-4V is not suitable for real-world medical diagnostics, given its suboptimal accuracy in responding to diagnostic medical questions.
Seven Unique Facets: The study delineates seven distinctive facets of GPT-4V's behavior in medical VQA, emphasizing its limitations in this intricate domain.
Mask Propagation for Efficient Video Semantic Segmentation (🔗 Read the Paper)
This paper addresses Video Semantic Segmentation (VSS), a task that involves assigning a semantic label to each pixel in a video sequence. While previous work extended image semantic segmentation models to video, they often came with significant computational costs. Here, the authors present an efficient framework for VSS called MPVSS.
Efficient Mask Propagation: MPVSS utilizes query-based image segmentation on sparse key frames to create precise binary masks and class predictions.
Segment-Aware Flow Estimation: A flow estimation module uses these learned queries to generate segment-aware flow maps, which are linked to mask predictions from key frames.
Temporal Cost Reduction: By reusing key frame predictions for non-key frames, the model avoids processing every video frame individually, substantially reducing computational costs.
Performance: Extensive experiments show that MPVSS achieves a strong balance between accuracy and efficiency. For instance, the Swin-L backbone model outperforms the current state-of-the-art by 4.0% mIoU while requiring only 26% FLOPs on the VSPW dataset. Additionally, it reduces FLOPs by up to 4x compared to the per-frame Mask2Former baseline with a minor 2% mIoU degradation on the Cityscapes validation set.
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