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Researchers from Johns Hopkins Medicine Developed a Machine Learning Model for Precise Osteosarcoma Necrosis Calculation
Researchers at Johns Hopkins Medicine have developed a machine learning model that accurately calculates the extent of tumor death in bone cancer patients. The model, trained on annotated pathology images, achieved 85% accuracy, which rose to 99% after removing an outlier. The innovative method reduces the workload for pathologists and has the potential to revolutionize…
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New report reveals how generative AI is being harnessed by terrorists
A new report by Tech Against Terrorism highlights that violent extremists are increasingly using generative AI tools to create content, including images linked to groups like Hezbollah and Hamas. This strategic use of AI aims to influence narratives, particularly relating to sensitive topics like the Israel-Hamas war. The report also raises concerns about the implications…
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The OECD has modified its definition of AI which will extend to the EU AI Act
The OECD has updated its definition of AI, which is expected to be included in the European Union’s AI Act. The new definition recognizes AI systems that can have emergent goals beyond their original objectives and expands the range of outputs AI can produce. It also considers the changes AI systems can undergo after deployment.…
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Cookie Permissions 101
Summary: The article highlights the importance of cookie permissions following data protection laws while striking a balance between user privacy and user-friendliness. With increased regulation, companies need to provide clear and simple choices for users to control data privacy without confusion or frustration. Cookies store data about user preferences and interactions and are used for…
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The 6 Types of Conversations with Generative AI
Summary: The article discusses the different types of conversations that users have with generative-AI bots, and how UI designs should accommodate these variations. The study involved analyzing 425 interactions with bots like ChatGPT, Bing Chat, and Bard, and found that varying conversation lengths can serve different user goals. The findings are reported in multiple articles.
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Use machine learning without writing a single line of code with Amazon SageMaker Canvas
Amazon SageMaker Canvas is a no-code environment that allows users to easily utilize machine learning (ML) models for various data types. It integrates with Amazon Comprehend for natural language processing tasks like sentiment analysis and entity recognition. It also integrates with Amazon Rekognition for image analysis, and Amazon Textract for document analysis. The ready-to-use solutions…
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Using LLMs to evaluate LLMs
The text discusses the challenges of evaluating language models and proposes using language models to evaluate other language models. It introduces several metrics and evaluators that rely on language models, including G-Eval, FactScore, and RAGAS. These metrics aim to assess factors such as coherence, factual precision, faithfulness, answer relevance, and context relevance. While there are…
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Reimagining Image Recognition: Unveiling Google’s Vision Transformer (ViT) Model’s Paradigm Shift in Visual Data Processing
The Vision Transformer (ViT) model is a groundbreaking approach to image recognition that transforms images into sequences of patches and applies Transformer encoders to extract insights. It surpasses traditional CNN models by leveraging self-attention mechanisms and sequence-based processing, offering superior performance and computational efficiency. ViT presents new possibilities for complex visual tasks, making it a…
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This AI Paper Introduces a Comprehensive Analysis of GPT-4V’s Performance in Medical Visual Question Answering: Insights and Limitations
A recent study evaluated the performance of GPT-4V, a multimodal language model, in handling complex queries that require both text and visual inputs. While GPT-4V has potential in enhancing natural language processing and computer vision applications, it is not suitable for practical medical diagnostics due to unreliable and suboptimal responses. The study highlights the need…
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Researchers from Stanford Introduce RT-Sketch: Elevating Visual Imitation Learning Through Hand-Drawn Sketches as Goal Specifications
Researchers at Stanford University have introduced RT-Sketch, a goal-conditioned manipulation policy that uses hand-drawn sketches as a more precise and abstract alternative to natural language and goal images in visual imitation learning. RT-Sketch demonstrates robust performance in various manipulation tasks, outperforming language-based agents in scenarios with ambiguous goals or visual distractions. The study highlights the…