Label Your Data

🌟 University Spotlight: Duke University 🌟 Duke University's Duke MEMS (Department of Mechanical Engineering and Materials Science) leads groundbreaking research at the intersection of AI and engineering. Their focus areas include: 🔹 Predictive modeling – Enhancing accuracy in simulations 🔹 Materials discovery – Leveraging AI for advanced material design 🔹 Automation systems – Optimizing control and efficiency 👩🔬 Key researchers of the department John Dolbow — Focuses on modeling quasi-static and dynamic fracture of structural components. Dr. Cate Brinson – Specializes in AI and mechanics of materials, emphasizing complex hierarchical materials and polymer-based systems.  Dr. Leila Bridgeman – Expert in robust and optimal control, linear matrix inequalities, and model predictive control. 🤝 Why Collaborate with Duke? - Access cutting-edge research - Tap into a skilled talent pool - Explore joint funding opportunities Explore the department here: https://lnkd.in/daQzg44k

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🔔 Top AI & ML News 1️⃣ Coding in the AI Era – Google’s Head of Research Yossi Matias reaffirms that learning to code remains a valuable skill, even with AI tools transforming the landscape. Read more: (https://lnkd.in/dwpAKp3F). 2️⃣ GPTs Scaling Limitations – Insights into OpenAI’s upcoming Orion model suggest a shift away from scaling as the primary improvement strategy. Read more: (https://lnkd.in/dpWkpy2g). 3️⃣ Spotify & Generative AI – Spotify integrates AI tools to enhance user recommendations and explores the future of AI-generated music. Read more: (https://lnkd.in/d7pN3mU6). There’s more in our ML Digest newsletter: - research overview - trending dataset - new blog articles Link in the comments!

🎉 The wait is over – we’re live on Product Hunt! 🎉 Introducing our Data Labeling Platform, built to simplify your annotation workflow: ✅ Computer vision focus (for now 😉) ✅ API access ✅ Free trial ✅ Cost calculator Perfect for ML engineers, AI-driven businesses, and academic researchers. 🥂 You're invited to our comment party there: (link in the comment)

🎉 The wait is over – we’re live on Product Hunt! 🎉 Introducing our Data Labeling Platform, built to simplify your annotation workflow: ✅ Computer vision focus (for now 😉) ✅ API access ✅ Free trial ✅ Cost calculator Perfect for ML engineers, AI-driven businesses, and academic researchers. 🥂 You're invited to our comment party there: (link in the comment)

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🧪 New Machine Learning Research: Breaking New Ground in AI Agent Capabilities! Researchers Adam Fourney, Gagan Bansal, and Hussein Mozannar at Microsoft Research introduce Magentic-One, a multi-agent system designed to solve complex tasks autonomously. This innovative framework combines multiple specialized agents to tackle real-world web and file-based tasks, offering significant advancements in AI systems that operate independently to achieve goals. - Research goal: Develop a generalist, multi-agent system capable of completing open-ended tasks autonomously in dynamic environments. - Research methodology: Magentic-One uses an orchestrator agent to manage other agents, including web browsing, file navigation, coding, and execution, coordinating them through a task ledger system for progress tracking and adaptive planning. - Key findings: Magentic-One achieved competitive results across multiple benchmarks. In the GAIA benchmark, it reached approximately 35% accuracy, closely aligning with non-open-source state-of-the-art models. In AssistantBench and WebArena, Magentic-One demonstrated similar performance, achieving around 35-40% accuracy.  - Practical implications: This agent-based framework enables applications in software engineering, research, and data analysis. For instance, Magentic-One can autonomously gather information, organize research documents, and even execute code, all within a structured workflow. #LabelYourData #AIResearch #MachineLearning #AgentSystems #MultiAgent #MicrosoftResearch #AIInnovation #MLResearch 

🤐 Not a word until the release.

🧪 New Machine Learning Research: Revolutionizing Data Annotation with the Model-in-the-Loop (MILO) Framework Researchers from Meta including Yifan Wang, David Stevens, and Wenwen Jiang, have introduced MILO, a cutting-edge data annotation framework that combines AI with human annotators for enhanced speed and accuracy. - Research goal: Develop a more efficient and precise annotation process by integrating AI-human workflows to support large-scale data projects. - Research methodology: MILO utilizes AI for pre-annotation guidance, real-time support, and quality checks, tested across three studies to ensure streamlined annotation. - Key findings: The MILO framework demonstrated that AI suggestions are most effective when aligning with human annotators’ initial judgments. Overall, MILO improved handling time by 12% with a 95% confidence interval, and achieved high model accuracy across various categories. - Practical implications: MILO is applicable to expansive AI projects, providing a faster, more reliable annotation process essential for sectors like natural language processing and autonomous systems. #LabelYourData #MachineLearning #Innovation #AIResearch #MLResearch #LLM

I’m honored to have my insights on synthetic data validation for Large Language Models (LLMs) featured in Label Your Data’s latest article from Featured. Now that many companies build specific models (e.g., Llama 3.1 405B) to facilitate synthesis dataset generation, integrating human expertise into the LLM development process is important. Human-in-the-loop (HITL) approaches ensure that domain experts guide and refine model outputs, leading to more accurate and contextually relevant results. As a GenAI researcher, I believe the concern of running of training data for future LLMs development in the future, raised by experts in the field like Chip Huyen, is valid and deserve significant awareness. The expert-informed data synthesis validation helps mitigate biases and enhances the model’s ability to handle complex, nuanced scenarios, for example thinking about if a biotech company needs to generate synthesis customer reviews for a niche healthcare product not lanuched yet or a forensic psychiatry research group aims to generate crime vignettes to assess the public’s perception of crime types and punishment relevant to demographic groups. Thank you again to the Label Your Data team for highlighting this important aspect of AI development. #AI #GenAI #SyntheticData #LLM #DataValidation #HITL #ResponsibleAI