​[[{“value”:” Data labeling involves annotating raw data, such as images, text, audio, or video, with tags or labels that convey meaningful context. These labels act as a guide for machine learning algorithms to recognize patterns and make accurate predictions. This stage is crucial in supervised… Read More »Understanding Data Labeling (Guide) Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Deploying machine learning models on edge devices poses significant challenges due to limited computational resources. When the size and complexity of models increase, even achieving efficient inference becomes challenging. Applications such as autonomous vehicles, AR glasses, and humanoid robots require low-latency and memory-efficient operations.… Read More »Meet FluidML: A Generic Runtime Memory Management and Optimization Framework for Faster, Smarter Machine Learning Inference Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have transformed artificial intelligence by enabling powerful text-generation capabilities. These models require strong security against critical risks such as prompt injection, model poisoning, data leakage, hallucinations, and jailbreaks. These vulnerabilities expose organizations to potential reputational damage, financial loss, and societal… Read More »NVIDIA AI Introduces ‘garak’: The LLM Vulnerability Scanner to Perform AI Red-Teaming and Vulnerability Assessment on LLM Applications Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Neural networks have traditionally operated as static models with fixed structures and parameters once trained, a limitation that hinders their adaptability to new or unforeseen scenarios. Deploying these models in varied environments often requires designing and teaching new configurations, a resource-intensive process. While flexible… Read More »NeuMeta (Neural Metamorphosis): A Paradigm for Self-Morphable Neural Networks via Continuous Weight Manifolds Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Google has introduced a ‘memory’ feature for its Gemini Advanced chatbot, enabling it to remember user preferences and interests for a more personalized interaction experience. This feature is available exclusively to Google One AI Premium Plan subscribers, and it is part of Google’s effort… Read More »Google Introduces ‘Memory’ Feature to Gemini Advanced Shobha Kakkar Artificial Intelligence Category – MarkTechPost

​[[{“value”:” AI-driven solutions are advancing rapidly, yet managing multiple AI agents and ensuring coherent interactions between them remains challenging. Whether for chatbots, voice assistants, or other AI systems, tracking context across multiple agents, routing large language model (LLM) queries, and integrating new agents into existing… Read More »AWS Releases ‘Multi-Agent Orchestrator’: A New AI Framework for Managing AI Agents and Handling Complex Conversations Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The machine learning community faces a significant challenge in audio and music applications: the lack of a diverse, open, and large-scale dataset that researchers can freely access for developing foundation models. Despite advances in image and text-based AI research, the audio domain lags due… Read More »LAION AI Unveils LAION-DISCO-12M: Enabling Machine Learning Research in Foundation Models with 12 Million YouTube Audio Links and Metadata Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:”This paper was accepted at the Foundation Model Interventions (MINT) Workshop at NeurIPS 2024. Instruction-following is crucial for building AI agents with large language models (LLMs), as these models must adhere strictly to user-provided guidelines. However, LLMs often fail to follow even simple instructions. To… Read More »Do LLMs Internally “Know” When They Follow Instructions? Apple Machine Learning Research

​We study private stochastic convex optimization (SCO) under user-level differential privacy (DP) constraints. In this setting, there are nnn users, each possessing mmm data items, and we need to protect the privacy of each user’s entire collection of data items. Existing algorithms for user-level DP… Read More »Faster Algorithms for User-Level Private Stochastic Convex Optimization Apple Machine Learning Research

​Learning with identical train and test distributions has been extensively investigated both practically and theoretically. Much remains to be understood, however, in statistical learning under distribution shifts. This paper focuses on a distribution shift setting where train and test distributions can be related by classes… Read More »Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization Apple Machine Learning Research