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​[[{“value”:”Natural language processing (NLP) remains one of the most quickly evolving fields in AI, as new research continues to rapidly advance large language models (LLMs), systems for speech recognition and generation, language agents, and more. This technology is essential to many of today’s AI experiences,… Read More »Apple Workshop on Natural Language and Interactive Systems 2025 Apple Machine Learning Research

​Recent advances in large language models (LLMs) have extended context lengths, enabling assistants to sustain long histories for coherent, personalized responses. This ability, however, hinges on Key-Value (KV) caching, whose memory grows linearly with dialogue length and quickly dominates under strict resource constraints. An active… Read More »EpiCache: Episodic KV Cache Management for Long Conversational Question Answering Apple Machine Learning Research

​This paper re-examines the first normalized incomplete moment, a well-established measure of inequality with wide applications in economic and social sciences. Despite the popularity of the measure itself, existing statistical inference appears to lag behind the needs of modern-age analytics. To fill this gap, we… Read More »Alternative Statistical Inference for the First Normalized Incomplete Moment Apple Machine Learning Research

​We present AToken, the first unified visual tokenizer that achieves both high-fidelity reconstruction and semantic understanding across images, videos, and 3D assets. Unlike existing tokenizers that specialize in either reconstruction or understanding for single modalities, AToken encodes these diverse visual inputs into a shared 4D… Read More »AToken: A Unified Tokenizer for Vision Apple Machine Learning Research

​[[{“value”:” This post was written with Sarah Ostermeier from Comet. As enterprise organizations scale their machine learning (ML) initiatives from proof of concept to production, the complexity of managing experiments, tracking model lineage, and managing reproducibility grows exponentially. This is primarily because data scientists and… Read More »Rapid ML experimentation for enterprises with Amazon SageMaker AI and Comet Vikesh Pandey Artificial Intelligence

​[[{“value”:” This post is co-written with Abhinav Pandey from Nippon Life India Asset Management Ltd. Accurate information retrieval through generative AI-powered assistants is a popular use case for enterprises. To reduce hallucination and improve overall accuracy, Retrieval Augmented Generation (RAG) remains the most commonly used… Read More »How Nippon India Mutual Fund improved the accuracy of AI assistant responses using advanced RAG methods on Amazon Bedrock Shailesh Shivakumar Artificial Intelligence

​[[{“value”:” Audio is being added to AI everywhere: both in multimodal models that can understand and generate audio and in applications that use audio for input. Now that we can work with spoken language, what does that mean for the applications that we can develop?… Read More »Generative AI in the Real World: Raiza Martin on Building AI Applications for Audio Ben Lorica and Raiza Martin AI & ML – Radar

​Machine learning practitioners spend countless hours on repetitive tasks: monitoring model performance, retraining pipelines, data quality checks, and experiment tracking. Machine learning practitioners spend countless hours on repetitive tasks: monitoring model performance, retraining pipelines, data quality checks, and experiment tracking.  Read More  

​[[{“value”:” How do you teach kids to use and build with AI? That’s what Stefania Druga works on. It’s important to be sensitive to their creativity, sense of fun, and desire to learn. When designing for kids, it’s important to design with them, not just… Read More »Generative AI in the Real World: Stefania Druga on Designing for the Next Generation Ben Lorica and Stefania Druga AI & ML – Radar

​Machine learning workflows often involve a delicate balance: you want models that perform exceptionally well, but you also need to understand and explain their predictions. Machine learning workflows often involve a delicate balance: you want models that perform exceptionally well, but you also need to understand… Read More »How to Combine Scikit-learn, CatBoost, and SHAP for Explainable Tree Models Vinod Chugani MachineLearningMastery.com