​[[{“value”:” LLMs enable interactions with external tools and data sources, such as weather APIs or calculators, through function calls, unlocking diverse applications like autonomous AI agents and neurosymbolic reasoning systems. However, the current synchronous approach to function calling, where LLMs pause token generation until the… Read More »Yale Researchers Propose AsyncLM: An Artificial Intelligence System for Asynchronous LLM Function Calling Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Video generation has improved with models like Sora, which uses the Diffusion Transformer (DiT) architecture. While text-to-video (T2V) models have advanced, they often find it hard to create clear and consistent videos without extra references. Text-image-to-video (TI2V) models address this limitation by using an… Read More »Researchers from UCLA and Apple Introduce STIV: A Scalable AI Framework for Text and Image Conditioned Video Generation Divyesh Vitthal Jawkhede Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Model Merging allows one to leverage the expertise of specific fine-tuned models as a single powerful entity. The concept is straightforward: teach variants of a base foundation model on independent tasks until they become experts, and then assemble these experts as one. However, new… Read More »TIME Framework: A Novel Machine Learning Unifying Framework Breaking Down Temporal Model Merging Adeeba Alam Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs), trained on extensive datasets and equipped with billions of parameters, demonstrate remarkable abilities to process and respond to diverse linguistic tasks. However, as tasks increase in complexity, the interpretability and adaptability of LLMs become critical challenges. The ability to efficiently… Read More »Meet AutoReason: An AI Framework for Enhancing Multi-Step Reasoning and Interpretability in Large Language Models Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have significantly advanced natural language processing, but tokenization-based architectures bring notable limitations. These models depend on fixed-vocabulary tokenizers like Byte Pair Encoding (BPE) to segment text into predefined tokens before training. While functional, tokenization can introduce inefficiencies and biases, particularly… Read More »Meta AI Introduces Byte Latent Transformer (BLT): A Tokenizer-Free Model That Scales Efficiently Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Neural networks face significant challenges in generalizing to out-of-distribution (OOD) data that deviates from the in-distribution (ID) training data. This generalization problem poses critical reliability issues in practical machine learning applications. Recent studies have uncovered interesting empirical laws describing model behaviors across distribution shift… Read More »Researchers from CMU and Bosch AI Introduce New Insights on Test-Time Adaptation for Distribution Shifts Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Language model routing is a growing field focused on optimizing the utilization of large language models (LLMs) for diverse tasks. With capabilities spanning text generation, summarization, and reasoning, these models are increasingly applied to varied input data. The ability to dynamically route specific tasks… Read More »Researchers at Stanford University Propose SMOOTHIE: A Machine Learning Algorithm for Learning Label-Free Routers for Generative Tasks Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Amazon SageMaker Pipelines includes features that allow you to streamline and automate machine learning (ML) workflows. This allows scientists and model developers to focus on model development and rapid experimentation rather than infrastructure management Pipelines offers the ability to orchestrate complex ML workflows with… Read More »How Amazon trains sequential ensemble models at scale with Amazon SageMaker Pipelines Bikram Singh AWS Machine Learning Blog

​[[{“value”:” Amazon SageMaker HyperPod is designed to support large-scale machine learning (ML) operations, providing a robust environment for training foundation models (FMs) over extended periods. Multiple users — such as ML researchers, software engineers, data scientists, and cluster administrators — can work concurrently on the… Read More »Implementing login node load balancing in SageMaker HyperPod for enhanced multi-user experience Janosch Woschitz AWS Machine Learning Blog

​[[{“value”:” This post was written with Darrel Cherry, Dan Siddall, and Rany ElHousieny of Clearwater Analytics. As global trading volumes rise rapidly each year, capital markets firms are facing the need to manage large and diverse datasets to stay ahead. These datasets aren’t just expansive… Read More »How Clearwater Analytics is revolutionizing investment management with generative AI and Amazon SageMaker JumpStart Darrel Cherry AWS Machine Learning Blog