[[{“value”:” I’ve been in a few recent conversations about whether to use Apache Beam on its own or run it with Google Dataflow. On the surface, it’s a tooling decision. But it also reflects a broader conversation about how teams build systems. Beam offers a… Read More »A “Beam Versus Dataflow” Conversation Aaron Black AI & ML – Radar
[[{“value”:” Table of contents What is catastrophic forgetting in foundation models? Why does online reinforcement learning forget less than supervised fine-tuning? How can forgetting be measured? What do experiments on large language models reveal? How does RL compare to SFT in robotics tasks? What insights… Read More »A New MIT Study Shows Reinforcement Learning Minimizes Catastrophic Forgetting Compared to Supervised Fine-Tuning Michal Sutter Artificial Intelligence Category – MarkTechPost
[[{“value”:” Table of contents Why is long context such a bottleneck for LLMs? How does REFRAG compress and shorten context? How is acceleration achieved? How does REFRAG preserve accuracy? What do the experiments reveal? Summary FAQs A team of researchers from Meta Superintelligence Labs, National… Read More »Meta Superintelligence Labs Introduces REFRAG: Scaling RAG with 16× Longer Contexts and 31× Faster Decoding Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” Latvian language-tech firm Tilde has released TildeOpen LLM, an open-source foundational large language model (LLM) purpose-built for European languages, with a sharp focus on under-represented and smaller national and regional languages. It’s a strategic leap toward linguistic equity and digital sovereignty within the EU.… Read More »Tilde AI Releases TildeOpen LLM: An Open-Source Large Language Model with Over 30 Billion Parameters and Support Most European Languages Maxime Mommessin Artificial Intelligence Category – MarkTechPost
[[{“value”:” Large language models (LLMs) very often generate “hallucinations”—confident yet incorrect outputs that appear plausible. Despite improvements in training methods and architectures, hallucinations persist. A new research from OpenAI provides a rigorous explanation: hallucinations stem from statistical properties of supervised versus self-supervised learning, and their… Read More »From Pretraining to Post-Training: Why Language Models Hallucinate and How Evaluation Methods Reinforce the Problem Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” In this advanced DeepSpeed tutorial, we provide a hands-on walkthrough of cutting-edge optimization techniques for training large language models efficiently. By combining ZeRO optimization, mixed-precision training, gradient accumulation, and advanced DeepSpeed configurations, the tutorial demonstrates how to maximize GPU memory utilization, reduce training overhead,… Read More »Implementing DeepSpeed for Scalable Transformers: Advanced Training with Gradient Checkpointing and Parallelism Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” Hugging Face has just released FineVision, an open multimodal dataset designed to set a new standard for Vision-Language Models (VLMs). With 17.3 million images, 24.3 million samples, 88.9 million question-answer turns, and nearly 10 billion answer tokens, FineVision position itself as one of the… Read More »Hugging Face Open-Sourced FineVision: A New Multimodal Dataset with 24 Million Samples for Training Vision-Language Models (VLMs) Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” Alibaba’s Qwen Team unveiled Qwen3-Max-Preview (Instruct), a new flagship large language model with over one trillion parameters—their largest to date. It is accessible through Qwen Chat, Alibaba Cloud API, OpenRouter, and as default in Hugging Face’s AnyCoder tool. How does it fit in today’s… Read More »Alibaba AI Unveils Qwen3-Max Preview: A Trillion-Parameter Qwen Model with Super Fast Speed and Quality Michal Sutter Artificial Intelligence Category – MarkTechPost
[[{“value”:” Table of contents What is a Personal Health Agent? How does the PHA framework operate? How was the PHA evaluated? Evaluation of the Data Science Agent Evaluation of the Domain Expert Agent Evaluation of the Health Coach Agent Evaluation of the Integrated PHA System… Read More »Google AI Introduces Personal Health Agent (PHA): A Multi-Agent Framework that Enables Personalized Interactions to Address Individual Health Needs Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” In this tutorial, we present a complete end-to-end Natural Language Processing (NLP) pipeline built with Gensim and supporting libraries, designed to run seamlessly in Google Colab. It integrates multiple core techniques in modern NLP, including preprocessing, topic modeling with Latent Dirichlet Allocation (LDA), word… Read More »How to Build a Complete End-to-End NLP Pipeline with Gensim: Topic Modeling, Word Embeddings, Semantic Search, and Advanced Text Analysis Asif Razzaq Artificial Intelligence Category – MarkTechPost