​[[{“value”:” Posted by Jason Jabbour, Kai Kleinbard and Vijay Janapa Reddi (Harvard University) Everyone wants to do the modeling work, but no one wants to do the engineering. If ML developers are like astronauts exploring new frontiers, ML systems engineers are the rocket scientists designing… Read More »MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering noreply@blogger.com (TensorFlow Blog) The TensorFlow Blog

​[[{“value”:” Planning and decision-making in complex, partially observed environments is a significant challenge in embodied AI. Traditionally, embodied agents rely on physical exploration to gather more information, which can be time-consuming and impractical, especially in large-scale, dynamic environments. For instance, autonomous driving or navigation in… Read More »John Hopkins Researchers Introduce Genex: The AI Model that Imagines its Way through 3D Worlds Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Using large language models (LLMs) has revolutionized artificial intelligence applications, enabling breakthroughs in natural language processing tasks like conversational AI, content generation, and automated code completion. Often with billions of parameters, these models rely on massive memory resources to store intermediate computation states and… Read More »This AI Paper from UC Berkeley Introduces Pie: A Machine Learning Framework for Performance-Transparent Swapping and Adaptive Expansion in LLM Inference Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Log-based anomaly detection has become essential for improving software system reliability by identifying issues from log data. However, traditional deep learning methods often struggle to interpret the semantic details in log data, typically in natural language. LLMs, like GPT-4 and Llama 3, have shown… Read More »LogLLM: Leveraging Large Language Models for Enhanced Log-Based Anomaly Detection Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” One popular term encountered in generative AI practice is retrieval-augmented generation (RAG). Reasons for using RAG are clear: large language models (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. The haphazard results may be… Read More »Unbundling the Graph in GraphRAG Paco Nathan AI & ML – Radar

​[[{“value”:” Effective lesson structuring remains a critical challenge in educational settings, particularly when conversations and tutoring sessions need to address predefined topics or worksheet problems. Educators face the complex task of optimally allocating time across different problems while accommodating diverse student learning needs. This challenge… Read More »Stanford Researchers Propose ‘POSR’: A Unique AI Framework for Analyzing Educational Conversations Using Joint Segmentation and Retrieval Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The rapid expansion of data in today’s era has brought with it both possibilities and difficulties. Businesses handle and use this data to their advantage with the help of some techniques. With their own unique architecture, capabilities, and optimum use cases, data warehouses and… Read More »Big Data vs Data Warehouse Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Foundation models (FMs) and large language models (LLMs) are revolutionizing AI applications by enabling tasks such as text summarization, real-time translation, and software development. These technologies have powered the development of autonomous agents that can perform complex decision-making and iterative processes with minimal human… Read More »This AI Paper Explores AgentOps Tools: Enhancing Observability and Traceability in Foundation Model FM-Based Autonomous Agents Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have advanced exponentially since the last decade. However, LLMs still need to improve regarding deployment and utilization, particularly in the areas of computational cost, latency, and output accuracy. This limits the accessibility of LLMs to smaller organizations, degrades the user… Read More »OptiLLM: An OpenAI API Compatible Optimizing Inference Proxy which Implements Several State-of-the-Art Techniques that can Improve the Accuracy and Performance of LLMs Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Drug discovery is a costly, lengthy process with high failure rates, as only one viable drug typically emerges from a million screened compounds. Advanced high-throughput (HTS) and ultra-high-throughput screening (uHTS) technologies allow rapid testing of large compound libraries, enabling Pharma and Biotech companies to… Read More »VirtuDockDL: A Deep Learning-Powered Platform for Accelerated Drug Discovery through Advanced Compound Screening and Binding Prediction Sana Hassan Artificial Intelligence Category – MarkTechPost