​ This post is co-written with Marc Neumann, Amor Steinberg and Marinus Krommenhoek from BMW Group. The BMW Group – headquartered in Munich, Germany – is driven by 149,000 employees worldwide and manufactures in over 30 production and assembly facilities across 15 countries. Today, the… Read More »Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio Marc Neumann AWS Machine Learning Blog

​ In today’s ever-evolving world of ecommerce, the influence of a compelling product description cannot be overstated. It can be the decisive factor that turns a potential visitor into a paying customer or sends them clicking off to a competitor’s site. The manual creation of… Read More »Automating product description generation with Amazon Bedrock Dhaval Shah AWS Machine Learning Blog

​ Amazon SageMaker Canvas is a rich, no-code Machine Learning (ML) and Generative AI workspace that has allowed customers all over the world to more easily adopt ML technologies to solve old and new challenges thanks to its visual, no-code interface. It does so by… Read More »Optimizing costs for Amazon SageMaker Canvas with automatic shutdown of idle apps Davide Gallitelli AWS Machine Learning Blog

​ This post was co-written with Greg Benson, Chief Scientist; Aaron Kesler, Sr. Product Manager; and Rich Dill, Enterprise Solutions Architect from SnapLogic. Many customers are building generative AI apps on Amazon Bedrock and Amazon CodeWhisperer to create code artifacts based on natural language. This… Read More »How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action Greg Benson AWS Machine Learning Blog

​ Potential energy surfaces (PESs) represent the relationship between the positions of atoms or molecules and their associated potential energy. PESs are essential in understanding molecular behavior, chemical reactions, and material properties. They describe how the potential energy of a system changes as the positions… Read More »This AI Paper Introduces a Groundbreaking Machine Learning Model for Efficient Hydrogen Combustion Prediction: Leveraging ‘Negative Design’ and Metadynamics in Reactive Chemistry Mohammad Arshad Artificial Intelligence Category – MarkTechPost

​ LLM models have been increasingly deployed as potent linguistic agents capable of performing various programming-related activities. Despite these impressive advances, a sizable chasm still separates the capabilities demonstrated by these models in static experimental settings from the ever-changing demands of actual programming scenarios. Standard… Read More »This AI Paper Proposes ML-BENCH: A Novel Artificial Intelligence Approach Developed to Assess the Effectiveness of LLMs in Leveraging Existing Functions in Open-Source Libraries Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

​ Artificial Intelligence and Deep learning have brought about some great advancements in the field of technology. They are enabling robots to perform activities that were previously thought to be limited to human intelligence. AI is changing the way humans approach problems and bringing revolutionary… Read More »This AI Paper Introduces Φ-SO: A Physical Symbolic Optimization Framework that Uses Deep Reinforcement Learning to Discover Physical Laws from Data Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​ The highly parameterized nature of complex prediction models makes describing and interpreting the prediction strategies difficult. Researchers have introduced a novel approach using topological data analysis (TDA), to solve the issue. These models, including machine learning, neural networks, and AI models, have become standard… Read More »Decoding Complex AI Models: Purdue Researchers Transform Deep Learning Predictions into Topological Maps Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​ Large Language Models (LLMs), such as GPT-4 and LLaMA, have undoubtedly transformed the technological landscape. However, sluggish processing speed is a recurring challenge limiting their widespread applicability. Despite their remarkable capabilities, the time it takes to obtain responses from LLMs hinders their effectiveness, particularly… Read More »Researchers from Microsoft Research and Tsinghua University Proposed Skeleton-of-Thought (SoT): A New Artificial Intelligence Approach to Accelerate Generation of LLMs Madhur Garg Artificial Intelligence Category – MarkTechPost

​ A group of researchers from Nvidia have developed a new technique called Tied-LoRA, which aims to improve the parameter efficiency of the Low-rank Adaptation (LoRA) method. The course uses weight tying and selective training to find the optimal balance between performance and trainable parameters.… Read More »NVIDIA AI Researchers Propose Tied-Lora: A Novel Artificial Intelligence Approach that Aims to Improve the Parameter Efficiency of the Low-rank Adaptation (LoRA) Methods Sana Hassan Artificial Intelligence Category – MarkTechPost