​[[{“value”:” In a world where visual content is increasingly essential, the ability to create and manipulate images with precision and creativity is invaluable. Black Forest Labs, with its FLUX.1 Tools, expands the possibilities of text-to-image generation. Designed to bring control and flexibility to their base… Read More »Black Forest Labs Release FLUX.1 Tools: A Suite of AI Models Designed to Add Control and Steerability to the Base Text-to-Image Model FLUX.1 Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​[[{“value”:” As generative AI models advance in creating multimedia content, the difference between good and great output often lies in the details that only human feedback can capture. Audio and video segmentation provides a structured way to gather this detailed feedback, allowing models to learn… Read More »Enhance speech synthesis and video generation models with RLHF using audio and video segmentation in Amazon SageMaker Sundar Raghavan AWS Machine Learning Blog

​[[{“value”:” Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to… Read More »Using responsible AI principles with Amazon Bedrock Batch Inference Ishan Singh AWS Machine Learning Blog

​[[{“value”:” Today, we’re excited to share the journey of the VW—an innovator in the automotive industry and Europe’s largest car maker—to enhance knowledge management by using generative AI, Amazon Bedrock, and Amazon Kendra to devise a solution based on Retrieval Augmented Generation (RAG) that makes… Read More »Revolutionizing knowledge management: VW’s AI prototype journey with AWS Rui Costa AWS Machine Learning Blog

​[[{“value”:” Fine-tuning foundation models (FMs) is a process that involves exposing a pre-trained FM to task-specific data and fine-tuning its parameters. It can then develop a deeper understanding and produce more accurate and relevant outputs for that particular domain. In this post, we show how… Read More »Fine-tune large language models with Amazon SageMaker Autopilot Hajer MKacher AWS Machine Learning Blog

​[[{“value”:” Recent advancements in natural language processing (NLP) have introduced new models and training datasets aimed at addressing the increasing demands for efficient and accurate language models. However, these advancements also present significant challenges. Many large language models (LLMs) struggle to balance performance with efficiency,… Read More »SmolTalk Released: The Dataset Recipe Behind the Best-in-Class Performance of SmolLM2 Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Quantum computing (QC) stands at the forefront of technological innovation, promising transformative potential across scientific and industrial domains. Researchers recognize that realizing this potential hinges on developing accelerated quantum supercomputers that seamlessly integrate fault-tolerant quantum hardware with advanced computational systems. These heterogeneous architectures are… Read More »Artificial Intelligence AI and Quantum Computing: Transforming Computational Frontiers Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In natural language processing (NLP), a central question is how well the probabilities generated by language models (LMs) align with human linguistic behavior. This alignment is often assessed by comparing LM scores with human acceptability judgments, which evaluate how natural a sentence feels. Previous… Read More »MORCELA: A New AI Approach to Linking Language Models LM Scores with Human Acceptability Judgments Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In the evolving field of machine learning, fine-tuning foundation models such as BERT or LLAMA for specific downstream tasks has become a prevalent approach. However, the success of such fine-tuning depends not only on the model but also heavily on the quality and relevance… Read More »Task-Specific Data Selection: A Practical Approach to Enhance Fine-Tuning Efficiency and Performance Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Vision Transformers (ViTs) have revolutionized computer vision by offering an innovative architecture that uses self-attention mechanisms to process image data. Unlike Convolutional Neural Networks (CNNs), which rely on convolutional layers for feature extraction, ViTs divide images into smaller patches and treat them as individual… Read More »Attention Transfer: A Novel Machine Learning Approach for Efficient Vision Transformer Pre-Training and Fine-Tuning Nikhil Artificial Intelligence Category – MarkTechPost