​[[{“value”:” In today’s data-driven business landscape, the ability to efficiently extract and process information from a wide range of documents is crucial for informed decision-making and maintaining a competitive edge. However, traditional document processing workflows often involve complex and time-consuming manual tasks, hindering productivity and… Read More »Scalable intelligent document processing using Amazon Bedrock Venkata Kampana AWS Machine Learning Blog

​[[{“value”:” Photo by Zbynek Burival on Unsplash Time series forecasting is a specific machine learning (ML) discipline that enables organizations to make informed planning decisions. The main idea is to supply historic data to an ML algorithm that can identify patterns from the past and… Read More »Use weather data to improve forecasts with Amazon SageMaker Canvas Charles Laughlin AWS Machine Learning Blog

​[[{“value”:” Document understanding is a critical field that focuses on converting documents into meaningful information. This involves reading and interpreting text and understanding the layout, non-textual elements, and text style. The ability to comprehend spatial arrangement, visual clues, and textual semantics is essential for accurately… Read More »This AI Paper from Snowflake Evaluates GPT-4 Models Integrated with OCR and Vision for Enhanced Text and Image Analysis: Advancing Document Understanding Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Predicting the scaling behavior of frontier AI systems like GPT-4, Claude, and Gemini is essential for understanding their potential and making decisions about their development and use. However, it is difficult to predict how these systems will perform on specific tasks as they scale… Read More »How Scale Impacts Predicting Downstream Capabilities of Frontier AI Models: Understanding the Elusiveness Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Analogical reasoning, fundamental to human abstraction and creative thinking, enables understanding relationships between objects. This capability is distinct from semantic and procedural knowledge acquisition, which contemporary connectionist approaches like deep neural networks (DNNs) typically handle. However, these techniques often need help to extract relational… Read More »This AI Paper from Georgia Institute of Technology Introduces LARS-VSA (Learning with Abstract RuleS): A Vector Symbolic Architecture For Learning with Abstract Rules Mohammad Arshad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have become increasingly prominent in natural language processing because they can perform a wide range of tasks with high accuracy. These models require fine-tuning to adapt to specific tasks, which typically involves adjusting many parameters, thereby consuming substantial computational resources… Read More »Training on a Dime: MEFT Achieves Performance Parity with Reduced Memory Footprint in LLM Fine-Tuning Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In large language models, understanding how they work and what they pay attention to is crucial for improving their performance. However, analyzing the attention patterns of these models, especially in large-scale scenarios, can be daunting. Researchers and developers often need to gain insights into… Read More »Inspectus: An Open-Sourced Large Language Model LLM Attention Visualization Library Niharika Singh Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Researchers from C4DM, Queen Mary University of London, Sony AI, and Music X Lab, MBZUAI, have introduced Instruct-MusicGen to address the challenge of text-to-music editing, where textual queries are used to modify music, such as changing its style or adjusting instrumental components. Current methods… Read More »Instruct-MusicGen: A Novel Artificial Intelligence AI Approach to Text-to-Music Editing that Fosters Joint Musical and Textual Controls Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In a groundbreaking development, Timescale, the PostgreSQL cloud database company, has introduced two revolutionary open-source extensions, pgvectorscale, and pgai. These innovations have made PostgreSQL faster than Pinecone for AI workloads and 75% cheaper. Let’s explore how these extensions work and their implications for AI… Read More »A New Era AI Databases: PostgreSQL with pgvectorscale Outperforms Pinecone and Cuts Costs by 75% with New Open-Source Extensions Asif Razzaq Artificial Intelligence Category – MarkTechPost