Recent methods have demonstrated that Large Language Models (LLMs) can solve reasoning tasks better when they are encouraged to solve subtasks of the main task first. In this paper we devise a similar strategy that breaks down reasoning tasks into a problem decomposition phase and… Read More »Divide-or-Conquer? Which Part Should You Distill Your LLM? Apple Machine Learning Research
[[{“value”:” The rapid growth of large language models (LLMs) has brought significant advancements across various sectors, but it has also presented considerable challenges. Models such as Llama 3 have made impressive strides in natural language understanding and generation, yet their size and computational requirements have… Read More »Meta AI Releases New Quantized Versions of Llama 3.2 (1B & 3B): Delivering Up To 2-4x Increases in Inference Speed and 56% Reduction in Model Size Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” Vision-language models (VLMs) are gaining prominence in artificial intelligence for their ability to integrate visual and textual data. These models play a crucial role in fields like video understanding, human-computer interaction, and multimedia applications, offering tools to answer questions, generate captions, and enhance decision-making… Read More »Salesforce AI Research Introduces BLIP-3-Video: A Multimodal Language Model for Videos Designed to Efficiently Capture Temporal Information Over Multiple Frames Nikhil Artificial Intelligence Category – MarkTechPost
[[{“value”:” Artificial intelligence will be the driving force behind India’s digital transformation, fueling innovation, economic growth, and global leadership, NVIDIA founder and CEO Jensen Huang said Thursday at NVIDIA’s AI Summit in Mumbai. Addressing a crowd of entrepreneurs, developers, academics and business leaders, Huang positioned… Read More »‘India Should Manufacture Its Own AI,’ Declares NVIDIA CEO Brian Caulfield – Archives Page 1 | NVIDIA Blog
[[{“value”:” Large language models (LLMs) are very large deep-learning models that are pre-trained on vast amounts of data. LLMs are incredibly flexible. One model can perform completely different tasks such as answering questions, summarizing documents, translating languages, and completing sentences. LLMs have the potential to… Read More »Super charge your LLMs with RAG at scale using AWS Glue for Apache Spark Noritaka Sekiyama AWS Machine Learning Blog
[[{“value”:” The AWS Generative AI Innovation Center (GenAIIC) is a team of AWS science and strategy experts who have deep knowledge of generative AI. They help AWS customers jumpstart their generative AI journey by building proofs of concept that use generative AI to bring business… Read More »From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 1 Aude Genevay AWS Machine Learning Blog
[[{“value”:” Machine learning, particularly the training of large foundation models, relies heavily on the diversity and quality of data. These models, pre-trained on vast datasets, are the foundation of many modern AI applications, including language processing, image recognition, and more. The effectiveness of foundation models… Read More »Adaptive Data Optimization (ADO): A New Algorithm for Dynamic Data Distribution in Machine Learning, Reducing Complexity and Improving Model Accuracy Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. Conventional ML development cycles take weeks… Read More »Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas Suresh Patnam AWS Machine Learning Blog
[[{“value”:” In this post, we set up an agent using Amazon Bedrock Agents to act as a software application builder assistant. Agentic workflows are a fresh new perspective in building dynamic and complex business use- case based workflows with the help of large language models… Read More »Create a generative AI-based application builder assistant using Amazon Bedrock Agents Shayan Ray AWS Machine Learning Blog
[[{“value”:” Amazon Rekognition people pathing is a machine learning (ML)–based capability of Amazon Rekognition Video that users can use to understand where, when, and how each person is moving in a video. This capability can be used for multiple use cases, such as for understanding:… Read More »Transitioning from Amazon Rekognition people pathing: Exploring other alternatives Fangzhou Cheng AWS Machine Learning Blog