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BRAG Unveils: Small Language Models (SLMs) Optimized for RAG Tasks Available for Under $25 each.

The BRAG series is a set of high-performance Retrieval Augmented Generation (RAG) models developed by Maximalists AI Researcher. They are a small language model designed to be a low-cost alternative for AI-driven language processing, proving effective in artificial intelligence due to their affordability and cost-effectiveness. They were created to meet the need for more powerful…

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The Progression of AI Agents: The Role of Workflow, Planning and Matrix Agents in Driving Business Automation

Artificial Intelligence (AI) is transforming a multitude of industries at an exponential rate. In particular, AI agents designed to streamline and automate various aspects of business operations are emerging as some of the most innovative recent developments. These agents broadly fall into three categories: Planning Agents, Workflow Agents, and Matrix Agents. Each type of agent…

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Improving Text Embeddings in Compact Language Models: A Comparative Refinement Method using MiniCPM.

Researchers from Tsinghua University have developed an approach to improve the performance of smaller language models such as MiniCPM, Phi-2, and Gemma by enhancing their text embeddings. By applying contrastive fine-tuning using the NLI dataset, the researchers significantly improved the text embedding quality across various benchmarks. In particular, MiniCPM showed a significant 56.33% performance improvement,…

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OpenPerplex.com’s Open-Source Launch: A Search Engine Powered by Artificial Intelligence

Despite their crucial function in our digital lives, many search engines still struggle to deliver relevant and accurate results, leading to user frustration. Often, these issues stem from limitations in the underlying technologies used by these search engines. Several have tried to address these problems by integrating advanced algorithms and machine learning models into their…

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OWLSAM2: An Innovative Progress in Zero-Shot Object Detection and Mask Creation through the Integration of OWLv2 and SAM2

OWLSAM2 is an innovative project that combines the strengths of OWLv2 and SAM2, two advanced models in the field of computer vision, to create a text-promptable model for zero-shot object detection and mask generation. OWLv2 stands out for its zero-shot object detection abilities that enable it to identify objects based on textual descriptions alone, without…

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OWLSAM2: A Groundbreaking Progress in Zero-Shot Object Identification and Mask Creation via the Integration of OWLv2 and SAM2

Introducing OWLSAM2: An unparalleled project that merges the sophisticated zero-shot object recognition attributes of OWLv2, renowned for its ability to identify objects in images without needing specific dataset training, and the highly advanced mask generation proficiencies of SAM2 (Segment Anything Model 2). This novel integration consequently leads to the creation of a text-prompted model that…

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To develop a superior AI assistant, begin by emulating the unpredictable actions of human beings.

Researchers at MIT and the University of Washington have developed a method to model the behavior of agents, either human or artificial, accounting for potential unknown computational constraints that could affect their problem-solving abilities. This model can predict an agent’s future behavior based on a few instances of their past actions - what they term…

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This compact microchip can protect user information while promoting effective computing on a mobile phone.

MIT and MIT-IBM Watson AI Lab researchers have invented a machine-learning chip resistant to the most common forms of cyberattacks. The technology caters to increasing demand for secure health-monitoring apps for individuals with chronic diseases or fitness goals, as well as for hardware-heavy uses such as autonomous vehicles and virtual reality. Health records and other…

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A dataset for Artificial Intelligence paves innovative ways for identifying tornadoes.

Tornadoes are one of nature's most destructive and unpredictable forces, causing billions of dollars in damages and claiming lives every year. Though they are difficult to predict, a new open-source dataset created by researchers from the MIT Lincoln Laboratory, known as TorNet, offers hope in improving our detection and prediction abilities. Composed of radar images…

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