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Intel Researchers Propose a New Artificial Intelligence Approach to Deploy LLMs on CPUs More Efficiently
Large Language Models (LLMs) have gained popularity for their text generation and language understanding capabilities. However, their adoption is challenging due to the large memory requirements. Intel researchers propose using quantization methods to reduce computational power on CPUs. Their approach includes INT-4 weight-only quantization and a specialized LLM runtime for efficient inference. Experimental results show…
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Towards Generative AI for Model Architecture
“Intelligent Model Architecture Design (MAD)” explores the idea of using generative AI to guide researchers in designing more effective and efficient deep learning model architectures. By leveraging techniques like Neural Architecture Search (NAS) and graph-based approaches, MAD aims to accelerate the discovery of new breakthroughs in model architecture design. The potential implications of self-improvement in…
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Comprehensive Guide: Supporting Customers on Social Media
Summary: Supporting customers on social media has become crucial for businesses. Social media platforms provide a convenient and direct way for customers to seek help and voice concerns. It allows for real-time problem-solving and provides opportunities to showcase expertise and personalize interactions. Businesses must identify their target audience, set up a dedicated support team, and…
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Steady the Course: Navigating the Evaluation of LLM-based Applications
LLM-based applications, powered by Large Language Models (LLMs), are becoming increasingly popular. However, as these applications transition from prototypes to mature versions, it’s important to have a robust evaluation framework in place. This framework will ensure optimal performance and consistent results. Evaluating LLM-based applications involves collecting data, building a test set, and measuring performance using…
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Humane, an OpenAI and Apple collaboration, drop the “AI Pin”
Humane, a startup led by former Apple innovators, has unveiled the AI Pin, a wearable projector priced at $699. The device functions as a personal assistant and comes with features like ultrawide camera capabilities, text/email communication, and AI responses. It was developed in collaboration with OpenAI and Microsoft. The AI Pin will start shipping in…
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Meta & GeorgiaTech Researchers Release a New Dataset and Associated AI Models to Help Accelerate Research on Direct Air Capture to Combat Climate Change
The OpenDAC project, a collaboration between Meta and Georgia Tech, aims to reduce the cost of Direct Air Capture (DAC) by identifying novel sorbents that efficiently remove CO2 from the air. They have created the ODAC23 dataset, the largest collection of Metal-Organic Framework (MOF) adsorption calculations, and released it to the research community to facilitate…
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The State of Sustainability in Agile – Reflections on SoSA 2023
The SoSA 2023 conference brought together the Agile community to address sustainability in social, environmental, and economic areas, setting a direction for global responsibility. This update was originally published on Agile Alliance. (51 words)
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A Key Start to MLOps: Exploring Its Essential Components
MLOps is a set of techniques and practices used to design, build, and deploy machine learning models efficiently. This tutorial provides a clear and comprehensive overview of MLOps, covering key topics such as the workflow, principles, tools, and best practices. It is a great resource for beginners looking to learn about MLOps.
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Structuring Your Cloud Instances’ Startup Scripts
The text discusses the separation between first launch and reboot when using startup scripts in cloud servers. It explains how user data is used to configure instances during the first launch and reboot, and provides an example of structuring the scripts to differentiate between the two. It also discusses centralizing startup scripts and overcoming character…
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OpenAI Data Partnerships
Collaboration to develop open-source and private datasets for AI training is emphasized.