Trevor Vickers’ Post

Artificial intelligence (AI) technology is no longer considered futuristic as it’s applied in many business sectors. Ground-breaking AI applications can process images, detect objects, and recognize human speech.  In 2021, the global artificial intelligence market reached a value of $56.5 billion, according to IMARC Group. By 2027, this figure could reach $308.5 billion.   One of the essential trends in software development this year is the use of TensorFlow for AI development. TensorFlow is an open-source deep learning framework developed by Google. This complex mathematical tool is currently the leading platform for building and training neural networks. On the one hand, this is a comprehensive ecosystem of libraries, tools, and various resources needed to create intelligent models and algorithms. On the other hand, the framework is flexible and user-friendly, making it easy for developers to build advanced software. Why is TensorFlow so popular? Because it: ---Supports several programming languages, including Java, C++, and Python.  ---Runs on multiple CPUs, GPUs, and mobile operating systems. ---Can be deployed to the cloud, locally, in a browser, or on a device. ---Has built-in intuitive APIs. ---Ensures immediate model iteration and easy debugging. ---Allows for conducting complex research experiments.

Artificial intelligence (AI) technology is no longer considered futuristic as it’s applied in many business sectors. Ground-breaking AI applications can process images, detect objects, and recognize human speech.  In 2021, the global artificial intelligence market reached a value of $56.5 billion, according to IMARC Group. By 2027, this figure could reach $308.5 billion.   One of the essential trends in software development this year is the use of TensorFlow for AI development. TensorFlow is an open-source deep learning framework developed by Google. This complex mathematical tool is currently the leading platform for building and training neural networks. On the one hand, this is a comprehensive ecosystem of libraries, tools, and various resources needed to create intelligent models and algorithms. On the other hand, the framework is flexible and user-friendly, making it easy for developers to build advanced software. Why is TensorFlow so popular? Because it: ---Supports several programming languages, including Java, C++, and Python.  ---Runs on multiple CPUs, GPUs, and mobile operating systems. ---Can be deployed to the cloud, locally, in a browser, or on a device. ---Has built-in intuitive APIs. ---Ensures immediate model iteration and easy debugging. ---Allows for conducting complex research experiments.

Just came across an insightful blog discussing the training of LLMs using Intel's Gaudi2 with a 3D parallelism approach. Here's what's covered at a high-level: ✅ A Dive deep into Data Parallelism, Tensor Parallelism, and Pipeline Parallelism. ✅ Explore the power of Habana SynapseAI(R) and DeepSeed in bringing this 3D parallelism to life on the Gaudi2. For anyone passionate about AI training and optimizations, this is a must-read! Feel free to share and let's discuss the innovations in the comments! #AI #Intel #Gaudi2 #Parallelism #DeepLearning https://lnkd.in/g4xyDiJd

LLM based development tools: PromptFlow vs LangChain vs Semantic Kernel. Introduction Data is the power behind human civilization and knowledge. Over the past decades, we have seen an increase in data shared across the internet. With the massive availability of data as well as advanced compute, models were trained to take advantage of the data to process and understand natural language. Models were trained to produce new and original content bringing about Generative AI.   Globally, developers, data scientists, and engineers created new applications or advanced their existing applications to take advantage of LLMs. While building a... #techcommunity #azure #microsoft https://lnkd.in/gYSrGAhw

🚀 𝗚𝗿𝗼𝗾: 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗔𝗜 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗨𝗻𝗺𝗮𝘁𝗰𝗵𝗲𝗱 𝗦𝗽𝗲𝗲𝗱, 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆, 𝗮𝗻𝗱 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 🚀 I'm thrilled to share an innovative breakthrough that's setting new benchmarks in artificial intelligence and machine learning - Groq's latest development, a game-changer in AI processing that's all about speed, efficiency, and beyond! Founded with the mission to revolutionize real-time AI applications, Groq introduces the Groq chip, a tensor processing unit designed to accelerate deep learning applications with unparalleled speed and energy efficiency. 🌟 🔍 𝗪𝗵𝘆 𝗚𝗿𝗼𝗾 𝗦𝘁𝗮𝗻𝗱𝘀 𝗢𝘂𝘁: Speed & Efficiency: Their cutting-edge Language Processing Unit (LPU) is specifically optimized for processing large language models (LLMs), significantly outperforming traditional hardware in both speed and efficiency. Ease of Use: With GroqPy, a high-level programming language, developers can easily program the Groq chip using Python, making advanced AI accessible to a wider range of programmers. Support for Standard ML Frameworks: Integration with popular ML frameworks like PyTorch, TensorFlow, and ONNX ensures a smooth experience for AI development. 🚀 𝗚𝗿𝗼𝗾𝗖𝗹𝗼𝘂𝗱 & 𝗔𝗣𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Get started in minutes with Groq's user-friendly API, supporting top-tier models like LLaMA2–70b and Mixtral-8x7b. Plus, their innovative Conversational Chatbot, powered by LangChain and LlamaIndex, offers a conversational interface with contextual, human-like responses. This leap in technology isn't just about improving processing power or efficiency; it's about enabling new possibilities in AI that were previously unthinkable. Discover more about how Groq is paving the way for the future of AI at Groq's website https://groq.com/ . 🔗 Dive deeper into how Groq is shaping the future of AI, and let's discuss the endless opportunities this opens up for developers and businesses alike. #AI #MachineLearning #DeepLearning #Groq #TechnologyInnovation #FutureOfAI

Learn with me #LearnWithMe Unlocking AI's Potential with Phi-3, C#, and ONNX: A Game-Changer for Text and Vision Samples In a recent blog post on the .NET Blog, Microsoft highlights the power of combining Phi-3, C#, and ONNX to accelerate AI development for text and vision samples. You will find use cases and source code! Here's a breakdown of the key points and their implications: =>What's Phi-3? Phi-3 is a Phi-3 Small Language Model (SLM) https://lnkd.in/dXsWjTye =>What's ONNX? ONNX is an open-source format that allows AI models to be portable and interoperable across different frameworks and hardware. https://lnkd.in/d5wQ6-Kh =>The Power of Phi-3 and C# with ONNX The Phi-3 Cookbook repository showcases how these powerful models can be utilized for tasks like question-answering and image analysis within a .NET environment using C# and ONNX. https://lnkd.in/dhSm-dYv Developers will: - Accelerate AI Workloads - Simplify AI Integration - Enhance Model Performance => Full article: https://lnkd.in/dNWuvrHe #AI #llms #Productivity #Technology #ArtificialIntelligence #msftadvocate

Thrilled to complete the 'Fundamentals of AI & ML: Advanced Data Science Methods' course! Excited to leverage AI techniques to elevate embedded software development, ensuring higher quality and efficiency. 🌐💻 #AI #ML #EmbeddedSystems #SoftwareDevelopment"

Meet vLLM: An Open-Source Machine Learning Library for Fast LLM Inference and Serving - MarkTechPost: Large language models (LLMs) such as programming assistants and universal chatbots have a significant impact on daily lives and careers. However, operating these applications can be expensive due to hardware requirements. Researchers have proposed PagedAttention, an attention algorithm inspired by virtual memory and paging techniques, to optimize memory utilization and boost throughput. This approach redefines the state of the art in LLM serving systems and enables more efficient GPU utilization. The researchers also found that the system improves the throughput of LLMs and decoding algorithms, especially for larger models and longer sequences. - Artificial Intelligence topics! #ai #artificialintelligence #intelligenzaartificiale

AI technology is rapidly advancing, and machine learning is a key driver of this progress. Through machine learning algorithms, AI systems can analyze vast amounts of data, extract insights, and expand their knowledge base. With more data, AI systems can make more accurate predictions and decisions. Programming languages used in machine learning, such as Python and R, are crucial for developing and implementing AI systems. These languages provide the tools and frameworks necessary to build and train machine learning models, process data, and deploy AI solutions. In a highly competitive landscape, AI companies differentiate themselves through their expertise, innovation, access to quality data, domain-specific knowledge, ethical considerations, and collaborations. Companies that prioritize ethical practices and demonstrate responsible AI deployment may have a competitive edge, especially when working with clients who value ethical considerations. While competition exists, the AI landscape is also characterized by collaboration and cooperation. Companies often work together to advance the field, share knowledge, and address common challenges. Stay ahead of the game by keeping up with the latest trends and innovations in AI and machine learning. #AI #MachineLearning #CompetitionInAI

TensorFlow Boosts AI Development Artificial intelligence (AI) technology is no longer considered futuristic as it’s applied in many business sectors. Ground-breaking AI applications can process images, detect objects, and recognize human speech. In 2021, the global artificial intelligence market reached a value of $56.5 billion, according to IMARC Group. By 2027, this figure could reach $308.5 billion. When creating such software systems, programmers implement deep learning concepts using special AI libraries and frameworks. And one of the essential trends in software development this year is the use of TensorFlow for AI development. TensorFlow is an open-source deep learning framework developed by Google. This complex mathematical tool is currently the leading platform for building and training neural networks. On the one hand, this is a comprehensive ecosystem of the libraries, tools, and various resources needed to create intelligent models and algorithms. On the other hand, the framework is flexible and user-friendly, making it easy for developers to build advanced software. Why is TensorFlow so popular? Because it: **Supports several programming languages, including Java, C++, and Python. **Runs on multiple CPUs, GPUs, and mobile operating systems. **Can be deployed to the cloud, locally, in a browser, or on a device. **Has built-in intuitive APIs. **Ensures immediate model iteration and easy debugging. **Allows for conducting complex research experiments.