DataMind AI

phidata, founded by Ashpreet B., is revolutionizing how we build and deploy AI agents! With an open-source foundation, phidata simplifies the entire process, empowering developers to create impactful AI-driven agents quickly and effectively. Their GitHub repository has a range of exciting issues that welcome contributions from the community — a great way to get involved and make a difference! Check it out: https://lnkd.in/d9tVc4H9

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Function is Hiring! Function founded by Yusuf Olokoba, a startup transforming how enterprises deploy AI, is looking for an AI Inference Engineer (contract position). The company helps package AI models into self-contained functions that can be deployed seamlessly across any platform—from VPC to on-premise to users' devices. The ideal candidate will help bring more open-source AI models to the platform, enabling flexible AI deployment at scale. If you know someone passionate about AI and model deployment in diverse environments, encourage them to apply! You can read more about the JD here - https://lnkd.in/d3b4dUjv #AIJobs #AIInference #OpenSourceAI #MachineLearning #Hiring

Exploring the Chain of Thought (CoT) Paradigm in AI Inference As Large Language Models (LLMs) continue to evolve, a crucial paradigm is transforming the way we think about AI reasoning: Chain of Thought (CoT) Sampling. Instead of focusing solely on generating direct answers, the CoT paradigm encourages models to "think out loud" — breaking down complex reasoning tasks into intermediate steps. This approach not only enhances the accuracy of model outputs but also significantly improves transparency, allowing us to better understand how an LLM arrives at its conclusions. ✨ Key benefits of Chain of Thought sampling: Improved Reasoning: By working through logical sequences step by step, the model mimics human-like critical thinking. Enhanced Interpretability: Greater insight into how AI processes information, making it easier to trust the outcomes. Handling Complexity: Better performance on complex tasks that require multi-step solutions. The CoT paradigm represents a shift in AI-driven decision-making and opens up new opportunities for more reliable and interpretable AI applications. At DataMind AI, we're excited about the possibilities that Chain of Thought holds for the future of AI inference! 🔗 Read more about the paradigm and its implications here( written by Harold B.) : https://lnkd.in/dZb8RZwR #AI #MachineLearning #ChainofThought #LLM #ArtificialIntelligence #DataMindAI #AIInference #AIResearch

Breaking ground in Vision Transformers! The latest research on SATA: Spatial Autocorrelation Token Analysis offers a powerful new method to enhance ViTs’ robustness and performance. With SATA, pre-trained ViTs now achieve a stunning 94.9% top-1 accuracy on ImageNet-1K, all while reducing computational costs and improving resistance to complex datasets. What’s even better? No retraining or fine-tuning needed. This could be a huge leap forward for vision-based AI applications! Check it out here: https://lnkd.in/detqv6zQ #AI #VisionTransformers #DeepLearning #MachineLearning #Innovation

🌟 Introducing the Kolmogorov-Arnold Transformer (KAT)! 🚀 I'm thrilled to share insights from the recent paper by Xingyi Yang and Xinchao Wang, which presents the Kolmogorov-Arnold Transformer (KAT)—a significant evolution in transformer architecture. Traditionally, transformers use multi-layer perceptron (MLP) layers for channel information mixing. KAT replaces these MLP layers with Kolmogorov-Arnold Network (KAN) layers, enhancing both expressiveness and performance. Key Challenges Addressed: Base Function: The standard B-spline function used in KANs was not optimized for parallel computing, leading to slower inference speeds. Parameter and Computation Inefficiency: Each input-output pair required a unique function, resulting in large computational demands. Weight Initialization: The initialization of weights in KANs posed challenges, crucial for ensuring convergence. Innovative Solutions Proposed: Rational Basis: The introduction of rational functions in place of B-splines enhances compatibility with modern GPUs, improving computation speed. Group KAN: By sharing activation weights among groups of neurons, computational load is reduced without sacrificing performance. Variance-Preserving Initialization: This approach maintains activation variance across layers, promoting better convergence. With these advancements, KAT demonstrates superior scalability and performance compared to traditional MLP-based transformers. I'm excited to see how these innovations will shape the future of deep learning! What are your thoughts on the implications of KAT in the AI landscape? paper: https://lnkd.in/djZJaZyr code: https://t.co/VeZeBUCMd7 #AI #DeepLearning #Transformers #Innovation #DatamindAI

🚀 𝗧𝘄𝗼 𝘄𝗮𝘆 𝗩𝗼𝗶𝗰𝗲-𝗘𝗻𝗮𝗯𝗹𝗲𝗱 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝗮𝘁𝗯𝗼𝘁 🚀 𝗟𝗟𝗠 - 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 9 We're thrilled to introduce our Voice-Enabled Conversational Chatbot! This AI-powered chatbot allows you to interact seamlessly by speaking and receiving responses in both voice and text formats. Whether you're looking for a hands-free experience or just want to engage in natural conversation, this project delivers a simple yet dynamic chatbot interaction. 🔊 Speak to interact – Ask your questions or chat like you would with a friend!  🗣️ Voice and text responses – Receive replies in both voice and text for a fully immersive experience.  ⚙️ Powered by 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 and 𝗘𝗹𝗲𝘃𝗲𝗻𝗟𝗮𝗯𝘀, making the conversations sound natural and fluid. 📂 Bookmark our GitHub repository and try it yourself! 🔧 Have questions? We’re here to help! Join us as we redefine how we consume video content—one conversation at a time. 🔔 Follow us on LinkedIn for more exciting AI projects, tutorials, and updates. GitHub Link - https://lnkd.in/dNiiekum #AI #MachineLearning #LangChain #ElevenLabs #VoiceChatbot #AIChatbot #TechInnovation #OpenSource #GitHub

🚀 𝗖𝗵𝗮𝘁 𝘄𝗶𝘁𝗵 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗩𝗶𝗱𝗲𝗼𝘀 𝗨𝘀𝗶𝗻𝗴 𝗔𝗜 🚀 𝗟𝗟𝗠 - 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 8 We’re excited to introduce our 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝘁𝗯𝗼𝘁 𝗔𝗽𝗽, a tool that allows you to converse with YouTube videos using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 and 𝗢𝗽𝗲𝗻𝗔𝗜! Now, you can easily extract meaningful insights, summaries, or detailed discussions from videos in a chat-like interface. Here’s how it works:   1) Simply paste a YouTube link.   2) Transcribe the video with OpenAI Whisper.   3) Engage with the content in a dynamic chatbot format. 📂 Bookmark our GitHub repository and try it yourself! This app is ideal for extracting insights, reviewing long content, and interacting with videos in an intuitive way. 💡 Pro Tip: Using the Whisper API offers faster results but might incur costs. Explore the flexibility of local transcription too! 🔧 Have questions? We’re here to help! Join us as we redefine how we consume video content—one conversation at a time. 🔔 Follow us on LinkedIn for more exciting AI projects, tutorials, and updates. GitHub Link - https://lnkd.in/dTHXnWDr #AI #MachineLearning #LangChain #OpenAI #YouTubeChatbot #WhisperAPI #Streamlit #Python #LLM #AIProjects #TechInnovation #GitHub #ArtificialIntelligence #OpenSource #Coding #TechCommunity #AIResearch

🚀 𝗧𝗿𝗮𝗻𝘀𝗰𝗿𝗶𝗯𝗲 & 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝘇𝗲 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗩𝗶𝗱𝗲𝗼𝘀 𝘄𝗶𝘁𝗵 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 𝗮𝗻𝗱 𝗢𝗽𝗲𝗻𝗔𝗜 🚀 𝗟𝗟𝗠 - 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝟳 We’re thrilled to announce our latest project video where we demonstrate how to build a powerful tool that can transcribe and summarize YouTube videos effortlessly using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 and 𝗢𝗽𝗲𝗻𝗔𝗜 𝗪𝗵𝗶𝘀𝗽𝗲𝗿. Whether you're looking to extract key insights from long-form content or just want to get a quick summary, this tool has you covered! 📂 Bookmark our GitHub repository for daily updates, and try the code yourself to see how easily you can integrate this into your projects!  💡 Pro Tip: Use the Whisper API option for even faster results, but keep in mind it may incur additional costs. 🔧 Facing any issues? We’re here to help! Don’t hesitate to reach out as you explore the exciting possibilities of transcribing and summarizing video content with AI.  🔔 Follow us on LinkedIn for the latest updates, tutorials, and cutting-edge AI projects. Join us in pushing the boundaries of what's possible with AI, one project at a time. The future of content consumption is here—simplified and powered by AI! GitHub Link - https://lnkd.in/dMyRW8SB #AI #MachineLearning #LangChain #OpenAI #YouTubeTranscription #VideoSummarization #WhisperModel #Streamlit #Python #LLM #AIProjects #TechInnovation #GitHub #ArtificialIntelligence #Coding #OpenSource #TechCommunity #AIResearch

🚀 𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗮 𝗩𝗼𝗶𝗰𝗲-𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗱 𝗖𝗵𝗮𝘁𝗯𝗼𝘁 𝘄𝗶𝘁𝗵 𝗟𝗮𝗻𝗴𝗰𝗵𝗮𝗶𝗻 𝗮𝗻𝗱 𝗢𝗽𝗲𝗻𝗔𝗜 🚀 𝗟𝗟𝗠 - 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝟲 We're excited to unveil our latest video where we build a voice-controlled chatbot using 𝗟𝗮𝗻𝗴𝗰𝗵𝗮𝗶𝗻. This innovative chatbot combines the power of Large Language Models (𝗟𝗟𝗠𝘀) with 𝗦𝗽𝗲𝗲𝗰𝗵𝗥𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 python library, allowing for seamless and intuitive conversations. The chatbot can handle multiple voice-based chat sessions, making interactions more natural and engaging. 🎤 Watch the video now to discover how you can create your voice-controlled chatbot and revolutionize user interactions!  📂 Bookmark our GitHub repository to follow along with daily updates and try the code yourself.  🔧 If you encounter any challenges while building your chatbot, don’t hesitate to reach out—we’re here to support your AI journey!  🔔 Follow our LinkedIn page to stay informed about our latest projects and developments. Let's push the boundaries of AI together, one project at a time. The future is voice-powered! GitHub Link - https://lnkd.in/d9Th6zrj #AI #LLMs #LangChain #Chatbot #VoiceAI #ConversationalAI #ArtificialIntelligence #MachineLearning #AIInnovation #TechDevelopment #OpenAI #DailyProjects #Innovation #TechCommunity