BentoML’s Post

Simon Willison's latest piece on transforming files into AI prompts offers a fascinating glimpse into how generative AI can extend its capabilities beyond traditional boundaries. As professionals in the AI sector, it's crucial to recognize and leverage these evolving tools to enhance our workflows and deliver innovative solutions. This approach not only streamlines interactions between complex data and AI models but also opens up new avenues for creativity and efficiency in our projects. How do you see the integration of file-based inputs shaping the future of AI in your work? For a deeper dive into Simon Willison's insights, check out the full article: https://lnkd.in/gNv2JgiY #TechSherpa #AI #DataScience #Innovation

#LangGraph , a new framework by @LangChainAI , provides an easy way to create cyclical graphs useful when creating #AI #agent runtimes

🚀 Excited to share my latest Medium blog on "Custom Object Detection with YOLOv7: A Step-by-Step Guide!" 🎯 In this comprehensive guide, I walk you through the process of training YOLOv7 on custom data for object detection. Whether you're new to AI or looking to enhance your skills, this step-by-step tutorial covers everything: ✅ Setting up your environment and installing dependencies. ✅ Preparing and labeling your custom dataset using YOLO format. ✅ Configuring YOLOv7 for training with your specific data. ✅ Training the model and evaluating performance metrics like mAP@0.5:0.95. ✅ Making inferences with your custom-trained YOLOv7 model. If you're ready to dive into the world of custom object detection and boost your AI capabilities, check out the full guide here : https://lnkd.in/gJtpxsZ2 #AI #ObjectDetection #YOLOv7 #MachineLearning #ComputerVision

Very good intro guide with example to AI agent with ReAct pattern https://lnkd.in/dZPMdnPF

Just a moment...: The text describes effortlessly mastering the intricacies of dockerization from inception to deployment. For further details, please refer to the article "Artificial Intelligence in Plain English." - Artificial Intelligence topics! #ai #artificialintelligence #intelligenzaartificiale

What isn't smart about AI? I can't help but think about the low-level AI model values we set (hyperparameters), which seem more like a black box solved with brute force methods compared to the rest of the end-to-end process. Yes, there are papers that guide us toward optimal hyperparameters, but behind these papers, there is often a lot of testing with random values. This isn't new. I even spent time trying to solve this using reinforcement learning to determine the best hyperparameters with the project Convstruct (https://lnkd.in/gX-7pGwF). The issue with using AI to solve this is that it takes time to learn what parameters a model should have, and this time is expensive. NVIDIA released a paper (https://lnkd.in/gwQJNxZr) that looks at this issue in a different way. Instead of learning the parameters before training a model, they focus on the parameters during training. It's clever, and I think it's a great direction! Check out their open-source code here: https://lnkd.in/gXHD9gvH

When it comes to developing a machine learning model that performs well on unknown data, there are crucial steps that must be taken. One of those steps is splitting data into training and testing sets. In this post, we'll focus on the importance of this step when working on a supervised learning problem. Read the article here: https://lnkd.in/duYEjBz8 #MachineLearning #DataScience #SupervisedLearning #AI #ArtificialIntelligence #technology

Kedro is featured in a new blog post by QuantumBlack, AI by McKinsey that highlights how it can be used to accelerate the path to production for AI. > Organizations face significant challenges when scaling their AI efforts beyond experiments and proof-of-concept models. This article examines how Kedro, an open-source Python framework created by the QuantumBlack Labs team to create reproducible, maintainable, and modular code, can help. Kedro has almost ~17M downloads and 10K stars on GitHub to date. It is used in many different fields. Developers from over 250 different companies have worked with Kedro as super-users in the last year. Continue reading on Medium: https://lnkd.in/eddzh59e

The remarkable "zero-shot" performance of multimodal models like CLIP and Stable-Diffusion hinges on vast web-crawled pretraining datasets. Yet, the significance of this "zero-shot" generalization is uncertain. Researchers investigated how well multimodal models perform on downstream concepts relative to their occurrence in pretraining data. Their study, spanning 34 models and five standard datasets, revealed a sobering truth: these models don't achieve "zero-shot" generalization. Instead, they need exponentially more data for marginal performance gains, defying efficiency. Even controlling for dataset similarity, the trend persists. Benchmarking against a long-tail dataset showed universal poor performance. The study contributes the Let it Wag! benchmark, highlighting the need for deeper understanding in achieving "zero-shot" capabilities under large-scale training paradigms. https://lnkd.in/ePbVuyVy #multimodal #CLIP #StableDiffusion #zeroshot #generalization #research #benchmarking #LetItWag #AI #machinelearning #LinkedIn

Combining logical reasoning with LLMs with Neuro Symbolic AI and Defeasible Reasoning (with a Python code example). https://lnkd.in/e2K4YJU5 #llm #logic #reasoning #chatgpt #neurosymbolic #ai