Union.ai’s Post

Menlo VC just dropped a deep dive on AI agents 2 insights: 1. A nice market map x-axis = vertical, horizontal y-axis = more or less LLM autonomy 2. Common AI agent architectures: - RAG - RAG + Promp Chaining - Tool Use - Decisioning Agent

Power system Operations to become easier and control room operators would get the best real time support from Gen AI soon.

Our latest Forum on Artificial Intelligence was a thought-provoking event, with Sébastien Pinto da França Roux from LIMSEN presenting a fascinating comparative study of AI-powered building design platforms. ️ The discussion highlighted the growing relevance of AI in architecture. Its ability to process vast amounts of data, automate repetitive tasks, and generate creative solutions has the potential to revolutionise the industry. We don't know how far AI will go, but we know we'll be there.

As someone deeply immersed in the world of data science, I'm excited to share our take on AI agents in our latest blog post: https://lnkd.in/d7JkwT7C Here's what you'll learn: 🔸 Architectural choices for multi-agent systems 🔸 How we blend GenAI with traditional ML for optimal results 🔸 Ensuring transparency and reliability in automated workflows While we're making great strides, it's important to remember that we're still in the early stages of this technology. What we are doing is cutting-edge, so best practices are being worked out as we go. That's why collaboration and knowledge-sharing in our industry are more crucial than ever!

Imagine you had millions upon millions of sq feet of real estate visual data. Imagine you had a strong AI team with years of building ML/AI pipelines for multimodal data. Imagine you had some of the largest contractors and builders as customers. Imagine you could build workflow pipelines on top of all the data, simulation, and analytics. Imagine you were OpenSpace! With cofounder/CEO Jeevan Kalanithi.

Pinecone CTO Ram Sriharsha sat down with Richard Cotton at DataCamp to discuss common use cases for vector databases, RAG in chatbots, steps to create a chatbot, static vs dynamic data, and many more critical components of today's AI infrastructure landscape. 👀 👂

AI is already deeply integrated into our lives, but it is important to understand how it works rather than accepting it as a “black box.” TRANSFORMER EXPLAINER is a powerful tool for cultivating the “ability to understand,” which is essential in this age of AI. It will be a great help not only to AI developers but also to those of us who use AI.

Going from a Cool Demo to Production grade RAG is one of the biggest challenges for enterprises adopting GenAI. Nexla takes on three of the following biggest challenges with that in collaboration with partners, including Pinecone - the leading innovator in vector databases: 1. 𝐒𝐜𝐚𝐥𝐞 & 𝐑𝐢𝐜𝐡𝐧𝐞𝐬𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 with converged feeds across unstructured documents, structured databases, APIs, and real-time sources. 2. 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 to ensure RAG flows only fetch data that the user has access to 3. 𝐅𝐮𝐭𝐮𝐫𝐞-𝐩𝐫𝐨𝐨𝐟 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐨𝐬𝐚𝐛𝐥𝐞 to easily switch LLMs, Embedding Models, Re-rankers, evaluation steps, agents, and other modules. Interested in learning more? This Thursday - Aug 15th at 11am PT, Amey Desai from Nexla and 🏳️🌈 Corbett Waddingham from Pinecone will take on one of the key data challenges in going to production when working with large scale data. Be sure to register here: https://lnkd.in/gpvgz-gC

Announcing Matthias Niehoff, Head of Data & AI at codecentric AG, as speaker at #VoxxedDaysBrussels! 🚀 Did you ever ask yourself the question: Is software engineering fundamentally different from data engineering and machine learning, or are they more similar than we think? Join Matthias as he explores the intersections between these domains, uncovering valuable insights for both data and AI initiatives. See you soon! ➡️https://lnkd.in/ehpxfRxE

FixedIT is featured in Activeloop's latest newsletter for our article on object detection. Detecting objects in a camera's view is today a crucial part of utilizing your network cameras efficiently. FixedIT has been a long-time user of the Activeloop / Deep Lake platform for dataset management as it offers all the features you expect such as versioning, branching, visualization and much more. In difference from many other dataset storage systems we have found Activeloop to work very well in the distributed systems that edge AI often leads to. Accessing or adding new data from any device is easy and performant. Check out the article by Márk Mészáros and Daniel Falk here: https://lnkd.in/dqQpypaA