TWIL: July 31, 2023
This week I bring you another episode from Lex Fridman’s podcast, and a set of really interesting articles. One on how to build a landing zone for Azure OpenAI, two on Vector Databases and another on AI Anomaly Detector in Azure. Finally, an article on how to use an LLM to create a digital version of you, and the awesome Seeing AI app from Microsoft. Have fun!
Episode 387: George Hotz: Tiny Corp, Twitter, AI Safety, Self-Driving, GPT, AGI & God
George Hotz is a programmer, hacker, and the founder of comma-ai and tiny corp discusses multiple topics with Lex Fridman. From artificial intelligence, AI friends, AI safety, self-driving, programming and video games to the meaning of life, a very interesting conversation.
Azure OpenAI Landing Zone reference architecture
Azure Landing Zones provide a solid foundation for your cloud environment. When deploying complex AI services such as Azure OpenAI, using a Landing Zone approach helps you manage your resources in a structured, consistent manner, ensuring governance, compliance, and security are properly maintained. In this article, we delve into the synergy of Azure Landing Zones and Azure OpenAI Service, building a secure and scalable AI environment. unpacking the Azure OpenAI Landing Zone architecture, which integrates numerous Azure services for optimal AI workloads. Furthermore we will also explore security measures and the significance of monitoring for operational success.
AI Anomaly Detector
Easily embed time-series anomaly detection capabilities into your apps to help users identify problems quickly. AI Anomaly Detector ingests time-series data of all types and selects the best anomaly detection algorithm for your data to ensure high accuracy. Detect spikes, dips, deviations from cyclic patterns, and trend changes through both univariate and multivariate APIs. Customize the service to detect any level of anomaly. Deploy the anomaly detection service where you need it—in the cloud or at the intelligent edge.
Using Azure Search for vector search with Azure OpenAI and LangChain
Recently, Azure Cognitive Search introduced vector search for indexing, storing, and retrieving vector embeddings from a search index. In this post, we’ll look into how we can use this to chat with your private data, similar to ChatGPT. So besides Azure Cognitive Search we’ll be using LangChain and Azure OpenAI Service.
Explaining Vector Databases in 3 Levels of Difficulty
Vector databases have been getting a lot of attention recently, with many vector database startups raising millions in funding. Chances are you have probably already heard of them but didn’t really care about them until now —at least, that’s what I guess why you are here now…
Create a Clone of Yourself With a Fine-tuned LLM
This article aims to illustrate how to fine-tune a top-performing LLM efficiently and cost-effectively on a custom dataset. We will explore the utilization of the Falcon-7B model with LoRA adapters using Lit-GPT. Ever wondered what it would be like to have a digital twin? A virtual replica of yourself that can have conversations, learn, and even reflect your thoughts? Recent advances in artificial intelligence (AI) have made this once-futuristic idea attainable.
Seeing AI is a Microsoft research project that brings together the power of the cloud and AI to deliver an intelligent app, designed to help you navigate your day. It is a camera-based app with multiple channels that can be an essential companion to someone with visual impairment.
Have an awesome week!