All Things Microsoft AI

Q: concerned about what Microsoft and / or the US government can do with the data for a custom copilot. I’ve looked at the Microsoft copilot documentation but I didn’t find anything that clearly states what Microsoft can and cannot do with data used in custom copilots, do you have any resources that you can share?   A: Microsoft posted info about this topic specifically at https://learn.microsoft.com/en-us/legal/cognitive-services/openai/data-privacy?context=%2Fazure%2Fcognitive-services%2Fopenai%2Fcontext%2Fcontext#see-also   In a nutshell: Your prompts (inputs) and completions (outputs), your embeddings, and your training data: are NOT available to other customers. are NOT available to OpenAI. are NOT used to improve OpenAI models. are NOT used to improve any Microsoft or 3rd party products or services. are NOT used for automatically improving Azure OpenAI models for your use in your resource (The models are stateless, unless you explicitly ...

Interested in building your own Copilot using Azure AI Studio?  Listed below are some useful links: Azure AI Studio Architecture Quickstart: Create a project and use the chat playground in Azure AI Studio Tutorial: Deploy an Enterprise Chat web app AI Studio FAQ  

User Groups May 2: Twin Cities .NET User Group May 7: Ohio North Database Training May 8: Azure Cleveland May 16: GLUG.NET May 22: Cleveland C# User Group   Conferences May 3: Stir Trek Jun 27-28: Kansas City Developer Conference Jul 26: Cincy Deliver  

User Groups Apr 2: Ohio North Database Training Apr 4: Twin Cities .NET User Group Apr 10: Azure Cleveland Apr 18: GLUG.NET Apr 24: Cleveland C# User Group   Conferences May 3: Stir Trek  

Machine learning tasks like regression and classification contain various algorithm implementations.  Some tasks may utilize the same algorithm, such as the SDCA algorithm in both Binary Classification and Regression tasks   In some cases, the problem you are trying to solve and the way your data is structured does not fit well into the current algorithm. If so, consider using a different algorithm for your task to see if it learns better from your data.   A trainer identifies a single algorithm used for a single task (i.e. Trainer = Algorithm + Task). Listed below is a summary of trainers available in ML.NET. For more info, see guidance on which algorithm to choose .   Trainer Algorithm Task ONNX Exportable SdcaLogisticRegressionBinaryTrainer SDCA Binary classification Yes SdcaNonCalibratedBinaryTrainer SDCA Binary classification Yes SdcaMaximumEntrop...

Auto-GPT is an experimental project developed by Significant Gravitas.  It’s an open-source Python application powered by GPT-4.   Unlike ChatGPT, Auto-GPT does not rely on human prompts to operate. It can self-prompt and tackle subsets of a problem without human intervention.  It works by pairing GPT with AI agents that can make decisions and take actions based on a set of rules and predefined goals. Auto-GPT is important and relevant because it showcases the potential of language models like GPT-4 to autonomously complete different types of tasks. It has the ability to write and execute its own code using GPT-4, allowing it to debug, develop, and self-improve recursively. One of the advantages of Auto-GPT is its ability to continuously self-improve. It can debug, develop, and enhance its own capabilities recursively.   Accessing Auto-GPT requires specific installed software and familiarity with Python, and an API key from OpenAI.  It runs...

ML.Net has the capability of utilizing 7 different Machine Learning Tasks via the MLContext object: Binary Classification Multi-class/text Classification Regression and Recommendation Clustering Ranking Anomaly Detection sentence similarity   Each task offers various performance metrics for evaluating the model after training is completed These metrics are properties accessible via the Evaluate() method within each task object (i.e. MLContext. MLTask .Evaluate()   Sample Code Snippet     static void Main(string[] args)     {         MLContext mlContext = new MLContext();           // 1a. Create training data         HouseData[] houseData = {                new HouseData() { Size = 1.1F, Price = 1.2F },       ...