Tuesday, July 23, 2024

Questions on Copilot Data Privacy

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 fine-tune models with your training data).
  • Your fine-tuned Azure OpenAI models are available exclusively for your use.

The Azure OpenAI Service is fully controlled by Microsoft; Microsoft hosts the OpenAI models in Microsoft’s Azure environment and the Service does NOT interact with any services operated by OpenAI (e.g. ChatGPT, or the OpenAI API).

 

 

 

Q: Does Microsoft have the same Data Privacy policy for Copilot studio as Azure AI Studio?  Is there similar documentation for custom copilots created in copilot studio?

 

A: It’s seeming like it’s the same.  After logging into Copilot Studio, browse to https://www.microsoft.com/licensing/terms/product/PrivacyandSecurityTerms/all .  From there, you can download the Data Protection Addendum from https://aka.ms/DPA. (see attached).  On P.5 it states:

 

Nature of Data Processing; Ownership

Microsoft will use and otherwise process Customer Data, Professional Services Data, and Personal Data only as described and subject to the limitations provided below (a) to provide Customer the Products and Services in accordance with Customer’s documented instructions and (b) for business operations incident to providing the Products and Services to Customer. As between the parties, Customer retains all right, title and interest in and to Customer Data and Professional Services Data. Microsoft acquires no rights in Customer Data or Professional Services Data, other than the rights Customer grants to Microsoft in this section. This paragraph does not affect Microsoft’s rights in software or services Microsoft licenses to Customer.

 

 

Wednesday, March 27, 2024

Apr '24 Regional Tech Events

Trainers in ML.NET

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

SdcaMaximumEntropyMulticlassTrainer

SDCA

Multiclass classification

Yes

SdcaNonCalibratedMulticlassTrainer

SDCA

Multiclass classification

Yes

SdcaRegressionTrainer

SDCA

Regression

Yes

AveragedPerceptronTrainer

Averaged Perceptron

Binary classification

Yes

LbfgsLogisticRegressionBinaryTrainer

L-BFGS

Binary classification

Yes

LbfgsMaximumEntropyMulticlassTrainer

L-BFGS

Multiclass classification

Yes

LbfgsPoissonRegressionTrainer

L-BFGS

Regression

Yes

SymbolicSgdLogisticRegressionBinaryTrainer

Symbolic stochastic gradient descent

Binary classification

Yes

OnlineGradientDescentTrainer

Online gradient descent

Regression

Yes

LightGbmBinaryTrainer

Light gradient boosted machine

Binary classification

Yes

LightGbmMulticlassTrainer

Light gradient boosted machine

Multiclass classification

Yes

LightGbmRegressionTrainer

Light gradient boosted machine

Regression

Yes

LightGbmRankingTrainer

Light gradient boosted machine

Ranking

No

FastTreeBinaryTrainer

Fast Tree

Binary classification

Yes

FastTreeRegressionTrainer

Fast Tree

Regression

Yes

FastTreeTweedieTrainer

Fast Tree

Regression

Yes

FastTreeRankingTrainer

Fast Tree

Ranking

No

FastForestBinaryTrainer

Fast Forest

Binary classification

Yes

FastForestRegressionTrainer

Fast Forest

Regression

Yes

GamBinaryTrainer

Generalized additive model

Binary classification

No

GamRegressionTrainer

Generalized Additive Model

Regression

No

MatrixFactorizationTrainer

Matrix Factorization

Recommendation

No

FieldAwareFactorizationMachineTrainer

Field Aware Factorization Machine

Binary classification

No

OneVersusAllTrainer

One Versus All

Multiclass classification

Yes

PairwiseCouplingTrainer

Pairwise Coupling

Multiclass classification

No

KMeansTrainer

KMeans

Clustering

Yes

RandomizedPcaTrainer

Randomized Pca

Anomaly detection

No

NaiveBayesMulticlassTrainer

Naive Bayes Multiclass

Multiclass classification

Yes

PriorTrainer

Prior

Binary classification

Yes

LinearSvmTrainer

Linear Svm

Binary classification

Yes

LdSvmTrainer

Ld Svm

Binary classification

Yes

OlsTrainer

Ols

Regression

Yes

 

 

Tuesday, March 12, 2024

What is Auto-GPT?

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 locally on a Mac, PC, or Docker image.

 

For a complete tutorial on how to use AutoGPT, visit https://youtu.be/v-5AWQlTFw8

 

For more info, see What is Auto-GPT and What Is the Difference Between ChatGPT vs Auto-GPT?

 

Thursday, March 7, 2024

ML.NET Task Metrics

ML.Net has the capability of utilizing 7 different Machine Learning Tasks via the MLContext object:

  1. Binary Classification
  2. Multi-class/text Classification
  3. Regression and Recommendation
  4. Clustering
  5. Ranking
  6. Anomaly Detection
  7. 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 },

               new HouseData() { Size = 1.9F, Price = 2.3F },

               new HouseData() { Size = 2.8F, Price = 3.0F },

               new HouseData() { Size = 3.4F, Price = 3.7F } };

 

        // 1b. Import training data

        IDataView trainingData = mlContext.Data.LoadFromEnumerable(houseData);

 

        // 2. Specify data preparation and model training pipeline

        var pipeline = mlContext.Transforms.Concatenate("Features", new[] { "Size" })

            .Append(mlContext.Regression.Trainers.Sdca(labelColumnName: "Price", maximumNumberOfIterations: 100));

 

        // 3. Train model

        var model = pipeline.Fit(trainingData);

 

        //***** Model Evaluation

        HouseData[] testHouseData =

        {

            new HouseData() { Size = 1.1F, Price = 0.98F },

            new HouseData() { Size = 1.9F, Price = 2.1F },

            new HouseData() { Size = 2.8F, Price = 2.9F },

            new HouseData() { Size = 3.4F, Price = 3.6F }

        };

 

        var testHouseDataView = mlContext.Data.LoadFromEnumerable(testHouseData);

        var testPriceDataView = model.Transform(testHouseDataView);

 

        var metrics = mlContext.Regression.Evaluate(testPriceDataView, labelColumnName: "Price");

        double rs = metrics.RSquared;

        double rmse = metrics.RootMeanSquaredError;

    }

 

Metrics Summary

Listed below is a summary of 6 various ML.NET Tasks and their metrics:

 

BinaryClassification

MulticlassClassification

Regression

Accuracy

Micro Accuracy

R-Squared

AUC

Macro Accuracy

MAE (Mean Absolute Error)

AreaUnderPrecisionRecallCurve

Log-Loss

MSE (Mean Squared Error)

F1-score

Log Loss Reduction

RMSE (Root Mean Square Error)

 

 

Clustering

Ranking

AnomalyDetection

Avg Distance

DCG
(Discounted Cumulative Gains)

Area Under ROC Curve

Davies Boulding Index

Normalized DCG
(Discounted Cumulative Gains)

Detection Rate At False Positive Count

NMI
(Normalized Mutual Information)

 

 

 

 

Reference: https://learn.microsoft.com/en-us/dotnet/machine-learning/resources/metrics

Thursday, November 30, 2023

Azure AI Content Safety Service

Microsoft introduced a new AI service called “Azure AI Content Safety Service” at the Build conference in May 2023.  This new service will inspect for questionable content in any of the following categories.

  1. Violent content
  2. Hateful content
  3. Sexual content
  4. Self-harm content

 

The Content Safety service is intended to protect customers’ web sites and social media apps from receiving questionable comments or images.

Content maybe text, images, audio, video, or a combination of items (i.e. multi-modal). 

 

Users can utilize filters to tweak the severity levels.  For example, an outdoor equipment provider may allow images of knives or guns uploaded to their social media, but a school or church may like to prevent those images. Filters are set to Medium by default and can be increased.  Turning the filter settings to be less restricted or turned off requires a written application to Microsoft to ensure the customer is trusted and low risk.

 

The AI Content Safety Service is built into Open AI and most Microsoft AI products.  It’s used internally at Microsoft as well in public products like Bing Chat. The purpose is to uphold responsible AI principles provided by Microsoft.

 

 


 

Code Example

  1. Using the Azure AI Content Safety Service is accessible through the Azure portal. After logging into the portal, simply create a “Content Safety” Resource in an existing group or a new group.

 

  1. Once the resource is created, the Keys and Endpoints will be accessible in the Resource Management pane

 

 

  1. To access the Safety Content API, I created a console application with the following NuGet packages

 


 

  1. The code will utilize an API call, using the key and endpoint from step #2

 

using Azure;

using Azure.AI.ContentSafety;

using Microsoft.Extensions.Configuration;

using System.Reflection;

 

 

namespace AIContenSafety.ConsoleApp

{

    internal class Program

    {

        static void Main(string[] args)

        {

            var config = new ConfigurationBuilder().AddJsonFile("appsettings.json").Build();

            string endpoint = config["AppSettings:endpoint"];

            string key = config["AppSettings:key"];

 

            string datapath = Path.Combine(Path.GetDirectoryName(Assembly.GetExecutingAssembly().Location), "Images", "TestImage1.jpg");

 

            ImageData image = new ImageData();

            image.Content = BinaryData.FromBytes(File.ReadAllBytes(datapath));

 

            var request = new AnalyzeImageOptions(image);

 

            Response<AnalyzeImageResult> response;

            try

            {

                ContentSafetyClient client = new ContentSafetyClient(new Uri(endpoint), new AzureKeyCredential(key));

                response = client.AnalyzeImage(request);

            }

            catch (RequestFailedException ex)

            {

                Console.WriteLine("Analyze image failed.\nStatus code: {0}, Error code: {1}, Error message: {2}", ex.Status, ex.ErrorCode, ex.Message);

                throw;

            }

 

            Console.WriteLine("Hate severity: {0}", response.Value.HateResult.Severity);

            Console.WriteLine("SelfHarm severity: {0}", response.Value.SelfHarmResult.Severity);

            Console.WriteLine("Sexual severity: {0}", response.Value.SexualResult.Severity);

            Console.WriteLine("Violence severity: {0}", response.Value.ViolenceResult.Severity);

        }

    }

}

 

 

Testing the Application

Provided in the solution is a folder containing a test image (shown below), called TestImage1.jpg.  Naturally this image should be classified as violent content.

 

Running the Application

Executing the console application will load the test image specified above.  All results are posted in the console window showing the type of content violating the safety guidelines.  In addition, it shows the severity level of the content.

 

Additional Resources

Get started in Studio https://aka.ms/contentsafetystudio

Visit product page to learn more https://aka.ms/contentsafety

Read the eBook https://aka.ms/contentsafetyebook