Author: ultroni1

As AI continues to evolve, it isn’t only transforming how you perform tasks but also creating entirely new roles and processes that were previously unimaginable.

AI is transforming industries by introducing new roles and methodologies that boost efficiency and foster innovation. Whether in finance, healthcare, energy, manufacturing, retail, the public sector, or agriculture, AI is catalyzing advancements and propelling growth. As you explore this environment, it’s important to be an agile learner, prepared to adapt and enhance your skills. Using AI’s capabilities enables you to accomplish more and reshape your professional journey.

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Today, AI advancements make technology more accessible for everyone. These innovations are breaking down barriers and creating new opportunities for individuals with diverse abilities. AI is proving to be a catalyst for inclusivity, enabling people to access information, communicate, and participate in various aspects of life more easily. Through these efforts, technology is becoming a powerful ally for inclusiveness and accessibility.

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As technology advances, human and AI interaction grows more important. AI isn’t just for automation; it’s transforming industries, improving our lives, and sparking innovation. This video examines AI’s societal impact and the key considerations for its integration.

AI is changing industries by enabling data-driven decisions, automating processes, and fostering innovation. By following these guidelines, we can harness the power of AI responsibly and shape a future that aligns with our shared values and aspirations.

Data privacy: Balance the need for data with the protection of individual privacy rights.

Algorithmic bias: Detect and mitigate biases in AI systems to prevent societal biases from being reflected.

Transparency: Ensure AI decision-making processes are clear and understandable to foster trust and accountability.

Legal liability: Address responsibility for AI decisions, considering the roles of developers, users, and the AI itself.

Innovation and accountability: Striking a balance between innovation and accountability is essential for responsible AI usage.

Data sharing: Encourage data sharing while safeguarding privacy to improve AI systems without compromising individual rights.

AI research: Invest in AI research to fuel innovation and ensure the benefits of AI are accessible to all.

Digital education: Promote digital education and workforce development to equip people with the skills needed in an AI-transformed job market.

AI advisory committees: Establish AI advisory committees to provide oversight, insights, and guidance on AI development and deployment.

Government engagement: Engage with government officials to shape policies that affect AI use in communities.

These guidelines are designed to ensure the responsible and ethical use of AI, fostering a positive and fair influence on society.

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Deepfake technology added complexity to the rapid production and distribution of digital content. AI-generated deepfakes, which can mimic real people, pose serious ethical and legal issues, especially concerning copyright and intellectual property.

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As AI becomes more integrated into our lives, it’s important to understand how to use it ethically and effectively.

Responsible collaboration with AI is essential. Here are some key best practices:

Understand AI: It’s important to grasp the basics of AI and its broad capabilities. This knowledge is the foundation for using AI effectively.

Stay informed: Keep up with the latest advancements and ethical discussions in AI. This will help you leverage AI responsibly.

Recognize AI’s blind spots: AI can reflect societal biases present in the data it learns from. Actively seek unbiased information and understand how AI uses data to navigate these blind spots.

Prioritize safety and privacy: Your data is precious. Choose AI services that value user privacy and prioritize security and transparency.

Cross-verify AI-generated content: Don’t accept AI-generated content at face value. Always cross-verify information from various sources and engage your critical thinking skills.

Evaluate and refine content: Use critical thinking to evaluate and refine AI-generated content by verifying facts and sources, understanding the content’s goals and target audience while considering a range of viewpoints.

Ensure clear policies: Make sure the AI tool or service you’re using has clear policies and guidelines for secure usage.

Promote AI for good: AI should be a tool for good, aiding in areas like healthcare, education, and environmental conservation.

Join the conversation: Start discussions in your community and workplace about responsible AI use. Encourage people to think about how AI will be utilized and take steps to prevent misuse.

These methods will enable you to use AI responsibly and contribute positively to society.

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After uploading data to Azure AI Foundry and creating an index on your data using the integration with Azure AI Search, you can implement the RAG pattern with Prompt Flow to build a generative AI application.

Prompt Flow is a development framework for defining flows that orchestrate interactions with an LLM.

A flow begins with one or more inputs, usually a question or prompt entered by a user, and in the case of iterative conversations the chat history to this point.

The flow is then defined as a series of connected tools, each of which performs a specific operation on the inputs and other environmental variables. There are multiple types of tool that you can include in a prompt flow to perform tasks such as:

• Running custom Python code
• Looking up data values in an index
• Creating prompt variants – enabling you to define multiple versions of a prompt for a large language model (LLM), varying system messages or prompt wording, and compare and evaluate the results from each variant.
• Submitting a prompt to an LLM to generate results.

Finally, the flow has one or more outputs, typically to return the generated results from an LLM.

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When you’ve created an Azure AI Search index for your contextual data, you can use it with an OpenAI model. To ground prompts with data from your index, the Azure OpenAI SDK supports extending the request with connection details for the index.

In this example, the search against the index is keyword-based – in other words, the query consists of the text in the user prompt, which is matched to text in the indexed documents. When using an index that supports it, an alternative approach is to use a vector-based query in which the index and the query use numeric vectors to represent text tokens. Searching with vectors enables matching based on semantic similarity as well as literal text matches.

To use a vector-based query, you can modify the specification of the Azure AI Search data source details to include an embedding model; which is then used to vectorize the query text.

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When you want to create an agent that uses your own data to generate accurate answers, you need to be able to search your data efficiently. When you build an agent with the Azure AI Foundry, you can use the integration with Azure AI Search to retrieve the relevant context in your chat flow.

Azure AI Search is a retriever that you can include when building a language model application with prompt flow. Azure AI Search allows you to bring your own data, index your data, and query the index to retrieve any information you need.

Using a vector index

While a text-based index will improve search efficiency, you can usually achieve a better data retrieval solution by using a vector-based index that contains embeddings that represent the text tokens in your data source.

An embedding is a special format of data representation that a search engine can use to easily find the relevant information. More specifically, an embedding is a vector of floating-point numbers.

For example, imagine you have two documents with the following contents:

• “The children played joyfully in the park.”
• “Kids happily ran around the playground.”

These two documents contain texts that are semantically related, even though different words are used. By creating vector embeddings for the text in the documents, the relation between the words in the text can be mathematically calculated.

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Language models excel in generating engaging text, and are ideal as the base for agents. Agents provide users with an intuitive chat-based application to receive assistance in their work. When designing an agent for a specific use case, you want to ensure your language model is grounded and uses factual information that is relevant to what the user needs.

Though language models are trained on a vast amount of data, they may not have access to the knowledge you want to make available to your users. To ensure that an agent is grounded on specific data to provide accurate and domain-specific responses, you can use Retrieval Augmented Generation (RAG).

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This unit explains how to sign up for GitHub Copilot, how to configure GitHub Copilot by using VS Code, and how to troubleshoot GitHub Copilot by using VS Code.

Sign up for GitHub Copilot

Before you can start using GitHub Copilot, you need to set up a free trial or subscription for your account.

To get started, select your GitHub profile photo, and then select Settings. Copilot is on the left menu under Code, planning, and automation.

After you sign up, you need to install an extension for your preferred environment. GitHub Copilot supports GitHub.com (which doesn’t need an extension), VS Code, Visual Studio, JetBrains IDEs, and Neovim as an unobtrusive extension.

For this module, you’ll just review extensions and configurations for VS Code. The exercise that you’ll complete in the next unit uses VS Code.

If you’re using a different environment, you can find specific links to set up other environments in the “References” section at the end of this module.

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