Generative AI

###Generative AI

Generative AI describes a category of capabilities within AI that create original content. These applications take in natural language input and return appropriate responses in a variety of formats such as natural language, images, or code.

###Generative AI Applications

Generative AI can be used in several different domains, including natural language, images, and code generation:

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  a. Natural Language Generation:
  Generative AI can create natural language responses based on a given input. For example, you might submit a request like, "Write a cover letter for a person with a bachelor's degree in history." The AI would then generate a professional cover letter in response.

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  b. Image Generation:
  Some generative AI applications can interpret natural language requests and generate corresponding images. For instance, a request like "Create a logo for a florist business" could result in the AI generating an original logo based on that description.

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  c. Code Generation:
  Generative AI can assist software developers by automatically generating code. For example, if you request "Write Python code to add two numbers," the AI could return the following:

  python

  def add_numbers(a, b): return a + b

###Language Models

Generative AI applications rely on language models, which are specialized machine learning models used to perform natural language processing (NLP) tasks. These tasks include:

• >Determining sentiment or classifying natural language text.
• >Summarizing text.
• >Comparing multiple text sources for semantic similarity.
• >Generating new natural language.

###Transformer Models

Transformer models are at the core of the latest advances in natural language processing. These models build on and extend previous techniques for modeling vocabularies and supporting NLP tasks. They are particularly effective in generating language and have become the foundation for large language models like GPT.

###Responsible AI in Generative Models

Microsoft provides guidance for developing and deploying generative AI in a responsible and ethical manner. This guidance is based on a four-stage process:

1. >Identify potential harms relevant to your solution.
2. >Measure the presence of these harms in the outputs generated by the solution.
3. >Mitigate the harms at multiple layers of the solution to minimize their impact, ensuring transparent communication about potential risks to users.
4. >Operate the solution responsibly, following a deployment and operational readiness plan.

The first stage of this process involves identifying potential harms, which includes four key steps:

1. >Identify potential harms.
2. >Prioritize identified harms.
3. >Test and verify the prioritized harms.
4. >Document and share the verified harms.

###Image Generation Models

Generative AI models can take prompts, base images, or both to create new content. These models can generate both realistic and artistic images, edit existing images, and create variations of a provided image.

###DALL-E

DALL-E is a generative AI model specifically designed for working with images. Like GPT models for text, subsequent versions of DALL-E (e.g., DALL-E 2) provide enhanced image capabilities. These capabilities generally fall into three categories:

• >Image creation: Generating original images based on textual prompts.
• >Image editing: Modifying or adjusting existing images.
• >Image variations: Creating different versions of a provided image.

###Capabilities of OpenAI AI Models

OpenAI’s AI models exhibit a variety of capabilities, including:

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   Generating Natural Language:
   Tasks include summarizing complex text, suggesting alternative wordings, and providing detailed explanations at different reading levels.

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   Generating Code:
   Tasks include translating code between programming languages, troubleshooting and identifying bugs, and providing code suggestions or completions.

3. >

   Generating Images:
   Tasks include generating original images based on text descriptions, creating visual content for publications, and more.