Learning Objectives

After completing this lecture, you will be able to:

• Understand the key concepts of generative ai deep dive in a government context
• Connect generative ai deep dive to your agency's AI initiatives
• Identify next steps for applying these concepts in your role

Key Topics Covered

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Text generation, image generation, code generation

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How diffusion models work

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Government use cases for each

Why This Matters for Government

Government agencies face unique challenges when it comes to AI adoption. This lecture addresses these challenges head-on by providing analysts, project leads, team supervisors with the knowledge and frameworks needed to navigate AI in the public sector responsibly and effectively.

As part of the L2 (AI Practitioner) curriculum, this lecture builds on the foundational principle that every AI system in government ultimately serves citizens. Whether you are working with AI tools daily or setting strategy for your agency, understanding generative ai deep dive is essential for responsible, effective government AI adoption.

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TRANSCRIPT: Generative AI Deep Dive

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What you will learn: How different types of generative AI work (text, image, code); diffusion models conceptually; when generative AI is appropriate versus risky; government applications for each modality; hallucinations and safety measures specific to each type.

You now understand transformers and language models. But generative AI is broader than just text. Your agencies will increasingly use AI to generate images, code, videos, and more. Each modality has different characteristics, different risks, different governance implications. This lecture gives you a conceptual architecture for all generative AI.

By the end, you'll evaluate any generative AI system for your agency and understand its technical fundamentals.

WHY THIS MATTERS FOR GOVERNMENT

Generative AI is expanding beyond text. Agencies are exploring image generation for training simulations, diagrams for policy documents, or synthetic data. Agencies are using code generation to accelerate software development. Agencies are exploring video synthesis for training materials and public communications.

The challenge: each modality has unique failure modes. An image model might generate plausible-looking but counterfactual diagrams. A code-generation model might produce secure-looking but vulnerable code. A video-generation model might create deepfakes that spread misinformation.

Understanding generative AI across all modalities is essential for responsible deployment.

DIFFUSION MODELS

Most image generation systems use diffusion models. Here's the conceptual idea:

Take a clear image. Gradually add noise until it becomes pure random static. That process (adding noise) is easy. Now reverse it: start with random static and gradually remove noise until you get a real image. That's what generative AI learns to do.

Training: The model learns to predict: "If I add noise and get this noisy version, what's the original image?" It learns this at many noise levels.

Inference: To generate an image from a text description:

• Start with pure random noise.
• The model uses your text to guide denoising.
• Step by step, it removes noise, refining the image.
• After many steps, you have a detailed image matching your description.

Key limitation: Diffusion models predict "what image is likely given this text," based on training examples. If you ask for something unusual, the model blends similar examples and produces something plausible but potentially weird.

IMAGE GENERATION RISKS

Government agencies might use image generation for creating diagrams, generating training scenarios, or creating illustrations for public materials.

Risk 1: Visual plausibility without accuracy. An image looks real but depicts something false. A diagram looks professional but represents incorrect relationships.

Risk 2: Bias in visual representation. Image models trained on internet data encode biases about how different groups look. Generated images might systematically represent professions with certain demographics, reinforcing stereotypes.

Risk 3: Bias and copyright concerns. Some models trained on copyrighted images. Generated images might resemble training data, creating IP issues.

Risk 4: Deepfake concerns. Image generation used to create images of real people in situations they weren't in spreads misinformation.

CODE GENERATION

Many agencies explore code-generation AI (GitHub Copilot, Claude, Codex) to accelerate development. The model is trained on billions of lines of public code. When you provide context, the model generates likely-to-be-correct completions.

Opportunities: Accelerating development, helping with unfamiliar languages, generating test cases, documenting code.

Risks:

Risk 1: Security vulnerabilities. Generated code looks right but has vulnerabilities. SQL injection, weak validation, or weak cryptography.

Risk 2: License compliance issues. Generated code might be similar to open-source code with specific licenses. You might violate the original license.

Risk 3: Outdated code. The model's training has a cutoff date. Generated code might use deprecated libraries or outdated patterns.

Risk 4: Difficult-to-debug code. Developers sometimes don't understand what generated code does, making debugging harder.

AUDIO AND VIDEO

Overview

Audio generation: Text-to-speech, voice cloning, music generation.

Government applications: Accessibility, multilingual support, training scenarios.

Risks: Deepfake audio, voice cloning used for impersonation.

Video generation: Text-to-video, image-to-video, deepfake video.

Government applications: Training scenarios, public education, policy visualizations.

Risks: Deepfake video, misinformation, manipulation of records.

PRACTICAL USE CASES WITH GOVERNMENT SCENARIOS

Use Case 1: Accessibility Through Text-to-Speech

Scenario: A state agency publishes regulatory documents online. Citizens with visual impairments use screen readers. But screen readers sometimes struggle with formatting, tables, or diagrams.

Approach: Use text-to-speech to create high-quality audio versions.

Application: A citizen with visual impairment listens to natural-sounding audio instead of relying on robotic screen readers.

Challenge: Quality varies. The model must handle acronyms correctly. Punctuation and emphasis must be appropriate. Audio must be reviewed for accuracy.

Use Case 2: Accelerating Compliance Code Development

Scenario: An agency needs compliance checking software. Developers manually write code validating regulatory requirements.

Approach: Use code-generation AI to accelerate development, with human code review for security.

Application: A developer writes a docstring describing validation requirements. The code-generation model suggests a complete function. The developer reviews, adjusts, and commits it.

Challenge: Code must be verified as correct and secure. No generated code goes to production without human review.

Use Case 3: Creating Policy Document Illustrations

Scenario: An agency publishes a detailed policy document on a new benefits program. To help stakeholders understand, they want illustrations of key concepts and workflows.

Approach: Use image generation to create custom illustrations tailored to the agency's style.

Application: The policy team describes a workflow. The image-generation model creates illustrations of these steps.

Challenge: Illustrations must be accurate to the actual process. If inaccurate, stakeholders get confused. Illustrations must not perpetuate biases. Human review and iteration are essential.

ANTI-PATTERNS AND MISUSE RISKS

Anti-Pattern 1: Deploying Generated Content Without Quality Review

RISK: Generated content has defects not obvious without careful review. These defects make it to production.

WHY IT HAPPENS: Generative AI is fast. You can generate dozens of options instantly. Teams skip review, especially when content looks good on first glance.

WHAT GOES WRONG: A diagram is published. Later, someone notices it's inaccurate, misrepresenting workflow. Or generated code causes a security incident. Or generated audio has mispronunciation.

HOW TO AVOID

-- Establish quality review processes for all generated content.

-- For code: security review and testing before deployment.

-- For images: verify images match intent, no biases, no copyright concerns.

-- For audio/video: verify accuracy, no deepfakes, no misrepresentation.

-- Involve subject matter experts in review.

-- Document what was generated, reviewed, and approved.

Anti-Pattern 2: Treating Generated Code as Secure Without Testing

RISK: Generated code looks functional but has subtle security vulnerabilities.

WHY IT HAPPENS: Security is hard. Code-generation models were trained on internet code, both secure and insecure. Models have no inherent preference for security.

WHAT GOES WRONG: Generated code is committed. Later, a security audit reveals vulnerabilities.

HOW TO AVOID

-- Run security scanning tools on all generated code.

-- Have security experts review code, especially code handling user input, authentication, or sensitive data.

-- Test extensively, including with malicious inputs.

-- Use generated code as a starting point, not a finished product.

-- Be especially careful with cryptographic code, authentication, and data validation.

Anti-Pattern 3: Using Generated Audio/Video Without Deepfake Concerns

RISK: Generated audio or video of real people can be used to create deepfakes, spreading misinformation.

WHY IT HAPPENS: Audio and video generation is becoming realistic. Teams use generated content without thinking about deepfake potential.

WHAT GOES WRONG: An agency uses voice cloning for training scenarios. Someone extracts the audio and uses it in a deepfake, claiming an official said something they didn't.

HOW TO AVOID

-- Be extremely cautious about generating audio or video that could be mistaken for actual events.

-- If generated, clearly mark it as synthetic.

-- Don't use voice cloning or video synthesis for impersonation.

-- Train staff on deepfake risks and how to verify authenticity.

-- For sensitive applications, use watermarking or digital signatures to verify authenticity.

-- Consider banning certain uses of audio/video generation entirely.

Anti-Pattern 4: Assuming Generated Images Are Unbiased

RISK: Generated images reflect training data biases and might depict inaccurate or harmful content.

WHY IT HAPPENS: Image models are trained on internet images, which contain biases. The model learns to predict images matching descriptions, based on training data.

WHAT GOES WRONG: Public materials perpetuate stereotypes. Or illustrations show inaccurate or harmful content the model generated.

HOW TO AVOID

-- Before deploying image-generation systems, audit them for bias.

-- Have people from diverse backgrounds review images for stereotypes.

-- Document that images are generated and illustrative only.

-- Don't use generated images where they might be mistaken for real events.

-- For high-stakes applications (recruiting, representation), avoid AI-generated images.

-- Be transparent about when images are generated vs. real.

PRACTICE AND REFLECTION PROMPTS

Prompt 1: Modality Assessment

What content does your agency create? Which modalities (text, image, code, audio, video) are most common? For each, what are the risks if that content is generated by AI?

Prompt 2: Hallucination Prevention

For a use case where your agency might use generative AI, list the most critical accuracy requirements. How would you detect inaccuracy? Would it be caught before or after publication?

Prompt 3: Bias Audit

If your agency deployed an image-generation model for public materials, how would you audit it for bias? What demographic descriptions would you test?

Prompt 4: Code Review Protocol

If your agency deployed code-generation AI, what would your code review process be? Who would review? What security checks would you require?

Prompt 5: Deepfake Preparedness

Does your agency publish content in audio or video format? What would you do if someone created a deepfake claiming something false? How would you protect against deepfakes?

KEY TAKEAWAYS

• Generative AI extends beyond text to images, code, audio, and video. Each modality has different technical foundations, applications, and risks.
• Diffusion models work by learning to remove noise from images. They generate by starting with random noise and iteratively refining to match text descriptions.
• Image generation risks include visual plausibility without accuracy, bias in representation, copyright concerns, and deepfake potential.
• Code-generation AI accelerates development but introduces security risks, license concerns, and maintenance challenges. All generated code must be reviewed and tested.
• Audio and video generation enable accessibility but create deepfake risks. Synthetic audio/video must be clearly marked as generated.
• All generated content must be reviewed for quality, accuracy, bias, and appropriateness before publication.
• Generative AI is a tool for accelerating creation, not a replacement for human judgment and review.

TERMS AND GLOSSARY

• Diffusion Model: A generative model that learns to reverse noise addition, enabling generation of realistic images from text descriptions.
• Text-to-Image: A generative system that creates realistic images from text descriptions. Uses diffusion models or other architectures.
• Code Generation: Using AI to generate source code based on descriptions, docstrings, or function signatures.
• Deepfake: Synthetic media showing a person saying or doing something they didn't actually do. Created using generative AI.
• Voice Cloning: Using generative AI to create synthetic speech sounding like a specific person's voice.
• Text-to-Speech: Converting written text into realistic-sounding spoken audio. Used for accessibility and multilingual support.
• Watermarking: Embedding information in generated content to mark it as synthetic and verify authenticity.

Generative AI is expanding rapidly across all modalities. Your agencies will increasingly use these technologies. Understanding the technical fundamentals of each modality helps you evaluate systems, set appropriate governance, and manage risks.

Key principle: Generative AI is good at producing plausible content quickly. But plausibility is not accuracy or appropriateness. Generative AI should accelerate creation but not bypass human review.

Take two minutes: Think about one type of generative AI your agency might use. What would be the most damaging failure mode? How would you detect that failure before publishing? Write a brief governance checkpoint.

You've completed Lecture 2.1.3. You now understand how different types of generative AI work and their specific risks. Next lecture (2.1.4: Multimodal AI), we'll look at systems that work across multiple modalities simultaneously -- systems that can understand text and images together, analyze video with text, or process audio with visual context.

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