Fundamentals of Modern AI

Module 1: Foundations of AI & Introduction to Modern Generative AI

Lecture and Demo

• AI Foundations: Definitions (AI, machine learning, deep learning), history, key milestones.
• The Rise of Large Language Models (LLMs): Intro to GPT (including GPT-4), Claude 2, Llama 2, Google Bard/Gemini, etc.
• Generative AI Today: How generative AI creates text, images, audio, and more. Real-world examples in content creation, coding assistance, etc.
• Foundation Models: What they are, why they matter, and the shift toward “foundation model” thinking.

Lab

• Hands-On with Generative AI:
  • Experiment with GPT-4 or ChatGPT for text generation.
  • Use an image-generation tool (e.g., DALL·E, Adobe Firefly, or Midjourney) to create basic images.
  • Gain familiarity with prompt-and-response interactions and best practices for prompt writing.

Module 2: Survey of Public, Proprietary & Open-Source Models/Tools

Lecture and Demo

• Popular AI Frameworks and Platforms: TensorFlow, PyTorch, Hugging Face, Google Vertex AI, Azure AI Services.
• Open-Source vs. Proprietary Models:
  • Examples of open-source LLMs (e.g., Llama 2, Falcon)
  • Proprietary platforms (e.g., Anthropic’s Claude, OpenAI GPT-4, Microsoft Azure’s hosted models).
• Choosing the Right Ecosystem: Key considerations (licensing, community support, enterprise features).

Lab

• Setting Up and Experimenting:
  • Spin up a cloud-based notebook environment (e.g., Google Colab or Azure Notebooks) and install an open-source model from Hugging Face.
  • Perform simple inference tasks (e.g., text classification or summarization) to understand the model’s performance.

Module 3: Multi-Modal Models & Prompt Engineering

Lecture and Demo

• Multi-Modal AI: Models that handle text, images, audio, and/or video (e.g., GPT-4’s image understanding, Google Gemini’s image input).
• Prompt Engineering Best Practices:
  • Strategies (system vs. user prompts, chain-of-thought prompts)
  • Techniques to improve accuracy and manage context (e.g., using external knowledge bases, embeddings).
• Live Demos: Show how modern LLMs process both textual and visual prompts.

Lab

• Prompt Crafting Exercises:
  • Work with a multi-modal model or tool (e.g., GPT-4 with image input or Stable Diffusion for advanced image prompting).
  • Experiment with different prompt styles to control output (e.g., role-based prompting, step-by-step instructions).

Module 4: Survey of Specialty & Niche AI Tools

Lecture and Demo

• Advanced Image & Media Generation: Tools like Midjourney, Stable Diffusion, Adobe Firefly for brand design, marketing, etc.
• Audio & Voice Tools: Speech synthesis and voice cloning (e.g., Microsoft Azure Cognitive Services Speech, ElevenLabs).
• Industry-Specific AI: Finance (fraud detection), healthcare (diagnostic AI), marketing (predictive analytics), etc.

Lab

• Hands-On with Specialized Tools:
  • Explore a niche AI tool (e.g., an AI-driven video creator or an automated audio transcription service).
  • Configure settings, experiment with outputs, and discuss potential use cases.

Module 5: Data Analysis & Visualization with AI

Lecture and Demo

• AI-Driven BI & Analytics: Integrating AI with Tableau or Power BI to automate insights.
• Natural Language to Query: Using LLM-based query features (e.g., Power BI’s “Copilot,” GPT-based SQL generation) to interact with data.
• Augmented Analytics: Automated data cleaning, anomaly detection, predictive models for forecasting.

Lab

• Practical Data Exercise:
  • Import a real-world dataset into a BI tool (Tableau, Power BI).
  • Use AI-driven functionality (like natural language queries) to explore data, generate charts, and identify trends.

Module 6: Responsible AI, Accuracy Testing, & AI Security/Privacy

Lecture and Demo

• Ethical AI & Policy: Introduction to emerging regulations (e.g., EU AI Act, U.S. Executive Orders), alignment and safety (RLHF—Reinforcement Learning from Human Feedback).
• Accuracy & Bias Testing: Methods to evaluate model performance, detect biases, and manage drift over time.
• Security & Privacy: Data governance, threat vectors (prompt injection, data poisoning), and zero-trust AI setups.

Lab

• Evaluating AI Models:
  • Practice measuring bias or error rates using a pre-trained model on a sample dataset.
  • Brainstorm mitigation strategies (e.g., balanced data collection, model refinement).

Module 7: Disinformation & Critical Thinking in the Era of Generative AI

Lecture and Demo

• AI & Disinformation: Deepfakes, AI-generated news, synthetic media.
• Critical Thinking Skills: Identifying authentic vs. manipulated content.
• Verification Tools: Techniques and software for detecting manipulated text or images.

Lab

• Hands-On Disinformation Detection:
  • Review various AI-generated samples.
  • Use available detection tools (e.g., reverse image search, AI content detectors) to differentiate genuine vs. fabricated content.

Module 8: AI Copilots & Next-Gen Productivity Assistants

Lecture and Demo

• Emergence of AI Copilots: Overview of Microsoft 365 Copilot, GitHub Copilot X, Google Duet AI, ChatGPT Enterprise, Anthropic’s Claude Pro, etc.
• Workflow Automation: Leveraging AI assistants for email drafting, coding, meeting notes, scheduling, and more.
• Future Directions: Personalized AI agents, auto-GPT-based solutions, multi-agent systems.

Lab

• Hands-On with AI Copilots:
  • Use Microsoft 365 Copilot (or GitHub Copilot in a coding environment) to complete a guided task (e.g., drafting a document, analyzing text, writing code snippets).
  • Discuss best practices, limitations, and how to integrate AI into daily workflows.

Final Exam & Wrap-Up

• Exam Format:
  • A combination of multiple-choice questions, short answers, and scenario-based problem-solving to test conceptual understanding.
  • May include a mini-lab exercise (short prompt-engineering challenge, model evaluation, or data analysis task).
• Course Completion & Next Steps:
  • Recap main learnings and milestones.
  • Suggestions for continuing education (online courses, certifications, workshops).