Overview
This lecture explains generative AI (GenAI): what it is, how it works, its history, current applications, limitations, concerns, and possible future developments.
What is Generative AI?
- GenAI uses advanced algorithms to create new content (text, images, audio) in response to prompts.
- It encodes existing data into a vector space and decodes new content by exploring data correlations.
- Popular GenAI tools include OpenAI ChatGPT, Google Gemini, and Dall-E.
Key Technologies and History
- GenAI emerged from early chatbots (Eliza, 1960s) and saw major advances with GANs in 2014.
- Transformers and large language models (LLMs) enabled training on massive text datasets, improving results.
- Innovations like diffusion models support high-quality image generation and multimodal AI.
How GenAI Works
- Foundation models (e.g., GPT, Gemini) are trained on large, diverse datasets.
- GenAI models include encoders (convert input to intermediate vectors) and decoders (generate output).
- Algorithms include transformers (self-attention), diffusion models, VAEs, GANs, and KANs.
Major GenAI Tools
- ChatGPT: Multimodal text, image, and voice generation.
- Google Gemini: Multi-domain AI tools, image, and audio generation.
- Microsoft Copilot: GenAI embedded in Office, GitHub, and business apps.
- Perplexity, Anthropic Claude, DeepSeek: Specialized or efficient GenAI systems.
Applications and Business Benefits
- Automates customer service, document drafting, code generation, and personalized marketing.
- Increases efficiency, boosts personalization, and improves risk management.
- Used across industries: finance, legal, manufacturing, and education.
Limitations and Concerns
- Often lacks source transparency and can generate plausible but incorrect or biased information.
- Risk of hallucinations, fake citations, copyright/ToS violations, data privacy issues, and high energy use.
- Raises ethical challenges: bias, deepfakes, data sweatshops, security threats.
Best Practices
- Clearly label GenAI-generated content.
- Always vet accuracy, consider biases, and check code/content with other tools.
- Understand tool strengths/limitations and implement security guardrails.
The Future of Generative AI
- GenAI adoption is rapid but faces scaling, cost, and accuracy challenges.
- Research explores agentic AI frameworks, improved trust, and integration into existing tools.
- Expect ongoing evolution across translation, content creation, 3D modeling, and more industries.
Key Terms & Definitions
- Generative AI (GenAI) — AI that creates new, original content using learned data patterns.
- Prompt — User input or query that initiates content generation.
- Vector space — Mathematical space mapping data based on correlations.
- Transformer — AI model architecture excelling in NLP and sequence tasks using self-attention.
- Large Language Model (LLM) — A GenAI specialized in understanding and generating text.
- GAN (Generative Adversarial Network) — Two-network model generating realistic synthetic data.
- Diffusion Model — AI model for image generation via transformation between data spaces.
- VAE (Variational Autoencoder) — Model encoding data into probabilistic latent spaces.
- Hallucination — AI-generated information that is factually incorrect or made up.
Action Items / Next Steps
- Review frequently asked questions and key differences between GenAI, LLMs, and traditional AI.
- Explore recommended readings: generative AI vs. machine learning, GPT-4o vs. GPT-4, and ethical AI frameworks.