Basics of Artificial Intelligence - Summary of Google's 4-Hour AI Course

Overview

• Google's 4-hour AI course distilled into key points.
• AI basics can improve the use of tools like ChatGPT and Google Bard.
• Dispelling common misconceptions about AI, machine learning, and large language models (LLMs).

Key Concepts

What is Artificial Intelligence?

• Field of study, like physics.
• Machine Learning (ML): A subfield of AI (like thermodynamics in physics).
• Deep Learning: A subset of ML.
• Discriminative Models & Generative Models: Subsets of deep learning.
• Large Language Models (LLMs): Fall under deep learning and intersect with generative models.
• Examples: ChatGPT and Google Bard.

Machine Learning (ML)

• Definition: Uses input data to train a model that makes predictions on new data.
• Supervised Learning: Uses labeled data.
  • Example: Predicting tips based on historical restaurant bill and tip data.
• Unsupervised Learning: Uses unlabeled data.
  • Example: Grouping employees based on tenure and income without predefined labels.
• Key Difference: Supervised models compare predictions to training data to close gaps; unsupervised models do not.

Deep Learning

• Definition: Type of ML using artificial neural networks inspired by the human brain.
• Neural Networks: Composed of layers of nodes (neurons).
  • More layers = more powerful models.
• Semi-Supervised Learning: Combines small labeled datasets with large unlabeled datasets.
  • Example: Fraud detection in banking with a mix of labeled and unlabeled transaction data.

Discriminative vs. Generative Models

• Discriminative Models: Classify data points based on labels.
  • Example: Classifying images as cats or dogs.
• Generative Models: Generate new data based on patterns in training data.
  • Example: Creating new images of dogs based on patterns in existing data.

Generative AI

• Definition: Generates new samples similar to training data (text, images, audio).
• Applications:
  • Text-to-Text Models: ChatGPT, Google Bard.
  • Text-to-Image Models: MidJourney, DALL-E, Stable Diffusion.
  • Text-to-Video Models: Google's Imagen Video, CogVideo, Make-A-Video.
  • Text-to-3D Models: OpenAI's Shap-E Model (game assets).
  • Text-to-Task Models: Performing specific tasks (e.g., summarizing emails).

Large Language Models (LLMs)

• Subset of Deep Learning.
• Pre-Training: General language tasks (text classification, question answering, etc.).
• Fine-Tuning: Specialized tasks in various industries (healthcare, finance).
  • Example: Hospitals fine-tuning LLMs for diagnostic accuracy using specific medical data.
• Economic Model: Big tech develops general LLMs; smaller institutions fine-tune them with domain-specific data.

Pro Tips

• Course Navigation: Copy video URL at current time while taking notes.
• Course Structure: 5 modules, each awarding a badge upon completion.
• Further Learning: Video on mastering prompting available.

Conclusion

• Understanding basic AI concepts enhances the practical use of AI tools.
• Google's AI course provides a foundational knowledge essential for anyone interested in the field.