Exploring Key AI Concepts: Deep Learning, Gen AI,& More

Concepts with broader applications for users exploring AI further.

1. Deep Learning: Using multi-layered neural networks to analyze data.
   • Example: Detecting fraud in credit card transactions.
2. Generative AI: AI creating new content, like images or text.
   • Example: ChatGPT generating responses or MidJourney creating digital art.
3. Supervised Learning: Training models on labeled data.
   • Example: Email spam filters identifying unwanted messages.
4. Unsupervised Learning: Identifying patterns in unlabeled data.
   • Example: AI grouping customers into segments based on behavior.
5. Semi-Supervised Learning: Combining labeled and unlabeled data for training.
   • Example: Identifying anomalies in network traffic for cybersecurity.
6. Reinforcement Learning: Learning through rewards and penalties.
   • Example: AI playing and mastering chess or Go.
7. Embedding: Representing data as numerical vectors.
   • Example: Word embeddings enabling AI to understand relationships like “Paris is to France as Berlin is to Germany.”
8. Tokenization: Breaking text into smaller units for analysis.
   • Example: Splitting a sentence into words for sentiment analysis.
9. Feature Extraction: Identifying key attributes in data.
   • Example: Analyzing customer age and income for loan predictions.
10. Explainability: Understanding how AI systems make decisions.
    • Example: AI in healthcare explaining why a diagnosis was made.