Overview
This lecture explains the differences and relationships among artificial intelligence (AI), machine learning (ML), deep learning (DL), and generative AI, including recent advancements and applications.
Artificial Intelligence (AI)
- AI aims to simulate or surpass human intelligence with computers.
- Intelligence involves learning, inferring, and reasoning.
- Early AI efforts included programming languages like Lisp and Prolog.
- Expert systems in the 1980s-90s were major early AI technologies.
Machine Learning (ML)
- Machine learning allows computers to learn patterns from data without explicit programming.
- ML excels at pattern recognition, making predictions, and spotting outliers.
- ML is especially useful for detecting anomalies, such as in cybersecurity.
- ML became widely known and used starting in the 2010s.
Deep Learning (DL)
- Deep learning uses neural networks to mimic the human brain’s structure and process.
- Neural networks have multiple layers, enabling more complex learning.
- Deep learning results can sometimes be unpredictable or difficult to interpret.
- DL gained significant traction in the 2010s and underpins many modern AI advancements.
Generative AI and Foundation Models
- Generative AI refers to systems that generate new content (text, audio, video) rather than just analyzing data.
- Foundation models, like large language models, predict extended forms of text instead of just single words.
- Generative AI can create new outputs like documents, audio, images, and deep fakes.
- Deep fakes use generative AI to realistically mimic voices or faces, which can be used for both positive and negative purposes.
- Foundation models have accelerated AI adoption and applications like chatbots and content summarization.
Key Terms & Definitions
- Artificial Intelligence (AI) — Computers performing tasks that require human-like intelligence.
- Machine Learning (ML) — Subfield of AI where systems learn from data rather than being explicitly programmed.
- Deep Learning (DL) — Subfield of ML using multi-layered neural networks to process data in complex ways.
- Neural Network — A computer system modeled after the human brain, consisting of interconnected nodes (neurons).
- Generative AI — AI that creates new content such as text, audio, or images.
- Foundation Model — A large-scale, generalized AI model used as a base for specific tasks.
- Large Language Model — A type of foundation model trained on massive amounts of text to generate or predict language.
- Deep Fake — Synthetic media where a person's likeness is convincingly replaced using AI.
Action Items / Next Steps
- Review definitions and distinctions among AI, ML, DL, and generative AI.
- Look into real-world applications of generative AI, such as chatbots and deep fakes.
- Suggested: Explore additional resources or assigned readings on foundation models and their impact.