Understanding AI, Machine Learning, and Deep Learning

Introduction

• AI and Machine Learning: Hot topics in technology.
• Deep Learning: A subset of these technologies.
• Generative AI: A new explosion in AI technology.
• Goal: Address common questions and misconceptions.

Artificial Intelligence (AI)

• Definition: Simulating with a computer something that matches or exceeds human intelligence.
• Intelligence Aspects:
  • Ability to learn
  • Ability to infer
  • Ability to reason
• History:
  • Started as a research project.
  • Early programming languages: Lisp, Prolog.
  • Evolution into expert systems (1980s-1990s).

Machine Learning (ML)

• Definition: Machines learn patterns and make predictions without being explicitly programmed.
• Functionality:
  • Observes data to predict outcomes.
  • Good at finding patterns and spotting outliers.
• Applications:
  • Cybersecurity: Identifying unusual user behavior.
• Popularity: Gained traction in the 2010s.

Deep Learning

• Definition: Uses neural networks to mimic human brain functions.
• Characteristics:
  • "Deep" due to multiple layers in neural networks.
  • Complex and sometimes unpredictable outputs.
• Significance: Important advancements in AI, popularized in the 2010s.

Generative AI

• Definition: AI that generates new content.
• Foundation Models:
  • Example: Large language models for text prediction.
  • Can predict sentences, paragraphs, entire documents.
• Analogy with Music: Generates new content from existing patterns.
• Applications:
  • Audio models, video models (e.g., deep fakes).
  • Chatbots and more.
• Impact: Major factor in AI adoption and attention.

Conclusion

• AI Evolution: From unknown to ubiquitous due to generative AI.
• Importance of Foundation Models: Key to rapid AI adoption.
• Future Prospects: Harness benefits of generative AI technologies.

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