Lecture 07 - AI - Intro to AI

Introduction

• Title: Introduction to AI

• Presenter: Prof. May El Barachi

• Institution: University of Wollongong in Dubai

What is Artificial Intelligence?

• Definition: Intelligence is the capacity to learn and solve problems; AI involves building intelligent agents.

• Two main approaches: Engineering vs. Cognitive Modeling.

Key Concepts of AI

• Interacting with the real world: AI can perceive, understand, and act in environments.

• Key abilities:

  • Speech recognition

  • Image understanding

  • Reasoning and planning

  • Learning and adaptation

AI Sub-fields

• Machine Learning: Automatically learns from experience; significant applications in various domains.

• Natural Language Processing (NLP): Interaction between computers and human languages.

• Computer Vision: Computers gaining understanding from digital images/videos.

AI Application Areas

• Utilizes techniques from numerous disciplines: Statistics, Engineering, Mathematics, etc.

• Applications include:

  • Facial recognition

  • Personalized shopping

  • Credit scoring

  • Autonomous surgery

Importance of AI

• AI enhances competitive advantage by improving products, reducing costs, increasing efficiency, and providing insights.

• Key benefits include:

  • Business automation

  • Error reduction

  • Enhanced customer experiences

Notable Quotes

• Larry Page (CEO, Google): "AI would be the ultimate version of Google."

Big Tech Companies in AI

• Major players include Google, Meta, OpenAI, Microsoft, and others.

AI Technologies

• Generative AI

• Natural Language Generation

• Machine Learning Platforms

• Biometrics

• Robotic Process Automation

Machine Learning Overview

• Definition: Sub-field focused on systems learning from data without explicit programming.

• Types of learning:

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning

Deep Learning

• Branch of machine learning using neural networks to understand complex data patterns.

Natural Language Processing (NLP)

• Definition: Study of interaction between computers and human languages.

• Challenges include ambiguity and complex language variations.

Goals of AI Systems

• Systems that think/act like humans or act rationally, utilizing the Turing Test for human-like reasoning.

Evolution of AI

• Key milestones:

  • Turing Test, Machine Learning advancements, IBM's Watson, AlphaGo, etc.

Robotics

• Definition: Mechanisms that perform tasks autonomously.

• Types:

  • Industrial, Educational, Medical, Military Robots.

Conclusion

• AI continues to evolve and has the potential to significantly impact future work environments.