Credit: Dam Minh Tien

The course "Introduction to Artificial Intelligence (AI)" provides an overview of AI's significance in today's world and its applications across various industries. Here are the key points:

• Course Purpose: To help learners understand AI concepts, terminology, and applications, enabling them to enhance their careers or transform their businesses.
• Content Overview:
  • Module 1: Introduction to AI applications in industries, including tools like ChatGPT and smart home devices.
  • Module 2: Core AI concepts such as deep learning, machine learning, and neural networks, along with generative AI models.
  • Module 3: Real-world use cases of AI in content generation, data analysis, and customer service, along with career opportunities in AI.
  • Module 4: Ethical considerations and governance of AI, addressing issues like fairness, transparency, and privacy.
• Learning Format: The course includes videos, readings, hands-on activities, quizzes, and a final project to reinforce learning.

By the end of the course, learners will be able to describe AI, its applications, and the ethical concerns surrounding it.

Outline

Module 1: Introduction and Applications of AI

1.1. Introducing AI

In the lecture on Introducing AI, you learned that:

• AI Definition: Artificial Intelligence (AI) is defined as augmented intelligence, which enhances human capabilities and automates tasks that typically require human intelligence, such as learning, reasoning, and decision-making.
• History of AI: The journey of AI began in the 1950s with Alan Turing's proposal of the Turing test and John McCarthy's coining of the term "artificial intelligence." Key developments include:
  • Early programs in the 1960s (e.g., ELIZA)
  • Rise of expert systems in the 1970s
  • Surge in machine learning in the 1980s
  • Introduction of neural networks in the 1990s
  • Growth of deep learning from 2010-2020

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timeline
    title History of AI
    1950: Turing Test proposed & term "AI" coined by McCarthy
    1960: Early program ELIZA developed
    1970: Expert systems rise in popularity
    1980: Surge in machine learning techniques
    1990: Introduction of neural networks
    2010-2020: Growth and breakthrough of deep learning

• Types of AI: AI can be categorized based on strength:
  • Weak AI (Narrow AI): Specialized in specific tasks (e.g., language translators).
  • Strong AI (Generalized AI): Capable of performing diverse tasks and learning new skills.
  • Super AI (Conscious AI): Hypothetical AI with human-level consciousness, which is not yet achievable.
• Learning Methods: AI learns through methods like supervised learning, unsupervised learning, and reinforcement learning.
• Impact of AI: AI is reshaping industries and enhancing decision-making by providing experts with relevant information and automating time-consuming tasks.

1.2. Artificial vs. Augmented Intelligent

In the current lecture, the speaker discusses three forms of intelligence encountered during a drive to work:

1. Human Intelligence: The ability to operate a vehicle, including tasks like steering and checking mirrors.