Unit 1 Introduction to AI (1)_compressed

Introduction to AI

• Since ancient times, humans have sought to create tools to enhance their capabilities.

• Efforts to make machines intelligent led to the development of artificial intelligence (AI).

• Smart machines have emerged that can think and respond similarly to humans.

• This chapter explores the evolution of AI.

What is Intelligence?

• Definition: Intelligence is the ability to learn from experience, recognize problems, and solve them.

• According to Sternberg: "Intelligence is the capacity to learn from experience, using metacognitive processes to enhance learning, and the ability to adapt to the surrounding environment."

• Example: A student is considered intelligent if they understand the content and achieve above-average grades.

Artificial Intelligence (AI)

• Definition: AI simulates human intelligence in machines, enabling them to think and perform tasks like humans.

• Goals: Develop machines with human-like intelligence covering perception, reasoning, and learning.

• Founder: John McCarthy coined "Artificial Intelligence" in 1956, defining it as the science of making intelligent machines.

Types of AI

• AI encompasses subsets like Machine Learning, Big Data, and Natural Language Processing (NLP).

Weak AI

• Also known as Narrow AI.

• Characteristics:

  • Performs dedicated tasks with limited intelligence.

  • Cannot operate beyond its training and fails in unpredictable situations.

• Examples:

  • Apple's Siri is a digital assistant with predefined capabilities.

  • IBM's Watson utilizes machine learning and NLP.

Strong AI

• Referred to as General AI.

• Characteristics:

  • Machines perform a wide range of tasks with human-like intelligence.

  • Focused on problem-solving, learning, and development.

• Currently in research stages, with no developed systems yet.

• Example: A machine responding to "good morning" by turning on the coffee maker.

Super AI

• Characteristics:

  • Hypothetical systems that surpass human intelligence.

  • Ability to understand and evoke emotions and desires.

• Considered the next evolution beyond Strong AI, with significant developmental challenges.

How do Machines Become Intelligent?

• Machines are provided with sufficient data and accurate algorithms for intelligence development.

AI Around Us

1. Smartphones: Smart assistants (e.g., Siri), portrait modes in cameras.

2. Email Spam Filters: Categorizes emails using AI.

3. Virtual Assistants: Control smart home devices, manage tasks.

4. Social Media: Tagging suggestions, content personalization.

5. Music and Media Streaming: Recommends content based on user preferences.

6. Video Games: AI controls characters based on player input.

7. Navigation: AI assists in route planning and traffic management.

8. Security and Surveillance: Smart cameras analyze movement in real-time.

9. Social Media Filters: AI-based effects in applications like Snapchat.

What is Not AI?

• Not every automated tool is AI-based:

  • Fully Automatic Dishwashers: Require human input for operations.

  • IP-enabled Security Cameras: Need human oversight.

  • Industrial Robots: Task-specific robots with no intelligence.

The Turing Test

• A test to determine if a machine demonstrates human intelligence through effective human-like conversation.

Big Data and AI

• Big Data refers to vast data collections that grow over time.

• Deep Learning: A machine learning technique that mimics human learning by example.

• Machine Learning: Utilizes data and algorithms for human-like learning.

• Data Science: Deals with vast data to recognize patterns and derive information.

World Famous AI Machines

• IBM Watson: Question-answering system for data analysis.

• Google’s Driverless Car: AI technology enables autonomous driving.

• Sophia: A humanoid robot designed by Hanson Robotics.

• Virtual Assistants: Alexa, Siri, Google Home that carry out commands.

• Honda Asimo: A humanoid robot created for various tasks.

Importance of AI

• Increased Efficiency and Accuracy: Solves complex problems quickly and accurately.

• Robots for Human-inaccessible Tasks: Use in hazardous situations like pandemic roles.

• Automation: Benefits everyday life through virtual assistants and chatbots.

• Support for Differently Abled: Enhances capabilities through AI-driven software.

AI in India

• India's favorable ecosystem encourages global companies to set up AI labs.

• NITI Aayog promotes AI in medical and agricultural sectors with the #AIforAll strategy.

• India’s AI Initiatives: Included in CBSE curriculum since 2019.

Future of AI

• Expected to profoundly change global dynamics and economies by replacing humans in risky jobs.

• Data is critical for AI machine function; AI processes diverse data types to provide insights.

Domains of AI

• Human-Machine Interaction (HMI): Interaction methods between humans and machines.

• AI Domains: Include Data, Computer Vision, and Natural Language Processing.

Data in AI

• Importance: Greater data volume enhances prediction accuracy.

• AI Game: Rock Paper Scissors game demonstrates AI's learning capabilities.

Computer Vision in AI

• Enables machines to interpret and understand visual data.

• Applications include self-driving cars and security systems.

Natural Language Processing (NLP)

• Allows machines to understand human language.

• Applications include chatbots and virtual assistants.

Smart Living

• Smart Homes: Enhance living standard through automated devices.

• Smart Cities: Improve urban living using technology and data.

AI Tasks and Project-Based Learning

• Assignments include researching AI's role in various industries and developing futuristic job ads.

Ethical Issues in AI

• Job Loss: Concerns about automation leading to unemployment.

• Personal Privacy: Issues with surveillance and data tracking.

• Mistakes: Instances where AI has made errors in judgment.

• Autonomous Weapons: Ethical dilemmas with military applications of AI.

• AI Bias: Challenges posed by biased data and algorithms affecting AI outcomes.

Advantages of AI

• Increased Automation and Productivity: Streamlines processes.

• Smart Decision-Making: Facilitates informed business choices.

• Problem-Solving Potential: Tackles complex issues effectively.

Disadvantages of AI

• High setup costs and risk of unemployment.

• Lack of human emotions and adaptability to new situations.

Conclusion

• The race to advance AI is global, with advancements bringing opportunities and ethical dilemmas.