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Class 9 Computer Science Unit 5 Notes: Applications of Computer Science Federal Board | FBISE | Download and View Online

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5.1 Artificial Intelligence and Machine Learning

Artificial Intelligence (AI)

Definition: A branch of computer science focused on creating computer-controlled machines that can perform human-like tasks.

Core Concept: Similar to fixing an "intelligent brain" into machines to provide decision-making capabilities for complex problems.

Function: Computers are trained to imitate human actions and develop intelligent software to solve problems with high precision.

Machine Learning (ML)

Definition: A field within AI that teaches computers to learn from data inputs and experience rather than direct programming.

Mechanism: Algorithms recognize patterns in large databases to build models that automate decision-making.

Growth: Capabilities improve over time as more sample data is input into the system.

Applications: Image/speech recognition, fraud detection, email phishing identification, translation, and self-driving cars.

Types of Machine Learning

1. Supervised Machine Learning

  • Uses known input data and known output responses to build a predictive model.
  • Goal: To learn the mapping from input to output so the model can make predictions on unseen data.
  • Example: Predicting car prices by training an algorithm on a dataset including engine power, seat number, and safety features.

2. Unsupervised Machine Learning

  • Uses unseen and unlabelled data (data not assigned to a category).
  • Goal: To find hidden patterns or groupings (known as clustering) within the data.
  • Example: Assessing loan applicants where irrelevant data (age, gender) is eliminated to focus on financial risk factors like income and debt.

The Machine Learning Process

The development of an AI algorithm follows these five stages:

  1. Collection of training data.
  2. Creating the algorithm.
  3. The learning process.
  4. Creating the training model.
  5. Predicting results.

Risks and Ethical Disadvantages

Because AI algorithms do not apply formulas like traditional algorithms, they can produce wrong results and lack transparency.

  • Human Bias: Developers may intentionally or unintentionally introduce bias, leading to discrimination based on ethnicity, gender, or age.
  • Safety Concerns: Errors in healthcare systems (wrong medicine) or self-driving car accidents can lead to death.
  • Cybersecurity: AI can be used by hackers to bypass security and launch malicious attacks.
  • Job Loss: Automation can replace human roles in manufacturing, education, and healthcare.
  • Loss of Skills: Dependence on AI may reduce human creativity and critical thinking.

Benefits of AI Systems

  • Efficiency: Increases productivity and helps in making correct decisions for complex problems.
  • Medical Advancement: Provides early-stage diagnosis and treatment for diseases like cancer.
  • Daily Convenience: Suggests shortest travel routes and provides real-time traffic updates.
  • Innovation: Enables new teaching techniques and the development of high-quality products in safe environments.

Popular AI Tools and Ethical Issues

ChatGPT

  • Description: A virtual assistant developed by OpenAI that provides text-based responses for essays, emails, and coding.
  • Ethical Issues: Misinformation, plagiarism in schools, and concerns regarding data privacy and security.

Grammarly

  • Description: An AI tool for correcting spelling and grammar errors.
  • Limitation: It is not 100% accurate and should not replace manual proofreading.

Lovo.ai

  • Description: An AI generator that converts text to realistic speech in over 100 languages.
  • Ethical Issues: Risk of copyright infringement and the need for unbiased training data.

Virtual Assistants (Siri, Alexa, Google Assistant)

  • Description: Interactive programs that understand natural language to perform tasks like making calls or search queries.
  • Ethical Challenges: Users often do not know where their information is stored or how it is protected against privacy risks.

5.2 Different Areas of AI

AI technology solves problems across various sectors, including healthcare, education, transportation, customer support, cybersecurity, and defense.

5.2.1 Speech Recognition

Definition: Technology that allows users to dictate text or provide verbal commands instead of typing.

  • Applications: Used by virtual assistants (Siri, Alexa) on mobile phones, tablets, and smartwatches. It is particularly helpful for individuals with physical disabilities.
  • Challenges: Difficulty understanding different pronunciations and occasional lack of accuracy in communication.

5.2.2 Computer Vision

Definition: A field of AI that enables computers to "see" and extract meaningful information from digital images and videos using cameras and algorithms.

  • Process: Large amounts of visual data are analyzed repeatedly until the system can recognize and distinguish specific images.
  • Applications: Facial recognition, self-driving cars, traffic flow analysis, healthcare, and manufacturing.

5.2.3 Natural Language Processing (NLP)

Definition: AI focused on giving computers the ability to understand, process, and translate spoken and written human language.

  • Core Purpose: To provide easy communication between humans and computers by automating workflows.
  • Daily Applications:
    • Language translation (Google Translate)
    • Email filtering and spam detection
    • Chatbots and Personal Assistants
    • Sentiment analysis and spell check

5.2.4 Expert Systems

Definition: Interactive AI programs designed to solve complex problems by mimicking the decision-making ability of human experts in a specific field.

  • Applications: Cancer detection in hospitals, loan analysis and fraud detection in finance, and monitoring plant operations.

5.2.5 Development of Expert Systems

  1. Knowledge Acquisition: Knowledge from human experts is collected and fed into a "knowledge database."
  2. User Input: An end user makes a query and provides required information via a questionnaire.
  3. Inference: The system applies rules and reasoning to the knowledge database to interpret the data.
  4. Solution: The system provides the final solution to the end user.

Applications of Artificial Intelligence (AI)

Astronomy

  • Definition: The scientific study of celestial objects (moon, sun, planets) outside Earth's atmosphere.
  • Role of AI:
    • Analyzes vast data from telescopes and satellites.
    • Identifies celestial objects and studies galaxy formation.
    • Predicts the motion of asteroids and the behavior of stars.

Cybersecurity

  • Purpose: To manage and respond to increasingly complex digital threats.
  • Key Functions:
    • Provides real-time monitoring of digital systems.
    • Identifies patterns, anomalies, and potential cyber threats.
    • Uses machine learning to detect and manage malicious activities effectively.

GPS Navigation

  • Evolution: Transitions from traditional static satellite maps to dynamic, AI-based systems.
  • Advantages:
    • Adapts to real-time changes like traffic, road closures, and weather.
    • Provides smarter route planning and improved travel efficiency.
    • Integrates voice recognition and natural language processing for better user interaction.

Robotics

  • Definition: Computer-controlled machines designed to perform tasks with little to no human intervention.
  • Components: Robots integrate sensors, cameras, microphones, and complex control systems.
  • Usage Areas:
    • Automotive and manufacturing industries.
    • Warehouse transport and medicine packaging.
    • Handling hazardous materials in high-risk or extreme-temperature environments.

Healthcare

  • Diagnosis: AI software helps diagnose diseases like cancer and scans for harmful bacteria in blood samples.
  • Monitoring: AI systems in ICUs track real-time data such as heart rate, blood pressure, and temperature.
  • Patient Care: Provides personalized health management, reducing the need for frequent hospital visits.

Education

  • For Students: Enhances learning through individualization and global access.
  • For Teachers: Assists in grading assignments, examinations, and essays.
  • For Administration:
    1. Schedules courses and creates class timetables.
    2. Analyzes student data for better decision-making.
    3. Reviews enrollment applications for admissions.

Gaming

  • Player Experience: AI makes opponents seem intelligent, creating a more challenging and engaging environment.
  • Graphics: Powers high-quality 3D graphics and Virtual Reality (VR) for a realistic experience.
  • Genres: Widely used in racing, shooting, role-playing, and adventure games.

Agriculture

  • Goal: To produce high-quality crops and increase production while using land more efficiently.
  • Future Impact: Essential for solving food shortages and managing livestock through innovative farming ideas.
  • Specific Applications:
    • Disease Prevention: Scans images of insects to detect and prevent the spread of diseases in both crops and livestock.
    • Livestock Monitoring: Uses drones, cameras, and computer vision to monitor health.
    • Precision Farming: Utilizes AI-based drone technology for efficient pesticide spraying and driverless tractors.
    • Surveillance: AI systems provide security by preventing unauthorized access to fields.

AI in Daily Life

  • Communication: Automatically filters spam emails into trash or spam folders.
  • Device Security: Powers facial recognition on phones and laptops for secure access.
  • Information Management: Search engines like Google and Yahoo use AI to organize web data and provide personalized shopping recommendations.
  • Advertising: Displays ads based on a user's specific search history.
  • Banking: Detects fraud and ensures secure financial transactions.

Self-Driving Cars

  1. Data Collection: AI software connects with sensors, cameras, and radars to gather environmental data.
  2. Real-time Action: The software analyzes information to navigate and drive the vehicle without a human operator.
  3. Industry Adoption: Major companies including Tesla, BMW, Audi, and Volvo are developing this technology.

Note: The core of self-driving cars is a complex integration of Computer Vision, Sensor Fusion, and Real-time Decision Making algorithms.

AI in Business

  • Management: AI software handles day-to-day activities in e-commerce and marketing to improve productivity.
  • Key Benefits:
    • Machine Learning (ML): Improves decision-making and efficiency.
    • Customer Relations: Enhances Customer Relationship Management (CRM) and services.
    • Operations: Enables smart pricing, automated recruitment, and stronger cybersecurity.

AI in Defense

  • Threat Detection: Identifies suspicious activities and prevents hackers from stealing confidential information.
  • Military Technology: Used in advanced surveillance, armed military drones, and the development of modern weaponry.
  • Training: Provides computer-simulated war environments to train soldiers effectively.

Chatbots

  • Definition: AI programs that use natural language to mimic human conversation through text or voice.
  • Role: Serve as virtual assistants to provide instant answers to website visitors.
  • Business Impact:
    • Available 24/7 for customer service.
    • More cost-effective than hiring human employees.
    • Helps in selling products and automating customer responses.

Study Guide: Cloud Computing

This guide covers the fundamental definition of cloud computing and the primary models used by organizations to manage their data and digital services.

1. What is Cloud Computing?

Cloud computing is the delivery of various computing services over the Internet via third-party vendors. It serves as a modern alternative to traditional, in-house data processing.

  • Core Services Provided:
    • Servers and storage
    • Databases and networking
    • Software and applications
  • Key Characteristics:
    • Remote Access: Resources are accessed from remote physical servers rather than local hardware.
    • Universal Connectivity: Users can process data from any location using any device connected to the Internet.

2. Types of Cloud Computing

Cloud models are categorized based on how resources are owned, managed, and shared.

Public Cloud

  • Owned and operated by a cloud service provider.
  • Resources (servers, software, storage) are delivered over the Internet to multiple businesses.
  • Benefit: Offers high-speed connectivity for rapid access to data and applications.
  • Examples: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform.

Private Cloud

  • Dedicated exclusively to a single organization.
  • Resources are owned and used internally within that specific organization.
  • Management: The user organization is responsible for maintaining the infrastructure (hardware, network, etc.).
  • Benefit: Provides greater control over servers and network security.

Hybrid Cloud

  • A combination of both public and private cloud environments.
  • Uses technology that allows data and applications to be shared between the two different models.
  • Benefit: Offers flexibility, allowing businesses to keep sensitive data privately while leveraging public cloud resources for less critical operations.

Study Guide Complete. This comprehensive guide covers key concepts in Artificial Intelligence (AI), Machine Learning (ML), and Cloud Computing. Use it for exam preparation, project research, or general knowledge enhancement.

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