Introduction to Artificial Intelligence (EmTe1011/1012): Chapter 3

Introduction to Artificial Intelligence

• Artificial Intelligence (AI) is a branch of computer science that enables machines to think and behave like humans.
• AI can be defined as a man-made thinking power that can acquire knowledge, reason, learn, and solve problems.

Key Concepts in Artificial Intelligence

• Intelligence is composed of: reasoning, learning, perception, linguistic intelligence, and problem-solving.
• An AI system consists of an agent and its environment, with the agent being able to perceive its environment through sensors and act upon it through effectors.
• High-profile examples of AI include autonomous vehicles, medical diagnosis, creating art, and playing games like chess.

Need and Goals of Artificial Intelligence

• The need for AI arises from the desire to create expert systems that exhibit intelligent behavior, learn from experience, and apply knowledge.
• The goals of AI include replicating human intelligence, solving knowledge-intensive tasks, and creating a machine that can perform tasks that require human intelligence.

Disciplines Comprising Artificial Intelligence

• AI requires a multidisciplinary approach, combining mathematics, sociology, statistics, biology, neuron study, psychology, and computer science.

Advantages and Disadvantages of Artificial Intelligence

• Advantages of AI include:
  • High accuracy with fewer errors
  • High speed and fast decision-making
  • High reliability
  • Useful in risky areas
  • Digitally assistant
  • Public utility
• Disadvantages of AI include:
  • High cost
  • Limited creativity
  • Lack of emotional intelligence
  • Increase in dependence on machines
  • Limited original creativity

History of Artificial Intelligence

• The history of AI can be divided into several eras, including:
  • The maturation of AI (1943-1952)
  • The birth of AI (1952-1956)
  • The golden years (1956-1974)
  • The first AI winter (1974-1980)
  • A boom of AI (1980-1987)
  • The second AI winter (1987-1993)
  • The emergence of intelligent agents (1993-2011)
  • Deep learning, big data, and artificial general intelligence (2011-present)

Levels of Artificial Intelligence

• There are seven levels of AI maturity, including:
  • Stage 1: Rule-based systems
  • Stage 2: Context awareness and retention
  • Stage 3: Domain-specific expertise
  • Stage 4: Reasoning machines
  • Stage 5: Self-aware systems/artificial general intelligence
  • Stage 6: Artificial superintelligence
  • Stage 7: Singularity and transcendence

Types of Artificial Intelligence

• AI can be categorized into:
  • Based on capabilities:
    • Weak AI or Narrow AI
    • General AI
    • Super AI
  • Based on functionality:
    • Reactive machines
    • Limited memory machines
    • Theory of mind
    • Self-awareness### Cloud Computing and API
• Cloud computing is a general term that describes the delivery of on-demand services, usually through the internet, on a pay-per-use basis.
• Companies worldwide offer their services to customers over cloud platforms, including data analysis, social media, video storage, e-commerce, and AI capabilities.
• Application Programming Interfaces (APIs) enable software components to communicate with each other easily.
• APIs abstract the underlying workings of a service, application, or tool, and expose only what a developer needs, making programming easier and faster.

The Emergence of Data Science

• Data science has emerged as a new profession that combines several disciplines, such as statistics, data analysis, machine learning, and others.
• The goal of data science is to extract knowledge or insights from data in various forms, either structured or unstructured.
• Data scientists use learning algorithms on patterns that emerge from large volumes of data.

Applications of AI

• AI has various applications in sectors such as:
  • Agriculture: agriculture robotics, solid and crop monitoring, predictive analysis.
  • Healthcare: diagnosis, and treatment planning.
  • Education: automating grading, AI chatbots as teaching assistants.
  • Finance and E-commerce: automation, chatbot, adaptive intelligence, algorithm trading, and machine learning.
  • Gaming: strategic games like chess, where machines need to think of a large number of possible moves.
  • Data Security: AI can be used to detect software bugs and cyber-attacks.
  • Social Media: managing user profiles, and data analysis.
  • Travel and Transportation: making travel arrangements, suggesting hotels, flights, and routes.
  • Automotive Industry: providing virtual assistants for better performance.
  • Robotics: creating intelligent robots that can perform tasks with their own experiences.
  • Entertainment: AI-based applications in daily life with services like Netflix or Amazon.

AI Tools and Platforms

• AI platforms are defined as hardware architecture or software frameworks that allow software to run.
• These platforms simulate the cognitive functions that human minds perform, such as problem-solving, learning, reasoning, social intelligence, and general intelligence.
• Some AI platforms offer pre-built algorithms and simplistic workflows, while others require greater knowledge of development and coding.
• AI tools can include functionality for image recognition, natural language processing, and others.

Simple AI Application

• AI applications are used in various aspects of daily life, including:
  • Commuting: Google's AI-powered predictions, ridesharing apps, and commercial flights.
  • Email: spam filters, and smart email categorization.
  • Social Networking: Pinterest, Instagram, and Snapchat.
  • Online Shopping: recommendations, and search functionality.
  • Mobile Use: voice-to-text, smart personal assistants, and smart speakers.

Introduction to IoT

• IoT is the networking of physical objects or "things" embedded with software, sensors, and network connectivity that enables them to collect and exchange data.
• IoT devices are designed to work in concert for people at home, in industry, or in the enterprise.
• IoT devices can be categorized into three main groups: consumer, enterprise, and industrial.

History of IoT

• The concept of IoT has been around since the early 1800s.
• The term "Internet of Things" was first used in 1999.
• IoT has evolved into a system using multiple technologies, including the internet, wireless communication, and embedded systems.

Advantages and Disadvantages of IoT

• Advantages: improved customer engagement, technology optimization, reduced waste, and enhanced data collection.
• Disadvantages: security concerns, lack of international standards, and complexity in managing and analyzing data.

Architecture of IoT

• The IoT ecosystem consists of four major components:
  • Sensing layer: collects data from the real world using sensors.
  • Network layer: acts as a communication channel to transfer data.
  • Data processing layer: analyzes data and makes decisions.
  • Application layer: implements and presents the results of data processing to accomplish various applications.

IoT Devices and Networks

• IoT devices are categorized into three main groups: consumer, enterprise, and industrial.
• Consumer devices include smart TVs, smart speakers, and wearable devices.
• Enterprise and industrial devices include smart meters, commercial security systems, and smart city technologies.### IoT in Enterprise and Smart Home
• In the enterprise, smart sensors in a conference room can help an employee locate and schedule an available room for a meeting, ensuring the proper room type, size, and features are available.
• IoT Platform Solutions are based on the Internet of Things and cloud technology and can be used in areas like smart home, city, enterprise, home automation, healthcare, or automotive.
• Applications of IoT include:
  • Agriculture: monitoring microclimate, soil moisture, and nutrients, and controlling smart irrigation and fertilizer systems.
  • Consumer Use: private IoT devices like wearables and smart homes make life easier.
  • Healthcare: wearable IoT devices allow hospitals to monitor patients' health at home.
  • Insurance: offering discounts for IoT wearables like Fitbit.
  • Manufacturing: tracking products from factory floor to destination store using RFID and GPS technology.
  • Retail: controlling warehouse automation and robotics using IoT sensors.
  • Transportation: optimizing routes for trucks using GPS and traffic patterns.
  • Utilities: monitoring environmental conditions like humidity, temperature, and lighting using IoT sensors.

IoT in Smart Cities

• In smart cities, IoT sensors can aid in innovations in energy management, traffic management, and security.
• Applications of IoT in smart cities include:
  • Structural health monitoring
  • Lighting management
  • Safety monitoring
  • Transportation management
  • Waste management
  • Greenhouse monitoring
  • Animal tracking
  • Compost monitoring
  • Field monitoring

Augmented Reality

• Definition: Augmented Reality (AR) is a view of the physical real-world environment with superimposed computer-generated images, changing the perception of reality.
• Features:
  • Integrates digital information with the user's environment in real-time.
  • Convinces by ensuring virtual content aligns with real objects.
  • Can be experienced using smartphones or special AR headsets.
• Examples of AR experiences include:
  • Snapchat lenses
  • Pokémon Go

Virtual Reality (VR) and Mixed Reality (MR)

• Virtual Reality (VR):
  • Fully immersive
  • Tricks senses into thinking you're in a different environment
  • Uses head-mounted displays (HMD) or headset
• Mixed Reality (MR):
  • Merging real and virtual worlds
  • Interactive virtual and physical objects
  • Requires advanced sensing and imaging technologies
• Key differences:
  • VR is fully digital, immersive, and interactive
  • AR overlays digital content on the real-world
  • MR is a digital overlay that interacts with the real-world environment

Architecture of AR Systems

• Infrastructure Tracker Unit: collects data from the real world and sends it to the Processing Unit
• Processing Unit: mixes virtual content with real-world content and sends the result to the Visual Unit
• Visual Unit:
  • Video see-through: uses a Head-Mounted Display (HMD) that employs video-mixing
  • Optical see-through: uses an HMD that employs optical combiners

AR, VR, and MR Applications

• Education:
  • Interactive lessons
  • Higher engagement
  • Higher retention
  • Boost intellectual curiosity
• Medicine:
  • Improving healthcare
  • Enhanced surgery
  • Diagnosis
  • Diabetes management
  • Navigation
• Entertainment:
  • Games
  • Music
  • TV
  • eSports
  • Theater