Emerging Trends in Computer and Information Technology Lecture Notes

Emerging Trends in Computer and Information Technology important vocabularies and definition for exam

Artificial Intelligence

AI is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. It encompasses learning, problem-solving, perception, and understanding natural language.

Machine learning

A subset of AI that focuses on enabling computers to learn from data without being explicitly programmed. ML algorithms allow computers to identify patterns, make predictions, and improve their performance over time.

Neural networks

Computer programs modeled after the structure and function of the human brain. They consist of interconnected nodes (neurons) that process and transmit information, enabling complex pattern recognition and decision-making.

The core components and constituents of AI

AI's foundation lies in logic, which provides the principles of reasoning; cognition, representing the mental processes; and computation, which enables the execution of complex algorithms.

Chomsky’s linguistic computational theory

Noam Chomsky's work significantly influences NLP. His theory posits a model for syntactic analysis using regular grammar, contributing to how machines understand and process human language.

Reactive Machines

The most basic type of AI, these machines react solely to the present situation without memory of past experiences. An example is Deep Blue, IBM's chess-playing computer.

John McCarthy - Father of AI

Coined the term 'Artificial Intelligence' and defined it as the science and engineering of making intelligent machines, especially intelligent computer programs.

AI

A multidisciplinary field drawing from machine learning, deep learning, and neural networks to create systems that can perform tasks requiring human-like intelligence.

Information

Data that is meaningful and useful to humans. AI relies on information to make decisions, generate insights, and take appropriate actions.

Intelligence

The capacity to understand, learn, and reason; essential for AI systems to perceive their environment, make informed decisions, and control actions effectively.

Animation

While used in conjunction with AI for creating realistic simulations, animation is not a core element of an AI career.

Communication Skills (written & verbal)

Crucial for AI professionals to explain how AI services and tools can be applied within diverse industry settings, ensuring stakeholders understand the technology's value.

Pursuing a career in the field of AI

Requires a robust education in mathematics, science, critical thinking, technology, logic, and engineering principles, providing a solid foundation for AI development.

Heuristics

Problem-solving approaches that use practical methods or shortcuts to produce solutions that may not be optimal but are sufficient given time or resource constraints.

Think like Humans

The cognitive science approach in AI design aims to create systems that mimic human thought processes, improving problem-solving and decision-making capabilities.

GPS

Stands for General Problem Solver, an early AI program designed to solve a wide range of problems using human-like logical reasoning.

Act like humans approach

The behaviorist approach in AI seeks to build systems that emulate human behavior without necessarily replicating human thought processes.

ELIZA was coded at MIT by

Joseph Weizenbaum in the 1960s as one of the earliest natural language processing programs, simulating conversation using pattern matching and substitution.

ELIZA

An early natural language processing computer program developed at MIT from 1964 to 1966 to demonstrate the possibility of communication between humans and machines.

The compound components are built up through core components

Higher-level AI capabilities such as knowledge representation, reasoning, NLP, computer vision, and search algorithms are built upon fundamental components.

The philosophy of AI is

Explores the potential for machines to possess consciousness, sentience, and moral agency, contemplating AI's broader implications.

The AI x-direction consists of

The x-direction in AI encompasses logic, which drives reasoning; cognition, mirroring human thought; and computation, the engine for processing data.

The AI y-direction consists of

Knowledge representation, reasoning mechanisms, and the user interface through which humans interact with AI systems define the y-direction.

The AI z-direction consists of

Focuses on AI's sensory and perceptive capabilities, including natural language processing, computer vision, and perception systems.

The power of computation logic demonstrated by

Charles Babbage and George Boole’s work laid the groundwork for modern computing, enabling machines to perform logical operations and mathematical calculations.

The modern philosopher such as

Pioneered the integration of logic and mathematics, providing a theoretical foundation for AI's symbolic reasoning and knowledge representation capabilities.

Who

Alan Turing developed the theory of computation for mechanization, creating the conceptual framework for building intelligent machines.

In

Marvin Minsky, in the 1950s, advanced AI by integrating logical formalism with knowledge representation, enabling machines to reason and solve complex problems.

KBS stands for

Knowledge-Based System: An AI system that uses a knowledge base to store and manipulate information for problem-solving.

Which is a type of AI

AI can be categorized based on capabilities (Narrow, General, Super AI) or functionalities (e.g., problem-solving, learning, perception).

Narrow AI

Also known as Weak AI, it excels at specific tasks but lacks broader cognitive abilities. Examples include virtual assistants like Siri and recommendation systems.

Narrow AI

Also called “Weak AI,” is designed to perform a specific task intelligently. It does not possess consciousness, self-awareness, or true intelligence in the human sense.

Good examples of Narrow AI

Include Apple's Siri, IBM’s Watson in its Jeopardy! days, and AI algorithms that can play chess at a super-human level.

General AI

Also known as Strong AI, possesses human-level cognitive abilities and can perform any intellectual task that a human being can.

IBM’s Deep Blue system is an example of

A reactive machine with no memory of past moves; it assesses the current board and makes its next move based solely on the present situation.

Limited Memory

AI systems that can store past experiences or data for a short period of time to inform future decisions, improving performance over time.

Theory of Mind

Refers to the ability of an AI to understand human emotions, beliefs, and intentions, enabling more natural and socially intelligent interactions.

Self-Awareness

A hypothetical stage of AI development where machines possess consciousness, self-awareness, and an understanding of their own existence and capabilities.

Natural Language Processing

A branch of AI focused on enabling computers to understand, interpret, and generate human language, facilitating communication between humans and machines.

Vision systems

Computer systems that capture, process, and analyze visual information from the real world, enabling tasks such as object recognition, image classification, and scene understanding.

Speech Recognition

The ability of intelligent systems to accurately transcribe spoken language into text, enabling voice-controlled interfaces and speech-based interaction.

Handwriting Recognition

Software technology that converts handwritten text into digital text, enabling automated data entry and document processing.

Machine Learning

A subset of AI focused on developing algorithms that enable computers to learn from data and improve their performance without explicit programming.

Different ways to implement machine learning techniques

Includes supervised learning (learning from labeled data), unsupervised learning (finding patterns in unlabeled data), and deep learning (using neural networks).

Supervised Learning

A machine learning approach where an algorithm learns from labeled training data to make predictions or classifications on new, unseen data.

Unsupervised Learning

A machine learning approach where an algorithm explores unlabeled data to discover patterns, structures, and relationships without predefined guidance.

Supervised learning algorithms

Examples include neural networks, support vector machines (SVM), and naive Bayes classifiers, each suited for different types of data and tasks.

Deep Learning

A subfield of machine learning using artificial neural networks with multiple layers to analyze data with the goal of extracting high-level features.

Online Advertising is an application of

Machine learning and deep learning, enabling personalized ad targeting, fraud detection, and optimized ad delivery.

IoT stands for

Internet of Things: A network of physical devices embedded with sensors, software, and other technologies for connecting and exchanging data with other devices and systems over the internet.

IoT

Key features of IoT include connectivity, data processing, and sensor integration. The absence of AI is not a defining characteristic.

IoT Communication model

Publish-Subscribe, Request-Response, and Push are standard IoT communication models. 'Push-Producer' is not a recognized model.

WSN stands for

Wireless Sensor Network: A group of spatially distributed sensors that monitor physical or environmental conditions and cooperatively pass data through the network to a main location.

Actuators

Mechanical or electrical devices that control or move something in response to a signal, often used to convert electrical signals into physical actions.

Embedded System

A specialized computer system designed to perform a specific task or set of tasks, typically with real-time computing constraints.

Embedded system

Typically includes an input device, a microcontroller (the central processing unit), and an output device.

Sensor

A device that detects and measures a physical quantity, such as temperature or pressure, and converts it into an electrical signal for processing.

Embedded System

Relies on an integrated software and hardware platform to execute specific functions efficiently.

The embedded system programs are mainly written using programming software like Turbo c, TASM, Proteus or Lab-view or Eclipse

Embedded system programs

Are often developed in C, C++, or embedded C due to these languages' efficiency and direct hardware control.

Embedded operating systems

Also known as Real-Time Operating Systems (RTOS), are designed for embedded systems and provide deterministic, real-time performance.

RTOS stands for

Real-Time Operating System: An operating system designed to handle events and processes within strict time constraints, crucial for embedded systems.

PIC stands for

Peripheral Interface Controller: A family of microcontrollers made by Microchip Technology, popular for use in embedded systems due to their versatility and low cost.

RISC stands for

Reduced Instruction Set Computer: A CPU design that uses a small set of simple instructions, enabling faster execution and improved efficiency.

PIC microcontrollers

Compact, programmable microcontrollers used in a wide array of embedded systems for tasks ranging from simple control to complex data processing.

AVR

AVR was developed in the year 1996

AVR stands for

Alf-Egil Bogen Vegard Wollan RISC: A family of microcontrollers known for their ease of use and wide availability.

ARM

ARM is 32-bit or 64-bit RISC

ARM stands for

Advanced RISC Machine: A widely used processor architecture known for its efficiency and scalability, dominating the mobile and embedded markets.

ARM

First introduced by Acorn Computers in 1985, ARM processors have become a cornerstone of modern computing.

ASIC stands for

Application-Specific Integrated Circuit: An integrated circuit designed for a specific use, rather than for general-purpose applications.

IoT

Key characteristics include dynamic and self-adapting, self-configuring, easy to integrate, and information sharing and collaboration.

IoT

Security vulnerabilities, privacy concerns, and system complexity are major disadvantages associated with the Internet of Things.

Link Layer

Protocols in this layer govern how data is physically transmitted over the network, managing the physical medium and hardware addressing.

IEEE 802.3

A set of standards defining wired Ethernet connections and data transmission over local area networks (LANs).

IEEE 802.11

A collection of wireless local area network (WLAN) standards, commonly known as Wi-Fi, defining how wireless devices communicate.

Network layer protocol

TCP (Transmission Control Protocol) is a transport layer protocol, not a network layer protocol, which handles reliable data transmission.

Network Layer

Responsible for logical addressing and routing of packets between different networks, ensuring data reaches its intended destination.

Transport Layer

Provides reliable, end-to-end data transfer between applications, independent of the underlying network details.

Transport layer

Key functions include error control, segmentation, flow control, and congestion control to ensure reliable and efficient data transmission.

TCP

Provides reliable, ordered, and error-checked delivery of data, using acknowledgements and retransmissions to ensure data integrity.

UDP

Offers a connectionless, datagram-oriented service with minimal overhead, ideal for applications where speed is more important than reliability.

UDP

Known for its speed and efficiency, it does not guarantee delivery, order of messages, or duplicate elimination, making it suitable for real-time applications.

HTTP

Defines how web browsers and servers communicate, enabling users to access and interact with resources on the World Wide Web.

HTTP

Encompasses commands like GET, PUT, POST, DELETE, HEAD, and TRACE for requesting and manipulating web resources.

COAP stands for

Constrained Application Protocol: A specialized web transfer protocol for use with constrained nodes and constrained networks in the Internet of Things.

WebSocket protocol

Enables real-time, two-way communication between a client and server over a single TCP connection, ideal for interactive web applications.

MQTT stands for

Message Queuing Telemetry Transport: A lightweight messaging protocol for IoT devices, designed for low-bandwidth, high-latency networks.

MQTT

Operates on a publish-subscribe model, where devices publish messages to topics, and subscribers receive messages from those topics.

XMPP stands for

Extensible Messaging and Presence Protocol: A communication protocol designed for real-time communication and streaming XML data over a network.

IoT functional blocks

Include devices (sensors and actuators), communication networks (wired and wireless), and services (applications and data processing).

IoT model

Known as Request-Response is based on the client-server architecture, enabling bi-directional communication over network.

Cloud Computing

Provides on-demand access to computing resources over the internet, enabling scalability, flexibility, and cost-effectiveness.

IoT Level-1

A base level automation involving a single node/device that does not provide analysis but only local processing.

Raspberry Pi, Arduino, Node MCU, Uno

Popular platforms for building digital devices that can sense and control objects in the physical and digital world.

Arduino

Including cost-effectiveness, cross-platform compatibility, and a user-friendly programming environment.

Raspberry Pi

A low-cost, credit-card-sized computer that can perform many of the functions of a desktop computer.

Temperature Sensors

Used to measure the amount of heat energy emitted by an object or area, converting temperature into an electrical signal.

sensor

Often utilize a humidity sensor that gauges that reads water vapor in air.

Forensic Science

Encompasses a range of scientific disciplines applied to analyze physical evidence for criminal and civil legal cases.