Emerging Technologies & Industrial Revolutions

Emerging Technologies

• Emerging Technologies are new technologies with different meanings used in media, business, science, or education
• Emerging Technologies are developing and expected to be available within 5-10 years
• These technologies are creating significant social economic effects, and is a theory of radical transformation of society through technological development

Industrial Revolutions (IR)

• Transportation grew by leaps and bounds with the invention of the plane and car
• Mechanical production grew in speed through mass production
• The Industrial Revolution began more than 200 years ago.
• The IR had a profound impact on how people lived
• The IR revolution changed how people worked and the technologies available to them
• Communication technology and transportation saw advancements
• Industry 1.0 began in around 1780 and focused on industrial production on machines powered by steam and water
• Industry 2.0 occurred 100 years later, in 1870, and was based on electrification and mass production through assembly lines
• Industry 3.0 occurred 100 years later, in 1970, and saw automation through the use of computers and electronics, enhanced by globalization involving offshoring production to low-cost economies
• Industry 4.0 involves digitalization, automation, artificial intelligence techs (AI), connected devices, data analytics, cyber-physical systems, and digital transformation
• Industry 5.0 is entering with a focus on man and machines working together
• Robots are free to deliver value-added tasks
• This iteration includes increased resilience, a human-centric approach, and a focus on sustainability

Emerging Technologies in 2025

• Generative Artificial Intelligence
• Blockchain
• Augmented Reality and Virtual Reality
• Cloud Computing
• Angular and React
• DevOps (Automation)
• Quantum Computing
• Internet-of-Things (IOT)
• Intelligent Apps (I-Apps)
• Big Data
• Robotic Processor Automation
• Cybersecurity
• Nanotechnology
• 5G and Beyond
• Edge Computing

Data and Information

• Data and information definitions will be discussed: data types and representation, acquisition and storage, data science prerequisites and tools
• Data is a set of random and unorganized values, quantities, and figures that do not carry any meaning
• Data is the first level of knowledge, not dependent on additional information
• Data may not be useful, it is input of information, does not provide meaning, and is vague in nature
• Information is a processed and organized form of quantities, values, or figures that carry a meaning
• Information is the second level of knowledge and is dependent on data
• Information is always useful and is an output of data
• Information provides logical meaning and is specific
• Data types tell the computer what kind of data it is handling
• This includes a number (integer), text (string), or a true or false value (boolean)
• Representations are how these data types are expressed or stored in a computer

Data Acquisition and Storage

• Data acquisition involves the field of data science and engineering
• Data acquisition depends on specialized software that quickly captures, processes, and stores information
• Data acquisition systems include measurement devices, sensors, computers, and software to acquire, store, visualize, and process data
• Data acquisition is the process of converting real-world signals to the digital domain for display, storage, and analysis
• Data Storage captures and retains digital information on electromagnetic, optical or silicon-based storage media
• Storage is in offices, data centers, edge environments, remote locations and people's homes, and mobile devices
• Data Storage: Cache Memory, RAM, Magnetic Tape, Magnetic Disk, Optical Disc, Flash-based SSD, Network-Attached Storage (NAS), Storage-Attached Network (SAN), Cloud Data Service Provider

Data Science

• Data Science involves asking the right questions and exploring the data, modeling the data using various algorithms, and communicating and visualizing the results
• Prerequisites for Data Science include machine learning, mathematical modeling, statistics, computer programming, and databases
• A data scientist takes real-world data, processes and analyzes it, and gets meaningful data and useful insights
• A data driven approach is based on these steps: Concept Study, Data Preparation, Model Planning, Model Building, Communicate Results, Operationalize
• Tools/Skills used include Skills for ETL, SQL, Hadoop Statistics and R
• Tools used consist of Informatica/ Talend, AWS Redshift, SAS, RapidMiner and Jupyer

Artificial Intelligence (AI)

• Artificial Intelligence (AI) is the simulation of human intelligence in machines
• AI is present in everyday life through virtual assistants like Siri and Alexa, recommendation systems on Netflix and YouTube, and smart home devices
• The significance of AI in daily activities enhances convenience, efficiency, and automation
• Narrow AI operates under a limited set of constraints and cannot generalize beyond its programming
• General AI would have the ability to understand, learn, and apply intelligence across different domains
• Super AI would surpass human intelligence, possessing the ability to innovate, create, and even make independent decisions
• AI learns from vast amounts of data and improves decision-making without explicit programming
• AI mimics the structure of the human brain using layers of nodes (neurons) to recognize complex patterns
• AI continuously refines its performance through trial and feedback

Uses of AI

• Used to create personalized experiences that cater to individual customer needs
• AI collects behavior, preferences, and interests
• AI algorithms use the collected data to create customized recommendations, content, and offers
• Al enables tailored messaging and interactions across different channels
• AI is used in Entertainment, Healthcare, Automotive, Customer Service, Manufacturing and Cybersecurity

Pros and Cons of AI

• Pros involve Al handling repetitice tasks quickly and accurately, Al systems can analyze and process data at speeds beyond human capability, Al reduces error in tasks that require precision, and does not need breaks, allowing for continuous operation
• Cons include systems can inherit biases, privacy concerns, over-reliance on AI leading to vulnerabilities, and job loss

Internet of Things (IoT)

• IoT refers to the interconnection of everyday physical objects embedded with software, sensors, and network connectivity
• It allows devices to seamlessly communicate, and is contributing to the rapid adoption across industries and daily life
• Key features include connectivity, automation and control, real-time data exchange, scalability, and integration with AI/ big data
• Improves efficiency, enhance convenience, supports data-driven decisions, and enables innovation
• Key IoT components comprise of: sensors/devices capturing data, connectivity via wireless protocols, data processing with edge or cloud computing, and user interfaces

IoT Communications and uses

• IoT Communication models: device-to-device, device-to-cloud, device-to-gateway, edge & fog computing
• It is used in Smart Homes, Smart Gadgets, machines/sensors, hospitals, surveillance/security, transportation

IoT Pros and cons

• Pros include automation/efficiency, data-driven insights, cost reduction, enhanced security
• Cons include security vulnerabilities, data privacy issues, interoperability challenges, and high infrastructure costs

IoT Challenges

• Security breaches
• Privacy concerns involving data collection and user consent
• Interoperability issues between devices
• Scalability requiring infrastructure Emerging trends include 6g, AI-Integrated IOT , and Quantum-Secured IOT
• Market growth exceeding $1.5 trillion by 2030 in developing economies

Platform Architecture

• Platform architecture integrates hardware, software, connectivity, and data
• Platform architecture enables smart devices with underlying structure for functionality and integration
• Key Components: Hardware Layer, Software Layer, Connectivity Layer, Data Processing & AI, Security & Privacy
• The hardware layer consists of physical components that handle data, the "brain" of the device that processes data and executes commands, sensors that collect environmental data, Communication Modulesthat enable devices to send and receive data
• The software layer manages the hardware resources and OS, middleware acts as a bridge between the OS and applications
• The connectivity layer defines connectivity for exchanging data is transmitted, How It Works -ensures devices can send and receive data , different protocols like Wi-Fi, Bluetooth, Zigbee, Z-Wave and allows updates and user connection

Platform Architecture: Data

• Data Processing and AI involves smart devices analyzing data and use for decision making.
• Data processing includes data from sensors converted into useful information
• AI enables smart devices to learn from data patterns and improve decision-making by automating actions
• Security and data privacy protects unauthorized access and includes data encryption, authentications, firewalls, data privacy laws like GDPR and HIPAA
• Properties involve simplicity, resilience, maintainability, and evolvability