AI and IoT Flashcards

Flashcards about AI and IoT

Artificial Intelligence (AI)

Computer systems or machines designed to mimic human intelligence, performing tasks that typically require human thought.

How AI works

Algorithms and models, like machine learning, help it improve and adapt over time without constant reprogramming.

Integration of AI into IoT

Enhances the system's ability to learn from data, make intelligent decisions, and automate actions.

IoT Structure

Sensors/Devices, Connectivity, Data Processing, IoT App, Actuators

Preprocessing at Edge

Edge devices use AI-based preprocessing to filter noise, remove outliers, or perform initial calculations.

AI Tasks for Data Transmission

Dynamic Channel Selection, Bandwidth Optimization

AI in IoT for Plant Watering

AI decides when to alert the user via the app if the plant needs water and learns the best watering schedule based on plant type and local weather conditions.

Edge Processing AI

Analyzing data immediately and making real-time decisions.

Cloud Processing AI

Handles more resource-intensive computations running deep learning models.

Cloud/Edge Computing AI Tasks

Data Aggregation, Predictive Analytics, Optimization, Real-Time Decision Making

AI role on Actuators

Optimizes how actuators respond, which ensures better performance, efficiency, or user satisfaction.

Efficiency & Productivity

Completing tasks faster and more accurately than humans, reducing effort in repetitive or time-consuming work.

Communication & Smart Assistants

Al-powered assistants and chatbots help with tasks like setting reminders, sending messages, and answering questions.

Personalisation & Convenience

Enhancing user experience by recommending content, products, and services based on personal preferences.

Safer & Smarter Transportation

Improving traffic management, navigation, and self-driving technology to reduce accidents and congestion.

Healthcare Advancements

Helping doctors diagnose diseases, personalise treatments, and predict health risks more accurately.

Security & Fraud Detection

Enhancing cybersecurity by detecting and preventing fraud, hacking, and identity theft.

Increasing Accessibility

Al-powered tools like speech-to-text software assist people with disabilities, making technology more inclusive.

Education & Learning

Al tutoring systems provide personalised learning experiences to help students understand difficult subjects.

Science & Environment

Analysing data to solve complex global challenges, such as climate change, space exploration, and disaster prediction.

Dependence on Data Quality

Poor-quality, incomplete, or outdated data can lead to inaccurate predictions.

Lack of Human Understanding

It struggles with common sense reasoning, emotions, and abstract thinking.

Lack of Creativity and Innovation

It relies on existing data and patterns rather than original thought, meaning it cannot invent entirely new ideas or solve problems in unconventional ways.

Limited Adaptability and Flexibility

Al excels in specific tasks but struggles with new or unfamiliar situations that require adaptability.

Bias and Discrimination

Al systems learn from historical data, which can reinforce and amplify societal biases, leading to unfair or discriminatory treatment of certain groups of people.

Privacy and Surveillance

Al-driven surveillance can track individuals without their consent, raising serious privacy concerns.

Deepfakes and Misinformation

Cybercriminals can use Al for deepfake scams or to spread false information, manipulate public opinion, or damage reputations.

High Costs & Energy Consumption

Data centers that train and run Al models use vast amounts of electricity and water, raising concerns about sustainability.

Job Displacement

Al and automation are replacing many repetitive and manual jobs, like manufacturing, retail and customer service, leading to concerns about unemployment.

Accountability and Decision Making

Who is responsible when Al makes a mistake?