Unit 3: Artificial Intelligence, IoT & Computer Vision
Artificial Intelligence (AI)
Artificial Intelligence is the ability of machines to perform tasks that normally require human intelligence such as learning, reasoning, problem-solving, and decision-making.
Machine Learning (ML)
| Feature | Description |
|---|---|
| Definition | Subset of AI that enables systems to learn from data without being explicitly programmed |
| Goal | Learn patterns and make predictions/decisions |
| Example | Netflix recommending movies based on past viewing |
Types of Machine Learning
| Type | Meaning | Example |
|---|---|---|
| Supervised Learning | Model learns from labeled data | Email spam detection |
| Unsupervised Learning | Model finds patterns in unlabeled data | Customer segmentation |
| Reinforcement Learning | Model learns by reward-punishment | Self-driving car, AlphaGo |
Deep Learning (DL)
| Feature | Description |
|---|---|
| Definition | Advanced ML technique using artificial neural networks inspired by the human brain |
| Strength | Handles huge data, images, speech |
| Example | Face recognition, voice assistants (Alexa, Siri) |
Difference (Simple)
AI → Machine intelligence
ML → AI learns from data
DL → ML that uses neural networks for complex tasks
Business Applications of AI
| Business Area | AI Applications |
|---|---|
| Marketing | Personalized ads, customer segmentation, chatbots |
| Finance | Fraud detection, credit scoring, robo-advisors |
| HR | Recruitment screening, employee attrition prediction |
| Operations | Demand forecasting, automation |
| Customer Service | Virtual assistants, call bots |
| Healthcare | Disease diagnosis, drug discovery, medical imaging |
| E-commerce | Product recommendations, dynamic pricing |
| Manufacturing | Predictive maintenance, robotics |
Internet of Things (IoT)
IoT = Network of physical devices connected to the internet that collect and exchange data. Example: Smart watches, CCTV cameras, smart refrigerators, industrial sensors
IoT Architecture (4-Layer Model)
| Layer | Function | Example |
|---|---|---|
| 1. Perception Layer | Sensors & devices capture data | Temperature sensor, RFID |
| 2. Network Layer | Transfers data to cloud/servers | Wi-Fi, Bluetooth, 5G |
| 3. Processing Layer | Data storage & analytics | Cloud (AWS, Azure), Edge computing |
| 4. Application Layer | User application | Smart home app, hospital monitoring app |
IoT Devices
| Type | Examples |
|---|---|
| Consumer Devices | Smart TV, Alexa, Smart AC |
| Industrial Devices | Factory robots, energy meters |
| Healthcare Devices | Smart insulin pumps, wearables |
| Retail Devices | RFID inventory scanners |
Enabling Networks & Technologies
| Technology | Use |
|---|---|
| Wi-Fi | Home & office connectivity |
| Bluetooth & BLE | Wearables & smart devices |
| RFID / NFC | Inventory tags, contactless payments |
| 4G / 5G | High-speed mobile IoT |
| LPWAN (LoRa, NB-IoT) | Long-range industrial IoT networks |
| Cloud computing | Data storage & AI processing |
| Edge computing | On-device processing for speed |
Business Implementations of IoT
IoT in Supply Chain
| Application | Impact |
|---|---|
| Real-time shipment tracking | Reduced delays |
| RFID warehouse tracking | Efficient inventory |
| Fleet sensors | Fuel efficiency |
| Cold chain monitoring (food & pharma) | Quality & safety |
Example: Amazon uses IoT robots in warehouses.
IoT in Healthcare
| Application | Benefit |
|---|---|
| Wearables (Fitbit, smart watch) | Track health vitals |
| Smart hospital systems | Real-time patient monitoring |
| Connected medical devices | Remote treatment |
| Smart beds | Automatic patient movement alerts |
Example: ICU patient remote monitoring systems.
IoT in Smart Cities
| Application | Benefit |
|---|---|
| Smart traffic & parking sensors | Reduced congestion |
| Smart streetlights | Saves energy |
| Air quality sensors | Environmental monitoring |
| Smart waste bins | Better waste management |
Example: Smart traffic system in Singapore & Delhi sensor projects.
IoT in Manufacturing (Industry 4.0)
| Application | Benefit |
|---|---|
| Predictive maintenance | Avoid machine breakdowns |
| Smart machines & robotics | Increased efficiency |
| Production line monitoring | Quality control |
| Digital twins | Virtual simulation of equipment |
Example: Tata & Mahindra use IoT factories.
MBA Quick Revision Notes
AI enables machines to think; ML learns from data; DL handles complex patterns using neural networks.
IoT connects physical devices to gather & analyze data.
IoT applications span supply chain, healthcare, smart cities, and manufacturing, improving efficiency & decisions.
Computer Vision
Computer Vision (CV) is a field of AI that enables machines to see, interpret and make decisions from visual data (images, video).
Goal: Turn pixels → meaningful information → business action.
Core building blocks
- Image acquisition — cameras, scanners, sensors.
- Pre-processing — resizing, normalization, noise reduction, color conversion.
- Feature extraction — edges, shapes, textures (classical) or learned features (deep learning).
- Modeling / Learning — classification, detection, segmentation, tracking.
- Post-processing & decisioning — filter results, trigger alerts, actuate devices.
- Human-in-the-loop — review, feedback, retraining.
Key techniques (brief)
- Classical CV: Filters, edge detection (Sobel, Canny), HOG, SIFT, SURF — useful for constrained tasks.
- Machine Learning: SVM, Random Forest on hand-crafted features.
- Deep Learning: Convolutional Neural Networks (CNNs), R-CNN / Faster R-CNN (object detection), YOLO / SSD (real-time detection), U-Net / Mask R-CNN (segmentation), Transformers (vision transformer).
- Video analytics: Object tracking (SORT, DeepSORT), optical flow, activity recognition.
Performance metrics
- Accuracy, Precision, Recall, F1-score (classification/detection)
- mAP (mean Average Precision) — object detection quality
- IoU (Intersection over Union) — segmentation/detection overlap
- Latency & FPS (frames per second) — real-time requirement
- Throughput & scalability — enterprise requirement
Business Applications of Computer Vision
1. Quality Control (Manufacturing)
What it does: Inspect products on production lines using cameras + CV models to detect defects, misalignments, missing parts, scratches, color inconsistencies.
Why it matters:
- Faster than human inspection, 24/7 operation, higher consistency, fewer false negatives.
- Reduces returns, improves yield, maintains brand reputation.
Typical architecture: High-speed camera → edge device (preprocess + inference) → PLC/Manufacturing Execution System → reject/accept actuator & dashboard.
Key CV tasks: Defect detection (classification), anomaly detection, segmentation to localize defect.
Exam points:
- Mention reduction in manpower cost, improved defect detection rate, ROI via reduced scrap and warranty claims.
- Challenges: lighting variability, need for labeled defect data, false positives, integration with conveyor/PLC systems.
2. Retail
What it does: Shelf analytics, planogram compliance, automatic checkout (cashier-less stores), footfall analysis, customer demographics and behavior (gaze, dwell time).
Business benefits:
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Better inventory replenishment, reduced stockouts, targeted promotions, reduced shrinkage (theft detection), frictionless shopping.
Example flows: CCTV → CV models detect empty shelf → alert restocking; Camera at exit → item recognition for checkout.
Exam points:
-
Emphasize privacy/regulatory considerations (face recognition vs anonymous analytics), cost of deployment vs incremental sales uplift.
3. Automation (Robotics, Vehicles, Warehouses)
What it does: Visual navigation for robots/AGVs, object picking (robotic grasping), traffic sign & pedestrian detection in autonomous vehicles, drone vision.
Business benefits:
-
Automates repetitive tasks, reduces human risk (hazardous environments), speeds up logistics.
Key requirements: Low-latency inference (often on edge), robust models for varying conditions, sensor fusion (LIDAR + camera).
Exam points:
-
Discuss sensor fusion, SLAM (simultaneous localization and mapping) basics, safety & redundancy.
Deployment Architectures & Practical Considerations
Edge vs Cloud inference
- Edge (on-device): Low latency, privacy-preserving, works offline (example: factory camera + edge GPU).
- Cloud: Centralized model management, scalable compute for heavy models, easier retraining and analytics.
- Hybrid: Edge for real-time inference + cloud for periodic retraining, dashboarding, long-term storage.
Integration elements
- Cameras & sensors (specs matter: resolution, frame-rate, IR for low light).
- Compute: Edge CPU/GPU/TPU, or cloud GPUs.
- Connectivity: Wired (industrial) or wireless (Wi-Fi, 5G).
- Data pipeline: Ingestion → storage (object storage / video lake) → annotation → model training → deployment → monitoring.
- MLOps: Continuous monitoring, drift detection, versioning, rollback strategies.
Governance, Ethics & Risks
- Bias & fairness: training on unrepresentative images leads to errors.
- Privacy: video of people—need anonymization, consent, local processing, avoid face ID if not permitted.
- Security: camera tampering, model theft.
- Regulatory: sector-specific rules (healthcare, public surveillance).
Integration of AI & IoT
Theme: Combine AI (algorithms/models) with IoT (sensors, connectivity, actuators) to create systems that sense — analyze — act autonomously or with minimal human input.
Conceptual architecture (three layers)
- Perception/Edge Layer (IoT devices): Cameras, sensors, actuators, edge compute.
- Connectivity & Data Layer: Networks (Wi-Fi, 4G/5G, LPWAN), gateways, secure transport to cloud/edge.
- Intelligence & Application Layer: Cloud/edge AI models, analytics platforms, dashboards, APIs, business applications.
Typical integration patterns
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Edge intelligence: lightweight AI runs on device for immediate action (e.g., reject defective product).
-
Cloud intelligence: heavy analytics and model retraining in cloud; insights pushed to devices.
-
Digital twin: IoT data feeds a virtual replica; AI simulates and prescribes actions.
-
Event-driven automation: Sensor event → AI decision → actuator command (example: detect slip in factory → stop conveyor).
Intelligent Products
- Smart thermostat: learns occupancy patterns (ML) + IoT sensors → optimizes HVAC, reduces energy bills.
- Smart camera systems: local CV models detect unauthorized access → trigger alarms and notify security.
- Connected medical devices: wearable sensor → AI detects arrhythmia → alerts patient/doctor.
Smart Services
- Predictive maintenance service: IoT sensors measure vibration/temperature → AI predicts failure → schedule maintenance → reduce downtime.
- Smart logistics: IoT trackers + CV at warehouses → automated sorting + route optimization for delivery.
- Smart city services: traffic sensors + camera CV → dynamic signal control, reduced congestion.
Business benefits & ROI drivers
- Cost reduction: less downtime, lower manual inspection costs.
- Revenue uplift: better product availability, personalized services.
- New business models: product-as-a-service, predictive maintenance contracts.
- Operational efficiency: optimized energy, labor, inventory.
Implementation checklist for managers
- Define clear KPIs (reduction in defects %, time saved, revenue uplift).
- Start with pilot / PoC in controlled environment.
- Choose right sensors & compute (resolution, fps, edge GPU).
- Plan data strategy: storage, labeling, privacy & compliance.
- Design for MLOps: retraining schedule, monitoring, version control.
- Cross-functional team: operations + IT + data scientists + domain experts.
- Measure ROI & scale gradually.
Challenges & Limitations
- High-quality labeled data is expensive.
- Environmental sensitivity (lighting, occlusion).
- Model drift — needs retraining as product lines or contexts change.
- Integration complexity with legacy systems (ERP, MES).
- Privacy and legal compliance hurdles.
Exam-ready Short Answers & Long-answer Points
- What is Computer Vision? Computer Vision is AI enabling machines to interpret images/video to extract information for tasks like detection, classification, and segmentation used in automation and analytics.
- Why use CV in quality control? CV provides faster, consistent defect detection, reduces human error, supports 24/7 inspection, and lowers rework and warranty costs.
- Edge vs Cloud for CV? Edge: low latency, privacy; Cloud: high compute, centralized model management; Hybrid combines benefits.
Long-answer
- Explain integration of AI & IoT for predictive maintenance: Start with IoT sensors capturing vibration/temperature; edge preprocesses and sends features to cloud; AI model predicts failure probability; system schedules maintenance; dashboard reports cost savings. Discuss KPIs (MTTR, MTBF), pilot approach, and data labeling challenges.
- Discuss CV in retail with privacy concerns: Describe applications (shelf, checkout, analytics), architecture (cameras, edge inference, cloud dashboard), benefits (sales uplift, shrinkage reduction). Then discuss privacy: anonymize data, avoid persistent face recognition without consent, comply with local laws, and present mitigation strategies.
Quick one-page summary
- CV = AI for images & video. Pipeline: capture → preprocess → feature/model → action.
- Business uses: quality control, retail (shelf & checkout), automation (robots & vehicles).
- Deployments: Edge (real-time) / Cloud (scale) / Hybrid.
- AI + IoT = intelligent products & smart services (sense → analyze → act).
- Key concerns: data quality, integration, privacy, monitoring & ROI.