Artificial Intelligence (AI) and data are deeply interconnected, with AI systems relying on data for training, learning, and decision-making. This document explores the journey of AI and data from basic concepts to advanced applications.
• AI uses data to learn patterns and make decisions.
• Types of AI:
Rule-Based AI (Expert Systems)
Machine Learning (ML)
Deep Learning (DL)
Structured Data: Organized in tables (e.g., databases, spreadsheets).
Unstructured Data: Text, images, audio, video.
Semi-Structured Data: JSON, XML, log files.
Data sources: Web, sensors, surveys, enterprise systems.
Methods of data collection: APIs, web scraping, surveys, IoT devices.
Storage systems:
Databases: SQL (MySQL, PostgreSQL) & NoSQL (MongoDB, Cassandra)
Data Warehouses: Amazon Redshift, Google BigQuery
Data Lakes: AWS S3, Azure Data Lake
Handling missing data: Mean imputation, interpolation.
Removing duplicates & inconsistencies.
Normalization & standardization.
Normalization & standardization.
This stage focuses on developing AI models, feature engineering, and advanced data processing.
Identifying important features for AI models.
Techniques: Principal Component Analysis (PCA), Mutual Information, Recursive Feature Elimination (RFE).
Example: Extracting sentiment from text data for NLP models.
Supervised Learning: Requires labeled data (e.g., image classification, speech recognition).
Unsupervised Learning: Clusters unlabeled data (e.g., anomaly detection, customer segmentation).
Semi-Supervised & Active Learning: Efficient labeling with limited data.
Supervised Models: Decision Trees, Random Forest, SVM, Neural Networks.
Unsupervised Models: K-Means Clustering, DBSCAN, Autoencoders.
Model Evaluation Metrics: Accuracy, Precision, Recall, F1-score, AUC-ROC.
Big Data Technologies: Hadoop, Apache Spark, Dask.
Parallel Processing: Handling large datasets efficiently.
Data Pipelines: ETL (Extract, Transform, Load) processes.
Cloud AI Services: AWS SageMaker, Google Vertex AI, Microsoft Azure ML.
Edge AI: Running models on IoT devices (e.g., NVIDIA Jetson, Raspberry Pi).
Federated Learning: Training AI models without sharing raw data (privacy-focused AI).
At this level, AI leverages real-time data, multimodal learning, and autonomous decision-making.
• Convolutional Neural Networks (CNNs): Image & video recognition.
• Recurrent Neural Networks (RNNs) & Transformers: NLP & time-series analysis.
• Generative AI: GANs, Diffusion Models (e.g., Stable Diffusion, DALL·E 3).
Streaming Data Processing: Apache Kafka, Flink, Pulsar.
AI for Predictive Analytics: Stock market forecasting, demand prediction.
Autonomous AI Decision Systems: Self-driving cars, robotics.
• Homomorphic Encryption: Performing AI computations on encrypted data.
• Differential Privacy: Preventing AI from leaking sensitive information.
• AI in Cybersecurity: Detecting fraud, phishing, malware.
Making AI Decisions Interpretable: SHAP, LIME, Model Explainability.
Bias Mitigation: Fairness-aware ML models.
Regulations & Compliance: GDPR, CCPA, AI Act.
Quantum AI: Leveraging quantum computing for AI.
Self-Learning AI: AI that improves autonomously.
AI-Generated Data: Synthetic data for model training.
| Level | Key Techniques | Examples/Models |
|---|---|---|
| Basic | Data Collection, Cleaning, Storage | SQL, NoSQL, ETL |
| AI Basics, Rule-Based AI, ML Introduction | Decision Trees, SVM | |
| Intermediate | Feature Engineering, Model Training | PCA, NLP, Deep Learning |
| Big Data, Distributed Computing | Spark, Hadoop | |
| Cloud AI, Federated Learning | AWS SageMaker, Edge AI | |
| Advanced | Deep Learning, GANs, Transformers | GPT-4, CNNs, RNNs |
| AI for Security, Explainable AI | SHAP, Differential Privacy | |
| Quantum AI, Self-Learning AI | Quantum ML, AI Regulation |
