Lecture on AI, Digitalization, and Data

Jul 11, 2024

Lecture on AI, Digitalization, and Data

Introduction

  • Discussion during the initial waiting period
  • Digital Twin: Understanding and real-time replication
  • Clarification of concepts and the use of AI in predicting outcomes

Digitalization and Digitization

  • Digitization: Converting analog to digital (e.g., scanning documents)
  • Digitalization: Using digital data to improve processes, workflows, and business models
  • Examples: Expense report automation (digitization) vs. end-to-end process automation (digitalization)

Technologies in Digitalization

  • IoT: Collects data (e.g., smartwatches)
  • Cloud: Stores data (internal and external clouds, regulatory compliance)
  • AI/ML/DS: Processes data (Artificial Intelligence, Machine Learning, Data Science)
  • BI/AR/VR: Decision making and visualization (Business Intelligence, Augmented Reality, Virtual Reality)
  • RPA: Automates processes (Robotic Process Automation, e.g., chatbots)
  • Cyber Security: Secures data
  • Blockchain: Verifies and connects systems

Historical and Current Perspectives on AI

  • Statistics: Analyze past data (e.g., average height in a class)
  • Machine Learning: Predict future outcomes based on training data (e.g., loan approvals)
  • Data Science: Combines machine learning with engineering tools for large-scale data analysis
  • Deep Learning: Analyzes unstructured data (e.g., images, texts)

Importance of Business Collaboration

  • Features and data selection should be guided by domain experts
  • Understanding and involvement of domain experts are critical for project success

Data Types

  • Nano Data: <5,000 rows
  • Medium Data: ~50,000 rows
  • Big Data: >100,000 rows

Data Challenges and Methodologies

  • Handling missing data and outliers
  • Anomaly detection and its importance
  • Use of public data for comparative analysis
  • Feature selection by understanding the domain

AI Models and Techniques

  • Classification Models: Logistic regression, decision trees, K-nearest neighbors
  • Anomaly Detection: Identifying outliers and unusual patterns
  • Ensemble Models: Combining multiple models for better prediction
  • Regression Models: Predicting a value using linear regression

Assignment 1 Overview

  • Create a business problem solution with AI integration
  • Include elements like Blueprint, Infrastructure, Data Requirements, Policy, etc.
  • Guidelines for creating slides/papers

Closing Remarks

  • Explanation to continue in the next class with more on unstructured data and Generative AI

Please use the provided guidelines and ensure clarity in responses for the assignment.