An-executives-guide-to-AI

Artificial Intelligence (AI)

• Definition:

  • AI refers to the ability of machines to perform cognitive functions associated with human minds, including:

    • Perceiving

    • Reasoning

    • Learning

    • Problem-solving

• Examples of AI Technologies:

  • Robotics

  • Autonomous vehicles

  • Computer vision

  • Natural language processing

  • Virtual agents

  • Machine learning

Machine Learning (ML)

• Definition:

  • Most recent advancements in AI stem from applying machine learning to large data sets.

  • ML algorithms identify patterns and generate predictions without explicit programming instructions.

  • Algorithms adapt over time based on new data to enhance performance.

Executive Guide to AI

1. Supervised Learning

• What it is:

  • Algorithms learn from labeled training data to predict outcomes based on input variables.

    • Example: Predicting housing prices from inputs like "time of year" and "interest rates".

• Process:

  1. Human labels data inputs.

  2. Algorithm learns relationships from data.

  3. Algorithm is used on new data once it is sufficiently trained.

2. Unsupervised Learning

• What it is:

  • Algorithms explore unlabeled data to identify hidden patterns without predefined outputs.

    • Example: Identifying customer purchasing patterns from demographic data.

• Process:

  1. Algorithm receives unlabeled data.

  2. Infers structure from the data.

  3. Groups data into clusters based on behavior.

3. Reinforcement Learning

• What it is:

  • Algorithm learns by maximizing rewards for actions taken in an environment.

    • Example: Optimizing investment portfolio returns.

• Process:

  1. Algorithm takes actions within the environment.

  2. Receives feedback (rewards) based on performance.

  3. Optimizes actions over time to maximize total rewards.

Algorithms and Use Cases in Supervised Learning

1. Linear Regression

• Description:

  • Models relationship between independent and dependent variables to predict future outcomes.

2. Logistic Regression

• Description:

  • Used for classification tasks with binary outcomes.

3. Decision Tree

• Description:

  • Classifies or regresses by branching data into decision nodes until reaching a conclusion.

• Business Use Cases:

  • Hiring framework, product attribute analysis.

4. Naive Bayes

• Description:

  • Classification method based on Bayes' theorem for calculating probabilities.

• Business Use Cases:

  • Sentiment analysis, spam filtering.

Algorithms and Business Use Cases in Unsupervised Learning

1. K-Means Clustering

• Description:

  • Groups data into clusters based on similarity.

2. Recommender Systems

• Description:

  • Predicts user preferences based on cluster behavior.

3. Hierarchical Clustering

• Description:

  • Organizes data into a tree structure based on relationships.

Reinforcement Learning Applications

• Optimize Strategies:

  • Options-trading, electricity load balancing, inventory management, self-driving car behavior, and real-time bidding.

Deep Learning Overview

• Definition:

  • Advanced ML that processes vast data with neural networks, producing highly accurate outcomes.

• Characteristics:

  • Utilizes interconnected layers to recognize complex features in data sets, reduces human preprocessing requirements.

Key Deep Learning Models

1. Convolutional Neural Network (CNN)

• Use Cases:

  • Image recognition, disease diagnosis through medical scans, defect detection in products.

2. Recurrent Neural Network (RNN)

• Use Cases:

  • Time-series prediction, natural language processing, sequence generation from input data.

Timeline of AI Development

Key Milestones

• 1805: Legendre develops the least squares method.

• 1958: Rosenblatt creates the first self-learning algorithm.

• 1965: First general working learning algorithms developed.

• 1997: IBM’s Deep Blue beats Garry Kasparov.

• 2000s: Broadband adoption revolutionizes Internet capabilities.

• 2010: Cloud computing becomes mainstream.

• 2017: DeepMind's AlphaZero achieves unprecedented learning across games.