AI/ML Primer

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In this primer, we would learn about Artificial Intelligence and Machine Learning technologies from career perspective.

Artificial Intelligence (AI) and Machine Learning (ML) are closely related fields, but they serve different purposes and work in distinct ways. Let me explain them clearly:

Artificial Intelligence (AI)

  • Definition: AI is a broad field of computer science aimed at creating machines or systems that can perform tasks that typically require human intelligence. These tasks include reasoning, decision-making, understanding language, recognizing patterns, and problem-solving.
  • Goal: To build systems that simulate human-like intelligence, often making decisions or solving problems autonomously.
  • Examples:
    • Virtual assistants (like Siri or Alexa)
    • Chatbots
    • Autonomous vehicles
    • Smart recommendations (like Netflix suggesting movies)

Machine Learning (ML)

  • Definition: ML is a subset of AI that focuses on enabling machines to learn from data and improve their performance on specific tasks without being explicitly programmed.
  • How It Works:
    1. ML systems are trained on large datasets using algorithms.
    2. The system identifies patterns in the data and uses these patterns to make predictions or decisions.
    3. The more data it gets, the better it becomes.
  • Goal: To create models that can generalize and predict outcomes based on data inputs.
  • Examples:
    1. Face recognition on social media
    2. Spam email detection
    3. Predicting stock market trends
    4. Product recommendations on e-commerce platforms

Computer Vision

Today, almost all practical computer vision systems rely on Machine Learning and Deep Learning (such as Convolutional Neural Networks and Vision Transformers) to recognize patterns, detect objects, and understand context automatically

How They Relate:

  1. AI is the big picture, while ML is one of the tools used to achieve AI.
    • For example, AI might need to make decisions (like in a chatbot), and ML might be the mechanism that teaches the chatbot how to respond based on prior conversations.
  2. Not all AI is ML:
    • Rule-based systems (like if-else logic) can still be AI, even if they don’t use ML.
  3. Not all ML leads to AI:
    • A machine learning model predicting housing prices is ML but doesn’t necessarily involve broader AI concepts like reasoning or awareness.
    • Computer Vision – Computer Vision[1] is a specialized domain within AI focused on giving machines the ability to “see,” interpret, and extract meaningful information from visual inputs like images and videos.

References:

https://ieeexplore.ieee.org/document/11375661 [1]