What is AI? Core Concepts & Terminology

Artificial Intelligence (AI) refers to the domain of computer science focused on creating systems that can perform tasks that typically require human intelligence. Think reasoning, learning, problem-solving, perception, understanding language, and even making decisions.

Now, within this big umbrella of AI, there are important subdomains:

Machine Learning (ML): This is a subset of AI. Instead of explicitly programming rules for every possible scenario, ML involves creating algorithms that allow computers to learn from data. The more data they process, the better they get at a specific task (like identifying spam emails or predicting house prices).

  • Example: Think of ML like teaching a child by showing them examples of cats and dogs. You don’t write down rules for “cat” vs. “dog”. You show them lots of pictures, and they eventually learn to distinguish them.

Deep Learning (DL): This is a subset of ML, inspired by the structure of the human brain (neural networks). DL uses complex, multi-layered “neural networks” to learn patterns from vast amounts of data. It’s particularly powerful for tasks like image recognition and natural language processing (like what powers sophisticated chatbots or self-driving car vision).

  • Example: If ML is learning from examples, DL is like having many layers of understanding. The first layer might learn basic shapes, the next combines shapes into objects, and a deeper layer might recognize complex scenes.

Generative AI is a subset of Deep Learning. The below diagram illustrates this concept.

Unraveling AI Complexity - A Comparative View of AI, Machine Learning, Deep Learning, and…
English: This diagram represents a comparative view of AI, Machine Learning, Deep Learning, and Generative AI. Through…commons.wikimedia.org

We also distinguish between types of AI based on capability:

  • Narrow AI (or Weak AI): This is the AI we see all around us today. It’s designed and trained for one specific task. Examples include virtual assistants (like Siri or Alexa), recommendation engines (like Netflix or Amazon), image recognition software, or self-driving cars (even though complex, they are focused on the task of driving). They excel at their specific job but can’t operate outside it.
  • Artificial General Intelligence (AGI): This is the hypothetical type of AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a human level or beyond. I wrote more about AGI in a separate article.

Socratic Questions

  • Could you explain in your own words the difference between programming a computer with explicit rules versus using Machine Learning?
  • If someone says they are using "AI" in their product, why might it be useful to ask if they are specifically using ML or DL? What might that tell you?
  • Think about your smartphone. Can you identify an application that likely uses Narrow AI? What specific task does it perform?

This article is part of a series about becoming an AI Consultant.