AI Crash Course: Basic Terminology for Artificial Intelligence Investors

One of my top rules for digital asset investors is to be able to explain your investments, but with AI advancing as fast as you can say artificial intelligence, it's easier said than done.

Especially with phrases like deep learning, neural networks, and natural language processing being thrown around like they're basic English.

The AI learning curve can be even steeper for new investors. When I first got into this market, I understood maybe 10% of what I was reading. But once I could define some basic AI-related jargon, that's when I finally grasped the magnitude of what this tech could do. And then I was able to explain my investments.

To help you do the same, I put together flashcards with basic AI terminology to help you understand how it works and why it's valuable.

There's also a quick video I want you to watch where I'll walk you through each definition and provide examples of how it relates to AI.

Start your AI crash course here...

Step One: Start by watching the 15-minute crash course where I'll cover 16 basic definitions every AI investor should know.

Step Two: Use the flashcards below to study these definitions. You don't have to memorize them perfectly, but you should be able to explain the terms to someone else.

Here are the definitions for you to reference:

1. Machine Learning: A subset of AI that involves developing algorithms and statistical models that enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so.
2. Deep Learning: A subset of machine learning that uses artificial neural networks with many layers to enable computers to learn from large amounts of unstructured data.
3. Natural Language Processing (NLP): A subset of AI that involves teaching machines to understand, interpret, and respond to human language.
4. Robotics: A field of AI that involves the design and development of robots, which are machines that can perform tasks autonomously or with human guidance.
5. Computer Vision: A subset of AI that involves teaching computers to interpret and analyze images and videos.
6. Neural Networks: A type of machine learning model that is inspired by the structure and function of the human brain.
7. Reinforcement Learning: A type of machine learning that involves training agents to take actions in an environment to maximize a reward signal.
8. Natural Language Generation (NLG): A subset of natural language processing (NLP) that involves teaching machines to generate human-like language.
9. Expert Systems: AI systems that mimic the decision-making abilities of a human expert in a particular domain.
10. Data Mining: The process of discovering patterns and insights in large datasets using statistical and computational methods.
11. Big Data: Extremely large datasets that can be analyzed to reveal patterns, trends, and associations, especially relating to human behavior and interactions.
12. Artificial Intelligence Ethics: The study of the ethical, social, and political implications of AI systems and applications.
13. Explainable AI: AI systems and models that can provide explanations or justifications for their decisions or predictions.
14. Generative Adversarial Networks (GAN): A type of deep learning model that involves t…