Simplifying AI Terminology

February 1, 2024
min read

This edition of AI for Business is the first in a five-part series to make Artificial Intelligence more understandable for everyone, especially those with non-technical backgrounds.

In this first issue, we lay the foundation by clarifying common AI terms. Becoming familiar with AI is important because it will transform how businesses operate, from revenue management and customer services, to product development and supply chain optimization.

Let’s dive in.

We Break It Down Step-by-Step

Taking a structured approach is one of the best ways to become familiar with key AI terms. We start with the broad concept of AI, and then move to specific solutions like ChatGPT.

Artificial Intelligence
  • Artificial intelligence, or AI, focuses on creating machines and software capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.
Strong AI and Narrow AI

Strong AI and Narrow AI categorize two different levels of artificial intelligence:

  • Strong AI aims to have the breadth and depth of human-like general intelligence, with the ability to tackle a wide variety of tasks and learn new ones autonomously. Think of this as a system with comprehensive human-like intelligence and reasoning capabilities.
  • Narrow AI, also known as Weak AI or Artificial Narrow Intelligence (ANI), refers to AI systems that are designed and trained for a specific task or a limited set of tasks. Examples include language translation or image recognition.
Machine Learning
  • Machine learning, or ML, is a branch of AI that involves training algorithms using data to improve the performance of a specific task. Recommendation systems and predictive analytics are good examples.
Deep Learning
  • Deep learning is an advanced form of ML that uses neural networks to mimic human brain processing. By using data to autonomously learn and extract complex patterns, more accurate predictions or decisions are possible. Tasks like speech recognition and image classification are examples.
Generative AI
  • Generative AI refers to a class of artificial intelligence techniques that are capable of creating new content, such as images, text, or audio based on patterns learned from existing data.
Large Language Models
  • Also known by their acronym LLM, large language models are AI systems trained on extensive text data, capable of processing and generating human-like language used in applications like chatbots and writing assistance.
Generative Pre-trained Transformers (GPT)
  • A type of LLM known for generating coherent text. These systems are trained on large datasets to predict language patterns.
  • The latest and most advanced model in the GPT series. It offers enhanced language processing, but has limitations such as biases and factual inaccuracies.
  • An OpenAI application allowing interactive conversations with GPT models, showcasing AI's potential in customer service and content creation. ChatGPT was the fastest-growing consumer internet app of all time achieving 100 million monthly users in two months. It took Facebook four and a half years to hit this milestone.

Wrap Up

Now you have a basic understanding of the key AI concepts along with a few well-known applications. Remember, you don’t need to master the technical aspects of AI to appreciate how it has the potential to transform business.

Next week in part two of this series we will cover how you can take a hands-on approach to AI learning.

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