From Data to Intelligence: GPT (Generative Pre-Trained Transformers), ML (Machine Learning), and AI (Artificial Intelligence)

The AI Revolution: Exploring GPT, Machine Learning, and How They Shape our World

By Chris Dickhans, Yactraq

Ai generated image of woman with man beside herOne article in the QATC newsletter alone cannot fully cover the extensive topic that falls under the title; but let’s start the conversation. To begin, let’s talk about the image of me alongside the robot with a man standing in the background. The picture of me is real and the background was created by artificial intelligence.

DID YOU KNOW: “bot” is a condensed form of “robot,” denoting a robot without a physical body?

Let’s kick off this article by laying the groundwork for a comprehensive understanding of Generative Pre-Trained Transformers, commonly known as GPT. Many of you might have had the chance to engage with ChatGPT. If not, you’ve undoubtedly come across references to it in your role or news updates.

Fundamental or basic GPT models, such as ChatGPT, are versatile natural language processing models that can be used for a wide range of tasks. You may have used a processing model and didn’t even know it. Here are some common uses of a basic GPT model like ChatGPT. See if you can find any you have used in your everyday life or in your contact center.

Text Generation:

  • Generate human-like text for creative writing or content generation.
  • Redefine a paragraph you have written that needs more structure.
  • Automatic generation of a response to user queries or prompts.

Informational / Conversational:

  • Generate a comment, joke, or poetry to fit the context or style of your needs.
  • Create a short story about something that interests you.

Content Summarization:

  • Summarize long articles or documents into short summaries.

Language Translation:

  • Translate text from one language to another.

Sentiment Analysis:

  • Analyze the sentiment (positive, negative, neutral) of a given piece of text or a voice conversation.

Simulating Conversations:

  • Create conversational agents for simulations, training, or testing.

Answering Questions:

  • Answer questions posed in natural language based on given context.
  • Automatically determine the intent of a question or conversation and generate contextual responses for individual businesses.

Text Completion:

  • Complete sentences, paragraphs, or a short story by using a starting sentence or prompt.

Text-based Games and Chatbots:

  • Create interactive text-based games.
  • Construct collaborative chatbots for entertainment.
  • Build interactive bots for customer support.

Content Enhancement:

  • Improve or refine the readability and consistency of existing content.

Text Classification:

  • Classify or categorize text into predefined or predetermined categories, classes or labels.

Language Generation for Chatbots:

  • Use GPT for building chatbots that can engage in natural, human-like conversations.

Auto-Generating Code:

  • Automatically generate code snippets based on documentation.
  • Auto-generate code descriptions based on user requirements.

Text-Based Data Generation:

  • Create synthetic or fake text data for testing with data scientists and machine learning.
  • Generate text data in a style similar to the original dataset for training purposes.

Language Learning, Understanding, Correction and Proofreading:

  • Extract key information, entities, and facts from text.
  • Check and correct grammar, punctuation, and spelling errors in text.
  • Assist language learners with grammar, vocabulary, and language exercises.

Text Analysis and Insights:

  • Analyze and extract insights from large volumes of text data, such as social media content, customer reviews, training content, or an entire transcription of a conversation.
  • Summarize entire conversations of any duration into simple, consumable executive style insights.

Personal Assistants:

  • Develop virtual assistants that can assist with tasks like scheduling, reminders, and information retrieval.

DID YOU KNOW: behind Apple’s Siri voice recognition system there are complex data models, algorithms, speech recognition models, natural language processing models and machine learning components. Next time you use Siri as a personal assistant to set a reminder or schedule an event, know that there is so much going on behind your “hey Siri” request.

These examples represent a fraction of the ways basic GPT models, such as ChatGPT, can be utilized. The choice of application hinges on your unique requirements and the specific realm you aim to address. It’s worth noting that fine-tuning the model for particular tasks has the potential to significantly boost its performance in specialized applications.

If you crave additional excerpts, e-mail me at I’m eager to delve into the advantages and disadvantages of GPT, along with exploring its use cases in both your personal and professional life. Then we can move into a deeper dive on machine learning and artificial intelligence.

Chris Dickhans is Vice President of Sales for Yactraq. She may be reached at For more information on Yactraq, go to