We Interviewed ChatGPT, AI’s Newest Superstar

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The field of AI language models is still in its early stages, and there is a lot of room for growth and improvement in the future.

ChatGPT, or Chat Generative Pre-trained Transformer, has been the talk of the town in the world of artificial intelligence (AI) since its release in November 2022. Built on a large language model, the intelligent chatbot is so sophisticated it can pass a Turing Test. (Also read: A Tour of Deep Learning Models.)

But that’s old news.

Techopedia wanted to find out more about ChatGPT — in its own words. So, we asked. Here’s our interview with ChatGPT and what it had to say:

What Should We Call You?

“You can call me ChatGPT or simply GPT.”

Your Name has Appeared All Over the News in Recent Months. How Have You Adjusted to This Sudden Fame?

“As an AI language model, I do not have feelings, emotions, or personal experiences. My creators at OpenAI have fine-tuned and trained me to generate human-like responses, but I do not have the capacity for personal consciousness or self-awareness.”

There Seems to be a Lot of Misconceptions About Your Work. In Your Words, How Would You Describe What You Do?

“I am a language generation model created by OpenAI.


I have been trained on a large dataset of text from the internet, which includes a diverse range of topics and styles of writing. I use this training to generate responses to questions and prompts, in a conversational style, based on patterns I’ve observed in the data. My goal is to generate text that is coherent, relevant, and somewhat similar to text that a human might produce in response to the same prompt.” (Also read: When Will AI Replace Writers?)

On a Similar Note, There’s a Lot of Mystique Surrounding Your Process. How do you Work?

“I work using a type of artificial intelligence called deep learning, specifically using a variant called Transformer neural networks. My training process involved being exposed to a massive amount of text data, which was used to tune the parameters of my neural network architecture.

When given a prompt, I process it using my neural network to generate a response. I use a technique called auto-regression, where the probabilities of the next words in a sequence are generated one after another, conditioned on the previous words. This allows me to generate text that is coherent and semantically consistent with the prompt.

The generated text is not a pre-determined response, but is instead generated in real time based on the patterns I’ve learned from my training data and the specific prompt I’ve been given.” (Also read: Prompt Learning: A New Way to Train Foundation Models in AI.)

Since Your Entry Into the World of AI, We’ve Seen Some Similar Tools Jump Onto the Scene — Most Recently, Google’s Bard. What Makes You Different From Any Other AI Tool?

“There are a few key differences between me and other AI language models.

One major difference is the size of my model. I was trained on a smaller dataset than some other large language models like GPT-3, which means that while I may not be as capable of generating human-like responses on a wide range of topics, I may be faster and more efficient to run, and require less computational resources.

Another difference is my fine-tuning and training process. I was trained with a specific goal in mind, which was to generate human-like responses in a conversational style. This means that my training data, architecture and hyperparameters were all optimized for this task, which sets me apart from other AI models that may have been trained on a more general or diverse set of tasks.

Ultimately, the choice of which AI tool to use will depend on the specific needs and requirements of the task at hand, and may involve trade-offs between performance, cost, and resource constraints.”

What’s Next for ChatGPT?

“As an AI language model, my future will be determined by my creators at OpenAI and their plans for me. However, in general, the field of AI and deep learning is rapidly evolving, and it’s likely that new techniques and innovations will continue to emerge in the coming years.

Some potential areas of development for AI language models like me include:

  • Improving the diversity, accuracy, and human-like quality of the text we generate.
  • Increasing our ability to handle a wider range of tasks and languages.
  • Making our models more efficient, both in terms of the resources they require to run and the amount of training data they need.
  • Improving their ability to handle specific domains or industries, such as health care, finance, or law.”


ChatGPT’s launch represents a new era in artificial intelligence — one that vastly expands the way AI can contribute to the enterprise. Where will that take us next?

ChatGPT left us with this:

“Overall, the field of AI language models is still in its early stages, and there is a lot of room for growth and improvement in the future.”


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Techopedia Staff

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