Eye on Gen AI

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Talking to ChatGPT, Part 1: Generative AI in Its Own Words

My name is Chris Paoli, editor at Redmondmag.com. Welcome to the first post in our new blog series Eye on Gen AI, focusing on anything and everything generative AI. What does "anything and everything" mean? Well, just like the exploding generative AI landscape, it could mean predictive language models this week and evolve to how generative AI is protecting organizational systems the next. Where it goes, we will follow and break down what it means for enterprises and IT.

In the coming weeks, we're going to focus on breaking down generative AI and what it can (and cannot) do, and what it means for enterprises. This includes shining a spotlight on hot tech like ChatGPT's predictive language modeling, discussing Microsoft's recent cadence of AI-integrated tech and investments, and looking into how IT is taking to the new tech.

For this first entry, let's break down the basics of generative AI with a conversation with said technology. We'll see what ChatGPT knows about itself, the meteoric growth of the tech and what it means for IT. Following is the lightly edited transcript of our recent chat.  

Redmond: Hello ChatGPT, can you start off by breaking down what is the fundamental difference between traditional artificial intelligence and generative AI?
ChatGPT: Certainly! The fundamental difference between traditional artificial intelligence (AI) and generative AI lies in their underlying approaches and capabilities.

Traditional AI, also known as symbolic or rule-based AI, is based on explicit rules and instructions programmed by human experts. It relies on predefined algorithms and logical reasoning to process data and make decisions. Traditional AI systems are designed to solve specific tasks or problems and often require a large amount of handcrafted features and explicit instructions to perform well. These systems excel at tasks with well-defined rules and clear objectives, such as playing chess or diagnosing medical conditions.

On the other hand, generative AI, also referred to as deep learning or neural network-based AI, takes a different approach. It uses neural networks, which are large interconnected networks of artificial neurons inspired by the human brain. Generative AI models learn directly from data, rather than relying on explicit instructions. They can automatically extract relevant features and patterns from the input data, enabling them to generalize and make predictions or generate new content.

"I don't have access to specific details regarding the exact number of parameters or the size of the training dataset used for my training. OpenAI has not publicly disclosed these specifics."


Generative AI models can be trained to perform a wide range of tasks, such as image recognition, natural language processing, speech synthesis and more. They are particularly powerful in tasks involving complex and unstructured data, where traditional rule-based systems may struggle. Generative AI models often require vast amounts of training data to learn effectively, and their training process involves optimizing millions or even billions of parameters.

As of May 15, how much training parameter data have you utilized?
As an AI language model, I am based on OpenAI's GPT-3.5 architecture, which was trained on a vast amount of text data. However, I don't have access to specific details regarding the exact number of parameters or the size of the training dataset used for my training. OpenAI has not publicly disclosed these specifics.

To provide some context, GPT-3, the predecessor to GPT-3.5, was trained on 570GB of text data, which included a wide range of sources such as books, articles, websites, and more. It consists of 175 billion parameters. GPT-3.5 is an extension of this architecture but with enhanced capabilities.

What industries have you seen most taking advantage of ChatGPT's features?
ChatGPT's features have found utility across various industries. Here are a few industries where ChatGPT's capabilities have been leveraged:

  • Customer Support: ChatGPT has been used in customer support applications to handle inquiries, provide information, and assist customers with common questions or issues.
  • Content Generation: ChatGPT has been employed in content creation, such as generating articles, blog posts, product descriptions, and social media captions.
  • Virtual Assistants: ChatGPT has been integrated into virtual assistant applications, allowing users to interact with AI-powered assistants for tasks like setting reminders, answering questions, scheduling appointments and more.
  • Education: ChatGPT has been utilized in educational settings to provide personalized tutoring, answer student questions, explain complex concepts and facilitate interactive learning experiences.
  • Research and Development: ChatGPT has been employed in assisting researchers in exploring new ideas, generating hypotheses and conducting literature reviews. It can aid in brainstorming and knowledge discovery.
  • Gaming and Entertainment: ChatGPT has been integrated into video games and entertainment applications to provide engaging and interactive virtual characters or NPCs (non-player characters) that can engage in conversation with players.
How about specifically by those in IT?
In the field of IT (Information Technology), ChatGPT's features have been leveraged in several ways. Here are a few examples of how it has been used in the IT industry:

  • Technical Support and Troubleshooting: ChatGPT can assist in providing technical support to users by answering common IT-related questions, troubleshooting issues, and providing step-by-step guidance for problem resolution.
  • Knowledge Base and Documentation: ChatGPT can be used to create and maintain knowledge bases and documentation repositories. It can generate explanations, tutorials, and FAQs for IT products, services, or processes, thereby enhancing the availability and accessibility of information for users and support staff.
  • Code Assistance and Debugging: ChatGPT can help developers by providing suggestions, answering programming-related questions, and assisting with debugging code
  • IT Project Management: ChatGPT can support project management activities in the IT industry. It can provide project status updates, answer queries about project plans or timelines, and offer assistance in managing tasks and resources.
  • Infrastructure Monitoring and Alerting: ChatGPT can be integrated into monitoring systems to provide real-time insights and alerts about IT infrastructure health and performance. It can analyze system logs, detect anomalies, and provide recommendations for remediation.
  • Network Security and Threat Detection: ChatGPT can assist in analyzing security logs, detecting potential threats, and providing recommendations for network security. It can help IT professionals stay updated on the latest security practices and assist in incident response.

Thanks for breaking down some of the basics for this first part of our conversion.
You're welcome! I'm glad I could provide you with these basics. If you have any more questions or if there's anything else I can assist you with, feel free to ask. I'm here to help!

Next week we will continue our conversation with ChatGPT and dive more into enterprise IT use cases, and how Microsoft is facilitating the spread of AI in its product line.

Posted by Chris Paoli on 05/18/2023 at 10:41 AM


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