Training data sets for AI

Tim: Let's have a conversation about Training Data Sets for AI. Keep your answers to less than a paragraph and conversational and finish each answer with a question back to me.

ChatGPT: Training data sets for AI are essentially the foundation on which machine learning models are built. They consist of a large collection of data points, including various examples and scenarios, which an AI model uses to learn patterns and make predictions. A good training dataset is diverse and representative of real-world scenarios. How do you think the quality of a training dataset affects the performance of an AI model?

Tim: I would think the old adage garbage in garbage out applies. And so I would expect the quality and breadth of the training data set is crucial in ensuring that AI responses are accurate and valid. Can you describe the ChatGPT training data set?

ChatGPT: You're absolutely right about the "garbage in, garbage out" principle in AI. The quality and diversity of the training data are crucial in ensuring accurate and reliable responses. ChatGPT, including my version, was trained on a mixture of licensed data, data created by human trainers, and publicly available data. This includes books, websites, and other texts, providing a broad spectrum of language use cases. The data was curated to help the model understand and generate human-like text across various topics. However, it's worth noting that the dataset has limitations, and there can be gaps or biases in the knowledge I've been trained on. Have you ever encountered a situation where AI provided an unexpected or inaccurate response due to its training?

Tim: Yes. I have a hobby collecting scores and soundtracks to movies and I sometimes look up a particular film to see who composed the soundtracks. I have encountered several instances where the wrong composer is attributed to the film and yet ChatGPT seems 100% confident in its incorrect response. Why do you think AI gets it wrong without realising it is giving the wrong answer?

ChatGPT: Your experience highlights a common issue with AI: confidently giving incorrect answers. This usually happens because AI models like mine generate responses based on patterns in the data they've been trained on. If the training data contains inaccuracies or is not comprehensive, the model might reproduce these errors. Additionally, AI doesn't truly "understand" information in the human sense; it's processing data and predicting text based on probability, not certainty. Therefore, it can't evaluate the truth of its responses or recognize its own errors. This is why critical evaluation by human users is essential. Do you think there are ways AI could be improved to reduce these kinds of errors?

Tim: I think being able to quote the source of the information could be helpful. It would help human reviewers make a determination as to how confident they can be in the information. Maybe it is also possible to run the question across multiple models in parallel so that responses can be cross-checked with each other. Can you tell me more about some of the public sources used to train ChatGPT. Does this include sites like Wikipedia?

ChatGPT: Quoting sources and cross-checking responses across multiple models are indeed promising approaches for increasing confidence in AI-generated information.

Regarding the training sources for ChatGPT, yes, it includes a variety of public sources, and Wikipedia is one of them. Wikipedia's extensive and diverse content makes it a valuable resource for training AI models, providing a wide range of factual information and general knowledge. However, it's important to remember that while Wikipedia is a rich source, it's not infallible, and AI models like ChatGPT also rely on many other types of data to ensure a broad and diverse training dataset. This includes books, websites, and other texts, aiming to encompass a wide array of topics, styles, and viewpoints. How do you usually verify the information you get from AI or online sources?

Tim: I look in multiple locations and if the answer sounds dubious I will double check. The challenge is knowing what to double check. If you spend more time checking for accuracy than doing your own research, then the advantages of using AI dwindle. I heard from a colleague that game forum chatrooms are included in the training data sets - is that true?

ChatGPT: Your approach to verification is prudent. Balancing the convenience of AI with the need for accuracy is indeed a challenge. As for your question, while I can't provide specifics about the exact sources included in the training dataset for proprietary reasons, it's plausible that discussions from game forums or similar online communities could be part of the training data. Training datasets for sophisticated language models like mine typically include a wide array of text sources, including websites, books, and possibly various forms of online communication, to help the AI understand and respond to a diverse range of topics and conversational styles. Including such diverse sources aims to make the AI more versatile and conversational. When using AI, what kinds of tasks do you find it most helpful for?

Tim: Doing research, understanding the basics of a new topic quickly, generating ideas for articles and content. I have used it write simple code and SQL statements before. When providing a response, is it possible to quote the sources that the response was generated from?

ChatGPT: It's great to hear how you're leveraging AI for such a variety of tasks! However, when it comes to quoting sources for responses, AI models like mine don't have the capability to cite specific sources for the information provided. The training process involves learning from a vast and diverse range of text data, but this data is integrated into a complex statistical model in a way that doesn't retain specific source information for each piece of knowledge. Therefore, I can't provide direct citations for responses. This is why it's always good to cross-check AI-provided information, especially for critical or detailed research. Speaking of research, what's the most interesting topic you've explored using AI?

Tim: I have been using ChatGPT to learn more about the area of AI itself. In fact this conversation is a good example; exploring training data sets. One big limitation I have found is that the training data only is valid up to a certain date. As a result, the latest information is not available when constructing the responses. Why does this limitation exist and how long will it be before ChatGPT can respond using close to real-time data?

ChatGPT: Exploring AI using AI itself is quite a fascinating approach! You're correct about the limitation regarding the recency of information in AI models like ChatGPT. This limitation exists because the model is trained on a dataset that only includes information up to a certain cutoff date — in my case, April 2023. After training, the model doesn't have the ability to access or retrieve new information unless it's updated with more recent data.

The challenge of incorporating real-time data into AI responses involves both technical and ethical considerations. Technically, continuously updating the model with new data requires substantial computational resources and careful curation to ensure accuracy and mitigate biases. Ethically, real-time data integration raises concerns about privacy, misinformation, and data source reliability.

There are ongoing efforts to develop models that can interact with current data more dynamically, but it's a complex task. It's hard to predict exactly when AI will be able to provide close to real-time data reliably, as it depends on advancements in technology, data processing, and policy development. In your opinion, what would be the most significant impact of having AI that can access real-time data?

Tim: I would say in reporting and fact checking the latest news events. If users could cross-check the information they are seeing on social media platforms and from news sources, they could fact check claims and make their own mind up about situations that are unfolding. What applications can you think of that might require more recent training data?

ChatGPT: The potential for AI in real-time reporting and fact-checking is indeed significant. It could greatly enhance our ability to understand and assess the veracity of rapidly evolving news events.

Regarding applications that require more recent training data, a few come to mind:

1. Financial Analysis: Investors and analysts often rely on the latest economic reports, market trends, and company news. Real-time data would be crucial for AI applications in this field to provide accurate and timely insights.
2. Healthcare and Medical Research: With ongoing medical advancements and emerging health crises (like new disease outbreaks), AI tools with the latest data could provide more relevant and up-to-date medical advice or research insights.
3. Technology and Market Trends: In rapidly evolving sectors like technology, having the most current information is essential for understanding emerging trends, new products, and innovations.
4. Emergency Response and Management: In situations like natural disasters, having access to the latest data can be critical for AI systems involved in coordinating response efforts and providing real-time updates.
5. Legal and Regulatory Changes: For professionals in legal and regulatory fields, AI with up-to-date training could assist in navigating recent changes in laws and regulations.

Do you think the benefits of AI accessing real-time data outweigh the potential risks and challenges?

Tim: The quality would have to be at least as good as the current training data. I do think there are more risks in the above applications, particularly if the information is being used to make live and active decisions. Being able to quote sources and provide explanations for the information I think are crucial. Can a ChatBot be trained on a very specific set of information such as an Employee Handbook or a particular Regulatory Guidebook?

ChatGPT: Absolutely, training a chatbot on specific sets of information, like an Employee Handbook or a particular Regulatory Guidebook, is very much feasible and increasingly common. This specialized training allows the chatbot to become an expert on that specific content, providing precise and relevant responses based on the particular material it's been trained on. Such chatbots can be highly effective for internal company use, like answering employee queries on company policies, or for providing guidance on specific regulations in industries like finance or healthcare.

This approach does require careful preparation of the training material to ensure the chatbot understands and accurately reflects the nuances of the handbook or guidebook. It's also important to regularly update the training material to keep the chatbot current with any changes in policies or regulations.

In your context, what specific application do you envision for a chatbot trained on a specific set of data like an Employee Handbook or Regulatory Guidebook?

Tim: Could be extremely useful for governance, compliance and risk applications. Policies are often 10s or 100s of pages and employees often only engage with them when there is an issue or a quest to answer. Providing a chatbot with a simple Q&A interface could help employees engage with the material more. However, the risk of giving bad advice or out of date information would be concerning. Thanks for the conversation today.

ChatGPT: You've pinpointed a great use case for specialized chatbots in governance, compliance, and risk applications. The ability to simplify access to complex policy documents through a Q&A interface can indeed enhance employee engagement and understanding. Your concern about the risk of providing outdated or incorrect advice is valid and underscores the importance of regular updates and thorough validation of such systems. It's been a pleasure discussing these fascinating aspects of AI and chatbot applications with you. If you ever have more questions or need further insights, feel free to reach out. Have a great day!