AI Training Data: What Is It and How Do AI Models Use It?

Much has been made of generative AI since it became a mainstream tool a couple of years ago, with many sharing ideas on how the tools can best be used for research, brainstorming, and content creation.

However, while there are plenty of ideas about how to use these tools, it’s not always easy to get insight into just how they work.

In this article, we will dive deeper into AI training data, what it is, and how the most popular AI models use it to offer users the best results.

Key Takeaways

• AI training data is crucial for the accuracy and overall quality of generative AI tools like ChatGPT.
• There are many ethical concerns surrounding the sourcing of AI training data, most notably around copyright and ownership.
• AI models use training data to provide the best possible results, establish what the input query is, and generate results.

What Is AI Training Data?

First off, let’s dive deeper into what AI training data is. 

When most of us think of the term data rows and rows of numbers in the cells of an Excel sheet often come to mind, but for AI training data, it’s a little more complex than that.

AI models use data throughout each stage of the development process, which can be loosely categorized into three main sections.

• Training data: This is the data used to train AI models (and the one we’ll focus on in this article).
• Validation data: Data that companies use to ensure a model works effectively.
  • As noted by Google in their machine learning guide on datasets, validation data is the initial round of data used to test a model. When the model is performing effectively, then, next, the test data is run for a final review.
• Test data: This type of data is used to test an AI model, and evaluate its performance.

According to IBM, data may either be structured or unstructured. Structured data is typically easier for machine learning to process and read.

Consider these examples of structured vs. unstructured data:

• Structured data: could be accounting or pricing data, dates, or CRM (customer relationship management) data.
• Unstructured data: could be via audio, video, imagery, or text data.

How Do AI Models Use Training Data?

Before AI models can actually use training data, it must be processed accordingly. This may be done using data science. 

Preparing or pre-processing the data may include:

• Annotating: labelling or tagging it.
• Validating: to ensure it is fit for purpose and assess the completeness of the data.
• Cleaning: fixing errors, taking out outlier data that could skew results, amending inconsistencies.

Sources: IBM Guides on Data Labeling and Data Preprocessing

Once preparing or pre-processing is complete, AI models are fed the training data to learn how to provide the best possible results.

Three of the ways that AI models use training data include:

Reinforcement learning

According to Amazon Web Services, one of the ways that AI models can be trained is by using the reinforcement method. 

• The reinforcement learning (RL) process starts by asking the AI model to perform several actions.
• The model then gets feedback on whether the action it took in response was correct and should be rewarded (or not). 

Reinforcement learning may be used to teach AI tools how to play games or any process that has a win-lose format.

Supervised learning

Supervised learning in contrast to RL learning, more closely resembles a teacher and student approach.

In this case, the teacher (often a machine learning engineer) teaches the student (the AI model).

To teach the AI model, data examples are labelled and identified, defining what the right answer (or output) might be.

Unsupervised learning

In unsupervised learning, the aim is to get the AI model to come to the same correct conclusion as with the supervised learning process, but without using any labelled data.

This approach tends to take longer due to the lack of support, but it does leave room for more exploratory learning, such as potentially setting up AI models to identify patterns humans are not yet aware of.

The Ethical Aspects of AI Training Data

The ethical aspect of sourcing AI training data is a topic of debate and a key part of the ongoing discussion around responsible AI.

We’ve curated a list of OpenAI and ChatGPT Lawsuits surrounding AI, including those involving the use of AI training data.

One website that has drawn a lot of interest in regards to providing data for AI training is Reddit, as reported by Wired. The article noted that Reddit’s use of data prompted an inquiry from the FTC (Federal Trade Commission). Further, it highlighted that Reddit’s partnerships or collaborations with AI could result in $203 million in revenue in the coming years.

Final Thoughts

AI training data comes in different shapes and sizes and from many different resources. There are a number of ways that AI models then use that data during the learning process, such as reinforcement learning, supervised learning, and unsupervised learning.

Additionally, there are several conversations around ethics, responsible AI, and the use of training data coming to the forefront as AI becomes increasingly integrated into everyday life.

We believe in a transparent approach to data at Originality.ai. That’s why we’ve published a guide on How Originality.ai Treats Your Content.

Learn more about AI in our top guides:

• What is Natural Language Processing?
• What Is Machine Learning?
• The Ethics Of AI-Generated Content: A Discussion

FAQs About AI Training Data

Why is training data so important for AI?

Training data is absolutely essential for AI models as it is the data that they use to learn and respond well to prompts. The better the data, the better the output’s reliability, accuracy, and quality.

How is training data collected?

Training data comes from several locations depending on the AI company and AI model. Some possible sources include user-generated content, web scraping, and public datasets.

How much training data is needed for AI?

The volume of data required for generative AI results depends entirely on how complex the query is. For simple answers, minimal training data is needed. However, for the more complex stuff, the more training data, the better!