What is Data Collection, and How Is it Used in Machine Learning?
As humans and businesses interact with each other, they generate a tremendous amount of data. This data is incredibly valuable. Businesses like Google, Facebook and Amazon make billions collecting and selling user data.
Today, data collection has a new application in the development of artificial intelligence (AI) and machine learning. Human data is used to train AI models, which helps new AI technology make more accurate predictions. However, researchers are still trying to understand how to manage datasets to improve the reliability of AI.
As technology evolves and incorporates more machine learning, diverse human datasets will become essential for training accurate, unbiased, and efficient AI tools. Now more than ever, it’s becoming increasingly important to understand the basics of data collection and how it applies to the success of your business.
What is Data Collection? In More Detail
Data collection is the practice of gathering and measuring information to construct meaningful datasets. These datasets help us understand trends, predict outcomes and improve our perspective on just about anything.
Data collection services are essential for businesses as it enables them to make data-driven decisions tailored to their strategic goals.
For example, an eCommerce brand may collect data to improve the customer experience and increase sales. They may focus on website interaction, customer feedback and sales data to gain insight on customer trends.
Their ability to gather and construct datasets directly impacts the analysis they eventually perform. In turn, this will determine the effectiveness of the strategies that arise from their data analysis.
It’s important to remember that data collection is not just about gathering information. It’s about categorizing the right information in a way that reflects the real world.
What Does Data Collection Have to Do with AI?
Data collection has many, many applications in the field of AI. In fact, AI systems cannot be built without large amounts of data. Data serves as the foundational training material for creating algorithms that can predict outcomes. In essence, this is all AI does—it uses historical data to predict outcomes.
For example, an AI chatbot (such as ChatGPT) is an AI language model that predicts each word based on the words that came before. To do this, it analyzes massive datasets to make its predictions accurate and human-sounding.
AI models use data to do much more than chat. Another AI model might categorize emails into Primary, Urgent, and Spam based on the sender’s address and the words in each email. This AI model would need to be trained on large datasets of past emails in order to make predictions about the contents of your inbox today.
This is just a simple example. AI tools are being developed to predict countless outcomes and streamline services in every industry. In the near future, it is likely that the main application of data collection will be to train AI models.
Problems with Data Collection for AI
AI is still very new. Most engineers and researchers are still unclear on how to collect data to train AI models effectively.
Good AI data collection is essential for training AI models that make accurate and reliable predictions. Poor practices can lead to big problems. If the data used is incorrect or biased, it can lead to AI systems that are unfair or make wrong decisions.
For example, imagine an AI tool designed to evaluate job applicants and determine their suitability for a position within a company. If the training data for this tool predominantly included candidates from Ivy League schools, the AI could develop a bias. It may favor applicants with Ivy League backgrounds, regardless of whether other candidates have more relevant experience or specialized training.
Also, if data is not categorized or labeled correctly, AI models can get confused. This can result in wrong predictions or classifications. Mistakes like these are especially serious in areas like healthcare, where they can have major negative effects.
These issues and others like them are common, and they highlight the importance of careful and detailed AI data collection methods. Among the most important is the diversity of data. Data collected from around the globe and in multiple languages enables AI models to gain a more accurate and realistic understanding of the world. This is crucial for creating reliable and fair AI systems.
Wolfestone Group’s Global Datasets are Improving AI Data Collection
Wolfestone Group, a global linguistics agency, has been at the forefront of data collection for over a decade. Today, it employs AI specialists who curate and collect multilingual data specifically to train machine learning models.
Thanks to its global network of translators and clients across industries, Wolfestong Group has the unique ability to gather data in over 100 languages, including US and UK English, German and French. It also collects this data from a variety of sources, such as voice overs, translations, global studies and more.
This diversity of information enables Wolfestone Group’s AI team to compile datasets that are much more comprehensive and representative than even the data used by leading AI firms.
For example, ChatGPT, the most famous (and at times notorious) AI tool today, is plagued by issues arising from its datasets. Time and time again, it has been found to reflect biases inherent in its primary training data, which is often overwhelmingly in US English and full of American cultural references.
Training AI models with extensive, multilingual datasets is a key step towards reducing biases, particularly those related to language, culture and race. This is necessary for training AI models that accurately predict outcomes based on the big picture rather than a narrow, monocultural view of the world.
This is where Wolfestone Group excels. By integrating data from around the globe, their team is able to create datasets that train AI models to predict a broader spectrum of human interactions and nuances.
This approach significantly improves the models' effectiveness and fairness. This doesn’t just make the world a more equitable place—it also improves the accuracy of AI tools. In other words, AI models work better with global datasets (given, of course, that they are categorized effectively).
Another benefit of Wolfestone Group’s AI data collection, especially for businesses, is that they expand the usability of AI tools across different regions and cultures from day one. As the global “AI tech race” begins, firms offering international AI solutions will find themselves better positioned for success.
Global Data is a Must for Global AI Success
The amount of data required to train an AI model is unimaginably huge. Even with the help of machines, collecting and categorizing all of this data—even in a single language—is a gargantuan task. But this doesn’t change the fact that most AI models will need to be trained on global, multilingual datasets in order to make accurate predictions.
Businesses that want to develop successful AI tools should seek out data collection teams like Wolfestone Group, who have access to diverse global data and the expertise to categorize it—in any language.
