According to a report by Grand View Research, the annual average increase in the implementation of artificial intelligence (AI) by companies is expected to be 37.3% between 2023 and 2030. Businesses see the potential of AI-based solutions to increase their productivity and eliminate human errors. However, experts from Progress remind us that these benefits depend on the quality of the training data that AI uses. There is therefore a need for appropriate control and selection of this data, so that artificial intelligence can effectively support business operations.
All elements that make up AI training data, regardless of their number and form, have a real impact on the later functioning of solutions based on this technology. High-quality information increases the accuracy, credibility, and transparency of their results. However, inputting inappropriate data into the system will have the opposite effects. This is evident in cases of large (LLM) and medium-sized (MLM) language models, which when confronted with unlabeled, i.e. unclassified and undescribed content, can adopt contained biases (e.g., racial), stereotypes, and biased approaches to various issues. As a result, there is a risk that the content they generate will be biased and untrue. Experts from Progress are examining this phenomenon and suggesting ways to avoid it.
Data “noise” is information devoid of value, often in the form of unstructured texts. Their presence has no positive impact on the model’s performance, they only occupy the space available in disk memory. Since machines are not capable of correctly interpreting “noisy” data, exposing AI to it may result in unpredictable and undesirable behavior.
“Such a situation can be compared to the human reaction to information overload,” explains Niklas Enge, the regional director for Nordics and Poland at Progress. “When the human brain encounters too much data or numbers that go beyond the usual range, this results in a feeling of disorientation. Both significant and worthless information mix together, making focusing difficult and creating a feeling of being overwhelmed. The problem AI faces is somewhat similar. Noise in the content, inaccuracies, errors, and unnecessary information can significantly disrupt the machine’s work. Companies must be aware that any expansion of the AI training database involves the risk of such difficulties.”
To ensure the proper functioning of AI mechanisms, the data used for training should undergo selection, harmonization, and cleaning before being entered into the training base. These activities will help to reduce noise and save disk space.
Also crucial is the functionality of the platform used in the enterprise during the implementation of AI. It should be scalable, multi-model, and safe. The metadata support, also known as keywords, can significantly facilitate defining, categorizing, identifying, and finding information using tags.
Creating or expanding a training database for artificial intelligence requires appropriate preparation. To achieve this, Progress experts encourage two straightforward steps. The first is to set specific goals for which AI support is needed. Outlining plans at an early stage ensures that AI is indeed what the company needs and helps simplify the process of collecting, selecting, and preparing training data later on.
The second step is to estimate the amount of training content needed to begin work. The more complex the tasks AI is to assist the company with, the more precise the selection and implementation process of training materials should be. It is worth contacting experts in the field; their contribution can help evaluate the data regarding its usefulness.
In addition to these tips, it is worth considering collaborating with data analysis specialists. Their support will be extremely helpful in the process of developing the best possible AI implementation strategy for a given company.
