Data is a primary component of the development of AI systems. While computing
power and algorithms are necessary components to develop advanced AI
systems, the quality and reliability of the data used is the greatest determining
factor to understand how intelligent an AI system can be. The lack of high quality,
relevant data results in failure of even the most advanced AI models to function
properly.
AI systems learn through the identification of patterns within the data they are
trained on. These patterns may include text, images, video, audio, or other types
of structured business data. Through the training process, AI identifies
relationships, generates predictions, and produces output based upon its prior
experience with the data it was trained on. The greater quality and relevance of
the data used to train an AI system results in a better understanding of the real
world of the system.
While the quality of the data is significant, the importance of quality outweighs the
importance of volume. A large amount of poor-quality data can result in
inaccurate predictions and unreliable performance. Errors, missing values, or
biased data in an AI’s dataset can negatively impact AI performance. Therefore,
data cleaning, validation, and proper labeling of data are all critical steps in the
development of trustable AI systems.
Diversity of data is another important consideration. An AI trained on a limited or
repeated dataset will typically struggle with adapting to changes once it is
exposed to new conditions. Training an AI on diverse datasets enables the AI to
better generalize and ultimately increases the accuracy and adaptability of the AI
in various environments and scenarios for multiple users.
Continued learning and adaptation are also supported by the inclusion of new
data. Strong AI systems are not static – they evolve over time. Through
continued learning using new data, AI systems can adapt to current trends,
behaviors of users, and changes in the marketplace. Continued learning is critical
in rapidly evolving industries such as health care, finance, and e-commerce.
Finally, ethics, security, and regulatory compliance surrounding the management
of data play a critical role in establishing trust in AI systems.
Ultimately, the quality and responsible management of data provides the
foundation for powerful AI systems. With high-quality and responsibly managed
data, AI systems can effectively learn, make accurate decisions, and provide
meaningful value to individuals in the real world.
