Artificial intelligence must train with a lot of data to function as intended and demonstrate a semblance of evolving independent “thinking.” This data, however, cannot be fed to the system raw without labeling, logical sorting, and structure. For programs to achieve some level of “intelligence,” they must work with usable and consistent data in a compatible format.
Data needs to undergo organization to achieve the attributes listed above. This is possible through the following ways.
Ensuring data reliability with data cleaning and reduction
Data needs processing before an AI system can make use of it. As such, developers need to ensure that data is reliable for the intended purpose. Inaccurate data should be wedded out. It is also necessary to remove information that does not represent actual situations that should be in the memory of the AI system. Additionally, there is a need to remove unnecessary or irrelevant data.
Datasets play a vital role in the overall quality of an AI system. Artificial intelligence essentially learns from these datasets. The machine learning system cannot behave as designed if the information is incorrect or lacking.
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Facilitating machine learning through data annotation
After data accuracy and completeness verification, it is necessary to make the data identifiable to the AI system. The development team does this through data annotation or the identification and labeling of data. In the case of visual data, for instance, it may be necessary to clarify the identity of objects to the AI system. While some AI systems can readily identify objects or data, there are instances when the supplied data creates confusion. For example, the system may mistakenly detect a tree as a building or geographical feature in a satellite image.
When done correctly, data annotation eliminates confusion. It ensures that the machine learning system accurately identifies data or visual elements from the get-go. Otherwise, it may learn the wrong details, resulting in a faulty system.
Data annotation can be undertaken manually or with the help of automated systems, especially when massive amounts of data are involved. There are already existing data labeling solutions that make AI development faster and more efficient without compromising accuracy.
Ensuring proper context through data normalization and simplification
Data patterns on a small scale may not be the same on larger scales. Therefore, it is essential to conduct data normalization to ensure that data represents a consistent scale. Different data sets may be grouped according to specific attributes or categories to normalize them when taken by the AI system, as it learns to establish patterns or logic for making certain decisions. There are also instances when complex data needs to be broken down into simpler forms to capture more specific relationships.
Data quality and accuracy are significant challenges in AI development. It is crucial to overcome these with the proper methods, tools, and human-guided development. After all, building AI cannot be entrusted to another AI system. The outcome will either be unintelligent or perhaps threateningly advanced or incomprehensible, like what happened when Facebook’s AI created its own language.