Feeding The AI Beast
It’s been often said across many topics that garbage in equals garbage out. And this is particularly true for the data sets that are used in advanced analytics. At face value, data has been widely regarded as a central resource in the digital age. However, data is no longer just data; data now needs to be of a certain quality, extracted to suit the needs of artificial intelligence (AI) systems. As the focus on AI increases, data quality will become an essential component of optimizing AI.
AI has rapidly advanced in recent years and its need for data has grown exponentially. Further, these data that is accurate, up-to-date, and complete for AI to function properly. Companies and organizations will have to adjust their data management strategies accordingly to accommodate these new requirements. Data must be collected with specific objectives in mind such as deep learning or natural language processing, instead of simply gathering information without any clear purpose or direction. Too often, the aggregation of data is a retrospective process where “good enough” is the necessary evil of the process. Today, and certainly into the future, data sets that we generate — from medical imaging to fine art — must be crafted and uniquely “quantized” with AI in mind. “AI-centric data” will be the new buzzword in broad applications that uniquely align with the specific requirements of AI systems.
AI will use data from various sources to learn from different patterns and relationships between data points. This means data must be collected from both structured and unstructured data sets, as well as a variety of data formats such as text, images, videos, etc. These data, from multiple sources across different data types and formats, will require new levels of interoperability to optimize use. In some instances, these data themselves will be as important as the technology.
In the era of data-driven decision-making, it is clear that quality data is essential for AI systems to function optimally. The vast constellation of users need to focus on collecting high-quality data that can effectively feed into their AI models to achieve the best results possible. Feeding the beast of AI will require the expansion of data acquisition to new multi-dimensional arrays that uniquely provide a most interesting “food for thought” that suits the technology diet. And while asking the right question maybe half of the solution, providing the right data, in both form and function, will become that next axiomatic point of truth for the future of advanced analytics.