Resource Data

Feeding the AI ​​Beast. Unique, AI-Centric Datasets… | by JOHN NOSTA | November 2022

Unique, AI-centric datasets will advance utility and results.

It has often been said across many topics that trash in equals trash out. And this is especially true for datasets used in advanced analytics. At first glance, data has been widely seen as a central resource in the digital age. However, data is no longer just data; data must now be of a certain quality, extracted to meet the needs of artificial intelligence (AI) systems. As the focus shifts to AI, data quality will become a critical part of AI optimization.

AI has advanced rapidly in recent years and its need for data has grown exponentially. Moreover, this data is accurate, up-to-date and complete for the AI ​​to work properly. Businesses and organizations will need to adjust their data management strategies accordingly to meet these new requirements. Data should be collected with specific goals in mind, such as deep learning or natural language processing, instead of simply collecting information without a clear goal or direction. Too often, data aggregation is a retrospective process where the “good enough” is the necessary evil of the process. Today, and certainly in the future, the datasets we generate – from medical imaging to fine art – must be uniquely designed and “quantified” 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.

The AI ​​will use data from various sources to learn different patterns and relationships between data points. This means that data should be collected from structured and unstructured datasets, as well as a variety of data formats such as text, images, video, etc. This data, coming from multiple sources across different data types and formats, will require new levels of interoperability to optimize usability. In some cases, this data itself will be as important as the technology.

In the age of data-driven decision-making, it’s clear that quality data is essential for AI systems to perform optimally. The vast constellation of users must focus on collecting high-quality data that can effectively feed their AI models to achieve the best possible results. Feeding the AI ​​beast will require extending data acquisition to new multi-dimensional arrays that uniquely provide most interesting “food for thought” that suits the technological regimen. And while asking the right question may be half the answer, providing the right data, in both form and function, will become the next axiomatic point of truth for the future of advanced analytics.