
The Infinite Retina
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As the fusion of Generative AI and Spatial Computing continues to advance, it presents a host of challenges and considerations that must be navigated to harness its full potential while maintaining ethical standards and real-world applicability.
Data quality and bias are indeed pivotal in shaping the reliability and fairness of applications that leverage Generative AI and Spatial Computing. These two factors directly influence the performance and outcomes of AI systems, with a significant impact on real-world decisions and perceptions.
Data quality pertains to the suitability of data for making decisions. High-quality data must be accurate, complete, timely, consistent, and collected using robust methodologies. For Generative AI systems, which often learn and make inferences from large datasets, the adage “garbage in, garbage out” is particularly apt.
The use of poor-quality data can lead to...
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