In the era of artificial intelligence (AI), data has been frequently touted as the new oil. Just as oil has fueled industrial revolutions of the past, data is driving the digital transformation of our current age. However, just like oil, data must be refined to be truly valuable. In the context of AI, the quality of data is paramount. Poor data quality can lead to inaccurate models, faulty predictions and ultimately, erroneous decisions. This topic was brought to us by Global Security Review in their article, “Unveiling the Future: The Convergence of AI and Strategic Intelligence Operations.”
AI systems, especially those powered by machine learning, are only as good as the data they are trained on. High-quality data ensures that AI models are accurate, reliable and robust.
Organizations must prioritize data quality to harness the full potential of AI, ensuring that their models are accurate, fair and generalizable. By overcoming challenges and implementing best practices, businesses can build robust AI solutions that drive innovation and deliver meaningful outcomes. As AI continues to evolve, the focus on data quality will only intensify, underscoring its critical role in the future of intelligent systems.
Everyone is looking at AI. Everyone is getting mixed results. The main issue is that data science has not changed, and scientific content is very complex and needs more attention to get the most out of the new AI engines. This is not new for Access Innovations.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
