Artificial intelligence (AI) has evolved into a vast and multifaceted field, offering tools and solutions for everything from automating repetitive tasks to creating entirely new content. Two of the most prominent categories within AI are generative AI and analytical AI. While they share a common foundation, their purposes, functionalities and applications are distinct. The Machine Learning Times brought this topic to our attention in their article, “How Gen AI and Analytical AI Differ — and When to Use Each.”
Generative AI focuses on creating new content or data that mimics the characteristics of the input it’s trained on. Analytical AI, on the other hand, focuses on understanding, interpreting and making decisions based on existing data.
Generative AI and analytical AI aren’t mutually exclusive; in fact, they often work best together. For example, generative AI can create synthetic datasets that analytical AI uses to train models for better performance in scenarios with limited real-world data.
While one sparks innovation and creativity, the other drives precision and informed decision-making. Understanding their differences—and their complementary nature—is key to leveraging the full power of AI in today’s fast-evolving technological landscape.
The real challenge is that most organizations have little knowledge on how AI systems make decisions. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.
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.
