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Are You in the Dark About Your AI? Here’s Why That’s a Problem

In the domain of Artificial Intelligence (AI) and Machine Learning (ML), the call for transparency and interpretability is louder than ever. As AI systems become increasingly integrated into critical decision-making processes, the need for what is known as “Explainable AI” rises commensurately.

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Get your Ninja On! Understanding the Ninja’s Tricks Used Against Your AI/ML Systems

It’s no secret the social and security implications of powerful AI capabilities are very concerning for policy makers and businesses alike. While many of these vulnerabilities focus on the worst case scenario somewhere between a Terminator and Matrix style future.

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Is Your AI Model Doomed to Fail? Why Your Testing Data Might Be Holding You Back.

The rapid evolution of artificial intelligence and machine learning (AI/ML) has ushered in a new era of technology, from personal assistants in our pockets to advanced data analytics informing business decisions. But the true powerhouse behind successful AI models isn’t necessarily the algorithms—it’s the data. In this exploration, we’ll delve into the importance, intricacies, and challenges of testing data in AI/ML.

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