Last year, we explored the fundamentals of AI and ML in the "100 Days of AI" series. This year, the focus shifts to something even more exciting—practical applications of these technologies. If AI is genuinely going to be a trillion-dollar market, it must deliver measurable productivity advantages across industries.
A great example of this shift is from a recent interview I did with Elastic, where we discussed how their analytics platform tackles a common challenge: filtering false positives.
Why does this matter?
Traditional machine learning (ML) systems for fraud detection often rely on pattern-matching techniques to identify anomalies. While effective to a degree, these approaches are susceptible to being gamed and still generate thousands of false positives, overwhelming human reviewers.
Enter large language models (LLMs). By leveraging contextual understanding, LLMs can go beyond basic pattern recognition. They filter incidents with a higher degree of certainty, significantly reducing the workload on human teams. This approach represents a major leap forward in productivity and efficiency.
Want to dive deeper into how LLMs are reshaping security incident identification? Check out this insightful research: arxiv.org/abs/2311.16169.
Stay tuned for more practical examples and insights as we explore the real-world impact of AI throughout the year.
CTA: Have a question or a use case to share? Hit reply and let’s discuss!
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