Legacy rules engines offer predictable automation but lack scalability and personalization; ML revolutionized this by enabling adaptive, data-driven decisions.
Chain-of-thought (CoT) prompting enables LLMs to improve their reasoning capabilities. This paper explores various CoT techniques and their practical limitations.
LLMs are better at math with a "verified reasoning trajectory" — an opportunity to review their steps and determine if the math they're doing makes sense.
A Data-First IDP integrates governance, traceability, and quality into workflows, transforming how data is managed, enabling scalable, AI-ready ecosystems.
Distributed training accelerates machine learning training by splitting tasks across multiple devices or machines, improving performance and scalability.
Reasoner models enhance problem-solving by "thinking longer" with test-time compute, improving accuracy in math and coding tasks through iterative refinement.
This article introduces process mining, explaining its key elements and practical applications for discovering and analyzing workflows using event data.
Key security challenges in AI and strategies to protect systems, from data breaches to adversarial attacks, to ensure robust and secure AI integration.
AI enhances SQL Server performance through query optimization and predictive maintenance, boosts efficiency, reduces latency, and improves system scalability.
This article examines how QML can harness the principles of quantum mechanics to achieve significant computational advantages over classical approaches.