In the dynamic field of artificial intelligence (AI), fine-tuning pre-trained models is essential for customizing

Prompt engineering acts a key skill in generative-AI. Precise and meaningful prompts can effectively guide

This blog delves into Retrieval-Augmented Generation (RAG), a cutting-edge AI model that improves huge language models with real-time data retrieval for greater accuracy and relevance. It describes RAG’s design, practical applications, and implementation with Azure’s AI services, such as Azure AI Enrichments, Prompt Flow, Open AI, and custom solutions.

Generative Artificial Intelligence (AI) is currently a buzzword in the software industry. Professionals are exploring

Microsoft’s HoloLens 2 leads the way in mixed reality technology, enabling revolutionary holographic experiences. This transformative headset is reshaping industries from healthcare to marketing by merging virtual and real worlds. With a wide field of view and precise tracking, HoloLens 2 brings future potential to diverse sectors through immersive, collaborative and productive applications.

Generative AI creates new data from existing datasets, enabling automation across business functions like sales, marketing, HR, and legal. It powers applications like proposal generation, predictive analytics, document creation, and chatbots. Building a conversational chatbot involves steps like selecting a model, training it on curated data, iterating for accuracy, choosing a platform, and extensively testing the interface.

The article emphasizes the critical role of Artificial Intelligence (AI) in enhancing API security. AI detects threats in real-time, predicts vulnerabilities, and responds adaptively, offering efficiency and proactive protection. Despite challenges like privacy and integration complexity, real-world applications in finance, healthcare, e-commerce, and government showcase AI’s effectiveness. eInfochips provides API security solutions, incorporating best practices and Secure SDLC methodology for comprehensive protection during product development.

In recent years, Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized the field of medical imaging, bringing significant advancements in diagnostics, image interpretation, and patient care. Through the application of advanced algorithms and computational models, AI and ML have enabled precision medicine by automating tasks that were once solely reliant on human expertise.

Individuals and businesses have undoubtedly reaped numerous undeniable benefits from the advancement and evolution of technology. However, it has come with a significant disadvantage: an increase in cybercrime, cyberattacks, and malware infections, facilitated by the ever-increasing attack surface.The expansion of the network perimeter poses a significant problem, particularly for high-level business operations that need to consistently monitor hundreds of layers of code and security events to guard against intrusions. This task surpasses human capabilities and therefore requires a more efficient solution.