Applications & Impact

This article delves into the systematic process of identifying and prioritizing high-impact AI use cases for enterprise implementation, covering strategic imperatives, alignment with core strategies, measuring business value, feasibility, data readiness, risk management, ROI, scalability, ethical considerations, talent and skills, sustainability, adoption, and customer impact. It provides a comprehensive blueprint for leaders navigating AI adoption in organizations.

This article delves into the use cases of AI, focusing on image/media generation and AI customer support chat systems. The author shares insights from their AI deep dive, including building AI apps like chatbots on Telegram to showcase AI applications. AI developers can learn about practical AI implementations and the potential of AI in enhancing existing products.

This article provides insights for Product Managers (PMs) working on AI features, emphasizing the importance of understanding user needs and business value. It highlights the practical approach to learning AI through user-centric lenses and the significance of asking questions when faced with new challenges.

The article explores the future impact of AI on various industries, highlighting its role in data analysis, research, human care, household tasks, workplace efficiency, and safety. It discusses the increasing adoption of AI by enterprises and the influence of generative AI tools like ChatGPT, providing insights into the evolving landscape of artificial intelligence.

The MIT report highlights a concerning 95% failure rate of generative AI pilots in companies, emphasizing the challenges in implementing enterprise AI solutions. This article sheds light on the GenAI Divide, offering valuable insights into the core issues hindering the success of AI initiatives in organizations.

Amazon Web Services (AWS) now offers OpenAI's open weight models on Amazon Bedrock and SageMaker AI, providing customers with advanced AI capabilities for various applications like agentic workflows, coding, and scientific analysis. This collaboration expands the availability of powerful AI technologies to AWS users, shaping the future of GenAI technology.

This article from Tribe AI provides a comprehensive guide to advanced AI governance, focusing on essential strategies for ensuring organizational compliance with advanced AI systems. It covers core principles, challenges, and best practices in AI governance, emphasizing ethics, transparency, and regulatory standards, offering valuable insights for AI developers and leaders in shaping responsible AI adoption.

Mosaic AI Agent Bricks simplifies the extraction of structured data from documents like contracts and invoices without manual labeling or schema training. AI developers can learn how to enhance automation and reduce effort by leveraging schema feedback and AI-assisted evaluation for continuous improvement.

This article delves into the common reasons why AI projects fail, highlighting challenges such as unclear success metrics, scope creep, and the added layer of probabilistic uncertainty in AI projects. It provides insights on how organizations can avoid these pitfalls and improve the success rate of their AI initiatives, making it a valuable read for AI developers and builders.

The article introduces 13 foundational AI courses and resources from MIT Open Learning, covering topics like artificial intelligence, machine learning, machine vision, and algorithms. These resources are valuable for AI developers looking to grasp the basics and advance their knowledge in AI technologies.

The article delves into the fundamentals and best practices of AI governance, emphasizing the need for a robust framework to manage AI risks and ensure data protection. It provides insights for AI developers and builders on implementing governance strategies to address security threats and compliance requirements.

This article serves as a comprehensive guide for individuals looking to learn AI from scratch in 2025, offering insights from industry experts, practical advice, and tips on mastering AI skills and tools. It emphasizes the increasing relevance of AI in various industries and provides a roadmap for aspiring data scientists, machine learning engineers, AI researchers, and enthusiasts.

This article provides a comprehensive guide to Amazon Bedrock, a managed AWS service for accessing and managing foundation models (FMs) essential for generative AI applications. AI developers can learn how to leverage AWS Bedrock to simplify infrastructure management, access cutting-edge AI models, and develop scalable generative AI applications aligned with their goals.

GraphRAG, a retrieval mechanism enhancing GenAI applications by leveraging graph data structures, is discussed in this article. AI developers can learn how GraphRAG optimizes information retrieval in graph databases, offering insights into improving AI systems' performance.

This article provides a walkthrough on building an AI agent observability system using LangChain, Rudderstack, and Clickhouse. It explores the importance of monitoring AI agents in chat systems and delves into the concept of AI observability, offering practical insights for developers interested in enhancing AI system visibility and performance.

This article delves into the transformative impact of effective prompt engineering in AI for solving business challenges. It emphasizes the importance of using specific frameworks to optimize AI models like Claude, Copilot, Perplexity, and others, offering practical insights for AI developers to enhance their prompt creation skills.

Learn how to leverage the COMPLETE function in Snowflake Cortex AI to integrate large language models (LLMs) into SQL queries, enabling advanced AI-driven data interactions. This tutorial by Christopher Marland offers practical examples for generating insights from data and optimizing AI models within Snowflake.

The United Nations System White Paper on AI Governance delves into the institutional models and normative frameworks for global AI governance within the UN system. AI developers can learn about the importance of ethical considerations, data privacy, bias mitigation, and transparent decision-making processes in leveraging AI for positive impacts.

This article discusses the process of handling unstructured data using Snowflake Cortex AI for geocoding and geofencing tasks. It provides insights into breaking down complex data problems, utilizing marketplace apps for geocoding services, and scaling up data processing for large datasets.

This article from RAND delves into the root causes of failure for artificial intelligence projects, highlighting insights from data scientists and engineers. It offers recommendations to avoid common pitfalls in AI implementation, making it a valuable resource for AI developers and builders to enhance project success rates.

The article explains GraphRAG, a technique that enhances Retrieval Augmented Generation (RAG) with knowledge graphs, enabling AI models to handle complex tasks like multi-hop reasoning and answering comprehensive questions. AI developers can learn how GraphRAG improves information retrieval and understanding in large language models.

Snowflake Cortex is a fully-managed AI service integrated within Snowflake, enabling businesses to leverage machine learning and AI capabilities through simple SQL commands. It offers pre-built ML functions for tasks like forecasting and anomaly detection, making it easy to gain insights and automate tasks without specialized programming knowledge.

LangChain is a framework for building context-aware reasoning applications powered by LLM technology. It enables developers to create AI applications by chaining interoperable components and third-party integrations, simplifying AI development and future-proofing decisions as technology evolves.

This article from MIT Sloan Teaching & Learning Technologies provides beginner-friendly resources on key AI concepts like neural networks, natural language processing, and model architectures. It offers insights into generative AI foundations for teaching and practical tips on writing effective prompts and mitigating bias in AI tools.

This article clarifies the distinction between AI and machine learning, highlighting how machine learning is a subset of AI that focuses on training algorithms to learn from data. It provides insights into popular terms like neural networks, deep learning, and large language models, offering a foundational understanding of how these concepts interrelate.

This beginner's guide from Coursera provides a structured approach to learning artificial intelligence, emphasizing the importance of creating a learning plan, mastering prerequisite skills, and starting with AI fundamentals. It highlights the wide-ranging applications of AI in everyday life and the potential career opportunities in the field.

Graph RAG is a powerful approach that enhances large language models (LLMs) with external knowledge, enabling more relevant and accurate answers to natural language questions. This article delves into the importance of integrating domain-specific proprietary knowledge into conversational interfaces through the Graph RAG approach, offering valuable insights for AI developers looking to optimize question-answering systems.

Agentic RAG leverages AI agents to enhance retrieval augmented generation systems, enabling large language models to retrieve information from multiple sources and handle complex workflows. This article delves into the concept of RAG, its components, and the benefits of incorporating AI agents for improved accuracy and adaptability in AI models.

GraphRAG introduces a structured, hierarchical approach to Retrieval Augmented Generation (RAG), enhancing language models' reasoning abilities by leveraging knowledge graphs. This article delves into the process of extracting knowledge graphs from text, building community hierarchies, and generating summaries for improved question-and-answer performance.