InfoQ Homepage Artificial Intelligence Content on InfoQ
-
Applying AI to the SDLC: New Ideas and Gotchas! - Leveraging AI to Improve Software Engineering
Tracy Bannon discusses using Generative AI in software engineering with AI-assistance to meet the speed and quality of end-users demand.
/presentations/ai-sdlc/en/mediumimage/TracyBannon-medium-1720690485520.jpg)
-
Generative AI and Organizational Resilience
Alex Cruikshank discusses where GenAI is likely to have the greatest impact, steps to manage this change, and ways to leverage the shift to AI mediated work to better understand business processes.
/presentations/generative-ai-resilience/en/mediumimage/AlexCruikshank-medium-1720691409057.jpg)
-
Developer Experience in the Age of Generative AI
The panelists discuss challenges developers face that interrupt the development flow, slow things down, the tools available to help, and how to use AI-powered programming assistants to help.
/presentations/dev-ai-assistant/en/mediumimage/infoq-live-logo-medium-1720197272725.jpg)
-
Generative Search: Practical Advice for Retrieval Augmented Generation (RAG)
Sam Partee discusses Vector embeddings in LLMs, a tool capable of capturing the essence of unstructured data used by LLMs to gain access to a wealth of contextually relevant knowledge.
/presentations/vector-embedding-llm/en/mediumimage/SamPartee-medium-1718796896899.jpg)
-
Being a Responsible Developer in the Age of AI Hype
Justin Sheehy discusses the dramatic developments in some areas of artificial intelligence and the need for the responsible use of AI systems.
/presentations/responsible-development-ai-hype/en/mediumimage/JustinSheehy-medium-1719934649319.jpg)
-
Defensible Moats: Unlocking Enterprise Value with Large Language Models
Nischal HP discusses risk mitigation, environmental, social, and governance (ESG) framework implementation to achieve sustainability goals, strategic procurement, spend analytics, data compliance.
/presentations/risk-mitigation-llm/en/mediumimage/NischalHP-medium-1718109596117.jpg)
-
When AIOps Meets MLOps: What it Takes to Deploy ML Models at Scale
Ghida Ibrahim introduces the concept of AIOps referring to using AI and data-driven tooling to provision, manage and scale distributed IT infra.
/presentations/aiops-mlops/en/mediumimage/GhidaIbrahim-medium-1718631160878.jpg)
-
Reach Next-Level Autonomy with LLM-Based AI Agents
Tingyi Li discusses the AI Agent, exploring how it extends the frontiers of Generative AI applications and leads to next-level autonomy in combination with enterprise data.
/presentations/ai-agent-llm/en/mediumimage/TingyiLi-medium-1717748092234.jpeg)
-
Lessons Learned from Building LinkedIn’s AI Data Platform
Felix GV provides an overview of LinkedIn’s AI ecosystem, then discusses the data platform underneath it: an open source database called Venice.
/presentations/ai-venice/en/mediumimage/FelixGV-medium-1717061499976.jpeg)
-
The AI Revolution Will Not Be Monopolized: How Open-Source Beats Economies of Scale, Even for LLMs
Ines Montani discusses why the AI space won’t be monopolized, covering the open-source model, common misconceptions about use cases for LLMs in industry, and principles of software development.
/presentations/ai-monopoly/en/mediumimage/InesMontani-medium-1717136562480.jpeg)
-
Retrieval-Augmented Generation (RAG) Patterns and Best Practices
Jay Alammar discusses the common schematics of RAG systems and tips on how to improve them.
/presentations/rag-patterns/en/mediumimage/JayAlammar-medium-1716537728283.jpg)
-
Large Language Models for Code: Exploring the Landscape, Opportunities, and Challenges
Loubna Ben Allal discusses Large Language Models (LLMs), exploring the current developments of these models, how they are trained, and how they can be leveraged with custom codebases.
/presentations/llm-options-train/en/mediumimage/LoubnaBenAllal-medium-1716277121651.jpeg)