Generative AI Insights, an InfoWorld blog open to outside contributors, provides a venue for technology leaders to explore and discuss the challenges and opportunities presented by generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld’s technically sophisticated audience. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content.
Large language models like GPT-4 and tools like GitHub Copilot can make good programmers more efficient and bad programmers more dangerous. Are you ready to dive in?
Some AI coding assistants are toylike, while others are enterprise-class. Here’s how to tell the difference.
Perhaps the biggest thing since open source or Google, LLMs may have companies fighting for supremacy, but it’s the developers who come out ahead.
Building an elite development team starts with abandoning the fantasy of the 10x developer and embracing a more modern approach to developer productivity.
AI systems are not yet mature and capable enough to operate independently, but they can still work wonders with human help. We just need a few guardrails.
For data science teams to succeed, business leaders need to understand the importance of MLops, modelops, and the machine learning life cycle. Try these analogies and examples to cut through the jargon.
How can developers use generative AI to write better code, increase productivity, and meet high user expectations?
This alternative to training with huge data sets has potential for business, but data science teams will need to spend time on research and experimentation.