Generative AI Insights
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.
Are large language models wrong for coding?
When the goal is accuracy, consistency, mastering a game, or finding the one right answer, reinforcement learning models beat generative AI.
AI coding assistants: 8 features enterprises should seek
Some AI coding assistants are toylike, while others are enterprise-class. Here’s how to tell the difference.
Large language models are the new cloud battleground
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.
The AI singularity is here
The time to figure out how to use generative AI and large language models in your code is now.
From the 10x developer to the 10x team
Building an elite development team starts with abandoning the fantasy of the 10x developer and embracing a more modern approach to developer productivity.
How to babysit your AI
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.
How to explain the machine learning life cycle to business execs
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.
ChatGPT and software development
How can developers use generative AI to write better code, increase productivity, and meet high user expectations?
Zero-shot learning and the foundations of generative AI
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.
