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.
How AI will impact the developer experience
Generative AI is not the first new technology that has changed how software developers work. While developers have nothing to fear, the stakes will be high for their employers.
How to use GPT-4 with streaming data for real-time generative AI
For businesses and their customers, the answers to most questions rely on data that is locked away in enterprise systems. Here’s how to deliver that data to GPT model prompts in real time.
3 open source NLP tools for data extraction
Unstructured text and data are like gold for business applications and the company bottom line, but where to start? Here are three tools worth a look.
8 ChatGPT tools for R programming
The generative power of OpenAI's GPT 3.5 LLM is now available to R users, with a growing collection of ChatGPT packages and apps to choose from.
ChatGPT’s parasitic machine
What do ChatGPT and other large language models owe to the human creators who provide the information they train on? What if creators stop making their insights publicly available?
Build a Java application to talk to ChatGPT
Build your own Java-based chatbot and get a feel for interacting with the ChatGPT API in a Java client.
LLMs and the rise of the AI code generators
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?
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.
