Generative AI Insights
By Blair Sibille, Jim Webber, Kirill Skrygan, Michiel Prins, Rahul Pradhan, Stephen Chin, Uzi Dvir
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.
Addressing AI hallucinations with retrieval-augmented generation
The hallucinations of large language models are mainly a result of deficiencies in the dataset and training. These can be mitigated with retrieval-augmented generation and real-time data.
Democratizing AI with digital adoption platforms
Digital adoption platforms learn application usage patterns and user behaviors and walk workers through business processes in real time, offering guidance and automating tasks. They can help all of us get the most from AI.
How knowledge graphs improve generative AI
Large language models have immense potential, but also major shortcomings. Knowledge graphs make LLMs more accurate, transparent, and explainable.
What does generative AI mean for software companies?
AI and machine learning will boost the creativity and problem-solving abilities of software developers. It will also establish a new oligopoly over the software industry.
How generative AI changes cybersecurity
The impacts of large language models and AI on cybersecurity range from the good to the bad to the ugly. Here’s what to watch out for, and how to prepare.
What ChatGPT doesn’t say about Kubernetes in production
Generative AI is already proving helpful across many relatively basic use cases, but how does it hold up when tasked with more technical guidance?
How generative AI changes IT operations
Generative AI can provide valuable analysis and insights to IT operators. But what about data security, reliability, workflow integration, and the conditions needed for successful deployment?
How to get a handle on shadow AI
By allowing the use of AI tools proven to be safe, but requiring them to be used within explicit guidelines, you can alleviate both employee frustration and organizational risk.
Applying the lessons of open source to generative AI
The excitement and turmoil surrounding generative AI is not unlike the early days of open source, or the Wild West. We can resolve the uncertainty and confusion.
How to take action against AI bias
Humans must be the custodians for preserving high-quality data as AI use continues to advance.
What is generative AI? Artificial intelligence that creates
Generative AI models can carry on conversations, answer questions, write stories, produce source code, and create images and videos of almost any description. Here's how generative AI works, how it's being used, and why it’s more...
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.
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.
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.
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.
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