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.
Key to the success of any large organization is effective governance of a vast, distributed landscape of data stores. AI can help.
It’s no longer how good your model is, it’s how good your data is. Why privacy-preserving synthetic data is key to scaling AI.
Successful integration of AI into daily operations hinges on front-line employees, yet the impact on their morale is often overlooked.
Balancing performance, energy efficiency, and cost-effectiveness, CPUs adeptly handle the less-intensive inference tasks that make up the lion’s share of AI workloads.
The average user of AI lacks an adequate understanding of the tools they increasingly depend on for decision-making and work. We need to change that.
Fine-tuning and retrieval augmentation are time-consuming and expensive. A better way of specializing LLMs is on the horizon.
Generative AI not only makes analytics tools easier to use, but also substantially improves the quality of automation that can be applied across the data analytics life cycle.
Responsible AI isn’t really about principles, or ethics, or explainability. It can be the key to unlocking AI value at scale, but we need to shatter some myths first.
Through natural language queries and graph-based RAG, TigerGraph CoPilot addresses the complex challenges of data analysis and the serious shortcomings of LLMs for business applications.
Retrieval-augmented generation brings to generative AI the one big thing that was holding it back in the enterprise.
The key to reaping the benefits of AI while minimizing the risks is through responsible development and use. Here’s how SAS Viya puts ethical AI practices to work.
Hardware requirements vary for machine learning and other compute-intensive workloads. Get to know these GPU specs and Nvidia GPU models.
Generative AI promises to be transformative for software development, but only if we ensure that all code is analyzed, tested, and reviewed.
Proper context and data privacy should be top of mind for developers when writing applications on generative AI for B2B use cases.
Large language models can reshape business processes by automating substantial portions of complex tasks. But they can’t do it alone.
Developing AI and machine learning applications requires plenty of GPUs. Should you run them on-premises or in the cloud?
From managing data to scaling systems to funding initiatives for the long haul, every part of your generative journey will be a challenge.
Five key questions you should ask before embarking on the journey to create your own in-house large language model.
Generative AI will reshape how we develop AI-driven products for the physical economy, starting with the creation of synthetic data sets for challenging use cases.
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