Generative AI | News, how-tos, features, reviews, and videos
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.
You can learn a lot about programming from AI assistants. Take advantage of these tips and techniques to improve your experience.
Fine-tuning and retrieval augmentation are time-consuming and expensive. A better way of specializing LLMs is on the horizon.
Developing generative AI applications is very different from developing traditional machine learning applications. These are the steps.
New tools for filtering malicious prompts, detecting ungrounded outputs, and evaluating the safety of models will make generative AI safer to use.
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.
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