Generative AI Insights

How evolving AI regulations impact cybersecurity

How AI is impacting data governance

Key to the success of any large organization is effective governance of a vast, distributed landscape of data stores. AI can help.

Solving the data quality problem in generative AI

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.

Addressing workers’ concerns about AI

Successful integration of AI into daily operations hinges on front-line employees, yet the impact on their morale is often overlooked.

How CPUs will address the energy challenges of generative AI

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.

Responsible AI starts with democratizing AI knowledge

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.

The limitations of model fine-tuning and RAG

Fine-tuning and retrieval augmentation are time-consuming and expensive. A better way of specializing LLMs is on the horizon.

How generative AI is redefining data analytics 

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.

3 pernicious myths of responsible AI

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.

How TigerGraph CoPilot enables graph-augmented AI

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.

How RAG completes the generative AI puzzle

Retrieval-augmented generation brings to generative AI the one big thing that was holding it back in the enterprise.

SAS Viya and the pursuit of trustworthy AI

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.

Choosing the right GPU for AI, machine learning, and more

Hardware requirements vary for machine learning and other compute-intensive workloads. Get to know these GPU specs and Nvidia GPU models.

Avoiding the dangers of AI-generated code

Generative AI promises to be transformative for software development, but only if we ensure that all code is analyzed, tested, and reviewed.

How generative AI can promote inclusive job descriptions

Proper context and data privacy should be top of mind for developers when writing applications on generative AI for B2B use cases.

How LLMs can help streamline business processes

Large language models can reshape business processes by automating substantial portions of complex tasks. But they can’t do it alone.

Make the most of GPUs for machine learning applications

Developing AI and machine learning applications requires plenty of GPUs. Should you run them on-premises or in the cloud?

How to manage generative AI

From managing data to scaling systems to funding initiatives for the long haul, every part of your generative journey will be a challenge.

Is creating an in-house LLM right for your organization?

Five key questions you should ask before embarking on the journey to create your own in-house large language model.

How generative AI will benefit physical industries

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.