Developers and modelers face challenges when finding and validating data, collaborating across groups, and transferring work to an enterprise platform. Using a self-service, on-demand compute environment for data analysis and machine learning models increases productivity and performance while minimizing IT support and cost. In this Q&A, Joe Madden, Senior Product
Artificial Intelligence
The days of one-size-fits-all messaging in the pharmaceutical industry are fading. Today's patients and health care providers (HCPs) expect personalized content across a variety of channels. This is where generative AI (GenAI) can really help execute cross-channel marketing. Reaching the right audience with the right message Imagine a world where
Synthesizing data? Who does that? Aren’t we supposed to be running the experiments and measuring things to produce real data? While generally true, there are scenarios in which the use of generative AI (GenAI) is beneficial. Let’s explore the benefits via “what if” scenarios. Before we begin, it’s important to
There's a lot to gain for insurers that move fast enough to adopt promising applications of trustworthy AI.
Imagine if your job was to sort a massive pile of 40,000 stones into about 200 buckets based on their unique properties. Each stone needs to be carefully examined, categorized and placed in the correct bucket, which takes about five minutes per stone. Fortunately, you’re not alone but part of
Customers are buzzing with stories of how SAS Viya has transformed how people work with AI, data and analytics. But where are these stories coming from? Users across various industries have shared their experiences, highlighting the impact SAS Viya has had on their productivity, decision making and overall work experience.
Generative AI models have existed since the 1950s, but only in recent years have their application in marketing gained significant attention and media coverage. The impressive abilities of generative AI, particularly in content generation, have sparked excitement within the industry. However, the larger question that arises is: How can generative
Trustworthy AI is dependent on a solid foundation of data. If you bake a cake with missing, expired or otherwise low-quality ingredients, it will result in a subpar dessert. The same holds for developing AI systems to handle large amounts of data. Data is at the heart of every AI
Data quality is a cornerstone for integrating large language models (LLMs) into organizations. The adage "garbage in, garbage out" holds particularly true here. High-quality data is the lifeblood that ensures the accuracy, relevance, and reliability of the model's outputs. In a business context, this translates to insights and decisions that
While no one currently alive witnessed the beginnings of the Industrial Revolution in mid-18th century Britain, we’re all now spectators and participants in the AI revolution – AI is accessible and entrenched everywhere. While AI is not new, 2023 ushered in a tsunami of AI innovation with the emergence of