When I first started as a data scientist, there was a gap. I met with dozens of organizations who would invest time and resources into building accurate and tuned models and then ask, “What now?” They had a fantastic model in hand but couldn’t get it into a place and
People are starting to compile resolutions for the new year, focusing on evolving their own habits and goals. At SAS, we’ve also looked toward 2026 to gather predictions on how AI in the public sector might evolve over the next 12 months. Prediction: By 2026, governments will utilize large language
AI systems have problems. They don’t always work right. They hallucinate, lie, or simply forget the all-important closing bracket in a JSON payload. AI systems need accountability, safeguards and oversight. But what should that look like? Human-in-the-loop (HITL) and its companion human-on-the-loop are in a nascent stage. These strategies, designed
Back in the 80s and 90s, life was simple. Kids roamed the neighborhood. Parents didn’t track them with GPS. Commercials interrupted your TV shows and you watched them. If you wanted to know if Ross and Rachel got back together, you waited – patiently – for the next episode. Research
The financial services industry is undergoing a period of profound change, driven by a dynamic economic landscape, increased regulatory scrutiny, changing consumer behavior and rapid technological advances. Banks operating in this environment are under increasing pressure to transform their risk modeling and decision-making ecosystems in order to remain competitive. This
For the 12th annual SAS NC State College of Design project, students used generative AI within B2B products in varying industries. The annual NC State Design Day came back for its 12th year. Like many years before, the future designers brought their "A game" to SAS world headquarters, showcasing innovative
Developers are trying to help AI to speak all the world’s languages, but it's a complicated journey. While there are around 7,000 languages spoken globally, digital technology primarily revolves around just a few. English dominates over half of all written content online, even though only around 16% of the global
AI has been largely reactive for years: following human commands, assisting with tasks and providing insights based on predefined rules. But 2025 is shaping up to be the year of agentic AI, where AI agents exist to not just respond to human input but act independently. In the movie I, Robot,
AI agents are the tech trend of the moment. It is promising to reshape industries, streamline operations, enhance customer experiences and drive smarter decision-making in ways we couldn’t have imagined just a few years ago. As businesses, leaders, and innovators, it’s vital to understand how these systems actually function, not
The world of data and AI is evolving at breakneck speed, with 2025 shaping into a year of breakthroughs and significant challenges. From AI model hallucinations to the role of synthetic data in innovation, industry leaders are grappling with complex issues that will shape the future of technology. I recently
SAS's Ann Kuo walks you through how SAS Tech Support developed an email classifier to clean up spam and misaddressed emails using SAS Viya's NLP-based text classifier
What happens if an employee unknowingly enters sensitive information into a public large language model (LLM)? Could that information then be leaked to other users of the same LLM? For example, if you ask ChatGPT or Claude to read and summarize a confidential contract, a patient record or a customer
AI is revolutionizing insurance by tackling talent gaps, boosting efficiency and driving innovation.
SAS' Mary Osborne, Ali Dixon Ricke, and Franklin Manchester break down what insurers still need to learn about generative AI.
With a new year around the corner, it’s time to start thinking about what tech trends are coming in the months ahead. 2025 will be interesting, because now that AI has been around long enough to go from novel to normal, we should stop marveling at what it can do
Before rushing to invest in generative AI (GenAI), organizations must pause and take a step back. GenAI is powerful and has shown potential to revolutionize multiple industries – but it’s not a silver bullet. Now that we’ve finally gotten past the hype phase, it’s time to look at the realities
We often hear about cyberattacks, hackers, ransomware, and other nefarious deeds in the news, but not all data breaches are caused by third parties.
Foundation and domain models are transforming how businesses approach technology. These powerful tools are moving beyond the hype of GenAI, offering real solutions for a variety of tasks – whether generating text, creating visual content or even composing music. A new global survey of 1,600 organizations revealed critical insights into
When using LLMs, managing toxicity, bias, and bad actors is critical for trustworthy outcomes. Let’s explore what organizations should be thinking about when addressing these important areas.
Adding linguistic techniques in SAS NLP with LLMs not only help address quality issues in text data, but since they can incorporate subject matter expertise, they give organizations a tremendous amount of control over their corpora.
Remember when the popular catchphrase, “We have an app for that,” was the new big thing? You might not if you’re as young as the NC State University students who recently participated in the 11th annual SAS-NC State Design Project, but back when we could still count 21st-century years in
Generative AI (GenAI) is in its most popular era and many organisations across industry are looking for ways to unlock its potential value. McKinsey's projections suggest that GenAI could add a staggering $2.6 to $4.4 trillion in value to the global economy. In fact, banking is the number one industry
Across the world, investigators and law enforcement officers are tackling a rapidly evolving and expanding workload fueled by an increase in complex modern-day crimes. As technology alters the type and methodology of the crime itself – the evasion of tax payments, theft of public funds, erroneous disbursement of benefits, gaming
SAS' Federica Citterio answers the perennial data science question: "How can I trust (generative) LLM to provide a reliable, non-hallucinated result?"
Large language models (LLMs), like ChatGPT and Microsoft Copilot, have moved quickly out of the novelty phase into widespread use across industries. Among other examples, these new technologies are being used to generate customer emails, summarize meeting notes, supplement patient care and offer legal analysis. As LLMs proliferate across organizations,
As the old saying goes, “You wait ages for a bus and then two [or possibly three]come along at once.” This saying can be updated to reflect life in our increasingly digital world: "You wait ages for a genuine disruptive technology and then two [or possibly three]arrive simultaneously." This phrase
The ability of an organization to make informed decisions swiftly and accurately is crucial. Organizations across various industries rely heavily on advanced technologies to navigate complex data and enhance customer experiences. Decision trees and large language models (LLMs) are two technologies that play pivotal roles in empowering organizations to make
SAS Viya can allow users and organizations to more easily interface with the LLM application, build better prompts and evaluate systematically which of these prompts leads to the best responses to ensure the best outcomes.
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
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
