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
While large language models (LLMs) have become synonymous with advanced AI capabilities, their integration into various business and technological domains is often accompanied by significant costs. These costs arise from the extensive computational resources required for training and running these models. However, traditional natural language processing (NLP) techniques offer a
SAS' Julia Florou-Moreno shows you how to use generative AI to build a digital assistant that interacts with a model using natural language conversation.
Most people associate generative AI (GenAI) with large language models (LLMs). While LLMs focus specifically on generating text, GenAI encompasses a wider range of content generation tasks beyond just language, including images, music and more. Broadly speaking, GenAI uses machine learning algorithms to analyze and learn from existing data sets
Large language models (LLMs) are at the forefront of today’s AI, not merely as technological marvels but as transformative agents reshaping how businesses innovate, operate and deliver value. Think of them as the wizards of words, capable of understanding language and transforming it in ways that benefit organizations. However, as
In a global economy marked by fragile supply chains, scarce resources and rising energy costs, the spotlight is on forecasting to address these issues. In 2022, McKinsey & Company uncovered a staggering $600 billion annual food waste, equating to 33% – 40% of global food production, spotlighting the devastating consequences
SAS' Ali Dixon and Mary Osborne reveal why a BERT-based classifier is now part of our natural language processing capabilities of SAS Viya.
