The ancients’ practice of publicizing set-in-stone personal records would run anathema to modern data privacy laws. These days, in lieu of using contemporary personally identifiable records, I anonymized a 4,000-year-old tax record from ancient Babylon to describe three principles for effective data anonymization at scale: Embracing rare attributes: values and
When we think of something “new”, we tend to picture something clean, shiny, or efficient. The new normal of fraud only checks one of those boxes, and unfortunately, that’s “efficient.” Today, scams and cyber attacks are persistent, and part of a larger ecosystem of phishing and hacking attacks by both
Generative AI (GenAI) is booming. It’s not just a trend; it’s produced a seismic shift in how we approach innovation and technology. SAS Innovate 2024 has moved on from Las Vegas and is now on tour across the world. If you want a recap of what happened in Vegas or
SAS' Federica Citterio answers the perennial data science question: "How can I trust (generative) LLM to provide a reliable, non-hallucinated result?"
SAS® pairs perfectly with data and AI and many at SAS Innovate agree. SAS Innovate host and technical training consultant Dominique Weatherspoon welcomed attendees at the Aria in Las Vegas. SAS Chief Technology Officer Bryan Harris dove into a few important topics including innovation inspired by human need, how SAS
Over the past year, business leaders and technical minds have delved into the potential of generative AI (GenAI) beyond creative tasks like writing fun poems or generating silly images. We’ve been part of these conversations, actively exploring how GenAI can be used to help organizations manage their data, derive insights
It’s no secret, everyone is talking about generative AI (GenAI). In 2023, funding shot up five times year over year. According to a McKinsey report, “Generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually… by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion.”
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
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
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
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
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
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.
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
SAS' Varun Valsaraj demonstrates how to build a digital assistant for a warehouse space optimization use case.
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
The Co-Founder of Ladies Learning Code and Canada Learning Code talks about strides in Canadian computer science education, AI, the future of coding, and more. Companies use many legacy processes to empower their employees, and that's just one of the many barriers employees face in the workplace. Organizations that prioritize
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.
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
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
In recent years, generative AI and AI chatbots like ChatGPT have ignited a flurry of conversation on college and university campuses. We’ve been talking about it a lot at SAS, too, and for good reason – generative artificial intelligence has garnered significant attention due to its considerable promise and possible risks.
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
Let me ask: did you believe Facebook CEO Mark Zuckerberg really boasted about the company’s power in a 2019 video, saying, “Imagine this for a second: One man, with total control of billions of people’s stolen data, all their secrets, their lives, their futures.” The video of Zuckerberg purportedly saying
Organizations continuously search for innovative ways to optimize their operations and elevate efficiency. One promising frontier is the integration of digital twins for predictive maintenance. However, the true potential of this technology often remains untapped, with many organizations settling for what can be described as “digital shadows.” In this exploration,
Whether for better or for worse, many people agree that generative AI is a game changer that will revolutionize the way we live and work. Optimists believe that generative AI is an opportunity to improve and expand our technological knowledge. At the same time, catastrophists fear that AI in general
See why we think the use of AI assistants will take off in 2024.
