SAS CMO Jennifer Chase and a panel of chief marketing officers offer insights on how they’re weaving AI into the business at American Marketing Association event Recent Salesforce research highlights a fact that didn’t seem to surprise the full-house audience at an American Marketing Association Annual CMO panel at SAS
Stop bias in its tracks – learn about the value of synthetic data for insurance.
A recent SAS survey revealed that 80% of business leaders are anxious about GenAI's data privacy and security implications Owing to these and other concerns, Wells Fargo Chief Data Officer Brian Gibbons and an esteemed panel of AI experts, hosted by Wells Fargo Technology Banking, addressed an audience of the
Synthetic data generation has intrigued across industries for its many use cases, including fraud detection, clinical trials, worker safety and law enforcement. One of the main benefits is the low cost of creating synthetic data, which is often cheaper than collecting actual demographic, psychographic or behavior-based information. With such data,
Step into the vibrant, insightful platform that is theCUBE, where industry leaders don’t just talk tech; they unpack AI’s impact on our world. Eight quotes aren’t enough to describe all the knowledge shared by SAS leaders during theCUBE’s session at SAS Innovate 2024. But this blog post would go on
생성형 AI는 우리의 업무 환경과 사회를 변화시키고 있습니다. 사람과 기술이 상호작용할 새로운 방법을 제시하며 상상을 능가하는 속도로 영향을 끼치고 있죠. 최근 실시한 조사 결과는 생성형 AI에 대한 흥미로운 시각을 제시하고 있는데요, 기업 의사결정자들이 체감하는 생성형 AI의 해결 과제와 기회를 동시에 확인하실 수 있습니다. 대다수의 응답자는 GenAI를 통해 직원 만족도가 향상되었고(82%),
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
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
