Remember the first time you held a smartphone in your hand? It wasn’t just a fancy phone – it was the beginning of a revolution. Before smartphones, we used separate calls, cameras, emails, and navigation devices. But almost overnight, this gadget transformed everything: how we communicate, consume media, work and
Everyone has heard it: Your organization needs to be more productive. But how? Businesses are constantly challenged with adopting AI technology, managing rising costs and closing talent gaps. While AI can boost performance, the need for faster, more performant models is often stymied by inefficient handoffs between key roles within
AI is no longer a futuristic concept – it’s a mainstay in our daily lives, both personally and professionally. In the business world, AI is revolutionizing workflows, driving efficiency and speeding up processes. However, as organizations rush to benefit from this modern technology, they must prioritize the ethical and transparent
Data science continues to be a pivotal force driving innovation across industries. From enhancing customer experiences to optimizing operational efficiencies, the role of data science is expanding, bringing with it new challenges and opportunities. This article explores the emerging trends and technologies that are shaping the future of data science
A woman arrives at the emergency room with chest pain. She immediately receives an x-ray. While the radiologist looks at the image, her AI assistant flags anomalies in the patient’s lungs – invisible without the technology. The chest pain turns out to be benign, but sophisticated imaging reveals early-stage lung
Everyone is talking about artificial intelligence (AI) for a good reason. It’s already revolutionizing the world as we know it. And governments are well-poised to use AI to improve services and operations. But we can expect the AI conversation to shift into 2025. Attend any conference of government leaders, and
We know that building trust in technology is a big deal. It’s no longer enough for AI to just work – we need to understand how it works, what it is doing and whether it’s performing as expected. That’s where model cards come in. If you remember from our previous
Model cards have been around for a few years now and while their purpose is clear – to increase machine learning transparency and to create a way to communicate usage, ethics-informed evaluation, and limitations – they're still evolving. Many companies have tried their hand at creating their own version of
The commercial real estate crisis is looming for insurers and banks. Learn how AI decisioning can help.
Learn how SAS and cloud computing can help insurers realize operational and business benefits.
