2023 was a breakout year for artificial intelligence. It dominated news headlines as well as LinkedIn feeds. But its impact goes beyond the professional—I often overhear conversations at coffee shops about AI from people who aren’t knee-deep in the field. Whoever you are, AI is likely having a transformative impact on your life.
Traditional AI models typically specialize in processing a specific type of user prompt, whether image- or text-based. However, a paradigm shift is underway with the emergence of a new generation of AI models known as "multimodal" systems. Unlike their predecessors, these advanced models can process a diverse range of inputs seamlessly. They can adeptly handle various media types, such as text, images, audio, video, and even code.
How generative AI brings radical new capabilities to the world of AI and machine learning.
The foundation of any successful AI initiative is a well-integrated and meticulously managed data platform.
The business landscape is undergoing radical change across industries, driven by artificial intelligence (AI) and automation. This article will differentiate AI and automation, debunk misconceptions, and highlight what business leaders need to know to navigate the challenges of integrating AI and automation across the value chain. So what’s the difference between AI and automation? AI and automation have distinct purposes.
AI is a groundbreaking technology that is ready to modernize the way federal government agencies operate. By automating tasks and optimizing workflows, artificial intelligence (AI) promises to enhance efficiency, minimize errors, and boost productivity without adding resources.
Ready to implement generative AI in your business processes? Starting with the right generative AI use cases is key to your success. You’ll want to find areas where you can achieve quick wins as you grow toward your larger AI vision. In this article, we’ll highlight five use cases where you can incorporate generative AI for increased process efficiency.
Imagine you’ve just started a new job working as a business analyst. You’ve been given a new burning business question that needs an immediate answer. How long would it take you to find the data you need to even begin to come up with a data-driven response? Imagine how many iterations of query writing you’d have to go through. In this scenario, you also have reports that need updating as well. Those contain some of the biggest hair-ball queries you’ve ever seen.
Customer service is an art—and a science. It isn’t just a transactional function. It's also a relationship building activity that’s deeply tied to physiological responses in our brains. And the stakes are high: these interactions shape the neural architecture of customer loyalty. So can you use AI for customer service? Let’s explore that question.
The rise of generative AI (gen AI) is inspiring organizations to envision a future in which AI is integrated into all aspects of their operations for a more human, personalized and efficient customer experience. However, getting the required compute infrastructure into place, particularly GPUs for large language models (LLMs), is a real challenge. Accessing the necessary resources from cloud providers demands careful planning and up to month-long wait times due to the high demand for GPUs.
