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
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
