Late last month, innovators from across the telecommunications spectrum — and all the industries that rely on connectivity to succeed — gathered at Mobile World Congress Barcelona (MWC), the biggest telecom conference of the year.

In an age of rapid technological transformation, governments are playing regulatory catch-up as they try to keep pace with technological developments and the increasing amount of personal identifiable information (“PII”) generated by our every-day lives. Privacy laws regulating the use of PII continue to strengthen (Gartner estimates that while 10% of the world’s population was covered by comprehensive privacy laws in 2020, by year-end 2024 it will be 75%).

Leveraging Codezero can significantly benefit AI developers in building better, larger, and more sophisticated models through its suite of features designed to simplify and secure microservices development. By addressing common challenges in AI model development, such as environment consistency, collaboration, scalability, and security, Codezero provides a conducive environment for innovation and efficiency. Here’s how AI developers can harness Codezero for these advantages.

In today's hyperconnected world, where negative reviews on social media can wreak havoc on a company’s reputation, delivering an exceptional customer experience isn't just a luxury—it's a business imperative. Companies are locked in a fierce battle for customers that is primarily based on their ability to deliver outstanding customer experiences (CX). According to research by The Conference Board, 65% of CEOs globally prioritize investing in strategies to improve CX.

Snowflake is committed to helping our customers unlock the power of artificial intelligence (AI) to drive better decisions, improve productivity and reach more customers using all types of data. Large Language Models (LLMs) are a critical component of generative AI applications, and multimodal models are an exciting category that allows users to go beyond text and incorporate images and video into their prompts to get a better understanding of the context and meaning of the data.

Having frequently worked with governments around the world over the course of my career, I’ve had all kinds of discussions about the global impact of generative AI. Today, I’m publicly wading into those waters to deliver my perspective, and my opinion is that … it’s incredibly hard to predict the future. Done. Wrapped up this entire post in a single sentence.

Data engineers and architects are being asked to do more with their enterprise data than ever before. Yet, the knowledge gap between what businesses want to do with data and how they can accomplish it is growing daily—especially considering today's AI hype cycle. With all that noise in the market, it's easy to see how organizations struggle to keep pace with innovation.

Underperforming AI programs/models built using low-quality or inaccurate data cost companies up to 6% of annual revenue on average.