Explore the essential AI use cases that every security leader should prioritize to safeguard their organization and streamline operations.

A brief history of digital twins and their role in training, simulating, planning, researching and testing complex AI/ML models at massive scale.

This article aims to inform, inspire and guide the MEG industry on how to leverage the latest technologies to create immersive experiences, achieve business outcomes and stay ahead of the competition.

The AI Proving Ground enables organizations to quickly, confidently and safely develop transformational AI solutions that deliver real business results in a fraction of the time.

Artificial intelligence (AI) has become a cornerstone of operations across a myriad of sectors. With this global trend, it's imperative that boards of directors everywhere take a proactive role in overseeing these systems.

AI is revolutionizing the way businesses interact with their customers. Here are the top ways that contact center leaders should leverage AI to yield the biggest ROI.

WWT's approach to full-stack excellence provides CX leaders with a framework for unlocking efficiency, growth and fresh digital experiences for their customers.

Protecting AI models and the development environment is a major challenge given the complexity of underlying big data platforms and the mathematics required to understand attacks against modern AI algorithms.

Holistic automation and AI capabilities are possible when organizations mature their data practices.

Learn how WWT's AI Proving Ground enables organizations to rapidly test, compare and validate the latest deepfake audio detection solutions from a growing roster of AI SaaS vendors.

Widely available GPU-accelerated servers, combined with better hardware and popular programming languages like Python and C/C++, along with frameworks such as PyTorch, TensorFlow and JAX, simplify the development of GPU-accelerated ML applications. These applications serve diverse purposes, from medical research to self-driving vehicles, relying on large datasets and GPU clusters for training deep neural networks. Inference frameworks apply knowledge from trained models to new data with optimized clusters for performance. The learning cycles involved in AI workloads can take days or weeks, and high-latency communication between server clusters can significantly impact completion times or result in failure. AI workloads demand low-latency, lossless networks, requiring appropriate hardware, software features, and configurations. This article will explain advanced queueing solutions used by all the major OEMs in the Network Operating Systems (NOS) that support ECN and PFC.

Looking for a flexible, cloud-based artificial intelligence solution to complement your Contact Center environment? Google CCAI might be for you.