Research

The AI revolution is here. CISOs must champion the secure and innovative use of AI across the entire organization to unlock its full potential amid the expanding threat landscape.

Healthcare delivery organizations should follow a zero trust approach to cybersecurity to safeguard patient data, intellectual property and more, especially as AI solutions become more integrated into everyday operations.

What does enterprise AI adoption look like in real life? Follow along as AI experts from WWT detail the evolution of two internally developed digital assistants.

CEOs must recognize AI as a critical component for staying ahead. Strategic AI investment, rigorous execution and organizational transformation centered around AI can drive operational efficiencies and enhance customer experiences.

Cyber attackers and defenders are utilizing AI, shaping both the offensive strategies employed by attackers and the defensive measures taken by security teams to combat evolving threats. As IT leaders evolve their security and data protection plans, implementing a cyber recovery plan is more critical than ever.

This report introduces Automated Prompt Tuner (APT), a framework that uses optimization techniques to refine prompts for large language models (LLMs), and demonstrates the application of APT to a request for proposal (RFP) text summarization use case.

A step-by-step guide to achieving automation success

Customer experience — encompassing every touchpoint and interaction within the customer journey — is an essential pillar of success for businesses and organizations of all types.

This report investigates how large language models (LLMs) can perform text classification tasks using parameter-efficient fine-tuning (PEFT) techniques. The report also compares the performance of open-source LLMs with proprietary models from OpenAI and Google.

We develop a generalizable machine learning method for route consolidation. The developed method is compared against a more traditional ad-hoc method. The machine learning method uses a deep autoencoder, K-means clustering and Procrustes distance. The machine learning method is shown to produce similar results to the more traditional method with the advantage of using a more generalizable approach.

Deploying the Kubeflow MLOps platform in AWS to enabled our Data Science team to create end-to-end ML workflows for automated delivery of machine-learning models.

This paper discusses methods to use AI to generate representative data that can be used safely for research and analysis.