Since the last time you logged in our privacy statement has been updated. We want to ensure that you are kept up to date with any changes and as such would ask that you take a moment to review the changes. You will not continue to receive KPMG subscriptions until you accept the changes.
We want to make sure you're kept up to date. Please take a moment to review these changes. You will not receive KPMG subscription messages until you agree to the new policy.
To make intelligent automation easy, we have compiled a glossary of key terms to help you navigate this new landscape. We’ll be expanding our list over time. If you’d like a key word adding, let us know on email@example.com.
Chatbots: AI software designed to simulate human conversation thanks to natural language processing
Cognitive automation: automation which mimics human activities such as recognizing, guessing, gathering evidence, hypothesizing and reasoning
Algorithm: a set of rules a computer must follow to solve problems
Artificial Intelligence (AI): an umbrella term for technologies that enable a computer to perform tasks that usually require human intelligence
Deep learning: a sub-category of machine learning which enables models to reach new levels of understanding through different layers of neural networks
Machine learning: algorithms created to solve problems, and which also have the ability to build new pathways of understanding and self-develop over time without human intervention
Natural Language Processing (NLP): a field of computer science focusing on interactions between human languages and computers
Neural networks: a circuit of artificial neurons that functions like the human brain and imitates behaviors such as pattern identification
Robotics Process Automation (RPA): tried-and-true technologies that leverage business capabilities including workflow management, rule engines and screen scraping to automate existing manual processes
Structured vs. unstructured data: structured data simply refers to any information that has been organized (for example, spreadsheets, relational databases or anything with rows and columns). Unstructured data is the opposite: it is information that has not been organized (e-mails, social media content and text messages, for instance)