Artificial intelligence Augmenting human intelligence

whitePaper | July 31, 2020

The current COVID pandemic has paralyzed large parts of our industry with significant movement limitations, occupancy restrictions, and in some cases complete shutdowns. Now, as the initial threat seems to be subsiding, governments and businesses must determine what is required to get back to some sense of normalcy. The biggest hurdles will be regulatory and/or safety-driven. Although the timelines and requirements for the easing of restrictions vary greatly, there are two common threads: limiting occupancy levels and contact tracing individuals who have tested positive for the virus.

whitePaper | August 6, 2020

The purpose of any data acquisition system is to gather useful measurement data for characterization, monitoring, or control. The specific parameters of your application will dictate the resolution, accuracy, channel count, and speed requirements of a data acquisition system. A wide assortment of data acquisition components and solutions is available on the market, including low-cost USB modules, benchtop data loggers, and large channel systems. Before you start your search for a data acquisition solution, carefully analyze your application requirements to understand how much capability and performance you need to purchase.

whitePaper | January 8, 2020

AI in manufacturing is a game-changer. It has the potential to transform performance across the breadth and depth of manufacturing operations. However, the massive potential of this new Industrial 4.0 era will only be realized if manufacturers really focus their efforts on where AI can add most value and then drive the solutions to scale.

whitePaper | October 11, 2021

AI forms the bedrock to help organizations unlock business value from the deluge of unstructured information. Improving operational efficiency by extracting structured data from unstructured documents enables organizations with faster and contextrich decision-making to adapt and thrive in the new normal.

whitePaper | July 20, 2022

The growing diversity of actors and circumstances involved in AI/ML deployment makes establishing a set of ethical participatory practices especially difficult for this field. Even among practitioners working at the same organization, there can be substantial differences in both their knowledge about how AI/ML systems were created or will be used and their ability to incorporate inclusive practices. Additionally, the greater availability of AI development platforms, including “no-code” platforms, means that algorithmic models can be deployed without having deep expertise, expanding the number of circumstances automated systems are deployed in. This means many more instances where automated systems are being deployed without consideration of how the algorithms were developed, the provenance of the datasets and nature of the bias on which they were trained and tested, and the ethical implications of their development and deployment.

whitePaper | January 5, 2020

Machine learning (ML) is an application of artificial intelligence (AI) that allows systems to automatically learn and improve from exposure to more data without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn for themselves.1 While AI represents the broader concept of machines being able to carry out tasks in an intelligent way, machine learning is a current application of AI based on the idea that we can give machines access to data, and they can use that data to learn for themselves.