Containers: Where Linux host networking meets network infrastructure

whitePaper | June 28, 2022

Artificial intelligence (AI) has been accelerating across sectors. It has great potential to benefit society but the technology’s full potential can only be realized if it includes the diversity of the populations it represents throughout every step of its development. With growing concerns about bias, data privacy and lack of representation, it is critical to re-evaluate the way in which AI is both designed and deployed to ensure that all affected stakeholders and communities reap the benefits of the technology, rather than any harm.

whitePaper | July 28, 2022

Before the pandemic, the path to AI adoption used to be slow and steady. According to the survey of industryleading firms, only 48% of companies deemed AI and Big Data a source of innovation. In 2021, this number has climbed to almost 65 percent.

whitePaper | January 4, 2022

When people hear AI they often think about sentient robots and magic boxes. AI today is much more mundane and simple—but that doesn’t mean it’s not powerful. Another misconception is that high-profile research projects can be applied directly to any business situation. AI done right can create extreme return on investments (ROIs)—for instance through automation or precise prediction. But it does take thought, time and proper implementation. We have seen that success and value generated by AI projects is increased when there is a grounded understanding and expectation of what the technology can deliver from the C-suite down.

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 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.

whitePaper | October 20, 2022

The digital evolution of the financial services (FS) industry is heavily reliant on having quality information and analytics to deliver services to customers and manage operations in an efficient and risk-appropriate manner.