AI-driven Personalizationin Digital MediaPolitical and Societal Implications

whitePaper | September 15, 2022

No broad industry trend, not client/server computing, not affordable hardware, not even the cloud itself, promises to so completely reshape the enterprise than artificial intelligence (AI). Melding decades-old mathematical principles with cutting edge algorithms and readily available, high performance hardware, AI is creating a seismic shift in the way companies across all industries build, maintain, and understand their core and departmental business operations.

whitePaper | December 29, 2022

In our modern world, taking advantage of Artificial Intelligence (AI) to gain insights from data is becoming more prevalent day by day. Graphical Processing Unit (GPU) systems use multiple cores to perform parallel processing, running select workloads to decrease processing times. Compared to GPUs, Central Processing Units (CPUs) have fewer cores; previously, this resulted in less capacity for parallelized processing. To move beyond this limitation, Intel has released new hardware that runs typical AI mathematical computations more efficiently on the CPU, and has also released libraries with hardware optimizations that enable an additional increase in performance.

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 | June 23, 2020

Modern organizations face unprecedented threats to their critical information assets and data. In previous years, the mindset of many cybersecurity professionals and business leaders was focused on avoiding attacks and building a strong perimeter to deflect them. However, more recently this approach has shifted, and these leaders now understand that attacks are inevitable. With this fundamental shift in thinking, cybersecurity professionals must build strong incident response programs that are capable of detecting threats in a timely manner and responding effectively when they occur.

whitePaper | September 19, 2022

The field of artificial intelligence (AI) traces back to the 1950s. More recently, the increased availability of both data and computing power has bolstered rapid adoption of AI and machine learning (ML) solutions (collectively, AI/ML)

whitePaper | July 10, 2020

There’s no arguing that preventing failures and accidents is critical for industry. Unexpected incidents can grind operations to a halt for extended periods of time and necessitate expensive repairs. Just 12 hours of downtime for an oil production platform could cost six to eight million dollars in lost production opportunity alone. A single day of grounding for a plane costs roughly four to five million dollars. Because of these disruptions, industrial sectors are always on the lookout for newer, better maintenance methods, and the approach on everyone’s lips right now is predictive maintenance. While everyone agrees on the name, there is less consensus on what it means or how to implement it. But to truly unlock the potential of predictive maintenance, it needs to be paired with artificial intelligence (AI) and machine learning (ML).