There's a big connection between my love for water sports and hardware design -- both involve observing waves and planning several moves ahead. Four years ago, when we started sketching the POWER9 chip from scratch, we saw an upsurge of modern workloads driven by artificial intelligence and massive data sets. We are now ready to ride this new tide of computing with POWER9. It is a transformational architecture and an evolutionary shift from the archaic ways of computing promoted by x86. POWER9 is loaded with industry-leading new technologies designed for AI to thrive.

The media coverage of artificial intelligence (AI) would have you believe that most people in the room raised their hands for the first question. But from my (unscientific) count, only 20 people raised their hands for #1, while close to 500 raised their hands for #2. It is obvious that we need to help IT leaders get from here to AI. Which is exactly why we invited thought leaders and technical realists from NVIDIA, Hortonworks, IBM Cognitive Systems and CloudPulse Strategies to cut through the AI hype and give us tips on how to get started. Deep learning could be the easiest place to start.

In a few months, when the HPC community gathers in Denver for SuperComputing 2017, I expect it will become clear that the supercomputing field is poised to take the next giant step in its evolutionary path. For decades, the HPC community has spoken longingly of efficiently steering simulations, improving the interpretation of complex model outputs and building more efficient and representative models of complex phenomena. And now we are beginning to see these desires realized as researchers and commercial enterprises demonstrate the utility of melding AI with HPC in products and approaches across a broad spectrum of problems and industries. IBM has been focused on merging AI and HPC for some time. Our recently-announced PowerAI Vision is a natural adjunct to HPC simulations producing visual outputs. We announced Distributed Deep Learning, which exploits HPC architecture to achieve noteworthy learning performance, thereby speeding "time-to-insight," and of course, in IBM Watson we have the capability to ingest huge amounts of data to help guide model development or solution interpretation.

The seemingly boundless possibilities for artificial intelligence (AI) have arguably made it the most important general-purpose technology of our era. Yet the immediate question in many executives' minds has moved beyond the envisioned potential and towards how businesses are successfully implementing and using AI today. To gain insight into this topic, the IBM Global C-suite Study Program interviewed over 3,000 C-suite executives across 20 industries worldwide about their current and planned use of AI. Eleven percent of these organizations (the Reinventors) are using AI in their business today and plan to continue investing in AI. In this report, we examine the Reinventors' strategic and tactical movements and the driving forces that underpin them.

Hardly an industry today can escape disruption. New technologies and business models compel CTOs and IT leaders to seek innovative ways to stay ahead of the digital transformation curve. But they face challenges understanding the role and impact of cognitive technologies such as artificial intelligence (AI) and machine learning when integrating them into their existing infrastructure. An increasing number of those infrastructures include mainframe systems, and enterprises are looking to leverage the compute power and secure data protection mainframes offer for running cognitive applications. Cognitive technologies applied to transactional data are pivotal in capturing keen insights, building client relevancy, implementing cost-effective business models and more.

This is an era of data-centric computing. For those in hardware engineering who embraced disruption over the past few years – my colleagues at IBM Cognitive Systems and our partner NVIDIA included – this sudden rise of AI-inspired applications is a once or twice-in-a-career thrill. From a practical perspective, we quickly realize that unbounded bandwidth, a sort of information superhighway inside computers with no speed limit, is a condition of unbounded software and algorithm innovation. As my generation learned so painfully, trying to use digital audio and video media in the early days of the internet, the best computer or network is the one that you don't have to think about or wait on endlessly. We're excited about the launch of NVIDIA's Volta GPU accelerators.Together with the NVIDIA NVLink "information superhighway" at the core of our IBM Power Systems, it provides what we believe to be the closest thing to an unbounded platform for those working in machine learning and deep learning and those dealing with very large data sets.

Artificial intelligence (AI), technically known as "machine intelligence," is a buzzword in the tech industry today. Businesses are finding innovative ways of mining data to generate insights that help them understand their customers better. In an era in which marketing rules, business leaders see a strong need to personalize marketing content to make it more effective. Step back 10 years, when AI wasn't a buzzword because there wasn't adequate data available to understand people, products, networks and so on. With the explosion in smartphone usage, the adoption of Internet of Things (IoT) devices and extensive social media usage, that has changed completely.

Deep learning has taken the world by storm in the last four years, powering hundreds of consumer web and mobile applications that we use every day. But the extremely long training times in most frameworks present a hurdle that's curtailing the broader proliferation of deep learning. It currently may take days or even weeks to train large AI models with big data sets to get the right accuracy levels. At the crux of this problem is a technical limitation. The popular open-source deep-learning frameworks do not seem to run as efficiently across multiple servers.

From time to time, we invite industry thought leaders to share their opinions and insights on current technology trends to the In The Making blog. The opinions in these blogs are their own, and do not necessarily reflect the views of IBM. Getting the right tool for the job is essential for anything from home improvement projects to launching satellites. I view the new trend of applying AI, deep learning and cognitive techniques to enterprise IT solutions as following that basic principle. Some tools are more complex and difficult to create than others, but they should all be viewed as a means to an end, not the end in itself.

Hardly an industry today can escape disruption. New technologies and business models compel CTOs and IT leaders to seek innovative ways to stay ahead of the digital transformation curve. But they face challenges understanding the role and impact of cognitive technologies such as artificial intelligence (AI) and machine learning when integrating them into their existing infrastructure. An increasing number of those infrastructures include mainframe systems, and enterprises are looking to leverage the compute power and secure data protection mainframes offer for running cognitive applications. Cognitive technologies applied to transactional data are pivotal in capturing keen insights, building client relevancy, implementing cost-effective business models and more.