Last Tuesday, I had the privilege of leading an insightful conversation at the Gartner IT Symposium with a badass panel of AI technologists – Hillery Hunter, Hilary Mason and Margaret Dawson – about the obstacles that organizations need to overcome if they are to successfully implement AI. The foundation of our panel discussion was based on a study that we recently commissioned to understand how enterprise executives are approaching AI. I will share more of the research in coming weeks, but one big takeaway is that companies that see results from AI are keeping AI capabilities at the core of the business. While most organizations are still experimental, we are seeing a strong correlation between organizations with highly dedicated on-premise AI capabilities to high performance, measurable ROI and less failure. Net-net, on-prem AI capabilities and solutions are fulfilling the AI hype.

This week on the podcast we're running a series of shows consisting of conversations with some of the impressive speakers from an event called the AI Summit in New York City. The theme of the conference, and the series, is AI in the Enterprise, and I think you'll find it really interesting in that it includes a mix of both technical and case-study-oriented discussions. My guest for this first show in the series is, Hillery Hunter, IBM Fellow & Director of the Accelerated Cognitive Infrastructure group at IBM's T.J. Watson Research Center. Hillery and I met a few weeks back in New York and I'm really glad that we were able to get her on the show. Hillery joins us to discuss her team's research into distributed deep learning, which was recently released as the PowerAI Distributed Deep Learning Communication Library or DDL.

The race to make computers smarter and more human-like continued this week with IBM IBM claiming it has developed technology that dramatically cuts the time it takes to crunch massive amounts of data and then come up with useful insights. Deep learning, the technique used by IBM, is a subset of artificial intelligence (AI) that mimics how the human brain works. IBM's stated goal is to reduce the time it takes for deep learning systems to digest data from days to hours. The improvements could help radiologists get faster, more accurate reads of anomalies and masses on medical images, according to Hillery Hunter, an IBM Fellow and director of systems acceleration and memory at IBM Research. Until now, deep learning has largely run on single server because of the complexity of moving huge amounts of data between different computers.

IBM's latest Power servers come with an AI twist. Optimized for deep learning, a new so-called PowerAI toolkit will "help train the systems to think and learn in a more human-like way, at a faster pace," as announced at SC16, the International Conference for High Performance Computing, Networking, Storage and Analysis. I spoke with Hillery Hunter, IBM Research's director of Systems Acceleration and Memory and Memory Strategist, about her team's contribution to the software behind the world's fastest deep learning servers. Hillery Hunter: In this case, AI refers to a collection of deep learning frameworks and algorithms. Today's launch represents our first public offering in hardware-software co-optimization for deep learning.