As artificial intelligence continues to gain traction as a transformational set of technologies, IT teams and data science teams are tasked with implementing AI projects as soon as possible. At the same time, there are a plethora of reasons why AI projects can go awry (here, here, here, here, here, here and here). Most involve choices: the right use case; the right data; the right infrastructure, frameworks and tools.
Join us Tuesday, August 21, for a live online chat, “AI: Make the Right Choices,” to learn about and discuss some of the right choices to make when planning your AI application project. You can easily participate using a Twitter, LinkedIn or Facebook account. Hear from industry experts at Cray who are working with users implementing machine learning and deep learning in fields as far-ranging as insurance, autonomous vehicles, drug discovery, energy exploration and scientific discovery.
How to participate:
The chat will be hosted on CrowdChat, which organizes tweets into streams of conversations. We’ll start the conversation thread from the Cray Twitter handle (@cray_inc), and everyone who wants to participate can comment, ask questions, or just watch the discussion from the sidelines. You can participate by logging in with Twitter, LinkedIn or Facebook.
Bring your questions or offer your expertise alongside our panel of experts and chat hosts:
- Dr. Rangan Sukumar is a senior analytics architect in the CTO office at Cray. His role is three-fold: (i) solutions architect – creating bleeding-edge solutions for scientific and enterprise problems in the long tail of the big data market that requires scale and performance beyond what cloud computing offers; (ii) technology visionary – designing the roadmap for analytic products through evaluation of customer requirements and aligning them with emerging hardware and software technologies; and (iii) analytics evangelist – demonstrating what big data and HPC can do for data-centric organizations. Before his role at Cray, he served as a group leader, data scientist and artificial intelligence/machine learning researcher scaling algorithms on unique supercomputing infrastructures at Oak Ridge National Laboratory. He has over 70 publications in the areas of disparate data collection, organization, processing, integration, fusion, analysis and inference — applied to a wide variety of domains such as healthcare, social network analysis, electric grid modernization and public policy informatics.
- Ted Slater is the global head of scientific AI & analytics at Cray. He has held senior roles in large pharmaceutical companies, content providers, and biotechs including senior director of data sciences at Thomson Reuters IP&S; senior director and head of knowledge management aervices at Merck Research Laboratories; and vice president for knowledge engineering at Genstruct (now Selventa). He holds master’s degrees in molecular biology from the University of California at Riverside and in computer science from New Mexico State University. Over his career, he has held several positions in the field.
- Rajesh Anantharam heads AI product strategy at Cray and is responsible for the AI product roadmap, customer engagement and partner strategy. Previously, Rajesh led AI strategy, product and data science at Samsung for an AI lab focused on deep reinforcement learning and applications to multiple verticals including manufacturing and marketing. Rajesh also held multiple positions at NVIDIA, including product marketing for the automotive business and engineering for high-speed, mixed-signal IP. He has an MBA in technology innovation & marketing from the UC Berkeley Haas School of Business and a master’s in electrical engineering from Stanford.
During this chat, we’ll discuss:
- What issues IT staff should consider when selecting an infrastructure approach for AI projects
- Whether an AI workflow should frame your decisions about infrastructure
- Whether AI problems are high-performance computing problems
- Why a single system may not be sufficient for a successful AI project
- A few examples of “converged” AI applications
More on AI
Want to learn more about getting started with AI before the CrowdChat? Download this paper, “Considerations for Getting Started with AI,” from Tractica.