Speaker: Kaladhar Voruganti, Equinix
(Talk virtually over zoom; note: talk will be recorded; please register here)
Throughout history of AI, we have seen major transformational changes that have made AI algorithms more accurate and accessible to the masses. In first-generation AI systems, human experts manually entered rules (e.g. via LISP, Prolog languages) to control systems, but these systems were mostly brittle, and couldn’t solve real-life problems. Then with the advent of big data and big compute (e.g. re-purposing of GPUs for deep learning), we entered the realm of second-generation AI systems, where it became possible to improve the accuracy of AI systems to match human accuracy for many important everyday tasks like vision, speech recognition/translation, anomaly detection and trends prediction. Thus, AI has become mainstream. However, now we are entering the era of third-generation AI systems where in order to take the accuracy of AI models to the next level, it is necessary for multiple organizations to share data and trained AI models with each other. Thus, in addition to algorithm accuracy, data/model governance, provenance, and trust are paramount as organizations start to share data and algorithms with each other. However, many organizations are hesitant to share their data externally due to privacy and control concerns (use of data for unauthorized reasons). Similarly, organizations are hesitant to use external data due to lack of proper provenance information that could lead to biases and potential security vulnerabilities in the imported data. In this talk we present the concept of “AI Marketplaces” and how they help organizations to share both data and algorithms with each other, and thus, help to take AI solutions across organizational boundaries. We will present the fundamentals of AI marketplace architectures, different types of trust/governance models, security approaches, and how federated AI architectures help to move “Compute to Data” instead of as in the traditional “Data to Compute” computer architecture model. We will also share our experiences in how AI marketplaces are being used in various real-life use cases.
Bio: Kaladhar Voruganti is a Senior Fellow, Technology and Architecture, in the Office of the CTO at Equinix. Equinix is the largest retail data center company (like an airport) in the world where the large public clouds, networks, financial companies, media companies and enterprises come to interconnect with each other. He is currently working on Distributed AI and AI Marketplace architectures. He previously worked at IBM Research and NetApp CTO office on large scale autonomous systems. He obtained his BSc in Computer Engineering and PhD in Computing Science from University of Alberta, Canada. He has more than 70 patents filed/issued.