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September 10, 2021, from 1:00 – 6:30 PM (PDT)

Schedule and Speakers:

1:10 PM: AI Marketplaces – Kaladhar Voruganti, Senior Fellow, Technology and Architecture, in the Office of the CTO, Equinix Abstract: 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. 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.	2:30 PM: Wafer Scale Machine Learning – David Greenhill, Senior Director, Cerebras Systems Inc Abstract: Cerebras Systems has developed and delivered to customers worldwide the fastest AI compute system. The Cerebras CS-1 contains the Wafer Scale Engine (WSE), the largest chip ever made — 56 times larger than the competition, with 1.2T transistors, 400,000 AI-optimized cores and 18 Gigabytes of high-speed on-chip memory. WSE-2 more than doubles this performance with 2.6T transistors and 850,000 AI optimized cores. In this talk, we’ll give an overview of the design of the WSE. Bio: David Greenhill has 30 years of experience designing high-performance computing systems. He has worked on the Inmos Transputer, Sun Microsystems workstations and server CPUs, Texas Instruments applications processors and Altera/Intel FPGAs. Currently he leads the silicon team at Cerebras systems, developing wafer-scale engines for accelerating machine learning applications.
3:20 PM: AIOps: Automating and Optimizing IT Operations Management with AI – Rama Akkiraju, IBM Fellow and CTO, IBM Abstract: The vision of self-aware, self-healing, and self-managing Information Technology (IT) systems has remained elusive until recently. Recent advancements in Cloud computing, Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI) in general, are all making it possible to realize this vision now. AI can optimize IT operations management processes by increasing application availability, predicting and detecting problems early, reducing the time it takes to resolve problems, proactively avoiding problems, and optimizing the resources and cost of running business applications on hybrid Clouds. In this talk, I will discuss the opportunity for AI to optimize IT Operations Management. I will describe how semi-structured application and infrastructure logs can be analyzed to predict anomalies early, how entities can be extracted and linked from logs, alerts and events to reduce alert noise for IT operations admins, how NLP can be put to use on unstructured content in prior incident tickets to extract next-best-action recommendations to resolve problems, and how deployment change request descriptions can be analyzed in combination with past incident root cause information to predict risks of deployment changes to prevent issues from happening in the first place. Bio: Rama Akkiraju is an IBM Fellow, Master Inventor, and IBM Academy Member at IBM’s Automation Division where she is the CTO of AI Operations. AI Operations is about optimizing information technology (IT) operations management using Artificial Intelligence (AI). Prior to this role, Rama led the AI mission of enabling natural, personalized, and compassionate conversations between computers and humans where she and her team developed and delivered several differentiating AI Services such as Personality Insights, Tone Analyzer, Emotion Analysis, and Sentiment Analysis services to the IBM Watson platform. Before this, Rama led various projects and Research teams at IBM Watson Research Center and IBM Almaden Research Center in the areas of AI, analytics, business process optimization and delivered innovative analytical assets to IBM’s Global Business Services (GBS), and Global Technology Services (GTS) organizations. Rama has been named by Forbes as one of the ‘Top 20 Women in AI Research’ in May 2017, has been featured in ‘A-Team in AI’ by Fortune magazine in July 2018, and named ‘Top 10 pioneering women in AI and Machine Learning’ by Enterprise Management 360 in April 2019. Rama is also the recipient of the University of California, Berkeley’s Athena award for Technical and Executive Leadership for 2020. Rama is also the co-recipient of ISSIP Excellence in Service Innovation Award, 2019 and 2022, respectively for her work on building foundational AI Services. http://issip.org/issp-excellence-in-service-innovation-award-recipients/. Rama is also the recipient of ‘CompTIA’s Industry Advisory Council Leadership award’ in 2021. In her career, Rama has worked on agent-based decision support systems, business process management, electronic market places, and semantic Web services, for which she led a World-Wide-Web (W3C) standard. Rama has co-authored over 100 technical papers. Rama has 45+ issued patents and 25+ pending. She is the recipient of 4 best paper awards in AI and Operations Research. Rama served as the President for ISSIP, a Service Science professional society for 2018, and continues to actively drive AI projects through this professional society. Rama is presently the co-chair of CompTIA industry association’s AI Council. Rama holds a Master’s degree in Computer Science and has received a gold medal from New York University for her MBA for the highest academic excellence.	4:30 PM: A Layered Approach for Bi-Inspired Distributed Active Perception – Prof. Fumin Zhang, Georgia Institute of Technology Abstract: There is a perceivable trend for robots to serve as networked mobile sensing platforms that are able to collect data in challenging environments with difficulty for localization and communication. The need for undisturbed operation of search and monitoring creates higher goals for sustainable autonomy. We propose a layered approach to achieve signal propagation over large swarms through active perception. Biological evidence from fish swarms has shown that active perception is used by animals to allow fast response to stimulations when only a few members are stimulated. Active perception-based consensus has advantage over averaging consensus, such as reduced communication and faster signal propagation. After transferring this knowledge to the design of robotic swarms, we found that multiple perception layers can be overlaid on top of the feedback control layer to achieve complex swarm behaviors. The findings also lead to effective distributed optimization algorithms that are quite different from the known consensus-based algorithms. One key feature is the capability to handle vanishing and exploding gradients that often arise in machine learning. Our algorithms are rigorously analyzed and verified by experimental effort on mobile and flying robot platforms. The promising results demonstrate that bio-inspired autonomy might be preferred in an aquatic environment that features severe limitation in localization and communication. Bio: Dr.‬ ‪Fumin Zhang ‬ ‪is Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. He received a PhD degree in 2004 from the University of Maryland (College Park) in Electrical Engineering, and held a postdoctoral position in Princeton University from 2004 to 2007. His research interests include mobile sensor networks, maritime robotics, control systems, and theoretical foundations for cyber-physical systems. He received the NSF CAREER Award in September 2009 and the ONR Young Investigator Program Award in April 2010. He is currently serving as the co-chair for the IEEE RAS Technical Committee on Marine Robotics, associate editor for IEEE Journal of Oceanic Engineering, Robotics and Automation Letters, IEEE Transactions on Automatic Control, and IEEE Transactions on Control of Networked Systems.‬‬‬‬‬‬‬‬
5:20 PM: AI for Edge Service – Prakash Ramchandran, Co-Founder, Emerging Open Tech Foundation, and Technical Staff, Dell Inc. Abstract: The acceleration of Digital Transformation due to the pandemic has led to rapid standardization of Edge platforms to work in tandem with Cloud and to focus on service they can deliver to consumers and enterprises all working remotely. Edge Clou, which is located anywhere from end user device to core, has embraced the concept of nodes and clusters with micro to nano data center form factors to deliver overlay micro services (SaaS) from the Edge with containers and VM workloads. The built in provisioning and lifecycle management for Infrastructure with Kubernetes has evolved to capture the Management and Control plane for service management. The mix of virtualization and containerization leads to an interesting service delivery mechanism at the edge, both acceleration and offloads. The vision of Edge Service with dynamic AI, the emerging use cases such as contact tracing, innovative business models including standards for serverless platforms, edge native services, latency, throughputs, ID management, security and service mesh are driving the path to edge service as a utility for future networking generation. Bio: Prakash Ramchandran, Co-Founder of the “Emerging Open Tech Foundation,” has been a thought leader in Edge Service in IEEE INGR, open source and commercial organizations. With M.Tech (EE) from I.I.T Bombay & 40+ years of experience in the ICT industry, he brings a passion and steady hand to streamline the challenges faced by Industry to make AI for Edge Service a success. His past experience includes Startup ISPs, ASPs, System Integrators, 3G/4G and Mobile deployments for Public Safety with AT&T, Research & Development for Futurewei, 4G/Telco Cloud, Open Source efforts for Edge with OpenStack and CMU, Chair IEEE NFIC 2018 & Senior member IEEE, Independent Member Board of Directors (2018-2020) and 5G/Kubernetes SME at Dell Technologies. He is a prolific speaker in the Emerging Technologies area for the last 10 years at OpenStack/LF Summits, Open Source Forums in Silicon Valley, India, China and the EU and helps build collaboration globally. He is currently working on establishing collaboration for innovation and startups in global and regional markets to overcome AI based security and contact tracing applications.	6:10 PM: Closing Keynote: The State of Robotics@Googlee – Carolina Parada, Senior Engineering Manager, Google Robotics Abstract: Robotics@Google’s mission is to make robots useful in the real world through machine learning. We are excited about a new model for robotics, designed for generalization across diverse environments and instructions. This model is focused on scalable data-driven learning, which is task-agnostic, leverages simulation, learns from past experience, and can be quickly adapted to work in the real-world through limited interactions. In this talk, we’ll share some of our recent work in this direction in both manipulation and locomotion applications. Bio: Carolina Parada is a Senior Engineering Manager at Google Robotics. She leads the robot-mobility group, which focuses on improving robot motion planning, navigation, and locomotion, using reinforcement learning. Prior to that, she led the camera perception team for self-driving cars at Nvidia for 2 years. She was also a leader with Speech@Google for 7 years, where she drove multiple research and engineering efforts that enabled OK Google, the Google Assistant, and Voice-Search. Carolina grew up in Venezuela and moved to the US to pursue a B.S. and M.S. degree in Electrical Engineering at the University of Washington and her Ph.D. at Johns Hopkins University in Machine Learning.