Data Science

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Opening Keynote Address: Monitoring ML at the Edge

Today, vision models run everywhere. For ML Engineers who operate ML models in production this poses an interesting challenge - how do we monitor model, sensor, and data health on the edge? This talk will present an open source library that can be used to instrument ML inference at the edge and discuss how monitoring can be implemented for ML models deployed to a fleet of devices.

On Device ML

Edge Trade Offs

Software Engineering

Data Science

Strategy

Author:

Alessya Visnjic

CEO and Co-Founder

WhyLabs.ai

Alessya Visnjic is the CEO of WhyLabs, the AI Observability company building the interface between AI & human operators. Prior to WhyLabs, Alessya was a CTO-in-residence at the Allen Institute for AI, where she evaluated commercial potential for the latest AI research. Earlier, Alessya spent 9 years at Amazon leading ML adoption & tooling efforts. Alessya is also the founder of Rsqrd AI, a global community of 1,000+ AI practitioners who are making AI technology Robust & Responsible.

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Healthcare

New for 2022, these discussion groups will allow attendees to delve into industry focused debate exploring the challenges and innovation driving edge AI across four specific industries.

On Device ML

Edge Trade Offs

Vision

NLP and Speech

Connectivity and 5G

Chip and Systems Design

Innovation at the Edge

Data Science

Hardware and Systems Engineering

Software Engineering

Strategy

Industry & Investment

Author:

Hooman Sedghamiz

Senior Director of AI & ML

Bayer

Hooman Sedghamiz is Director of AI & ML at Bayer. He has lead algorithm development and generated valuable insights to improve medical products ranging from implantable, wearable medical and imaging devices to bioinformatics and pharmaceutical products for a variety of multinational medical companies.

He has lead projects, data science teams and developed algorithms for closed loop active medical implants (e.g. Pacemakers, cochlear and retinal implants) as well as advanced computational biology to study the time evolution of cellular networks associated with cancer , depression and other illnesses.

His experience in healthcare also extends to image processing for Computer Tomography (CT), iX-Ray (Interventional X-Ray) as well as signal processing of physiological signals such as ECG, EMG, EEG and ACC.

Recently, his team has been working on cutting edge natural language processing and developed cutting edge models to address the healthcare challenges dealing with textual data.

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Industrial & Manufacturing

New for 2022, these discussion groups will allow attendees to delve into industry focused debate exploring the challenges and innovation driving edge AI across four specific industries.

On Device ML

Edge Trade Offs

Vision

NLP and Speech

Connectivity and 5G

Chip and Systems Design

Innovation at the Edge

Data Science

Hardware and Systems Engineering

Software Engineering

Strategy

Industry & Investment

Author:

Christopher Nichols

Director, IT/OT Resiliency & Support

Stanley Black & Decker

Christopher Nichols is Director of IT/OT Resiliency & Support at Stanley Black & Decker, Inc. He has been employed by the Fortune 500 manufacturer, Stanley Black & Decker for over seven years, and is currently Director of IT/OT Resiliency & Support. In this role, he is responsible for deploying Level 2/3 system architectures and connecting all other level systems to level2/3, while supporting them. Additionally, he manages remediation with Edge components, and deploys servers and connectivity for all OT-Related systems, plus Level 1 support of all OT-Related SW applications.

Author:

Dominic Pajak

VP Developers

Ready Robotics

Dominic Pajak started as an engineer in Arm's CPU group in Cambridge, UK. He went on to launch energy-efficient processors that now ship in billions of electronic devices every year. Dominic has consulted with major OEMs in the automotive, consumer and industrial segments but is a big believer in making embedded technology accessible to everyone. Later at Arduino he led a collaboration with Google to launch the first developer-friendly Tiny ML library. Today as VP Developers at Ready Robotics he is working on connecting software innovators with industry-proven robotics hardware. Dominic holds a PhD in Computer Science from University of Leeds, UK.

Author:

Jake Hillard

CEO and Co-Founder

Red Leader Tech

Jake Hillard is an expert in Signal Processing and has launched multiple laser satellite missions to space. He is a Peter Theil Fellow and currently the CEO and Co-Founder of Red Leader Technologies

Author:

Sven Brunner

CEO and Founder

holo|one

Sven is founder and CEO of holo|one, a leading Mixed Reality software company. holo|one offers a standardized Mixed Reality collaboration platform targeting the high-value enterprise use cases – remote assistance, workflow guidance and live collaboration. Their solution has been adopted across the globe by Fortune 500 companies like Micron, Lenovo and the Renault Group, along with garnering several awards and accolades.

Before founding holo|one, Sven worked as a software engineer and product manager at Disney Research, ETH Zurich, and his own consulting firm. Sven holds a master’s degree in informatics from the University of Zurich and is a recipient of the Swiss National Bank’s Economics Award.

Author:

Anthony Valle

NALA Presales Senior Engineer

ATOS

Anthony Valle is a Senior Pre-Sales Engineer for North America and Latin America at Ipsotek, an Atos company. Anthony has over 20 years of experience in IT and security technology solutions for the rapidly growing tech-based world. He works closely with clients in developing solutions for AI at the Edge, utilizing a patented Scenario-Based Rule Engine (SBRE), a powerful tool to precisely define behaviors of interest as they would unfold in the real-world dynamic and complex environment.

Prior to joining Atos, he performed first Sales Engineering and later Application engineering roles for Avigilon, one of the world's largest security manufacturers. Throughout his career, he has held key management positions within the industry and sought many certifications to further his career in security technology.

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Automotive

New for 2022, these discussion groups will allow attendees to delve into industry focused debate exploring the challenges and innovation driving edge AI across four specific industries.

On Device ML

Edge Trade Offs

Vision

NLP and Speech

Connectivity and 5G

Chip and Systems Design

Innovation at the Edge

Data Science

Hardware and Systems Engineering

Software Engineering

Strategy

Industry & Investment

Author:

Roger Berg

Vice President, North American Research and Development

DENSO International America, Inc.

Roger Berg is Vice President of DENSO’s North American Research and Development group. His latest research interests and responsibilities include next generation connectivity, mobile edge computing, connected automated vehicles and decentralized ledger technologies.

Author:

Gaurav Singh

Product, AI

Ridecell

Gaurav is part of the Nemo product team at Ridecell, and does advanced AI-based data analytics for ADAS & AD data. He brings a strong AI and machine learning background to his role having previously worked on developing software for autonomous driving and computer vision applications. Gaurav is a Masters in Robotics from Carnegie Mellon University.

Author:

Prashant Tiwari

Former Executive Director, Software Platforms at Volkswagen

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PANEL DISCUSSION: Choosing accuracy and robustness over performance: where are the limits?

Vision

NLP and Speech

Edge Trade Offs

On Device ML

Software Engineering

Data Science

Hardware and Systems Engineering

Strategy

Speakers

Author:

Denis Gudovskiy

Senior Deep Learning Researcher

Panasonic AI Lab

Author:

Chandra Khatri

Co-Founder

Got It AI

Chandra Khatri is the Co-Founder at Got It AI, wherein, his team is building the world's first fully autonomous Conversational AI technology. Under his leadership, Got It AI is pushing the boundaries of the Conversational AI ecosystem and delivering the next generation of automation products. Prior to Got-It, Chandra was leading various kinds of applied research groups at Uber AI such as Conversational AI, Multi-modal AI, and Recommendation Systems.

Prior to Uber AI, he was leading R&D for the Alexa Prize Competition (Alexa AI) at Amazon, wherein he got the opportunity to significantly advance the field of Conversational AI, particularly Open-domain Dialog Systems, which is considered as the holy-grail of Conversational AI and is one of the open-ended problems in AI. Prior to Alexa AI, he was driving NLP, Deep Learning, and Recommendation Systems related Applied Research at eBay. He graduated from Georgia Tech with a specialization in Deep Learning in 2015 and holds an undergraduate degree from BITS Pilani, India.

His current areas of research include Artificial and General Intelligence, Reinforcement Learning, Language Understanding, Conversational AI, Multi-modal and Human-agent Interactions, and Introducing Common Sense within Artificial Agents.

Author:

David Martin

CTO and Founder

Age@Home

Author:

Mark Kurtz

Director of Machine Learning

Neural Magic

Leader of ML and engineering teams focused on the design, development, and implementation of cutting-edge technologies and products. With over 12 years of experience in software engineering and machine learning, well-practiced in building and managing teams for both closed source and open source software solutions. Currently the Director of Machine Learning at Neural Magic, Mark is focused on lowering the cost, improving the performance, and increasing the adoption of deep learning technologies through SOTA research and engineering. An active GitHub contributor, blogger, and researcher with published papers in top ML conferences.

Neural Magic

Author:

Bernease Herman

Senior Data Scientist

WhyLabs.ai

Bernease Herman is a Sr. Data Scientist at WhyLabs, the AI Observability company, and a research scientist at the University of Washington eScience Institute. At WhyLabs, she is building model and data monitoring solutions using approximate statistics techniques. Earlier in her career, Bernease built ML-driven solutions for inventory planning at Amazon and conducted quantitative research at Morgan Stanley. Her academic research focuses on machine learning evaluation and interpretability with specialty on synthetic data and societal implications. Bernease serves as faculty for the University of Washington Master’s Program in Data Science program and as chair of the Rigorous Evaluation for AI Systems (REAIS) workshop series. She has published work in top machine learning conferences and workshops such as NeurIPS, ICLR, and FAccT. She is a PhD student at the University of Washington and holds a Bachelor’s degree in mathematics and statistics from the University of Michigan.

Moderator

Author:

Jack Kang

Senior Vice President, Business Development, Customer Experience, Corporate Marketing

SiFive

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NLP AND SPEECH TRACK: Efficiently using large scale models on the edge

The size of the Deep Learning and AI models has increased substantially within the past couple of years. With recent advancements specifically around NLP/Conversational AI and Computer Vision applications powered by large scale models such as BERT, GPT-3 and Vision Transformers (ViT) having hundreds of millions to billions of parameters, deployment and management of such models is becoming challenging. In this talk, I will go over the state-of-the-art models, their Edge AI applications, deployment concerns and approaches on how to leverage them on Edge computing. I will share my experience of deploying such large models from Amazon AI, Uber AI, and Got It AI.

NLP and Speech

Edge Trade Offs

On Device ML

Software Engineering

Data Science

Strategy

Author:

Chandra Khatri

Co-Founder

Got It AI

Chandra Khatri is the Co-Founder at Got It AI, wherein, his team is building the world's first fully autonomous Conversational AI technology. Under his leadership, Got It AI is pushing the boundaries of the Conversational AI ecosystem and delivering the next generation of automation products. Prior to Got-It, Chandra was leading various kinds of applied research groups at Uber AI such as Conversational AI, Multi-modal AI, and Recommendation Systems.

Prior to Uber AI, he was leading R&D for the Alexa Prize Competition (Alexa AI) at Amazon, wherein he got the opportunity to significantly advance the field of Conversational AI, particularly Open-domain Dialog Systems, which is considered as the holy-grail of Conversational AI and is one of the open-ended problems in AI. Prior to Alexa AI, he was driving NLP, Deep Learning, and Recommendation Systems related Applied Research at eBay. He graduated from Georgia Tech with a specialization in Deep Learning in 2015 and holds an undergraduate degree from BITS Pilani, India.

His current areas of research include Artificial and General Intelligence, Reinforcement Learning, Language Understanding, Conversational AI, Multi-modal and Human-agent Interactions, and Introducing Common Sense within Artificial Agents.

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VISION TRACK: Partitioning Machine Vision AI Workloads Across the Cloud – Edge – Intelligent Device Hierarchy

Machine vision workloads are complex, and their performance requirements often present challenges in areas like latency, security, energy use and reliability. Hierarchal partitioning of those workloads often makes sense, where the machine vision software is split into multiple stages (for example, contrast enhancement, feature extraction, object recognition, threat detection), which are run at different layers of the [intelligent camera -> edge node -> MEC -> cloud] hierarchy.

This talk will introduce the hierarchal cloud - edge architecture, and discuss the properties and capabilities of its many layers. It will propose an example segmentation of machine vision algorithms, and investigate the tradeoffs of how we can map them onto the various layers of processing available in the hierarchy. Finally, it will look at the dual flows of model training and inference for AI applications, and discuss which portions of those flows make sense in different edge layers, and how they can be secured, orchestrated and managed.

Vision

Edge Trade Offs

Software Engineering

Hardware and Systems Engineering

Data Science

Strategy

Author:

Charles Byers

Chief Technology Officer

Industry IoT Consortium and Valqari

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