Agenda

I4.0 has created a plethora of applications and vendors competing for attention in the manufacturing space. Many vendors are pushing platforms and enablement yet what manufacturers really need are hard core scalable use cases that deliver measurable impact and value. This cannot be solved by an individual vendor or platform. I4.0 success is dependent on cooperation and partnership between different players in the I4.0 landscape and a new approach to continuous improvement in order to massively reduce the time it takes to derive action, impact and value from data. 

Scanning Electron Microscope (SEM) images are the primary means used by expert engineers to identify and diagnose defects in semiconductor variable shape beam (VSB) mask writers. Deep learning (DL) offers an attractive alternative to this tedious process. However, extremely robust mask writers preclude collecting a large variety of SEM images to train DL models. Using digital twins that can mimic SEM images provides an exceptional way to synthesize ample DL training data. This talk will take a deep dive into synthesizing SEM images and leveraging them to build DL models for VSB mask writer defects analysis.

Engineering design simulation generates enormous amounts of structure data, the bulk of which is never studied or mined for insights. Several case studies will be presented for advancing the belief that simulation data presents an opportunity to allow innovation, hidden insights and guidance to emerge thru the use of simple ML techniques. 

With the need to cater to a global audience, there is a growing demand for applications to support speech identification/translation/transliteration from one language to another. This talk starts off with a brief introduction to the topic of Machine Translation (MT), the evolution/application of Machine Translation. The focus will be on Neural Machine Translation (NMT). Then it moves on to introduce some of the typical customer cases and finally ends with how to embed such functionality in your application. The talk will showcase how maintenance and company records across various subsidiaries which might be in their local language can be standardized into a common language so that all the global data can be used for NLP models.

VIPR is a predictive reliability framework focusing on a boosted payload application. The project addresses and decreases high costs and flight vehicle maintenance downtimes due to reactive onboard sensing. By developing a proof of concept corresponding virtual sensor that matches an onboard sensor, predictive sensing and modeling can occur to address any number of fleet aging issues. This new capability will use enhanced machine learning and AI algorithmic framework, anchored with aero data, to develop a modular virtual sensing system which can be trained and applied to any vehicle with sufficient heritage onboard sensor data. 

Elder Research, a data science consultancy, and Sira-Kvina kraftselkap, a large Norwegian power producer, have deployed deep learning models as part of a predictive maintenance solution to identify likely component failures within hydroelectric generator and turbine systems. In this session, we discuss the design concept, modeling approach, solution architecture, and how it is implemented as part of the maintenance team workflow. We will also explain what makes machine learning so challenging in the utlities industry where labeled cases are in short supply. 

Unplanned downtime is one of the largest sources of lost production in the manufacturing world, leading directly to a loss of possible revenue. The rise of the Internet of Things has led to the development of advanced predictive maintenance solutions aiming to help manufacturers achieve the ultimate goal: zero unplanned downtime. While advances in technology have led to broader adoption of these IoT solutions, many of them are still not accurate enough to rely on. Markus Larsson from Xerox’s Palo Alto Research Center (PARC) talks about what innovators can do to bring IoT predictive maintenance to the next level. 

Machine teaching is a complementary approach to machine learning. It helps those without AI expertise break a complex problem into simpler tasks and give the AI model important clues about how to find a solution quickly. In this session, we talk about the current limitations with control systems and how AI is bridging that gap by training intelligent controllers .

Seagate is a multinational data storage solution company with over 40,000 employees from around the world. Seagate has embarked on the advanced analytics journey 5 years ago. In the last 5 years, the teams worked on many initiatives to help the company and its staff to transform digitally and get ready for industry 4.0. In this talk, we will share our business and technical journey and talk about some of the challenges we encountered in the last 5 years. We will explain the solutions we came up with and the initiatives we established to help with the digital transformation journey at Seagate. We will also share best business practices and approaches that work well for us and share some technical examples of our data science solutions. We hope our experience will serve as a good industrial reference for companies that are interested in starting their own digital transformation and analytics journey. 

Enhanced accessibility and availability to data has resulted in the increasing demand for data scientists within the logistical and supply end of industry. Yet, with this great reservoir of information, data scientists are faced with key challenges in the development of their solutions. In this session, we explore both the traditional as well as new challenges of the data scientist in this environment. The alignment of the right data infrastructure in solving the right business problem is just one example. New data sources and new tools exacerbate this issue but the disciplined process approach towards the data science process is the constant. By looking at a variety of industries such as insurance, travel , and health, we examine how to develop the best practices within this process. A number of examples and case studies will be presented to explore the increased shift and emphasis towards data science skills that focus less on the technical and more on identifying the business problem. 

Reducing unplanned downtime requires the proactive maintenance of assets to keep operation running smoothly. Unfortunately, many organizations rely on time-based or usage-based maintenance rather than actual need to determine when to best schedule a maintenance activity. Predictive maintenance instead uses data from operations to determine the health of the asset and can be used to determine when it is best to perform maintenance. We developed intelligent digital twin technology that utilizes sensor data to assess the health and prognostics of a system based on predictive models built with physics-based simulation data. A digital twin is a digital representation of a physical asset that acts as a “single source of truth” for the asset. By incorporating predictive analytics into a digital twin, we can calculate maintenance-related parameters that would allow for appropriate intervention. In this session we will discuss some of the technology available to implement digital twins. We will also discuss how to augment the basic capabilities of digital twins with predictive algorithms for condition-based maintenance. In particular we will discuss developing a digital twin of a naval vessel that assesses the efficiency of various propulsion system components to determine the state of decay of the components.

Webex Contact Center is a multi-tenanted Cloud Contact Center solution from Cisco, that scales to thousands of concurrent interactions. Improving Agent Performance directly impacts the efficiency and effectiveness of Contact Centers. We are building the next generation AI enabled interaction experiences to create super agents. With the help of AI, agents can now focus on the customer, understand their sentiment and answer questions quickly. AI capabilities that were built include Speech-To-Text transcriptions, sentiment analysis, Question-Answering and Topic Modeling. AI Models by themselves don't become a scalable solution. Serving these models at scale, specifically for a multi-tenanted cloud service, takes additional planning, architectures, capabilities and operations. This session discusses the unique challenges, solutions and takeaways in building and deploying these models and solutions. Key topics addressed are- Multi-tenanted Cloud Services: unique considerations- Model Scaling Challenges for real time- Architecture options for scaling Models- Adapting Models to serve at scale- Tuning Models for Resource & Cost scaling

This session will outline how Google’s Corporate Engineering team is using AI and machine learning to spur innovation within Google. Additionally, Rich will identify the work that his team does (the structure, example use cases etc.), and the research that’s driving the work his team does and the democratization of AI (work in ML Fairness, Privacy, Interpretability and AutoML technologies).