Artificial Intelligence and Machine Learning in IoT

Artificial Intelligence and Machine Learning in IoT Dr. Christine Preisach Vice President Data Science in IoT PUBLIC

What is IoT: Examples and Facts

Save time and improve quality of life with: Optimized traffic Less pollution Less energy consumption

Wearables are monitoring body functions, motion and environment

Smart homes allow to monitor and control devices for a safe and convenient living

Diabetes patients get alerted in case of upcoming low or high glucose values

Save resources by detecting failures and theft early

Optimize production process and product quality

The Internet of Things A Definition Connectivity Hardware Internet of Things The network of physical objects or "things" embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. Sensors Software Why now: 80% reduction in the price of sensors, microprocessors, and wireless technologies over the past four years 2017 SAP SE or an SAP affiliate company. All rights reserved. Source: Wikipedia 9

The Internet of Things is Growing 6.4 billion 4.1 Consumer devices 2.3 Industrial devices 20.8 billion 13.5 7.3 Consumer devices Industrial devices 2 Zetta Bytes Number of devices estimated by Gartner* 2016 Number of devices estimated by Gartner* 2020 Data collected** 2019 2017 SAP SE or an SAP affiliate company. All rights reserved. *Without mobile phones, tablets, computers **DeAngelis 2017 10

Machine Learning: A solution to the IoT Challenges

Challenges in IoT Volume - Scale Variety type of data Up to 40,000 sensors in the upcoming Airbus A380 Time series data collected from sensors 7 TB per day Images and videos Volume Variety Text data from warranty claims Relational business data Value Velocity analysis of streaming data Sensor data collected in msec High frequency vibration data Velocity Veracity Veracity - uncertainty Poor data quality Missing data Data collected for specific purposes doesn't suit targeted use cases Value 2017 SAP SE or an SAP affiliate company. All rights reserved. Data per se is not valuable How to extract real value from data? 13

Machine Learning is a Key to Unlocking Big Data

Data Science, Artificial Intelligence and Machine Learning interdisciplinary field about scientific methods to extract knowledge or insights from data...uses techniques from many fields mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning. Data Science Is a subfield of AI, it gives computers the ability to learn without being explicitly programmed Machine Learning Study of intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success... Artificial Intelligence 2017 SAP SE or an SAP affiliate company. All rights reserved. Source: Wikipedia 16

Why Deep Learning is currently not Leading to Great Results in IoT Water Travel No person Sea Landscape Source: Google blog Prerequisites for robust and accurate models using deep learning: millions of examples of the same thing 2017 SAP SE or an SAP affiliate company. All rights reserved. 17

Industrial IoT Use Cases that can be solved with Machine Learning Which machines or components show anomalies compared to healthy machines? Anomalies Forecasting What is the expected output of a plant in the next month? What is the expected process time? What is the expected demand? To which category does an asset belong? Search for similar assets. To which category does a trip of a vehicle belong? Classification Extraction of Insights Relationships What are the correlations in the data? Which failure events occur together, which lead to a warranty claim? Support for root cause analysis with automatically extracted rules. Predict the occurrence of failures of machines or components using sensor data. Failure Prediction Prescriptive Analytics & Optimization Recommend actions and optimize processes (e.g. maintenance schedule optimization). 2017 SAP SE or an SAP affiliate company. All rights reserved. 19

Machine Learning Challenges in IoT High dimensional data How to find the relevant features? Needs the involvement of domain experts and/or automatic feature selection techniques Data Quality is poor Data is not collected to be used for Machine Learning. There are no standards with regards to sensor data. Difficult to integrate different data sources. Rare event problem Standard Machine Learning algorithms achieve poor results. Needs special algorithms for unbalanced classes. No labels Use unsupervised learning algorithms (e.g. anomaly detection) Use case specific algorithms and data models Needs flexibility and extensibility The deployment challenge Needs an automatic system for model deployment and management 2017 SAP SE or an SAP affiliate company. All rights reserved. 20

Machine Learning Process in IoT Process & Explore Process & Explore Pre-process and explore data and detect patterns and outliers. 80% of the time of data scientists is spent with preprocessing Learn Learn Act Use domain user annotations as labels and sensor data as well as business data to learn machine learning models Predict Use the learned model and apply to new data Predict Feedback Recommend Feedback Ask domain users to annotate patterns and anomalies Recommend Recommend steps that should be done by the domain user Act Asses recommendations and act accordingly if appropriate 2017 SAP SE or an SAP affiliate company. All rights reserved. 21

Example How to Integrate Machine Learning into IoT Apply Machine Learning Process Output Anomaly Detection Prescriptive Analytics* Data Pre- Processing New Algorithms Extensibility Adaptive* Learning Domain expert feedback Model Management Failure Prediction Recommendation of Actions* Continuous Improvement & Learning 2017 SAP SE or an SAP affiliate company. All rights reserved. *Planned 22

Use Cases in the Chemical Industry Maintenance Product Quality Batches Sensors Assets Process Improvement Maintenance Laboratory Shift Logs Sustainability Goal: Minimize process time and laboratory cost by detecting which batches are abnormal (chemical reaction not complete) and only taking samples of those batches. Result: 25% savings in laboratory costs and increased production throughput.

The Future of IoT and How to Make it Happen

Integrating Machine Learning in More IoT Applications Will Add Value and Help to Personalize IoT Applications Energy-Saving Recommendations Based on your behavior in your smart home, upcoming events (calendar), weather Digital Medical Assistant Based on correlations learned from measurement data from your body, symptom descriptions, medical databases, your medical record Recommend where to park your car Based on historic data, information about traffic, sensors from public parking lots Recommend workout plan Based on your goal, health state, available time 2017 SAP SE or an SAP affiliate company. All rights reserved. 25

What Needs to be Done to Make it Happen? Built up standards and systems for capturing knowledge about IoT devices and processes that ease the use of ML Develop simple to use IoT systems Work on specialized algorithms for IoT scenarios Build up trust in IoT and Machine Learning That allow to build IoT apps To solve challenges like the rare event problem, active learning in the IoT space By working on security, ethical standards and explanation of ML results 2017 SAP SE or an SAP affiliate company. All rights reserved. 26

What Needs to be Done to Make it Happen? Bring Three Worlds Together Semantic Web of Things We need a common description and data representation in IoT area therefore apply information modelling and ontology design. Physical Models Physical models contain engineering knowledge about machines (e.g. mathematical models). Machine Learning Apply Machine Learning on historic sensor and business data. We IoT specific algorithms. 2017 SAP SE or an SAP affiliate company. All rights reserved. 27

Data Science and Machine Learning in the Internet of Things and Predictive Maintenance Thank you Contact information: Dr. Christine Preisach Vice President Data Science in IoT SAP SE Christine.Preisach@sap.com https://www.sap.com/documents/2016/10/8ec7f23 f-917c-0010-82c7-eda71af511fa.html