Day 1 - 19 June 2019
Data Analytics for AI and IoT: Chair’s Welcome and Opening Remarks
Divisional CDO, Credit Suisse (Schweiz) AG
10:00AM - Day 1
Bringing Offense and Defense together – Shoppable Data For Data Based Services in Analytics & ML
- Problem: 80% prep time for data analytics
- Root cause: Quality, sourcing, relevance of data
- Path forward: Bring DG to good data definitions, provide info architecture and drive modelling
- Use case: Framework to make data shoppable
Dr. Mohamed Anas
10:30AM - Day 1
Keynote: Analytics and AI Strategies for IoT, Catalyzed by Simulation
The buzz about Internet of Things (IoT), Analytics, and Artificial Intelligence (AI) is deafening. Predictions abound that these will power a massive shift in the roles that computers play in our personal and professional lives: enabling automated driving functionality, predicting maintenance of industrial equipment, delivering intelligent home health care systems and robots, and more. But, to get there, teams must combine specialized knowledge, domain expertise, and business objectives while navigating through numerous choices – security, communication, data acquisition and preparation, algorithms, processors, architectural allocation, and more. Simulation can help them to keep their eye on the application, enable reuse while exploring trade-offs, and create the game-changing value for their organization. In this keynote, Mohamed Anas looks at the exciting opportunities and practical challenges of building AI into our systems and services, from prototyping to production while targeting edge nodes to cloud, using simulation as an enabler.
Consultant Manufacturing Analytics
11:30AM - Day 1
How to start with Big Data Analytics in Manufacturing?
Due to the exponential growth of process data in production facilities, manufacturers are facing data challenges they didn’t face a few years ago. Nowadays a factory (shop) floor produces even more data than it produces products. Batch data, machine data, operational data, energy data and sensor data: all examples of valuable data that is captured in a factory. In this presentation you will learn how industrial data differs from traditional marketing & sales data, what the major challenges are and how Wonderware Benelux have helped several key players in the industry to take the first steps in analysing big data from the factory floor.
Enterprise Solutions Architect
04:40PM - Day 1
12:00PM - Day 1
09:50AM - Day 1
Head of Global business Development
12:00PM - Day 1
12:00PM - Day 1
Dr. Maher Chebbo
Senior Executive SVP, Chief Business Innovation Officer
12:00PM - Day 1
Panel: IoT and AI Data analytics for intelligent decision making
- Identifying target-rich, high-value data that can be used to generate business intelligence
- Using cloud analytics platforms to derive value from IoT data
- Discussing the barriers to widespread IoT/ AI /Big Data value delivery and how these might be overcome.
- Real time data analytics in practice – examples of how IoT / AI data is creating business efficiency and revolutionising working practices
Data Scientist and Blockchain Developer
12:40PM - Day 1
Smart Machine Bidding: An incentive solution for automated data transmission settlement in IoT networks
LPWAN (Low Power Wide Area Network) technology can be used together with blockchains as an infrastructure for IoT. This combination automates machine to machine transactions and provides a device data economy (e.g. in MXProtocol). Smart Machine Bidding (SMB), provides an incentive solution to automate the payments related to the flow of IoT devices data. By SMB, each gateway can offer different bids on its data transmission cost. The LPWAN devices can smartly choose a gateway to transmit their data with. Data driven algorithms are also used in the SMB to automate and optimize the procedure.
In general, the SMB helps to provide a cost effective shared LPWAN which both gateway owners and device owners can profit from. In this talk, SMB procedure, its applications, and corresponding data driven algorithms will be discussed.
02:00PM - Day 1
Event Stream Processing: the dawn of a new era for A.I.
In the data management area, Event Stream Processing based architectures are becoming popular: they are efficient, performant, mature and, most importantly, the natural choice for a growing set of business scenarios (from any IOT based use cases to smart supply chain, from smart meters and energy consumption measuring to manufacturing lines monitoring and maintenance). The analyst community predicts that in the next few years to come, streaming oriented systems will become a standard for any company willing to develop data applications and successfully use A.I.
Technologies like Online Learning and Reinforcement Learning are topics very well connected to continuous computation of data streams. On the operational level though, there’s the need for a tool aimed to serve A.I. models in order to produce predictive and prescriptive analytics within the same workflow that starts with Data Integration. RNA (Radicalbit Natural Analytics) is such a tool, designed to facilitate operations around data application development over streaming oriented architectures.
Why are algorithms that were basically invented in the 1950s through the 1980s only now causing such a transformation of business and society
Many of us know that ML algorithms were invented back in the ’50s to ’80s so we must ask ourselves: “Why are algorithms that were invented in the 1950s through the 1980s only now causing such a transformation of business and society?”
Artificial Intelligence and even more Machine Learning capabilities depend on how much computational power and how much data you can have at your disposition. Higher computational power applied to the availability of data means having the tools to develop and test also a new algorithm. Without the right computational power, there will be no progress in AI algorithms. Who said that the problem with ML and AI is just the needs of new algorithms without taking into consideration the holistic vision he does not know what is talking about.
Dr. Gwyn Evans
02:20PM - Day 1
Steel manufacturing; building a data architecture for AI
The use case of the steel manufacturing plant outlines the data architecture build to oversee the plant’s performance and production. It will zoom in on the overwhelming range of data sources and the struggles around unifying all of them in a timely manner. I’ll talk about the plants aspirations on data science / AI and frustrations with the old landscape, concluding with all the possibilities we opened up for the plant in a challenging timeframe.
02:50PM - Day 1
02:50PM - Day 1
Business developer Advanced Analytics
Nederlandse Spoorwegen (Dutch Railways)
02:50PM - Day 1
Panel: Big Data – Creating Intelligent Data Models
The increased need for big data analytics to drive AI & Machine learning
How to successfully unlock unstructured data & transform into learnable features
The advancement of self-service big data tools & its benefit for your organisation
Senior Data Scientist
03:30PM - Day 1
Data & AI at TomTom. Accelerating the Future of Mobility
Mobility is at the heart of our cities and their infrastructures, and as the location technology specialist, TomTom thrives for a safe, connected, autonomous world free of congestion and emissions. How have AI, machine learning and deep learning dramatically changed our way of working at TomTom? What data do we collect, process and analyse? This talk will be oriented towards my personal experiences as a TomTom data scientist.