Day 1 - 23 November 2021
08:30AM
(CET)
Registration Opens
09:40AM
(CET)
Joachim de Greeff
Senior Consultant AI
TNO Data Science
09:40AM - Day 1
View AI for Enterprise: Chairperson’s Welcome
AI for Enterprise: Chairperson’s Welcome
09:50AM
(CET)
Georgio Mosis
Principal Scientist & Team lead Next Gen AI & Data
Philips
09:50AM - Day 1
View Responsible use of AI in Healthcare – Hybrid Intelligent systems
Responsible use of AI in Healthcare – Hybrid Intelligent systems
At Philips, we believe the value of AI is only as strong as the human experience it supports. That’s why we combine the power of AI with deep clinical knowledge, gained through decades of experience in healthcare. We envision AI that is fully integrated into the workflows of healthcare professionals and people’s daily health routines – supporting them at every stage of the health continuum. With AI, we can help people take better care of their health and well-being, and enable healthcare professionals to do what they do best: prevent, diagnose, treat, and monitor. In this presentation we will highlight few important Ai principles that governs our AI Vision:
- Well-being – We design our solutions to benefit the health and well-being of individuals and to contribute to the sustainable development of society.
- Fairness – We develop and validate solutions using data that is representative of the target group for the intended use, and we aim to avoid bias or discrimination.
- Oversight- We design AI-enabled solutions to augment and empower people, with appropriate human supervision.
- Transparency- We disclose functions and features of our offerings that are AI-enabled, the validation process, and the responsibility for ultimate decision-taking.
- Robustness – We develop AI-enabled solutions that are intended to do no harm, with appropriate protection against deliberate or inadvertent misuse.
10:10AM
(CET)
Vincent Chio
Data Science Lead
Shopify
10:10AM - Day 1
View Accelerating AI driven product development: A journey of building Shopify Inbox
Accelerating AI driven product development: A journey of building Shopify Inbox
Vincent will walk through actionable takeaways for accelerating product development with AI that can be used at any organization. In his talk, Vincent will cover:
- A practical use case on the Natural Language Processing system that powers Shopify Inbox—a chat tool to manage customer conversations, create automated messages and get insights to focus on chats that convert
- How to build a data foundation to establish trust
- How to identify opportunities where only AI can solve at scale
10:40AM
(CET)
Joel Hodgson
UK MLOps Lead
Seldon
02:00PM - Day 1
View PRERECORDED PANEL: Built to scale – Ramping up AI projects
12:40PM - Day 1
View VIRTUAL: Deploying and Managing ML Models at Scale
10:40AM - Day 1
View Deploying and Managing ML Models at Scale
02:20PM - Day 1
View Panel: Built to scale – Ramping up AI projects
Sean Greaves
Lead Solutions Engineer
Seldon
12:40PM - Day 1
View VIRTUAL: Deploying and Managing ML Models at Scale
10:40AM - Day 1
View Deploying and Managing ML Models at Scale
Deploying and Managing ML Models at Scale
- This presentation will cover what organisations need to understand and have in place in order to deploy ML models at scale while minimising risk.
- Joel Hodgson will discuss the common challenges faced by organisations when scaling ML model deployment and how to overcome them. He will discuss different use cases at different stages of ML maturity, as organisations move into deploying explainers and monitoring alongside their models.
- Sean Greaves will then walk through the creation, deployment, monitoring and management of a ML model using a real life use case and covering a range of practical tasks including training models on pre-processed datasets, deploying model artefacts with Seldon Deploy and using explainers and outlier detection to gain insight into model decisioning.
11:00AM
Networking Break
11:20AM
(CET)
Douwe Mulder
Programme Director
Startupbootcamp Amsterdam
Mark Bakker
Regional AI Lead - Benelux
H20.AI
01:20PM - Day 1
View PRERECORDED PANEL: Responsible delivery – the ethics of AI
11:20AM - Day 1
View Panel: Responsible delivery – the ethics of AI
Frans van Bruggen
Policy Advisor, Artificial Intelligence & Fintech
De Nederlandsche Bank
01:20PM - Day 1
View PRERECORDED PANEL: Responsible delivery – the ethics of AI
11:20AM - Day 1
View Panel: Responsible delivery – the ethics of AI
Panel: Responsible delivery – the ethics of AI
- What is AI bias, and is it fair?
- Discussing liability and security
- Who benefits from AI, and could it result in a decrease in human-human interaction and employment issues?
12:00PM
(CET)
Lena Rampula
Senior Data Scientist
H2O.AI
10:50AM - Day 1
View VIRTUAL: Explainable Automated Machine Learning
12:00PM - Day 1
View Explainable Automated Machine Learning
Explainable Automated Machine Learning
- Developing Machine-Learning models is a very complex process. Regardless of industry and domain, developing a production-ready model that brings business value may take months. At H2O.ai, we have made it our mission to help companies around the world and across industries on their journey with AI adoption. We believe that automating Machine-Learning will let you develop better models faster.
- Speed and accuracy is not everything though. Trusting your model is equally important. With Automated Machine Learning Explainability, you can gain insight into the factors that drive your model, analyse its fairness and robustness, and get explanations for every prediction that you make.
- During this session, we will take a look at how Automated Machine Learning reduces the time to maximise business value. We will also discuss why it is important to have Machine Learning Explainability as an integral part of the overall process.
12:20PM
(CET)
Joris Krijger
AI & Ethics Specialist
de Volksbank
11:40AM - Day 2
View VIRTUAL: The Right Thing to Do – Operationalizing Responsible Data Science
12:20PM - Day 1
View The Right Thing to Do – Operationalizing Responsible Data Science
The Right Thing to Do – Operationalizing Responsible Data Science
- What are the ethical dilemma’s with the development and deployment of AI
- To what extent do frameworks, regulations and tools provide meaningful guidance?
- How can or should organizations deal with these dilemma’s/The organizational dimension of responsible data science
12:40PM
(CET)
Rik Van Bruggen
Regional Vice President
Neo4j
11:10AM - Day 1
View VIRTUAL: How graphs, graph data science and AI enabled the ICIJ’s “Pandora Papers”
12:40PM - Day 1
View How graphs, graph data science and AI enabled the ICIJ’s “Pandora Papers”
How graphs, graph data science and AI enabled the ICIJ’s “Pandora Papers”
Graphs are everywhere – everything is more connected every day and we increasingly struggle to make sense of the indirect connections that surround us, our businesses and our societies. Neo4j has been chipping away at this problem for decades and has created a Graph Platform that allows people to make sense of those connections. In this talk, we will explain
- How that platform works
- Why graph data science matters to reveal unknown insights from the data
- Demonstrate how the ICIJ used it to uncover yet another set of shady business practices, in the Pandora Papers
01:00PM
Networking Break
02:00PM
(CET)
Dr. Dorota Iskra
Senior Director, AI
DataForce by TransPerfect
01:00PM - Day 1
View VIRTUAL: Enough With the Big Data, Let’s Talk Good Data
02:00PM - Day 1
View Enough With the Big Data, Let’s Talk Good Data
Sofia Silva
Account Executive, AI
DataForce by TransPerfect
01:00PM - Day 1
View VIRTUAL: Enough With the Big Data, Let’s Talk Good Data
02:00PM - Day 1
View Enough With the Big Data, Let’s Talk Good Data
Enough With the Big Data, Let’s Talk Good Data
- Develop reliable AI products and processes
- Don’t waste your time, get things right from the start
- How to get good data; use cases from healthcare, automotive and other industries
- Global Scale through consistent data quality
02:20PM
(CET)
Freek Bomhof
Senior business consultant
TNO
Alexandros Poulis
Senior Director, AI
DataForce by TransPerfect
02:00PM - Day 1
View PRERECORDED PANEL: Built to scale – Ramping up AI projects
02:20PM - Day 1
View Panel: Built to scale – Ramping up AI projects
Joel Hodgson
UK MLOps Lead
Seldon
02:00PM - Day 1
View PRERECORDED PANEL: Built to scale – Ramping up AI projects
12:40PM - Day 1
View VIRTUAL: Deploying and Managing ML Models at Scale
10:40AM - Day 1
View Deploying and Managing ML Models at Scale
02:20PM - Day 1
View Panel: Built to scale – Ramping up AI projects
Michael Endres
Sales Director Germany
Bulk Infrastructure Group
02:00PM - Day 1
View PRERECORDED PANEL: Built to scale – Ramping up AI projects
02:20PM - Day 1
View Panel: Built to scale – Ramping up AI projects
Manoj Saxena
Chairman
Responsible AI Institute
02:20PM - Day 1
View Panel: Built to scale – Ramping up AI projects
Yizhar Toren
Senior Data Scientist
Shopify
02:00PM - Day 1
View PRERECORDED PANEL: Built to scale – Ramping up AI projects
02:20PM - Day 1
View Panel: Built to scale – Ramping up AI projects
Panel: Built to scale – Ramping up AI projects
- How to move your project from “experimentation” to “live”
- R&D budget signoff – understanding why you need AI
- Turning AI into ROI – what does value mean to your business?
- Top tips for moving your pilot to the next stage
03:00PM
(CET)
Warren Barrie
Sales Director – International Data Centers
Bulk Infrastructure Group
11:30AM - Day 1
View VIRTUAL: Building a Sustainable Digital Infrastructure for AI and Big Data
03:00PM - Day 1
View Building a Sustainable Digital Infrastructure for AI and Big Data
Building a Sustainable Digital Infrastructure for AI and Big Data
- Why is it important to consider sustainability when looking at digital services and infrastructure
- What are the key considerations and opportunities
- How Bulk helped a UK based Fintech deliver a sustainable AI platform and significantly reduce cost at the same time
03:30PM
(CET)
Corneel Braber
Senior Data Scientist
bol.com
03:30PM - Day 1
View The Challenges of introducing Data Science into the Fraud & Risk department @ bol.com
The Challenges of introducing Data Science into the Fraud & Risk department @ bol.com
- Bol.com was the first online bookstore of Europe in 1999
- 20 years later, bol.com is the largest online retail platform in the Benelux, selling millions of products in dozens of product categories
- As one can expect, with the growth of bol.com, the number of fraud cases is increasing. In addition, fraudulent behaviour is constantly changing
- To stay ahead in this cat-and-mouse game the Fraud and Risk department of bol.com aims to proactively prevent fraud from happening on the platform
- How did the Fraud and Risk department move from a rule-based only reactive approach to a more proactive data-driven approach?
03:50PM
(CET)
Alberto De Lazzari
Chief Scientist
LARUS Business Automation
03:50PM - Day 1
View Graph-AI to Combat Fraud in Fintech sector
Filippo Minutella
Artificial Intelligence Engineer
LARUS Business Automation
03:50PM - Day 1
View Graph-AI to Combat Fraud in Fintech sector
Graph-AI to Combat Fraud in Fintech sector
- Fraud, especially its dynamic nature, is a major area of concern requiring significant time and resources to isolate from an enormous volume of transaction data.
- We have developed an innovative new composite AI based solution that combines graph-rule-based with graph-supervised-learning coupled with explainability to address this problem.
- The talk is based on a real world Graph AI (GAI) project undertaken by Larus, Inc. and Fujitsu Research. The project evaluated the incorporation of GAI technology from Fujitsu (Deep Tensor) into an existing rules-based credit card fraud detection application developed by LARUS.
04:10PM
(CET)
Attila Zubor
Consultant
Blue Sky Global Retail Solutions
04:10PM - Day 1
View Increase your profit with Demand Forecasting: A case study
Increase your profit with Demand Forecasting: A case study
- What is demand forecasting and how does it work?
- How did a leading DIY retailer utilize it to make more profit?
- How can you utilize it for your own company?