The Global AI On Tour in Brussels, Belgium is a free event on 23 June 2020 from 18:00 - 21:00.

About the event

DataMinds is hosting this event alongside multiple other locations globally.

It is an excellent opportunity to build or extend your knowledge about AI, Machine Learning, Cognitive services, and more.


  • 18:00- Welcome
  • 18:05 - Lightning Talk - Data science in tutorials vs. in real life (Eve Pardi, MVP)
  • 18:25 - Session 1 - Using AI to classify your SharePoint Data - (Albert-Jan Schot, MVP)
  • 19:25 - Session 2 - Artificial Intelligence? - more like Artificial Stupidity! (Aiko Klostermann)

Lightning Talk:

Data science in tutorials vs. in real life

by Eve Pardi (Software Developer and Data Scientist.)

Eva is obsessed with data and passionate about AI. Her main interest is to save lives and support humanity with the help of AI and data. She is working full time at Laerdal. In her free time, she writes articles, speaks at conferences about data and AI. She aims to make people understand how intelligent applications affect our lives, how to use and control them.

About the Talk I meet a lot of people at conferences or webinars where I participate as speaker or when I consult at a customer, and I often have the feeling that some people are perplexed about what is involved in the everyday work of a data scientist.

Sometimes I ask these people where they heard all that strange information, and they claim to read it in some tutorials. It turns out that some tutorials include information that is not necessarily a lie or stupid, but it can easily be misleading for someone who just would like to get started in this field.

I collected some information that other data scientists and I found at different tutorials...

Session 1:

Using AI to classify your SharePoint Data

by Albert-Jan Schot (MVP)

Albert-Jan, also known as Appie, is someone who lives and breathes SharePoint to such a degree that it has become second nature to him. He has numerous certifications to his name. With his extensive knowledge, Albert-Jan is a valuable source of information for colleagues. Albert-Jan not only enjoys stepping up to the challenge of designing, developing, and building innovative SharePoint environments, he also has consultancy and training experience. As a real die-hard SharePoint developer, Albert-Jan is active on a range of SharePoint forums, blogs as well as on Twitter, where he shares his knowledge and passion with others.

About the Talk

So you might have heard of Project Cortex, allowing you to auto-tag information in SharePoint and extract knowledge from your content. But what if you can't wait for the preview? Or are you in an on-premises scenario? You can use the Azure Cognitive services directly from your SharePoint on-prem environment! In this session, you will learn how you can extend your on-prem data in SharePoint with the different cognitive services Azure offers, including Azure Text Analytics and LUIS.

Session 2:

Artificial Intelligence? - more like Artificial Stupidity!

by Aiko Klostermann (Technology Consultant)

Aiko works as a consultant and developer for ThoughtWorks. He is passionate about data science, software craftsmanship, clean code, and infrastructure engineering. While working with clients, he focusses on improving the development process and code quality of the teams he's working together with.

Nowadays, working in Singapore, Aiko has previously worked with clients in Germany, the UK, and India as well. He leveraged Artificial Intelligence to help clients gain a competitive advantage. Recently his focus moved onto infrastructure development for building (data) platforms to better enable client teams.

About the Talk

Nowadays, "Artificial Intelligence" is everywhere! And rightly so, it does enable us to do cool things, things we couldn't even imagine doing just a decade ago. It sometimes just feels like magic. This 'magic' behind it is often powered by "Machine Learning." But even "AI" has its limitations. I'll show examples where "AI" and ML have failed (sometimes with horrible consequences) and will explain why failures are unavoidable in ML but also mention what we can do to reduce them in the future. Furthermore, I'll showcase how current AI implementations discriminate against minorities and how that, in some cases, even leads to a higher risk of death for those groups. I'll cover the bias that humans introduce, and I'll explain how poor choice of data makes our world even more unjust than it already is.

The takeaway for the audience: AI can fail, and sometimes it has horrible consequences. Why is AI so hard to "do right"? How can we make AI better?

Code of Conduct:


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