The concept of “Artificial Intelligence” had a massive impact over the past several years. Marvelous tales about its prospective achievements abound, just as much as sceptical questions about whether (or when) the machines will finally substitute the humans on this planet. As a result of this heated discourse, leaders across many industries feel an urgent need to somehow incorporate AI technologies into their workflows, fearing to miss out on an important innovation step.

About our Blog

This blog is a place for us to reflect on data science, AI, and machine learning. Hence, it covers a broad array of topics: technical considerations, our view on certain industries, interviews with researchers, thought leaders, and industry experts, as well as light-weight visualisation.

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Signs of our times: of black holes and red flags (idalab seminar #22)

On April 13, 2019, the front pages of every major newspaper were adorned with the “first-ever” picture of a black hole – an invisible astronomical object popularized by J. Robert Oppenheimer around the same time that the nuclear bomb was developed. The simple red-orange circular blob was heralded as a triumph of modern data analysis techniques, involving terrabytes of data analyzed by a team of PhD students led by a media-savvy PhD student. In my talk, I will place this event in historical context as a representation of changing interpretations of the scientific method.

From code to client: Lea’s journey at idalab

Lea Helmers joined idalab as a data scientist in early 2016. With degrees in Mathematics, Computer Science and Linguistics, she was destined to specialize in Natural Language Processing (NLP). Her three-year journey at idalab has been a powerful testament of the data scientist’s potential to maximize value for clients by combining data science tools with strategic and conceptual thinking. We asked Lea about how this evolution took place, and how she’s coped with it on a personal level.

“Data is a small part of the story”: a glimpse into the life of a data scientist

Hagen Hoferichter is a data scientist, but not just any data scientist. He specializes in the intersection of Machine Learning and embedded devices in the IoT world. Instead of being an obstacle, Hagen’s background as an Electrical Engineer gives him a unique edge when it comes to creating innovative data-gathering devices together with clients. Based on extensive experience working with clients from the healthcare sector, Hagen categorically emphasizes that data science work is more than just programming or number crunching. The most crucial part of his work consists of explaining data science to clients and exchanging expertise to ultimately enable him to create groundbreaking solutions. We met up with Hagen to find out about the most essential ingredients that allows a data scientist to solve intractable client problems.

Fostering AI architects: Tina about her journey as a data strategy intern at idalab

As a Data Strategy Intern at idalab, Tina Emambakhsh explores how AI-/ML-based healthcare technology can be regulated in the future. Before joining idalab, Tina gained experience in a wide range of disciplines and sectors, having worked at the Austrian Embassy in Tehran, KPMG and Strategy&. Tina studied International Business Administration at WU Vienna and at Universidad del Pacífico, Peru, and is currently pursuing a master’s degree in International Economic Policy and Economics at SciencesPo and the Stockholm School of Economics. Her research interests lie at the interface between public policy and media.

idalab seminar #21: How TomTom is using AI to Create World-Class Maps and Traffic Services for Autonomous Driving

For over a decade TomTom has been creating consumer devices for navigation routing people from A to B as fast as possible. One of the key components in routing is the availability of a high-quality map.  While initially maps were being produced in a very laborious way involving a significant amount of manual work, map productization is nowadays becoming more and more automatized.

idalab seminar #20: Inventing the future, one visualisation at a time

Data visualisation is a young and buzzing field, or so it seems. Many related projects are focused on mastering new technologies, on navigating the unprecedented wealth of data and on supporting the human-machine-interaction of the future. Interestingly, in most professional debates and talks today, we can detect a near total lack of historical perspective or awareness. While everyone is looking forward, there doesn’t seem to be much use in looking back.

idalab seminar #19: Towards Neuroadaptivity – How we can connect computers directly to the human mind

Can computers learn to understand their users in a way we intuitively understand other people? The short answer is: Yes, they can. Teaching computers to adapt to the human mind is exactly what Brain-Computer Interfacing is about.

AI beyond pattern recognition #1: Interview with Christoph von der Malsburg

Today, the world seems enamoured  by the possibilities of Artificial Intelligence. As of now, this largely amounts to powerful, yet predictable and widely understood algorithms for search and pattern recognition, that inspired the era of Data Science and Machine Learning. While the effects of these techniques are undoubtedly great and in many use cases their potential has yet to be unlocked, their capabilities are far from human cognition:

idalab seminar #18: Generating music in realtime with Artificial Intelligence: What if music could change automatically with the emotional state in a video game?

In a time when creators of video games are trying to make gaming an ever more immersive and ever more real experience, AI is beginning to have an impact in the field. Everyone who’s ever played a video game knows: Music plays an important part in conveying the atmosphere of altering game states and thus needs to change dynamically as the gamer makes choices and variations of the storyline unfold.