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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Review: “Vis Week” in Berlin

In late October, a broad international research community dedicated to visualising information and data gathered in Berlin. In a great concurrence of events (termed “Vis Week” by some people), the grand annual IEEE vis conference was accompanied by the smaller interdisciplinary conference Information+ in Potsdam.

idalab seminar #15: What makes an algorithm ethical? – Defining and implementing a Quality Criteria Catalogue for Algorithms

Algorithms are increasingly relied upon in decision making processes that can have far-reaching implications for all of us. They help doctors diagnose diseases and develop treatment plans. They tell police officers where to patrol. They decide who is going to be invited to the job interview. If these decisions are made by people and the way they decide seems harmful or unjust, our laws enable us to hold them accountable for their actions and correct them if necessary.

Cutting through the laptop installation jungle: How our MacBook setup repository helps our Data Scientists to organise their tools in only half a day

For me, one of the most frustrating parts of learning to program in earnest was that I always had some tooling problem before I could start. When I found a cool Python package and was eager to wrap my head around how it could help me to solve a problem, for instance, I often lacked the right version of Python to just install the package version I wanted and dive right in.

idalab seminar #14: Academia to Industry: Looking back on a decade of machine learning

Machine Learning is one of that areas that has seen a rapid transformation from a purely academic topic to becoming a driving technology in the industry these days. Mikio has seen both sides of the coin and will share his experience. What is the difference between academic research and bringing a ML driven product live? What does it take to productionize ML? And finally, how close are we to true AI?

Hassle-free travelling: idalab teams up with DB Systel, DFKI and Door2Door for research project SIM3S

Using public transport can be a challenge. In case of disruptions, loudspeaker announcements are the critical source of information – but they can be hard to understand, even for native speakers. English translations aren’t always provided, except in bigger cities. Matters get even worse for handicapped or elderly people, because information about accessibility on the alternative routes is usually not available at all.

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

While Artificial Intelligence is continuing to transform the world as we know it, the need for “AI generalists”, who take the role of architects designing custom solutions becomes ever more acute. It is no wonder that AI architects are in short supply: AI architects combine profound expertise in AI-methodologies with a highly analytical, yet creative and solution-focused mindset, enabling them to see the bigger picture and make strategic decisions. Today, only few people have this kind of generalist skill set.

idalab seminar #13: Exploring Chemical Space with Deep Learning

What if we could build batteries for electric cars that would take us further than a full tank of gasoline? If we could grow affordable, tasty and nutritious meat in the laboratory instead of occupying one third of the land on our planet with animal farming? What if we could easily identify promising targets in the human body for new cancer drugs?

Die Sehnsucht nach Transparenz ist eine Sehnsucht nach Begründung. Warum Algorithmen lernen sollten, eine Geschichte zu erzählen.

Basis aller heutigen Künstlichen Intelligenz sind bekanntlich Algorithmen. Diese sind in den letzten Jahren in zunehmendem Ausmaß in den Fokus der öffentlichen Wahrnehmung gerückt. Dabei schwankt der Grad an thematischer Souveränität und Güte der einzelnen Wortmeldungen aus Presse, Politik und Gesellschaft teils erheblich.