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.

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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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: idalab’s first Data Strategy Summer Fellow

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?

idalab goes PyData

This year’s PyData Berlin conference, taking place from July 6th to 8th at the Charité-Campus (Virchow), was a huge success and with almost 600 participants the biggest PyData conference all over Europe. Concerned with providing a forum for python users and developers in the field of data analysis, a wide range of topics was covered in four simultaneous tracks, going from deep learning and scalability over data privacy and best practices to putting machine learning into production.

idalab seminar #12: The data-privacy dilemma: How full homomorphic encryption could bring healthcare into the digital era

Imagine this: The key to better cancer treatments is within reach, based on patterns from data that is scattered across various locations all over the world. This data could be digitalised, labelled, collected, stored and interpreted. However, this data belongs to a countless number of individuals – and their right to data privacy weighs just as much as the dream of curing a lethal disease.

What do we mean by “data”?

Some technical terms are so ubiquitous and (apparently) unambigious, that they almost become a transparent fluid: always used but never much reflected upon. Interestingly enough, the word “data” 1 is such a term. It is an abstract, weightless and unidentified mass of numbers (mostly digitally encoded), with a potent influence on our lives. It is also considered a rich source of insight that is worth being tapped. But what are the origins of the word “data” – and what are its implications?

idalab seminar #11: SELECT * FROM … natural language: databases, we need to talk!

Do you know the feeling? All you want is a break-down of last year’s sales numbers and suddenly you find yourself typing tedious heaps of SQL-statements, clicking through complicated dashboards or looking for the right number in an Excel sheet. The vast majority of decision makers has better things to do with their time. That’s why business intelligence software was originally born. But only 20% of users are actually coping with the solutions BI software provides. Instead, the BI team is spammed with ad hoc data requests. What if everyone had a personal virtual data analyst at hand?