Topic

Data Science

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.

Structuring, connecting and discovering knowledge: Our biotech NLP platform helps to identify therapeutic targets from millions of scientific papers

In biomedicine, much of the information generated is made publicly available by and to the scientific community. These open data resources are attracting significant attention, as the pharmaceutical industry finds itself continuously under pressure to speed up the costly process of drug discovery.

idalab seminar #17: Interactive data visualisation – Can new empirical approaches in macroeconomic research reveal the true nature of our financial systems?

The Global Financial Crisis of 2007-2008 did not only cause the biggest loss in output post World War II, its far-reaching ripple effects also revealed that interdependencies between individual players in financial systems were much higher than previously assumed. This led policy makers to push the reset button for much of macroeconomic research. The analysis of financial systems and the development of empirical approaches became top of the agenda. One of these new empirical approaches was interactive data visualisation.

How to unlock valuable personal data for analysis: shedding light on the byzantine world of privacy-enhancing technology

At the heart of privacy preserving data analysis lies a fundamental paradox: privacy preservation aims to hide, while data analysis aims to reveal. The two concepts may seem completely irreconcilable at first, but – using the right approach – they need not be. To help you find this right approach for your specific use case, I discuss both the potential and the limitations of different solution concepts, ranging from de-identification to encryption.

idalab seminar #16: How to unlock valuable personal data for analysis – Shedding light on the byzantine world of privacy-enhancing technology

At the heart of privacy preserving data analysis lies a fundamental paradox: privacy preservation aims to hide, while data analysis aims to reveal. The two concepts may seem completely irreconcilable at first, but – using the right approach – they need not be. Our Data Strategist Lisa Martin spent two months researching this topic extensively, conducting interviews with industry experts and Startups alike. In this talk, Lisa will share her insights and we invite you to join us in discussing one of the most pressing issues of the 21st century: data privacy.

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?