This event is free, no Crunch conference ticket needed to attend.
A few important updates:
We will need to start the meetup at 6:00pm as our first speaker will have to leave for Crunch Conference's speaker's dinner latest at 7:30pm. Sorry for the last minute change.
Also, we are pushing capacity, but I just wanted to let you know that it might happen that you'll need to stand if you arrive late.
With that said, here is the schedule:
5:30pm - Doors open
6:00pm - TALKS
Around 7:00: Pizza and beers
After: Crunch kick-off party @GRUND
Doing data science with Clojure: Simon Belak
Having programmers do data science is terrible, if only everyone else were not even worse. The problem is of course tools. We seem to have settled on either: a bunch of disparate libraries thrown into a more or less agnostic IDE, or some point-and-click wonder which no matter how glossy, never seems to truly fit our domain once we get down to it. The dual lisp tradition of grow-your-own-language and grow-your-own-editor gives me hope there is a third way.This presentation is a meditation on how I approach data problems with Clojure, what I believe the process of doing data science should look like and the tools needed to get there. Some already exists (or can at least be bodged together); others can be made with relative ease (and we are already working on some of these); but a few will take a lot more hammock time.
Application of Artificial Intelligence for Automated Driving: Anka Laszlo
Demographic change, urbanization, energy efficiency, and the need for more comfort and safety are the major drivers for automated driving. Not so many years ago, fully automated driving was only a vision due to challenges like legal requirements, moral questions and technical limitations. Recently, numerous alliances are started in the different regions worldwide to change legislations and addressing cross-country standardizations. Also the moral discussions are proceeding. On the technical side, the availability of high-performance technologies like machine learning and the implementation of deep networks on embedded systems are considered as a leap. With these technologies being implemented in an automated car, the car is able to cope with much more traffic situations. Even more than that: the car is able to improve its skills. We will show the application of current and upcoming technologies in the major building blocks of an automated car like sensing, perception, environmental representation, prediction, and trajectory planning.