Distinguished Lecturer Series · Wintersemester 2016/17

FlyerDLS-WS-2016-17Plakat als PDF herunterladen (180kb)

Alle Vorträge finden jeweils 17:00 Uhr in Hörsaal 7 in der Carl-Zeiss-Straße 3 statt!

show Content 9.11.2016 · Toby Walsh · University of New South Wales

DLS_16-17_WalshWill Artificial Intelligence end jobs, war or humanity?

Artificial Intelligence (AI) is in the zeitgeist. The Chief Economist of the Bank of England recently predicted AI will destroy 50% of jobs in the UK. In 2015, thousands of AI researchers signed an Open Letter predicting that AI could transform warfare and lead to an arms race of "killer robots". And Stephen Hawking and others have predicted that AI could end humanity itself. What should you make of all these predictions? What should we do to ensure a safe and prosperous future for all?

 

 

 

 

 

show Content 23.11.2016 · Felix Freiling · Friedrich-Alexander-Universität Erlangen-Nürnberg

DLS_16-17_FreilingCyberkriminelle und ihre Tricks

Cyberkriminelle entwickeln laufend neue Methoden, um an Geld zu
gelangen. Der Schaden, der Unternehmen dadurch entsteht, beläuft sich
auf Milliardenbeträge. Die Methoden, mit denen die Kriminellen an ihre
Daten gelangen, sind vielfältig. Sie installieren beispielsweise
Schadsoftware auf möglichst vielen Rechnern oder locken die
Internetnutzer auf gefälschte Seiten, um darüber an sensible
Informationen zu gelangen. In dem Vortrag wird darauf eingegangen,
welche Maschen besonders erfolgreich sind. Neben der Frage, wie die
Verbrechen technisch ausgeführt werden, geht es auch darum, wie
sich solche Straftaten verhindern lassen. Denn viele Rechner sind
unzureichend geschützt und bieten damit eine ideale Angriffsfläche für
Cyberkriminelle.

show Content 21.12.2016 · Martin Vingron · MPI für molekulare Genetik Berlin

DLS_16-17_VingronGene networks, epigenetic networks, and the inverse covariance matrix

Cellular processes are governed by interactions among genes and/or their
protein-products. With recent technological advances, extensive
measurements of gene activity can be made for large numbers of genes.
From such measurements one tries to deduce relationships among the genes,
commonly depicted in the form of a graph or network.
This problem is called the network reverse-engineering problem.
A simple mathematical object, the inverse of the
variance-covariance matrix, is at the heart of a whole
class of methods for attacking this problem. The talk
will present the biological questions and explain how
the inverse variance-covariance matrix comes into play. Novel
applications include epigenetic factors and the construction of networks
of epigenetic modifications and chromatin associated proteins.

show Content 25.1.2017 · Kristian Kersting · TU Dortmund

DLS_16-17_KerstingThinking Data Science Machines

Our minds make inferences that appear to go far beyond standard data science approaches. Whereas people can learn richer
representations and use them for a wider range of data science tasks, data science algorithms have been mainly employed in
a stand-alone context, constructing a single function from a table of training examples. In this talk, I shall touch upon an approach
to data science that can capture these human learning aspects by combining graphs, databases, and relational logic in general with
statistical learning and optimization. Here, high-level (logical) features such as individuals, relations, functions, and connectives
provide declarative clarity and succinct characterizations of the data science problem. While attractive from a modeling viewpoint,
this declarative data science programming also often assuredly complicates the underlying model, making solving it potentially very slow.
Hence, I shall also touch upon ways to reduce the solver costs.  One promising direction to speed up is to cache local structures in the
computational models. I shall illustrate this for probabilistic inference, linear programs, and convex quadratic programs, all working horses of data science.