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.
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?
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?
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.
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?
In idalab seminar #10, we turn to the field of Precision Medicine. Dr.
Nicole Krämer from Staburo GmbH will give a talk on how Data Science can
help physicians to predict a patient’s reaction to a certain
treatment in advance.
The next idalab seminar will take place on Friday April 27th at 5pm, as
always in our office at Potsdamer Straße 68. We will host a talk by
Benjamin F. Maier, PhD student at the Robert Koch Institute and freelance
On August 14th 2017, Felix Biessmann gave a talk at idalab about
“Canonical Trends: Detecting Trend Setters in Web Data”. The
talk was based on a paper that Felix has written in 2012,
together with Jens-Michalis Papaioannou (TU Berlin), Mikio Braun (TU
Berlin), Andreas Harth (Karlsruhe Institue of Technology):