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?
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.
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?
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.
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.
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.
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
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?