Data visualisation is a young and buzzing field, or so it seems. Many
related projects are focused on mastering new technologies, on navigating
the unprecedented wealth of data and on supporting the
human-machine-interaction of the future. Interestingly, in most
professional debates and talks today, we can detect a near total lack of
historical perspective or awareness. While everyone is looking forward,
there doesn’t seem to be much use in looking back.
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.
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
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?