Lea Helmers joined idalab as a data scientist in early 2016. With
degrees in Mathematics, Computer Science and Linguistics, she was destined
to specialize in Natural Language Processing (NLP). Her three-year journey
at idalab has been a powerful testament of the data scientist’s potential
to maximize value for clients by combining data science tools with
strategic and conceptual thinking. We asked Lea about how this evolution
took place, and how she’s coped with it on a personal level.
As a Data Strategy Intern at idalab, Tina Emambakhsh explores how
AI-/ML-based healthcare technology can be regulated in the future. Before
joining idalab, Tina gained experience in a wide range of disciplines and
sectors, having worked at the Austrian Embassy in Tehran, KPMG and
Strategy&. Tina studied International Business Administration at WU
Vienna and at Universidad del Pacífico, Peru, and is currently pursuing a
master’s degree in International Economic Policy and Economics at
SciencesPo and the Stockholm School of Economics. Her research interests
lie at the interface between public policy and media.
For over a decade TomTom has been creating consumer devices for
navigation routing people from A to B as fast as possible. One of the key
components in routing is the availability of a high-quality map.
While initially maps were being produced in a very laborious way involving
a significant amount of manual work, map productization is nowadays
becoming more and more automatized.
The concept of “Artificial Intelligence” had a massive impact over
the past several years. Marvelous tales about its prospective achievements
abound, just as much as sceptical questions about whether (or when) the
machines will finally substitute the humans on this planet. As a result of
this heated discourse, leaders across many industries feel an urgent need
to somehow incorporate AI technologies into their workflows, fearing to
miss out on an important innovation step.
Today, the world seems enamoured by the possibilities of
Artificial Intelligence. As of now, this largely amounts to powerful, yet
predictable and widely understood algorithms for search and pattern
recognition, that inspired the era of Data Science and Machine Learning.
While the effects of these techniques are undoubtedly great and in many use
cases their potential has yet to be unlocked, their capabilities are far
from human cognition:
Professor Stephan Breidenbach und Dr. Paul von Bünau nähern sich in
diesem Interview der Frage, was Künstliche Intelligenz heute im Kern
ausmacht und was Voraussetzungen für echtes Verstehen wären. Darüber
hinaus erörtern sie, welche Innovationen KI im Bereich der
Rechtsdienstleistungen heute ermöglicht und welche Potenziale in Zukunft
noch gehoben werden können.
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.
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.
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?