| Learning objectives |
- Explain and critically examine central Big Data concepts: what
is special about Big Data? How does Big Data relate to Moore's
Law?
- Describe and discuss key principles underlying relevant
Artificial Intelligence technologies, including the relation
between Machine Learning and AI
- Assess positive and negative views on the future potential of
AI and Big Data, including concepts such as the Turing Test and the
Singularity. Also assess views on the social and economic effects
of AI and Big Data.
- Assess the value and relevance of Big Data concepts and related
computational tools presented throughout the course in relation to
their application in specific cases/scenarios. This includes
technologies such as Social Media Analysis and Data Mining, 3D
Printing, Chatbots, and Automatic Translation.
- Demonstrate the ability to reflect on your own activities and
interactions throughout the course by identifying a portfolio of
own online contributions and arguing for their relevance to the
exam. These contributions must be made during the course period --
before the exam period begins.
|
| Examination |
|
Who Owns the
Future? The Promise and Perils of the Coming Big Data
Revolution:
|
| Exam
ECTS |
7,5 |
| Examination form |
Home assignment - written product |
| Individual or group exam |
Individual exam |
| Size of written product |
Max. 10 pages |
| Assignment type |
Report |
| Duration |
Written product to be submitted on specified date
and time. |
| Grading scale |
7-step scale |
| Examiner(s) |
One internal examiner |
| Exam period |
Winter |
| Make-up exam/re-exam |
Same examination form as the ordinary exam
A new case and/or a new series of
essay questions will form the basis of the re-exam. Please note
that the assessment will partly be made based on the student’s
online activities/interactions made throughout the teaching period
of the course. It will not be possible to make new online
contributions. However, if the student – in accordance with the CBS
rules on make-up exams – has documented that illness during the
teaching period has resulted in his/her not making any online
contributions during the teaching period, the student will be given
the opportunity to make online contributions prior to the
re-exam.
|
Description of the exam
procedure
Note that online activities and interactions posted on Learn
throughout the course form part of the basis for the assessment, as
stated in the learning objectives. Thus it is important that
the students actively participate in the course each
week.
|
|
| Course content and structure |
|
Scarcely a day goes by without reports of revolutionary new
technologies, many of which promise to transform whole industries,
from finance to health care to translation. This technological
development is powered by exponential growth in the availability of
Big Data, together with similar growth in the computing power to
exploit that data. Many believe that this development is ushering
in an era of genuine Artificial Intelligence (AI), with
unprecedented improvements in productivity and general living
standards. In this course we will critically examine this utopian
vision, focusing on two inter-related issues: the technology, and
its impact.
Technology: AI practitioners themselves are deeply
divided about the question of whether true AI is right on the
horizon, or is in fact a long way off. We will take a
detailed look at key AI technologies to better understand this
debate, and separate the hype and misunderstanding from the true
potential. An understanding of these technologies is rapidly
becoming essential for managers and decision makers in business and
government.
Impact: the utopian future being ushered in by Big
Data and AI seems almost inevitable. At the same time, there
appears to be a “dark side” to these developments: Big Data is
central to the abuses of privacy which also seem to be increasing
dramatically from both business and government. Also disturbing is
that the undeniable technological progress we are witnessing does
not seem to be contributing to general well-being -- instead, there
is the paradox of accelerating productivity improvements coinciding
with economic stagnation.
|
| Description of the teaching methods |
| This is a fully online course. The course will
run over 8 weeks. The course will consist of asynchronous and/or
synchronous online lectures, asynchronous and/or synchronous online
discussions, quizzes and individual and/or group assignments.
Literature on the specific topics will be assigned during the
quarter. The readings will also build the foundation on which we
will discuss cases online, and they provide the necessary knowledge
to work with home assignments. The lecturer will be available for
asynchronous and/or synchronous online discussions throughout the 8
weeks in which the course runs. Students will get hands-on
experience in the development, deployment and assessment of
computational tools.While students will gain an understanding of
key principles underlying these computational tools, students are
not required to know how to program, and the focus will be
conceptual rather than technical. Student participation will be
targeted at producing insights that are meant to be covered in the
final exam project. |
| Feedback during the teaching period |
| Feedback on contributions to online forums for
each session. |
| Student workload |
| Reading |
40 hours |
| Online Lectures and other videos |
46 hours |
| Online activities |
60 hours |
| Exam and Preparation for Exam |
60 hours |
| Total |
206 hours |
|
| Further Information |
|
As this is a online course, no rooms are booked.
Online course will run from week 44-51
|
| Expected literature |
|
Who Owns the Future? Jaron Lanier, Simon &
Schuster, 2013
The Second Machine Age: Work, Progress, and Prosperity in a Time of
Brilliant Technologies, Brynjolfsson, E., & McAfee, A. W. W.
Norton & Company, 2014
|
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