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2012/2013  KAN-CMIT_VAIM  Artificial Intelligence in the Marketplace

English Title
Artificial Intelligence in the Marketplace

Course information
Language English
Exam ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Course period Autumn
Changes in course schedule may occur
Tuesday 08.00-09.40, week 36-41, 43-48
Time Table Please see course schedule at e-Campus
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Daniel Hardt - ITM
Administrative contact person is Bodil Sponholtz, ITM, bsp.itm@cbs.dk
Main Category of the Course
  • Information Systems
  • Innovation and entrepreneurship
Last updated on 27-04-2012
Learning objectives
  • Independently identify, select and combine key theoretical concepts taught in the course, and apply them on real business cases
  • Explain in depth, assess and analyze the key ideas and techniques underlying Artificial Intelligence technologies
  • Critically evaluate and compare the development and impact of Artificial Intelligence technologies in different business areas
  • Identify and analyze the strategies and plans of leading businesses with respect to Artificial Intelligence technology
  • Describe and deploy tools for analysis and description of web-based data
  • Identify and analyze a specific problem which can be addressed by new Artificial Intelligence techniques
Examination
Oral exam on the basis of a mini project (group):
Type of test Oral with Written Assignment
Marking scale 7-step scale
Second examiner Second internal examiner
Exam period December/January
Aids Please, see the detailed regulations below
Duration 20 Minutes

Oral exam on the basis of a mini project (individual or group). Max. 10 pages per student. Max. 15 pages per 2-5 students. The mini project is written in parallel with the course. The student is not entitled to supervision. The date for handing in the project will be decided by the secretary. 

20 minutes per student incl. performance discussion. No preparation for the oral exam. The teacher will act as examiner at the oral exam. 2nd examiner is internal (CBS).

Even if it is a group exam, each student must be assessed individually. The students do not need to give an account of which parts of the project they are responsible for. The mini project and the oral exam are both included in the overall assessment.

The title question(s) and content of the project must be prepared by the student(s) within the framework of the syllabus, possibly together with the teacher. The oral examination will be based on a discussion and a perspective of the mini project. The examiner may ask questions that go beyond the project, but within the framework of the syllabus.

The re-exam takes place on the same conditions as the ordinary exam.
Course content
This course examines new Artificial Intelligence technologies that are rapidly transforming the digital marketplace. One highly publicized example is Watson, IBM’s intelligent question-answering system that recently won the game show Jeopardy!. Another high-profile example is Google Translate, which now translates more text than all the world’s human translators. A few short years ago Artificial Intelligence technology was primarily relegated to the realm of fantasy and science fiction – now it is driving new businesses and technologies in a wide range of areas, such as e-discovery, sentiment analysis, topic tracking, and summarization. We will see that the sudden emergence of successful AI systems involves two key factors: the application of massive computing power, and leveraging the wisdom of crowds.
 
Facebook, Twitter and similar services are generating an enormous amount of data, covering every imaginable topic in thousands of different languages and styles. Artificial Intelligence is the key that can unlock the information in these massive unstructured collections of data. Students will get hands-on experience with newly developed tools for doing this. We will look at how AI technology is central to the strategy of Google, Microsoft, Facebook and Apple – and we will look at speculation about how these developments may well accelerate in the near future.
Teaching methods
The class combines lectures, discussions and group work. Students will apply the course material in group presentations, and will get hands-on experience in the development, deployment and assessment of computational tools.
Expected literature
Anderson (2004). The Long tail, Wired, No. 10 (6 pages)
Asur and Huberman, 2010. Predicting the Future with Social Media. CoRR (10 pages)
Bollen et al. 2011. Twitter mood predicts the stock market, Journal of Computational Science, 2(1), March 2011, Pages 1-8
Grossman, L (2011)2045: The Year Man Becomes Immortal. Time Magazine. Feb. 10, 2011.
Halavy et al. (2009) The Unreasonable Effectiveness of Data. Intelligent Systems 24(2)
Huberman et al. (2009) Crowdsourcing, Attention and Productivity, Journal of Information Science.
Joy, Bill (April 2000), Why the future doesn’t need us. Wired Magazine (Viking Adult) (8.04), ISBN 0670032492, retrieved 2007-08-07
O’Reilly and Battelle (2009) Web Squared: Web 2.0 Five Years On. www.web2summit.com
Pang and Lee (2008) Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2.
Roush (2011) Inside Google’s Age of Augmented Humanity. Xconomy.com
 
Saunter.T. (2009) Assessing an Augmented Future.  Digital Cortex
Last updated on 27-04-2012