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2020/2021  KAN-CBUSV2028U  Artificial Intelligence in Business and Society (T)

English Title
Artificial Intelligence in Business and Society (T)

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 150
Study board
BUS Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Daniel Hardt - Department of Management, Society and Communication (MSC)
Main academic disciplines
  • Information technology
  • Innovation
Teaching methods
  • Blended learning
Last updated on 18-08-2020

Relevant links
Learning objectives
  • Explain and analyze the key ideas and techniques underlying Artificial Intelligence technologies, including basic approaches to Machine Learning, basic concepts of linguistics and language processing, and basic concepts of visual processing.
  • Critically evaluate and compare the development and impact of Artificial Intelligence technologies in different business areas
  • Identify and analyze specific problems which can be addressed by new Artificial Intelligence techniques; perform technical assessment of proposed AI solutions
  • Critically assess the future potential of AI; in particular the potential for Artificial General Intelligence, in the light of historical and current discussion.
Course prerequisites
Basic programming skills
Examination
Artificial Intelligence in Business and Society:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance.
Individual or group exam Individual oral exam based on written group product
Number of people in the group 2-5
Size of written product Max. 15 pages
Assignment type Project
Duration
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Students can hand in a revised or a new project for the re-exam.
Students who were ill at the ordinary oral exam may also hand in the same project.
Course content, structure and pedagogical approach

This course examines new Artificial Intelligence technologies that are rapidly transforming the digital marketplace. 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 machine translation, sentiment analysis, voice-based assistants, and facial recognition. We will explore the key ideas underlying this revolution in AI technology, looking at the historical roots of AI, and the cognitive revolution in the key fields of language processing and visual processing.
  

Students will get hands-on experience with newly developed tools for working with a variety of AI technologies. We will see how these technologies are central to the strategies of IT Giants like Google, Amazon, and Facebook  – and we will look at speculation about how these developments may well accelerate in the near future. A key point is that AI technologies are becoming widely available, with public descriptions and open-source implementations. This means that they are no longer the province of large powerful companies, and we will see how AI technologies are playing an increasingly important role now for many small companies. Students will have an opportunity with hands-on exercises to learn how to access and deploy many of the leading technologies.  They will also explore fundamental issues about the ultimate potential of AI and related ethical and societal questions.
 
Description of the teaching methods
The class is a mixture of online lectures, other online activities, and practical exercises in a hands-on session, where students get experience in the development, deployment and assessment of computational AI tools.
Feedback during the teaching period
Students submit result of hands-on exercises each week, and they receive detailed written feedback on their submissions before the following session. Students also receive informal feedback on preliminary plans for a course project.
Student workload
Lectures 24 hours
Workshops 24 hours
Class Preparation 98 hours
Exam and Preparation for Exam 60 hours
Total 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy any books.

 

Alan Turing (1950) Computing Machinery and Intelligence

 

Ferruci et al (2010) Building Watson: An Overview of the Deep QA Project

 

Asur and Huberman, 2010. Predicting the Future with Social Media. CoRR (10 pages)

Halavy et al. (2009) The Unreasonable Effectiveness of Data. Intelligent Systems 24(2)
 

Eric Breck and Claire Cardie (2017)  Opinion Mining and Sentiment Analysis . The Oxford handbook of computational linguistics


 
Last updated on 18-08-2020