MAI622: AI Entrepreneurship

Instructor: Dr. Marios D. Dikaiakos, Professor
Teaching Assistant:
ECTS Credits: 8
Semester: Spring
Academic Year: 2024-2025
Course Level: Post-graduate.
Course Type: Compulsory course for the M.Sc. in Artificial Intelligence; Advanced Elective for the M.Sc. in Data Science, M.Sc. in Computer Science, and Ph.D. of the University of Cyprus. Free elective for other post-graduate students of other programs.
Prerequisites: None.
Language of instruction: English.
Online Forum: Blackboard
Teaching Schedule: Three hours of lectures and one hour of guest seminar or precept per week.

Lectures:: Thursdays,15:00-17:59, Room 148, ΘΕΕ01.
Seminar/Precept:: Students are expected to attend Wednesdays' guest seminars by the Centre for Entrepreneurship (consult their News and Events for time and place), or precept meetings announced through Blackboard on Wednesdays at 16:30-18:00 in Room Β101, ΘΕΕ01.

Responsibilities: It is the responsibility of each student to study the course syllabus and be aware of and comply with the regulations therein.

Objectives

This course aspires to help students explore and master key concepts and challenges of relevance to innovation-driven entrepreneurship focusing on opportunities of AI and Data-Science technologies. The course introduces students to the world of AI entrepreneurship through case studies that demonstrate successes, failures and challenges. The course provides also an overview of and an introduction to key steps to develop a company, design a business model, explore product-market fit, manage intellectual property, and attract investment. Students will explore acknowledged innovation-driven entrepreneurship methodologies and experiment with them and associated tools to pursue the translation of their ideas into entrepreneurial endeavors. The course examines issues faced by Startup Founders and Chief Technology Officers who need to innovate at the boundaries of AI, Ιnformation Τechnology and Βusiness by understanding all perspectives. The syllabus follows the "blueprint" of MIT's Disciplined Entrepreneurship courses adopting their textbook and re-using their resources available online (worksheets, etc).

Learning outcomes and skills

The students who complete this course successfully, will be able to:

Recognize and define key concepts and terminology related to entrepreneurship.
Analyze and evaluate entrepreneurial ideas, especially for AI-based, innovative products, processes or services based on advanced technologies or scientific inventions.
Consider issues of Intellectual Property (IP) and IP protection.
Understand Business Planning and create Business Plans.
Define and apply techniques for market analysis, product design, value proposition definition, and customer acquisition.
Explore and apply methodologies and tools for innovative entrepreneurship, such as the Disciplined Entrepreneurship Methodology, the Lean Product Methodology, and the Business Model Canvas.
Explore and apply techniques for creative ideation and design of software applications, products and services, such as Design Thinking, Innovators’ Compass, and Sprint.
Understand the challenges of team formation and management.
Use state-of-the-art collaboration, ideation and rapid prototyping tools.
Prepare pitch-decks and present ideas in front of investors to attract funding.
Understand and explain the interplay between Big Data, Machine Learning and various application domains.
Recognize and undertake the steps of the Disciplined Entrepreneurship methodology, and manage the key activities required to bring an innovative product or service to the market:
- product definition and market segmentation;
- value proposition analysis and high-level product specification;
- market and competition analysis;
- business model definition and revenue models;
- customer and user acquisition;
- pricing strategies for a product, process or service;
- minimum viable product definition and product implementation planning.
Understand the basics of fundraising, financing, and ownership for a startup.
Understand the key challenges for attracting talent, establishing and managing a startup team.
Prepare pitch decks, and pitch in front of potential investors, an ΑΙ-related business idea/product/service.

Teaching and Learning Methods

Students will meet the expected learning outcomes through participation to lectures, invited talks, active participation to class discussions, reviewing videos, reading and writing assignments, and actual practice with innovation and entrepreneurship methodologies. The course comprises 3 hours of weekly lectures, 1 hour of seminar with invited speakers or precept, reading and writing assignments, and a semester-long group project in entrepreneurship. In particular:

Lectures: Students are expected to actively participate to Lectures, where the main concepts and methodologies covered by the course are presented and critically appraised.

Attendance to lectures and seminars/precepts is obligatory.
Students are required to study the materials for each lecture, as announced on Blackboard, with readings, videos and materials for each topic given in the Course Topics page.

Guest Seminars and Precepts: The precept time is dedicated primarily to Guest Seminars, which are part of the Series of Lectures in Innovation and Entrepreneurship (KEP101) of the Centre for Entrepreneurship, and bring to campus distinguished entrepreneurs and experts in various aspects of relevance to entrepreneurship. When there are no C4E seminars, the precept time will used by students to view videos assigned by the instructor through Blackboard or to meet and discuss their progress in the group project or to administer Quizzes.

Class Participation and Attendance Rules

Classroom attendance is required. Unavoidable absences due to illness or family emergencies are excusable but please let the instructor know ahead of time. More than three absences from the lectures will reduce your class participation mark to 0. Please observe and apply the following rules when attending classes:

Be on time to every class.
Attendance will be taken at the beginning of class.
Display a name-card in front of you.
Please refrain from using your laptops, mobile devices, and tablets during class.
Once you have a team, sit together in the same places throughout the semester.
Come prepared to class: do the readings and be ready to discuss how your team has dealt with the steps in question.
Contribute to class (there may be cold-calling) – just showing up is not enough.
More than three absences will reduce your final grade by 15%.

Individual Assignments

Each student is expected to undertake individually and independently a number of assignments, producing the deliverables described below. More information is provided in the Assignments page. Announcements, deadlines, instructions and technical specifications on the deliverables will be outlined on Blackboard.

Group-project Work, Deliverables, and In-class Presentations

Semester-long, group projects are carried out by teams of 3-4 students who are expected to develop an AI-related idea with a strong exploitation potential through a business venture or a social enterprise. The teams are required to undertake all necessary activities to develop a strong business plan by applying all the steps of the Disciplined Entrepreneurship methodology, prepare a final oral presentation to seek funding (Venture Capital pitch), and present their pitch in class and at the C4E's Student Innovators Competition (SINN) and/or other competitions approved by the professor. Group-project progress will be monitored continuously through the deliverables of assignments and peer evaluations, announced on Blackboard. More information is provided in the Assignments page.

Final Exam

Purpose: To ensure comprehension of course material and reinforce learning.
Details: A final exam will be administered to test individual understanding. They will cover key concepts from lectures, readings, and discussions.

Late Submission Policy

Assignments are due on the specified due dates/times, as announced on Blackboard. Late submissions will be penalized by a 15% reduction in the final assignment grade for each day they are late. Extensions will be granted only in the case of illness (with a doctor's note) or extraordinary circumstances. Please let us know ahead of time if illness or an extraordinary circumstance will cause you to submit a writeup or paper late, then you should discuss the matter with your instructor as soon as possible.

Evaluation and Grading

Student progress is evaluated continuously through class participation and the assessment of writing assignments and group project deliverables. The final grade is based on the following formula:

Individual Class Participation and Attendance:: 15%
Individual Assignments, Deliverables and Peer Reviews:: 10%
Term Project Group Deliverables (based on slides prepared for each of 24 steps of DE):: 30%
Term Project Final Deliverables:: 20%
Final Exam:: 25%

ECTS Analysis

One ECTS unit corresponds to 25-30 hours of work undertaken by an average student to complete successfully expected learning outcomes. Consequently, the successful completion of the class requires a total of 187.5-225 hours of work, on average. The workload of the average student is analysed as follows:

Class and Guest Seminar/Precept participation: 4 hours per week for 13 weeks, totalling to 52 hours.
Study at home: 2 hours per week for 14 weeks, totalling 28 hours.
Case Study Report: 40 hours.
Final presentation: 10 hours.
Group project: 70 hours

Consequently, the total workload for successfully completing this course, is estimated to 205 hours on average.

Bibliography

Bill Aulet, “Disciplined Entrepreneurship.” Wiley, 2013.
Alexander Osterwalder and Yves Pigneur, “Business Model Generation.” Wiley, 2010.
Kai-Fu Lee and Chen Qiufan, "AI 2041: Ten Visions for Our Future." Currency, 2021.
Dan Olsen, “The Lean Product Playbook. How to Innovate with Minimum Viable Products and Rapid Customer Feedback.” Wiley, 2015.
Ash Fontana, "The AI-First Company: How to Compete and Win with Artificial Intelligence." Penguin, 2021.
See also Resource page.

On Intellectual Property

The work completed in this course is intended solely for academic purposes. There is no requirement, explicit or implied, for class-formed teams to share intellectual property (IP) or equity in ventures that may develop from this course. Unless otherwise explicitly agreed upon with the course instructor, all marketing and business analyses produced are considered public domain. Students are responsible for ensuring that any technical intellectual property they bring into the course is appropriately protected prior to participation. This course does not provide mechanisms for IP protection, as its primary focus is on educational enrichment rather than functioning as an economic development agency or investment accelerator. It is important to distinguish technical IP from the knowledge and skills gained during the business planning process. Students are strongly encouraged to consult legal or patent professionals to safeguard their technical IP and take necessary precautions before engaging in disclosure or collaboration within the course.

On the use of Artificial Intelligence Tools

The field of Artificial Intelligence (AI) is undergoing rapid developments. Generative Artificial Intelligence tools, such as ChatGPT/ChatPDF, allow the generation of written text, audio and images with highly realistic results. The University of Cyprus (UCY) is committed in the ethical and responsible use of AI based on specific principles for its utilization and on the appropriate preparedness of its staff and students. The following principles/recommendations regarding the utilization of AI in the educational process are in effect, starting in the Fall Semester 2023-2025 , and will be revised at regular time intervals.

GENERAL CONTEXT OF USE

UCY encourages experimentation with available AI tools that aid the process of comprehension and learning, given that their use does not violate applicable ethical principles. It is noted that, AI tools must be utilised in combination with critical thinking, while also acknowledging their sometimes potentially serious weaknesses. Implementation of such practices, can potentially lead to the constructive enhancement of the teaching and learning process.
Instructors may allow students to use AI tools in coursework assignments for the purpose of searching and cross-referencing information, to the extent that such use contributes to the achievement of the learning objectives.

TRANSPARENCY AND QUALITY ASSURANCE

When AI tools are used in coursework assignments, students must explicitly and accurately declare any AI tools used and how these were utilized in the process.
It is noted that the use of AI tools by students can contribute to the cultivation of critical thinking skills, through the evaluation of the quality of the assistance/responses generated by the AI tool(s).
Specific instructions for the use of AI by students may be provided by the instructors as part of the course syllabus or as part of the written guidelines of a coursework assignment/examination, based on the unique characteristics of a course.
Instructors are encouraged to adapt the assessment methods for coursework assignments/examinations in order to ensure the integrity of the assessment process (e.g., take-home exams and thesis assessments may be supplemented by an oral examination).

ETHICAL USE AND ACCOUNTABILITY

Verbatim copying from text generated by AI tools is prohibited. Instructors have the option of using plagiarism detection tools that recognize AI-generated text. It is noted, however, that plagiarism detection tools may have a margin of error. In the event of plagiarism, further disciplinary action may be taken in accordance with the Undergraduate Studies Rules (pages 35-43) for undergraduate students and with the Postgraduate Studies Rules (pages 48-57) for postgraduate students (Additional information on how students can recognize and avoid plagiarism in general, are provided in the electronic booklet guide of the University of Cyprus Library entitled "ACADEMIC PLAGIARISM: How to identify and avoid it”.)
It is noted that AI tools should be utilized with great caution, with regards to the data submitted to them as input (personal or confidential data, copyrighted information, etc.) and to the results that arise from their use. It is also important to consider that, currently popular AI tools generate algorithmically-derived responses based on existing text which is available online, without confirmation of the accuracy and timeliness of these responses in comparison to valid scientific information sources.
Users of AI tools are solely responsible for confirming the accuracy of data and ensuring unbiased and ethical conduct.

Other instructions

Each student has the right to attend lectures and workshops without disturbance and unjustified interruptions. So everyone is requested to preserve this right, respecting the start and end time of the lectures and workshops, not disturbing the class, and preserving the cleanliness of the auditoriums and laboratory spaces and in general the academic freedom.
Plagiarism is strongly prohibited.
You will not get any credit for late submissions. We will grant extensions only in the case of illness (with a doctor's note) or extraordinary circumstances. Please let us know ahead of time if illness or an extraordinary circumstance will cause you to submit a writeup or paper late, then you should discuss the matter with your instructor as soon as possible.