BUSI70233 AI Ventures

In this project-based, team-oriented module, students will explore applications of artificial intelligence technologies that will improve or transform existing financial, health and other systems, markets and methods, by spending dedicated time building new business models, products or technical concepts.  Students will be exposed to key ideas, principles and frameworks from CEOs of leading startups, corporate leaders and Imperial instructors who are leading research in the future of artificial intelligence .  The class will leverage convening of diverse students and instructors from across campus and around London for a richly anti-disciplinary approach.

Students will be able to cogently pitch a new artificial intelligence venture that they wish to launch in either a corporate setting or as an independent startup. They will learn to:

(1) describe the principles of AI and the "Five Tribes of Machine Learning” theory of Pedro Domingos, and relate the principles of AI to a specific area of interest to the student; 

(2) examine the ethical and risk management implications of AI+human systems, and the new regulations and guidelines that are emerging around them, including the new EU framework, the OPAL project and the Trusted Data framework;

(3) assess real world case examples of AI in business, including ethical implications and potential paths for resolution;

(4) create an application of AI to a particular commercial domain space, including (for technical students) creation or elaboration of a functional AI project or (for nontechnical students) rudimentary nocode/locode prototyping, while applying innovation ideation and launch methodologies such as Outthinking (Krippendorff) and Lean Startup (Reis);

(5) argue the merits of their AI venture including what problem it solves, and how it is unique, applying techniques of literary and dramaturgical theory (Joseph Campbell, Marshall Ganz, Barbara Minto).

Speaking to a student audience with no prior technical knowledge required (but extensible to those students with more of a technical background as well), the module will briefly explain the historical context of AI, then dive into contemporary trends and applications, and engage the students in a 'futurecasting' exercise to aim at new developments emerging into a commercial context over the next 3 to 5 years.  Rudimentary frameworks for new venture development and presentation will accompany AI subject matter-specific content.  Ventures will be contextualized with SWOT and other management strategy frameworks. Concepts such as unintended consequences, digital and data privacy, big tech data oligopoly and its implications for new AI ventures, open source AI projects and libraries, and ecosystem development will be touched upon.  Working in teams, students will create an outline for a new AI venture.  At the conclusion of the module, students will pitch their venture to an expert venture capital and corporate audience, for experience and feedback.

Learning and Teaching Approach

The module will combine lecture, guest speaker ('live case') perspectives, and structured team project development including tutorial feedback sessions with instructors [and mentors]. The combination of academic framework and theory, worked example, team-based projects, weekly application to practice, and personalised feedback will provide a richly intensive introduction to the translation of AI concepts into real-world problem/solution spaces.

Class participation, written assignments/quizzes, and interim business plan components will be used to assess progress towards the capstone submission and presentation. Students will also submit a brief mini-case of an existing AI venture or application to consider as a model or contrast to their own enterprise. Students will be graded on their written work, on classroom and tutorial contributions, and on their final presentation and accompanying written submission. A team assessment tool will be used to disaggregate individual contributions to group work.

Students will receive written and verbal feedback on assignments/quizzes and interim components of their capstone venture project, then additional feedback on the final verbal & visual presentation to an expert audience and their separate written capstone submission.