Welcome to AI in Practice: Prepare for AI! Artificial Intelligence (AI) will determine our future prosperity and well-being, and it will have an impact on all business sectors, our private lives and society. But do you have the knowledge and confidence to support the integration of AI into your organization?
This course, AI in Practice: Prepare for AI, is the 1st course of the online education program AI in Practice. The course gives you a kaleidoscope of examples of applications of AI in various organizations, outlines the state of the art in modern AI research, and provides practical tools for integrating AI into your own organization. The program AI in Practice is built from two initial courses, AI in Practice: Prepare for AI and AI in Practice: Applying AI. The program will have a 1st run in November 2020 as a free MOOC on the edX platform (exluding the individual assignment), with a verified Professional Certificate available for learners that join the whole course, including an individual assignment.
The AI in Practice: Prepare for AI course is designed for people who want to apply AI in their own practical situation.
For the experienced manager who wants to know what AI can do for her own organization.
For the data analyst or business consultant who wants to understand how AI can be applied in the business processes of the company for which he works.
For the student who wants to understand how the results of AI research can be translated into practical applications.
Learners that follow this course should have prior knowledge of business processes in their organizations, although technical knowledge or AI skills are not required. After taking this course learners should be able to:
Describe the state-of-affairs in current AI research, in terms of context, problems, research approach, results;
Identify the implications of current AI research on improvement strategies for industry, academia, and education.
Your host during this course is Hennie Huijgens, developer of ICAI labs and AI researcher at the Delft University of Technology in the Netherlands. He is assisted by two Student Assistants. This course does not have one single instructor; the lessons are presented by a large number of top researchers in the field of AI who work within so-called ICAI labs, Innovation Centers for Artificial Intelligence in which specialists from public and private organizations work closely with top researchers from Dutch universities. You can find more information about ICAI labs here. Short bio's of all lecturers in this course are provided in the Meet your Teachers section.
The online education program AI in Practice is set as part of the so-called ICAI Academy. The program consisting of two consecutive 5-week courses will run as a Professional Certificate program on the edX platform in late 2020 and early 2021. Each course lasts five weeks and is composed of five modules. Every week a new module starts. Course AI in Practice: Applying AI is offered immediately following course AI in Practice: Preparing for AI. Both courses include an individual assignment.After successful completion of both courses, a verified certificate can be obtained.
In the Getting Started section you’ll get to know the program and course structure, get familiarized with the virtual learning environment, complete your profile, meet your fellow students and the e-moderators and get an overview of the presenters of video lessons. These introductory tasks should be completed in the beginning of the course, after your first login.
The program AI in Practice is built from two initial courses:
The course AI in Practice - Preparing for AI is the first course of a series of two course in the online education program AI in Practice. This first course lasts five weeks and is composed of five modules. Every week a new module starts. Each module consists of a learning sequence provided by lecturers from so-called ICAI labs (Innovation Centers for Artificial Intelligence in the Netherlands), working in industry or academia. A learning sequence is built from a series of videos, each video in the series is followed by a number of quick questions. Besides that, the course contains an individual assignment that is executed in weekly iterations in all modules. In some modules a bonus track is provided.
The course is built from five main topics on AI in Practice:
Multimodal Machine Learning and Image Analysis - the National Police Lab AI (National Police and University of Amsterdam) and AI for Scientific Discovery in Publishing - the Discovery Lab (Elsevier and Vrije Universiteit).
Exploiting Structure in Deep Learning - Delta Lab (Bosch and University of Amsterdam) and a Bonus Track on Questions for Data Scientists in Software Engineering (AI for FinTech Research).
Thematic Track on Compliance & Ethics of AI - a variety of labs (University of Twente, Vrije Universiteit, ING, and Dutch National Police).
AI applications in FinTech - the AI for FinTech Research (ING and Delft University of Technology).
Robotics - the AIRLab Delft (Ahold Delhaize and Delft University of Technology).
In each module of this course each topic is explained from the perspective of a selection of guest lecturers from ICAI labs, working in industry or academia.
The goal of this course is to learn to recognize and understand the implications of Artificial Intelligence for organizations, and the importance of compliance and ethics when AI is applied in practice. In this course we use concrete examples to illustrate how AI can be of value in both private as well as public organizations – no matter how large or small, simple or complex they may be. As one example we look at the Dutch National Police where the use of AI systems and techniques has helped remove the burden of routine administrative and operational tasks. AI helps in handling and processing large amounts of data, improves intake processes and investigation workflows and eases communication within the police and also deals with wider potential transparency and privacy issues.
As the first part of our two-course program AI in Practice, this course will prepare learners for the integration of AI in your organization by understanding what it can achieve and recognizing the potential implications, including compliance and ethical considerations. In the course we present a variety of case studies from public organizations, top Dutch universities and private enterprises in the financial, retail and publishing sectors such as the National Police, ING, Ahold Delhaize and Elsevier.
AI in Practice – Preparing for AI gives learners the ammunition to prepare for the many manifestations of artificial intelligence that we will be dealing with in the coming years and to understand the significance of this for the organization in which they work.
After taking this course learners will be able to:
Describe the benefits and challenges of performing AI research in organizations, in terms of context, organizational background and problems, research approaches and results.
Identify the implications of implementing AI in terms of improvement strategies for organizations in industry, academia and education.
Understand the aspects of AI compliance and ethics and their significance for a learners own organization.
Write an essay about the implications of AI for industry, academia, education, or compliance and ethics.
As an online learner we expect you to be an active participant in this course, contributing to a positive atmosphere by questioning, sharing and helping out others, engaging in meaningful discussions where knowledge construction is revealed.
Regarding deadlines, we expect you to keep on track in order to benefit from learning within a community. This course is meant to be a place where you learn with and from others. In this sense, we'd like you to experience collaboration and peer-feedback, so please make sure you follow with other participants in order to enrich the overall learning experience.
Furthermore we expect you to follow forum and collaboration guidelines, and to respect the course policies and academic integrity.
The e-Moderator and the Student Assistants will guide you throughout the course, launching the weekly content, promoting and engaging in discussions, and providing feedback regarding your performance after each week. Guidance and support will happen on a regular basis, mainly every day.
The course is organized in a number of topics, divided over 5 weekly modules, each week a new module is started. Modules stay open for answering quick questions and performing a team assignment for two weeks. A detailed overview of the course schedule, including modules, topics, presenters, and start and end dates, is presented below.
In the getting started section you’ll get to know the course structure, get familiarized with the virtual learning environment, complete your profile, meet your fellow students and the e-moderator. These introductory tasks should be completed in the beginning of the course, after your first login.
The following overview gives a holistic view on the content of all modules, including lessons and team assignment, lecturers, and the date on which a module will be opened for students. In the overview individual assignments are indicated by the symbol @.
3. Assessment: Write an essay on any implications of AI
3.1. Start thinking about implications of AI in Module 1
Besides being able to describe the state-of-affairs in current AI research, there is another learning objective of this course which is to identify the implications of current AI research on improvement strategies for industry, academia, and education. What can we learn from ongoing research on AI, and how can these learnings help to improve business processes in industry, public organisations, academia, or education?
In order to identify improvement strategies for their own organization we set up an individual assignment during the course that ask learners to write an essay about the implications of AI for industry, academia, education, or compliance and ethics. To support you, we provide some tools for writing such an essay, emphasizing that we would like learners to think creatively and use their own approach or style for writing an essay:
Essays usually begin with a question and seek to answer that question based on research into existing theories and through the writer’s own evaluation. An essay may include results of practical research but only in so far as it may help support the writer’s conclusions.
Essays can be descriptive, discursive, evaluative, etc. This is dependent on the process given in the essay question. Content usually involves a synthesis of knowledge gained from existing texts and from the author's own opinions and argument.
Usually essays use an introduction and conclusion format. The main content, findings, analysis etc. come in between. In an essay, the thought process taken from the question dictatesthe structure of the main body of an essay.
Prepare an essay that addresses any implications of AI and discuss this with peer learners. The goal of the individual assignment is to stimulate you to creatively think about the implications of AI, especially with regard to your own organization. We want you to be creative in both the structure and the content of your essay, as long as it adds value to your fellow learners. In order to support you as much as possible to write a high quality essay, we ask you to perform a self-assessment in Module 3 to discover where improvements can be made.In Module 4 you complete the essay based on points for improvement from the self-assessment and upload your final essay for final review. The final review - also referred at as peer review - is carried out in Module 5 where fellow learners conduct a final assessment of your essay.In Module 5, you also assess a number of essays written by fellow learners.
3.2. Think creatively with SketchDrive in Module 2
For the second assignment of this course in Module 2, you are asked to think creatively about the implications of AI in your own context which you will submit via SketchDrive. We use this tool in this course as a platform for visual interaction and collaborative learning and we use it to captivate learners, boost engagement and raise your course completion rates.
To help you maximize the quality of your essay, we ask you to perform a self-assessment in Module 3.In that self-assessment we use the following assessment criteria:
Criterion Name
Criterion Prompt
Poor (1)
Fair (3)
Good (5)
Purpose and question
What is the purpose of the essay and the question that the author seeks to answer? Essays usually begin with a question and seek to answer that question based on research into existing theories and through the writer’s own evaluation. An essay may include results of practical research but only in so far as it may help support the writer’s conclusions.
It is unclear what the purpose is of the essay; the question is missing or not very clear. There is no link with research into existing theories and the writer's own evaluation.
The purpose and question of the essay are defined, however, no clear link is made with research into existing theories and the writer's own evaluation.
The purpose and question of the essay are very clearly described, and a very clear link is made with research into existing theories and the writer's own evaluation. The essay includes results of practical research that help support the writer's conclusions.
Content
What is the content of the essay? Is it descriptive, discursive, evaluative, or does it look at the content in another way? Essays can be descriptive, discursive, evaluative, etc. This is dependent on the process given in the essay question. Content usually involves a synthesis of knowledge gained from existing texts and from the author's own opinions and argument.
The content of the essay is described in a very unclear way. It is not clear what parts of the content are a result of knowledge gained from existing texts or the author's own opinions and argument.
The content of the essay is described in a clear way. However, it is not always clear what parts of the content are a result of knowledge gained from existing texts or the author's own opinions and argument.
The content of the essay is described in a very clear way. It is clear whether the essay is descriptive, discursive, evaluative, or written in another way. The essay involves a clear synthesis of knowledge gained from existing texts and from the author's own opinions and argument.
Format and structure
What is the format and the structure of the essay? Usually essays use an introduction and conclusion format. The main content, findings, analysis etc. come in between. In an essay, the thought process taken from the question dictates structure of the main body of an essay.
The format and the structure of the essay are unclear and messy. There is no proper introduction and a conclusion is missing.
The format and structure of the essay are clear, however, a proper introduction is missing and the conclusion is not always clearly linked with the question and purpose.
The format and structure of the essay are very clear. The essay uses an introduction and conclusion format, with a very strong link with the question and purpose. The thought process is described very clear.
Idea and comprehensiveness
Determine if there is a unifying theme or main idea. Does the essay show content that is of added value for other learners? Is there significant information provided? Does it seem complete? How likely is it that others will find this deliverable really useful?
Difficult for the reader to discern the main idea. The essay is too brief or too repetitive to establish or maintain a focus. Not a great contribution for other learners. Some content is there but things are either limited, incomplete or maybe even incorrect. Not something to recommend to others. There is much better content elsewhere.
Presents a unifying theme or main idea, but may include minor tangents. The essays stays somewhat focused on topic and task. Presents a summarized and comprehensive overview of an AI topic. It is a contribution to the knowledge, but it could be better. Highly relevant information is missing or available elsewhere and not pointed at. It is ok, but not brilliant.
Presents a unifying theme or main idea without going off on tangents. The essay stays completely focused on topic and task. Presents a clear contribution to the knowledge of AI. Target audience that reaches this deliverable is likely to get a complete picture and no need to look much further. It seem rather complete and the knowledge of AI has been expanded with a good additional contribution.
3.4. Upload your final essay for peer review in Module 4
In Module 4 we ask you to improve your essay, based on the results of the self-assessment that you performed in Module 3. You can use the assessment criteria as used in the self-assessment to check whether you covered all aspects that will be assessed in the peer review by fellow learners in the next module; see section 3.3 of this syllabus for these assessment criteria. Once finalized we ask you to upload your final essay for peer review by fellow learners in the next module by 1st Dec 2020.
We explicitly point out to learners that the upload of the final essay in Module 4's upload point must be done before Module 5 starts: December 1, 2020 12:00 UTC at the latest. This is to allow for sufficient time to undertake the peer review.
In this last module we ask you to review at least 5 essays that were written by your fellow learners. Review will be performed according to the assessment criteria that are to be found in section 3.3. in this syllabus:
Purpose and question
Content
Format and structure
Idea and comprehensiveness
We strongly encourage you to share your ideas and thoughts about review that you perform with your fellow students
Please note: Peer Review of a final essay is only accessible for learners who enrolled for a professional certificate.It is possible for learners that enrolled for the free course to upgrade during the course to a professional certificate up to December 1, 2020.
As mentioned, we explicitly point out to learners that the upload of the final essay in Module 4's upload point must be done before Module 5 starts: December 1, 2020 12:00 UTC at the latest.
In order to successfully complete the course and to earn a certificate you need to score 65% or more in the final grade. All assignments are mandatory.
Quick Questions that follow each video lesson: 15%
@SketchDrive Assignment in Module 2: 15%
@Self-Assessment of your draft essay in Module 3: 30%
@Peer Review of your final essay in Module 5: 40%
Quick Questions will be graded based on the best 3 out of 6 individual series of lessons grades. Therefore, 3 Quick Question assignments may be missed without a grade penalty.
Grading will be finalized once the last unit is ended (December 15, 2020).
Learners who achieve a passing grade in AI in Practice: Preparing for AI earn a certificate of achievement. A certificate will indicate that you have successfully completed the course, but will not include a specific grade. Certificates will be issued by edX. There are two options for this course: an ID verified certificate, and a free honor code certificate. For more information, please see the Certificates & Credits section of the Student FAQ (https://www.edx.org/student-faq).
All educational resources will be available in the course. They consist of short videos and interviews with guest lecturers to support you in the completion of the weekly learning activities.
When video lessons are based on finalized scientific research, we provide a link to the applicable papers underneath the video.
No special tool or software will be needed to complete this course, except for the use of edX plug-in SketchDrive in Module 2.
If you choose to reuse or repost DelftX course materials you must give proper attribution. Please utilize the following citation and refer to this MOOC:
"[TITLE OF WORK –with hyperlink to material] by TU Delft / [LECTURER NAME -with hyperlink to lecturers page] is licensed under CC-BY-NC-SA 4.0. This material was created by or adapted from material posted on [TITLE MOOC- with link to MOOC start page].”
Or if it is a derivative please use following citation:
“This work [Your title] by [Your name] is a derivative of “TITLE OF WORK –with hyperlink to material] by TU Delft / [LECTURER NAME -with hyperlink to lecturers page] and (re)licensed under CC-BY-NC-SA 4.0. This material was created by or adapted from material posted on [TITLE MOOC- with link to MOOC page].”
