What are the ICAI modules

Sub-project assignment "Interdisciplinary Center for Artificial Intelligence" (ICAI)

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1 sub-project assignment “Interdisciplinary Center for Artificial Intelligence” (ICAI) IT education offensive focus III “Competence Center Applied Digitization” Artificial intelligence by Gerd Altmann for free commercial use. Guido M. Schuster (SCU) Founding Director of the Interdisciplinary Center for Artificial Intelligence Version: 9.0 Created on: Last change on: Approval ITBO-ProjA Approval ITBO-ProgrA Doc: ITBO_OST_TPA3_V9.0 Page 1 Print:

2 Proof of change version Reason for change short Z. Draft date, planning data, project costs, risks SCU input from core team, project management, PM / QM, comparison with SCU the other sub-projects OST format adopted from TP2 4.1 First review version SCU feedback from the first review entered SCU First release with ramp-up of the AI ​​modules SCU section "Organizational Change Management" added SCU spelling mistakes and ICAI logo SCU feedback from MSE Data Science profile leader added SCU university management Feedback from added SCU feedback from institute director IPM-FHS added SCU ITBO-ProjA feedback from added SCU ITBO focus III PL feedback added SCU New structuring, details added to the appendix SCU ITBO program management feedback, appendix SCU renamed to "Explanatory Supplement" and organized as a separate document 8.1 Feedback from the project management included SCU chapter 4.3 revised after feedback from the education department SCU 8.3 Feedback internal cantonal preliminary review: supplement KGA « Transf project »9.0 Final version after approval by the ITBO program committee SCU Doc: ITBO_OST_TPA3_V9.0 Page 2

3 Table of contents 1. Management summary 4 2. Initial situation Task objectives Framework conditions 6 3. Organization organs Project mandate OST, ITBO focus III organs Subproject Stakeholder Management 9 4. Subproject 3 “Interdisciplinary Center for Artificial Intelligence” Objectives, subgoals and measurement criteria Work packages and milestones overview Work packages and milestones Overview of phases and milestones Costs and required resources Transfer objects Project boundaries and interfaces Change management Change management processes Organizational change management Risk management Controlling Status reports Canton reporting Project committee OST communication Public communication Internal communication OST compliance Order placement Directories and references List of figures List of tables References 28 Doc: ITBO_OST_TPA3_V9.0 page 3

4 1. Management summary In the sub-project 3 Interdisciplinary Center for Artificial Intelligence (ICAI) there are five goals, all of which are aimed at establishing broadly applied artificial intelligence (AI; ​​broad-based AI) as a USP of the OST and thus to bring Eastern Switzerland into an optimal position for the upcoming AI wave. Compared to top AI, broad-based AI is not geared towards the world's best research results, but rather towards the broad application of established AI methods for solving important economic, industrial and social problems. Broad AI as a USP is very well suited for a university of applied sciences that is close to business, industry and society and is still completely vacant as a marketing term both nationally and internationally. The two most important ITBO goals of sub-project 3 are the training of OST lecturers (Z.1) and students (Z.2) in the AI ​​basics. For this purpose, new AI block courses (lecturers) and new AI modules (students) are offered and AI is also integrated into existing OST modules. In addition to these two fundamental training goals, there are three supporting goals: Establishing and maintaining an AI community at each location (Z.3), which organizes weekly AI consultation hours and public lectures. Establishing the AI ​​experience through interdisciplinary projects (line 4), which enable students and lecturers to apply what they have learned in practice, as well as publicizing the broad AI (line 5), which transmits the broad AI message of the OST to society and the economy and industry as well as to the potential students. This is achieved through an active website and presence on social media, as well as through an annual conference and an annual hackathon. All activities in sub-project 3 are to be transferred to regular operation in the long term. The project is processed in a core team made up of representatives from the six departments as well as the sub-project manager who belongs to the Interdisciplinary Cross-Sectional Topics (IQT) department. This means that each department and the IQT have the opportunity to directly influence the sub-project. The extended project team consists of all OST course directors, as they are responsible for the respective curricula. This is especially important for the achievement of the goals of the broad education of the OST students. Subproject 3 will receive funding from ITBO to the tune of 1.62 million francs until 2026. Additional funds for AI will be provided by the OST, as defined in the service mandate, in the amount of CHF 1 million. The financing by the ITBO is used exclusively for personnel expenses. It does not finance any infrastructures. The contribution is divided over the five different work packages of the sub-project each year, with exactly one work package being assigned to each of the five goals. The achievement of the respective goals and sub-goals is checked with six milestones, each of which was set at the end of an academic year (). Subproject 3 has an appropriate risk management system, which is regularly checked by the quality and risk officer. The most important risks relate to the availability of suitable employees, the parallel OST merger process and the Covid-19 risks, which make it difficult or impossible to hold lectures, exercises, projects and marketing events. Doc: ITBO_OST_TPA3_V9.0 page 4

5 2. Initial situation 2.1. Task The ITBO project mandate OST “Competence Center Applied Digitization” is divided into three sub-projects. The present sub-project order describes the content, planning and resources of the third sub-project (TP3) "Interdisciplinary Center for Artificial Intelligence" (ICAI) as defined in the ITBO project OST [1]. The following figure shows the overview of the ITBO project OST. Figure 1: Overview of ITBO project OST The sub-project order 3 consists of the present document, which is approved by the ITBO program committee, as well as the explanatory report, status, which is informative in character and represents a trend-setting component. Objectives Broad-based AI should become a USP of the OST become. This in turn will help Eastern Switzerland to counteract the brain drain from the region. Since broad-based AI can be marketed very well, it is also expected that the broad-based AI message will contribute to higher student numbers in all courses. In order to achieve the goal of the widely used AI for solving important economic, industrial and social problems at the OST, students and lecturers are trained in the basics of AI with the ITBO funds, an OST AI community is established, interdisciplinary AI projects are promoted and targeted Broad AI marketing activities supported at all OST locations. Doc: ITBO_OST_TPA3_V9.0 page 5

6 In the ITBO project order OST, the goals of sub-project 3 were defined, which are repeated in the table below for completeness. Objectives Z.1: AI training formats (modules) are available to all courses of study for their individual needs Z.2: All OST students know the possibilities and limitations of AI in their subject areas Z.3: Low-threshold access to AI advice and resources Z.4: Interdisciplinary, AI-based projects are funded Z.5: Profiling the OST and the Canton of SG Table 1: Subproject 3 Objectives according to the OST project results Structure of a module “Teach the Teachers”: Basic course in AI for all lecturers. Adapted to the previous knowledge and needs of the lecturer. Structure of a “Teach the Students” module: basic course for all students. Individualized according to previous knowledge per course. Development of the “Walk-in-Services” and “AI-Clinic” services: Contact persons at each location should provide uncomplicated assistance with AI. Defined criteria for projects worth funding and a description of the process for obtaining funding are available. The canton and university of applied sciences are perceived as a reference for training and using AI in practice. Objective Z.2 requires that all OST students know the possibilities and limitations of AI in their subject areas. This is achieved in stages and in conjunction with the OST's performance mandate, as the ITBO-financed AI courses are increased from year to year until around two thirds of the students receive AI basics (financed by the ITBO) at the end of the ITBO. For around half of the students, this will be done by attending a new basic AI module and for the other half by integrating AI into existing modules. The remaining students (around one third) can be found in computer science-related courses, such as computer science, business informatics, electrical engineering, system technology, economics and machine technology innovation, all of which have built up an AI / data science offer within the OST service mandate and / or are building. Thus, for this one third, no funds from the ITBO have to be used to achieve goal Z.2. Achieving these five goals should ensure that the OST can take on a national and international leadership role in the broad application of AI (broad-based AI). At the moment, companies, universities and states around the world are investing enormous amounts in top AI, so it is not a good strategy for the OST to want to run top AI with limited resources [2]. After the ITBO special program has been completed, the intention is to transfer all sub-project activities to normal operation. Framework conditions The political, legal, financial and personal framework conditions are listed in the ITBO program mandate and the OST project mandate. The relevant document for the sub-project order is the OST project order approved by the government council on [1]. Doc: ITBO_OST_TPA3_V9.0 page 6

7 3. Organization 3.1. Bodies OST project mandate, ITBO focus III Since the approval of the OST project mandate (ITBO focus III), additional roles have been filled. All organs that function at project level are listed below. The tasks of the respective bodies are already defined in detail in the project mandate and continue to apply unchanged. Function / role Designation Tasks Project client Government Approval of project order Approval of credit tranches Chair of the project committee Project committee Monitoring committee Quality assurance and risk management Prof. Dr. Daniel Seelhofer Rector OST Roger Trösch, Program Manager ITBO BLD SG Dr. Rolf Bereuter, Head of AHS Carlo Höhener, Administrative Director OST Prof. Dr. Luc Bläser, Head of Dep. Informatics OST Prof. Lothar Ritter, Head of Dep. Technology OST Prof. Dr. Sibylle Minder Hochreutener, Head of Intercultural Cross-Sectional Topics OST Prof. Alex Simeon (without voting rights) Representation from teaching, research, further education and services Roman Richiger Head of project committee Details see project assignment OST Control of overall assignment and implementation of sub-projects Details see project assignment OST Consultation according to technical needs ; Part of the concept of stakeholder management External specialist for quality and risk assessment Project management Prof. Alex Simeon Operational management Project details see project assignment OST Kommunikation Lic. Phil. Eva Tschudi communication concept; Responsible for internal and external communication Project support Dipl. Ing. Gabriele Kerschbaumer Operational support for the project management Table 2: Bodies project mandate OST 3.2. Organs Subproject 3 For subproject 3, the following project organization is established. The core team consists of representatives from the six departments (nominated by the respective department heads) and the sub-project manager (IQT). This means that every department / IQT has the opportunity to influence the sub-project at an early stage and every department / IQT is always fully informed about the status of sub-project 3. It is the task of the core team to actively represent sub-project 3 in their own departments / IQT. The extended project team consists of all OST course directors, as they are responsible for the respective curricula. This is especially important for achieving the goals of the broad education of OST students. Doc: ITBO_OST_TPA3_V9.0 page 7

8 Function / Role Designation Tasks Project Management Prof. Alex Simeon Operational Management Overall Project Chief of Staff OST Details see project assignment OST Management Subproject 3 Core Team Subproject 3 Extended Project Team Subproject 3 Table 3: Bodies Subproject 3 Prof. Dr. Guido M. Schuster, Founding Director of the Interdisciplinary Center for Artificial Intelligence (ICAI) Prof. Dr. Stiehler, Steve SGL BSc Social Work Prof. Dr. Brenner, Andrea, Teaching Department of Health Prof. Richter, Stefan, SGL Informatik Prof. Graf, Christian, Institute partner ILF Prof. Dr. Jaeschke, Peter Head of Institute IPM-FHS Prof. Dr. Frick, Klaus Institute partner ICE Prof. Wenk, Felix SGL civil engineering Prof. Bonderer, Reto SGL electrical engineering Prof. Dr. Nordborg, Henrik SGL Renewable Energies and Environmental Technology Prof. Richter, Stefan SGL Computer Science Prof. Petschek, Peter SGL Landscape Architecture Prof. Dr. Gysin, Hanspeter SGL Machine Technology Innovation Prof. Dr. Engelke, Dirk SGL Urban, Traffic & Spatial Planning Prof. Dr. Keller, Daniel F. SGL Industrial Engineering RJ Prof. Sonderegger, Urs SGL Industrial Engineering SG MNS Renz, Andrea SGL BSc in Nursing Prof. Dr. Baer-Baldauf, Pascale SGL BSc Business Information Systems Prof. Dr. Stiehler, Steve SGL BSc Social Work Prof. Jessen, Anna Head of ArchitekturWerkstatt Prof. Metzger, Thomas Head of Business Studies Prof. Dr. Wilhelm, Michael C. SGL BSc System Technology Operational Management Subproject 3 For details see project mandate OST Representation Department of Social Work Representation of Health Department Representation of Computer Science Department Representation of ABLR Department of Economics Representation of Technology Department Representation of Civil Engineering Representation of Electrical Engineering Representation of Renewable Energies and Environmental Technology Representation of Computer Science Representation of Landscape Architecture Representation Machine technology Innovation Representation in urban, traffic and spatial planning Representation in industrial engineering RJ Representation in industrial engineering SG Representation in care Representation in business informatics Representation in social work Representation in architecture Representation in business administration Representation in system technology Doc: ITBO_OST_TPA3_V9.0 Page 8

9 Two new courses are currently being planned at the OST, Physiotherapy and Management & Law. As soon as the managers are known, they will be integrated into the expanded project team so that these new courses are also represented. If necessary, other people can be called in for advice or reviews as required. Stakeholder Management The project committee develops a concept for stakeholder management. The tasks, responsibilities and information flows are defined for the various stakeholder groups. Subproject 3 then adopts these specifications. Doc: ITBO_OST_TPA3_V9.0 page 9

10 4. Subproject 3 “Interdisciplinary Center for Artificial Intelligence” 4.1. Goals, sub-goals and measurement criteria The goals of sub-project 3 were defined in the ITBO project OST. To make it easier to discuss these goals, a short form has been assigned to each goal. In the following table, each goal is compared with the corresponding short form. This short form then corresponds to the names of the work packages. Objectives Z.1: AI training formats (modules) are available to all courses of study for their individual needs Z.2: All OST students know the possibilities and limitations of AI in their subject areas Z.3: Low-threshold access to AI advice and resources Z.4: Interdisciplinary, AI-based projects are funded Z.5: Profiling the OST and the Canton of SG Short forms Z.1: Teach the Teachers Z.2: Teach the Students Z.3: AI Community Z.4: AI Projects Z.5: AI Marketing Table 4: Sub-project 3 objectives according to the OST project order with the respective short forms In the following five tables, sub-objectives and measurement criteria are defined for each of the five objectives in the OST project order. A detailed description of why these sub-goals were chosen and how the measurement criteria of these sub-goals can be met with the use of which resources can be found in the accompanying document “Explanatory supplement to the sub-project contract“ Interdisciplinary Center for Artificial Intelligence ”(ICAI) v1.1”. Doc: ITBO_OST_TPA3_V9.0 page 10

11 The point in time column refers to the point in time and the number of the relevant milestone (MS 1, MS 2, MS 6) and the APs column shows the work package involved.Objective Sub-goals Measurement criteria Time of APs Z.1: Teach the Teachers Item.1.1: "Teach the Teachers" AI develop module concept MK.1.1: Module concept is available MK.1.2.1: 2 modules were Item.1.2: 4 "Teach the Teachers" Carry out AI modules MK.1.2.1: 4 modules were accumulated Table 5: Subgoals and measurement criteria for goal Z.1: Teach the Teachers MS MS MS AP.1 Goal Subgoals Measurement criteria Time APs Z.2: Teach the students TZ.2.1 : Developing “Teach the Students” AI module concept TZ.2.2: Developing a concept for integrating AI content into existing modules TZ.2.3: 22 Carrying out “Teach the Students” AI modules TZ.2.4: Carrying out 22 modules with integrated AI MK.2.1 : Module concept is available Table 6: Sub-goals and measurement criteria for goal Z.2: Teach the Students MK. 2.2: Module integration concept is available MK.2.3.1: 3 modules became MK.2.3.2: cumulatively there were 7 modules MK.2.3.3: cumulatively there were 13 modules MK.2.3.4: cumulatively there were 22 modules MK.2.4. 1: 3 modules with integrated AI became MK.2.4.2: cumulatively there were 7 modules with integrated AI MK.2.4.3: cumulatively there were 13 modules with integrated AI MK.2.4.4: cumulatively there were 22 modules with integrated AI MS MS MS MS MS MS MS MS MS AP.2 Doc: ITBO_OST_TPA3_V9.0 page 11

12 Objective Sub-goals Measurement criteria Time APs TZ.3.1: Develop AI community concept MK.3.1: Concept is available MK.3.2.1: Weekly AI consultation hours at all locations MS MS Z.3: AI Community TZ.3.2: Weekly AI during the semester Carry out consultation hours at all locations MK.3.2.2: Weekly AI consultation hours at all locations MK.3.2.3: Weekly AI consultation hours at all locations MK.3.2.4: Weekly AI consultation hours at all locations MK.3.2.5: Weekly AI consultation hours at all locations with an average of 4 customers / week MK.3.3.1: 4 public AI lectures MS MS MS MS MS AP.3 MK.3.3.2: cumulatively 8 public AI lectures MS TZ.3.3: 20 public AI lectures at adult education center level MK.3.3.3: Cumulative were 12 public AI lectures MS MK.3.3.4: Cumulative were 16 public AI lectures MS MK.3.3.5: Cumulative were 20 public AI lectures MS Table 7: Partial goals and measurement criteria for goal Z .3: AI Community Doc: ITBO _OST_TPA3_V9.0 page 12

13 Objective Sub-goals Measurement criteria Time APs Z.4: AI projects TZ.4.1: 15 develop AI showcase project proposals TZ.4.2: 15 carry out AI showcase projects TZ.4.3: 20 develop student AI project proposals TZ.4.4: 20 Carry out student AI projects Table 8: Sub-goals and measurement criteria for goal Z.4: AI Projects MK.4.1.1: 3 implementable applications are available MK.4.1.2: Cumulatively there are 6 implementable applications MK.4.1.3: Cumulatively there are 9 implementable requests MK.4.1.4: cumulatively there are 12 implementable requests MK.4.1.5: cumulatively there are 15 implementable requests MK.4.2.1: 3 showcase projects were MK.4.2.2: cumulatively were 6 Showcase projects MK.4.2.3: cumulative 9 showcase projects MK.4.2.4: cumulative 12 showcase projects MK.4.2.5: cumulative 15 showcase projects MK.4.3.1: 4 adequate applications have been submitted MK.4.3.2: Cumulatively there are 8 adequate applications MK.4.3.3: Cumulatively there are 12 adequate applications MK.4.3.4: Cumulative left egen 16 adequate applications before MK.4.3.5: cumulatively there are 20 adequate applications MK.4.4.1: 4 student projects were MK.4.4.2: cumulatively there were 8 student projects MK.4.4.3: cumulatively were 12 students -Projects MK.4.4.4: 16 student projects were cumulative MK.4.4.5: 20 student projects were cumulative MS MS MS MS MS MS MS MS MS MS MS AP.4 MS MS MS MS MS MS MS MS MS MS Doc : ITBO_OST_TPA3_V9.0 page 13

14 Objective Sub-goals Measurement criteria Time APs Z.5: AI Marketing TZ.5.1 .: Put ICAI website & social media into operation TZ.5.2 .: ICAI website & operate social media TZ.5.3 .: Carry out annual conference TZ.5.4 .: Annually Perform hackathon Table 9: Sub-goals and measurement criteria for goal Z.5: AI Marketing MK.5.1.1: The ICAI website & social media is in operation MK.5.2.1: 3 showcase AI projects and 4 student AI projects Projects were published MK.5.2.2: 6 Showcase AI projects and 8 student AI projects were published cumulatively MK.5.2.3: 9 Showcase AI projects and 12 student AI projects were published cumulatively MK.5.2 .4: Cumulatively, 12 showcase AI projects and 16 student AI projects were published MK.5.2.5: Cumulatively, 15 showcase AI projects and 20 student AI projects were published MK.5.3.1: Conference was included around 50 participants MK.5.3.2: The conference was with around 100 participants MK.5.3.3: The conference was with around 150 participants MK.5.3.4: T The agung was with around 150 participants MK.5.3.5: The conference was with around 150 participants. MK.5.4.1: The hackathon was with around 10 participants. MK.5.4.2: The hackathon was with around 20 participants MK.5.4.3: Hackathon was with around 30 participants MK.5.4.4: Hackathon was with around 30 participants MK.5.4.5: Hackathon was with around 30 participants MS MS MS MS MS MS MS MS AP.5 MS MS MS MS MS MS MS MS Doc: ITBO_OST_TPA3_V9.0 page 14

15 4.2. Work packages and milestones Overview of work packages and milestones To achieve each of the five goals from the OST project order, a work package was defined and the following table shows the assignment of these work packages to the goals of subproject 3.As already mentioned, there is between the goals and the work packages a one-to-one assignment. Aim in short Z.1: Teach the Teachers Z.2: Teach the Students Z.3: AI Community Z.4: AI Projects Z.5: AI Marketing work package AP.1: Teach the Teachers AP.2: Teach the Students AP.3: AI Community AP.4: AI Projects AP.5: AI Marketing Table 10: Assignment of work packages to the sub-project goals As can be seen from the tables in Chapter 4.1, a milestone is set at the end of each academic year. The table below, as well as the illustration of the project phases on the next page, show this milestone planning. Milestone point in time Milestone Milestone Milestone Milestone Milestone Milestone Table 11: Milestone plan Doc: ITBO_OST_TPA3_V9.0 page 15

16 Overview of phases and milestones Subproject 3 follows the four phases of priority III “Competence Center Applied Digitization” as shown in the graphic below. Figure 2: Project phases The graph above also shows the six milestones at the end of the respective academic year. The timing of sub-project 3 can best be understood if the tasks for the various goals / work packages are shown in detail along a time axis. This can be seen in the illustration on the next page. In the figure, the horizontal lines colored in the same way correspond to the goals / work packages and the indicated vertical columns represent the two semesters per academic year. The timeline and the associated Gant chart are shown in the accompanying document “Explanatory supplement to the sub-project contract“ Interdisciplinary Center for Artificial Intelligence ”(ICAI) v1.1” in A3 format. Doc: ITBO_OST_TPA3_V9.0 page 16

17 1st line: AP.1 Teach the Teachers (TTT), with the block module in Rapperswil-Jona at the ICAI. 2nd line: AP.2 Teach the Students (TTS), with the new AI modules and the existing modules in which AI is integrated. 3rd line: AP.3 AI Community, with the AI ​​consultation hours and the lectures in St.Gallen, Rapperswil-Jona and Buchs. 4th line: AP.4 AI Projects, with the 3 showcase projects and the 4 student AI projects per academic year. 5th line: AP.5 AI Marketing, with the ICAI website & social media as well as the two events, the hackathon and the conference. 6th line: Subproject management Figure 3: Subproject 3 Timeline Doc: ITBO_OST_TPA3_V9.0 page 17

18 4.3. Costs and required resources Costs and how they are differentiated from the OST service mandate The costs for sub-project 3, which are billed via the ITBO, are estimated at CHF 2,620,000.- and thus correspond to the approved OST project mandate [1] from the ITBO funds are not used to finance any infrastructure costs. Rather, these funds should be used for the personnel costs for teaching the students and for the training of the lecturers in the AI ​​basics in the sense of a set-up financing (see costs broken down according to work packages and personnel categories). In the service mandate for the OST for the period, contributions for the ICAI in the amount of CHF 500,000 per year are provided (cf. [3], point 3.5 Most important projects and reforms; expansion of competencies in the field of artificial intelligence and table 13 OST financing model). These funds are used in particular for the required infrastructure, for operating materials, for hardware depreciation, for building up competencies in research, for marketing (proportionately) and for internationally significant collaborations. Since the ICAI is based on the specialist group of Prof. Dr. Guido M. Schuster (ICOM / HSR, Subproject Leader Subproject 3), some of the required rooms, PCs, specialized software and hardware are already available. The AI ​​high-performance computers, which are located on the OST campus Rapperswil-Jona, are also accessible to the ICAI. In addition, the Institute for Computational Engineering (ICE) on the Buchs campus has AI specialists and GPU clusters at their disposal, with whom they will actively contribute to the ICAI. Experts, hardware and rooms are largely available to enable subproject 3 to get off to an optimal start. The resources that are still required are drawn from the funds set in the service order. Personnel costs For the calculation of costs, it is assumed that teaching 24 ECTS / semester (48 ECTS / year) corresponds to 100% lecturer utilization. 1. Costs broken down according to work packages The following table shows the costs according to goals / work packages (and sub-project management) over the entire ITBO period. Objectives work packages Costs Z.1: Teach the Teachers AP.1: Teach the Teachers CHF 472,000 Z.2: Teach the Students AP.2: Teach the Students CHF 695,000 Z.3: AI Community AP.3: AI Community CHF 173,000 Z.4: AI Projects AP.4: AI Projects CHF 701,000 Z.5: AI Marketing AP.5: AI Marketing CHF 302,000 Sub-project management CHF 277,000 Total CHF 2,620,000 Table 12: Costs according to goals / work packages 1 Calculatory basis: students 30CHF / h, academic staff 75CHF / h, lecturers 105 CHF / h, 1920 bookable hours per year Doc: ITBO_OST_TPA3_V9.0 page 18

19 The preceding table can also be visualized as a bar diagram. Chart Title Figure 4: Costs according to work package As you can see in the previous figure, most of the resources are used for basic AI lessons (for lecturers and students), which seems right for an IT education offensive. Thus, the total costs of sub-project 3 with this planning amount to CHF 2,620,000 and correspond to the budget in the approved OST project contract [1]. Costs broken down according to personnel categories Alternatively, the costs can also be shown according to the respective personnel categories, as can be seen from the figure below Master Students Digital Marketing & Event Assistance WiMi Lecturers TPL Figure 5: Costs according to personnel categories Doc: ITBO_OST_TPA3_V9.0 page 19

20 The figure above shows that the majority of the ITBO funds are invested in the lecturers of the OST. Second most of it flows into the WiMi, which develop the AI ​​showcase projects. Costs as a function of the project duration The figure below shows the planned costs as well as the accumulated planned costs as a function of the project duration. It should be noted that the axis on the right applies to the costs per quarter in CHF, while the axis on the left applies to the cumulative costs in CHF. Cost Cumulative Cost Q20 3Q20 4Q20 1Q21 2Q21 3Q21 4Q21 1Q22 2Q22 3Q22 4Q22 1Q23 2Q23 3Q23 4Q23 1Q24 2Q24 3Q24 4Q24 1Q25 2Q25 3Q25 4Q25 1Q26 2Q26 3Q Figure 6: Costs as a function of the project duration DocA ITBO

21 4.4. Transfer objects The ITBO special loan sees itself as the development financing of the OST activities in the broad AI. After the end of the ITBO special program, the intention is to transfer all activities of the ITBO project to regular operation in the long term, provided that the activities are successful. The graphic below gives an overview of both the planned activities of the OST as part of its regular training mandate and those measures within the framework of sub-project 3 that are made possible by the ITBO. It is clear that all measures will continue after the ITBO has been completed. The university management of the OST has made appropriate fundamental decisions on these activities. Financing ITBO computer science-related AI modules FHO end financing further training af & e FHO FHO hardware FHO Figure 7: Overview of transfer objects In the area of ​​teaching, new AI modules are set up for the less computer-related courses and / or AI basics are built into existing modules. All of these activities are funded by the ITBO. There is already an AI / data science offering in the IT-related courses. This is continuously expanded as part of the curricular development of these courses. These teaching activities are financed by the OST. The further education offer in the field of AI is supported by the further education budget of OST. Project activities in the AI ​​environment are handled in the respective organizational units of the OST (institutes, competence centers, etc.). They are therefore part of the OST's regular budget. The ICAI as an organizational unit takes on a coordinating role both in advanced training and in af & e. Finally, the infrastructure, which is used by all service areas, is financed and maintained by the OST. Doc: ITBO_OST_TPA3_V9.0 page 21

22 4.5. Project boundaries and interfaces The OST would like to take a leading role in the broad application of AI nationally and internationally, this should become an OST USP. Thus, the ICAI is not about cutting-edge AI research, but about the broad application of established AI methods to solve important problems from business / industry and / or society (broad AI instead of top AI). Thus, all ICAI training content, which is financed by the ITBO, is on a generally accessible level. Neither higher mathematics skills nor programming experience are required. In addition, the focus is on the Bachelor level, as this will be the final academic degree for the majority of OST graduates and thus the greatest impact can be achieved at this level with the available resources. In-depth AI training takes place in the regular modules (from the OST's service mandate) of the various specialized courses and is therefore not part of the ITBO funded AI basics modules. For example, the OST offers the following for specialization. In computer science, the two modules AI Foundations and AI Applications, which focus on the most important parts of AI for software engineers. The statistical machine learning module, which was introduced in 2015, and the deep learning module for electrical engineering established in 2018, are geared towards the fundamental algorithms and the underlying mathematics. The electrical engineering modules Image Processing and Computer Vision I & II, which were introduced in 2011, convey the practical and theoretical basics for computer vision systems, which today are based to a significant extent on AI. A new course in Computational Engineering was introduced for the System Technology course, which, in addition to modeling and digitization, has AI as one of the main training objectives. This is not a complete listing of all AI / Data Science modules or plans of the OST, but this list shows that many computer science-related courses of the OST are moving in the direction of AI. Subproject 3 has direct interfaces with subprojects 1 and 2 of the OST project mandate: SP1 “Innovative teaching and learning environment”: the field of activity (a) “Digitally supported teaching and learning” has a direct relationship to the teaching methods of subproject 3, while the Field of activity (b) “Smart Factory” will be related to subproject 3 in terms of content, since “Smart” is often a synonym for AI. SP2 “Market expansion of IT offers”: here, cooperation with regard to the curricula is important in the sense of the propagated “broad AI”. For example, basics AI modules could be offered in the general education category in the computer science course in St.Gallen. The regular exchange between the three sub-project managers and the participation of core team members in the other sub-projects ensure internal coordination and cooperation. Possible points of contact and interfaces to other ITBO projects will probably only become apparent in the course of the entire program, e.g. for vocational training and / or for the PHSG. Doc: ITBO_OST_TPA3_V9.0 page 22

23 4.6. Change management Change management processes Changes must be requested from the program committee if the goals or the achievement of goals change or are in danger. Project management, sub-project management, as well as quality and risk management ensure that foreseeable changes are presented to the project committee and the program committee in good time. The procedure is then based on the cantonal program specifications. Changes that represent a shift within a sub-project without questioning the goals or achievement of goals are dealt with directly by the project committee in accordance with the OST project mandate Organizational change management Since September 1, 2020, the three sub-schools (FHS, HSR and NTB) have been the University of Applied Sciences Eastern Switzerland (FHO) has been transferred to the new OST Eastern Switzerland University of Applied Sciences.The current merger process at the various management levels and the merging of the operative business are in full swing and are therefore running parallel to ITBO focus III “Competence Center Applied Digitization”. Therefore, the merger process was also explicitly identified as risk R11 in the following risk management section. This merger is generally helpful for subproject 3, as it enabled the establishment of the ICAI outside of the departments. The ICAI belongs to the “Interdisciplinary Cross-Sectional Topics” (IQT) department and in this position has the opportunity to work with all degree programs. The new OST, with its non-IT-related departments, which represent society in general better than the Technology Department and / or the IT Department alone, enables the implementation of the “Broad AI Strategy” in Eastern Switzerland through the sub-project 3. Das Subproject 3 core team carefully observes the internal structure and politics within the OST in order to identify the changes which could have a direct or indirect influence on the success of the subproject. The basis for these observations is good and open internal communication from a well-networked sub-project manager. If changes with negative potential are identified, the sub-project planning is adjusted so as not to jeopardize the success of sub-project 3. In order for this to be possible, continuous planning and not fear of change is required. The university management of the OST is informed at regular intervals by the project manager about the ITBO. If there is a need for action that leads to a necessary change, this is initiated by the university management and applied for down to the respective decision level. Doc: ITBO_OST_TPA3_V9.0 page 23

24 4.7. Risk management In the OST project, risks of general project management were identified, while the specific risks for sub-project 3 are listed here. Covid-19 risks are currently to be taken very seriously (R1 in the table below). The risks were qualitatively assessed according to the probability of occurrence (probability: 1 to 5) and extent of damage (impact: 1 to 5), then multiplied and presented as a risk factor (risk score: 1 to 25). ID Title Description Responsible Category Indicators of occurrence Probability Impact Risk score Preventive Measure Corrective Measure Success factors Probability Impact Risk score R1 Pandemic still active It is currently unclear when the lessons and / or conferences can be carried out normally again, ICAI Project Management BAG and Canton St.Gallen prohibit classes and / or conferences Likely Critical 20 none We would conduct the classes and / or conferences online or even skip them So that everything can be online at any time Likely Moderate 12 R2 Shortage of employees Because of the tight Time horizons and the lack of skilled workers in AI, it is expected that the recruitment of suitable employees will be difficult ICAI Project Management Too few highly qualified employees Major 12 use existing networks. 0 page 24

25 ID Title Description Responsible Category Indicators of occurrence Probability Impact Risk score Preventive Measure Corrective Measure Success factors Probability Impact Risk score R7 Insufficient project entries Since these AI projects are new as such, not enough projects are proposed at the beginning ICAI Project Management Less than 3 project proposals e Likely Moderate 12 The ICAI must advertise the AI ​​projects and thus ensure that enough projects are proposed The ICAI itself proposes projects Sufficient (> = 3) realistic projects are proposed Minor 6 R8 AI consultation hours not used The public services are not used really used. Thus, such joint projects may be used for political maneuvers. Since Master AI / Data Science students are planned for both the AI ​​Walk-in Services and the AI ​​projects, the ICAI has to accept 2-4 Master AI / Data Science students every year. The marketing material is always up-to-date. The discussions are factual and constructive. Everyone is moving in the direction that broad-based AI becomes an OST USP. Sufficient Master's AI / Data Science students can be recruited every year. The projects entered can be solved by the students with a high degree of probability with established AI Enough student AI project inputs There are not enough inputs that students can solve with established AI methods ICAI Project Management Less than 4 student AI project inputs Likely Moderate 12 The ICAI must advertise these student AI projects Do projects, for example in the AI ​​modules that students attend The ICAI proposes such projects itself Sufficient (> = 4) student AI projects are entered Minor 6 Table 13: Risk table according to Innosuisse Doc: ITBO_OST_TPA3_V9.0 page 25

26 This table results in two risk matrices, one before the preventive and corrective measures, and a second after the proposed measures have been applied. According to the Innosuisse method, this is intended to express the effectiveness of the proposed measures. R8 R14 R7 R10 R1 R1 R13 R6 R9 R3 R12 R5 R2 R4 R11 R10 R14 R8 R9 R7 R2 R6 R12 R5 R3 R4 R13 R11 Figure 8: Likelihood v.s. Impact before the measures Figure 9: Likelihood v.s. Impact according to the measures Doc: ITBO_OST_TPA3_V9.0 page 26

27 5. Controlling 5.1. Status reports from the canton Regular status reports (according to the cantonal submission) are submitted to the program committee via project management via the project committee and program management. The basis for this is the internal reports of the sub-project managers, which are standardized and queried periodically (see 5.2) Reporting OST project committee Periodic short reports on the progress of the sub-project and for risk assessment are submitted to the OST project management. Status reports with the goals achieved in the period, the status of costs, resources and deadlines, the risk assessment and the goals for the next reporting period are reported for the regular meetings of the OST project committee. 6. Communication 6.1. High-public communication Subproject 3 follows the established communication process. External communication with the program manager in the BLD is coordinated and adequate publication is agreed (according to the communication process diagram) Internal communication OST Internal communication within the core team and the extended team takes place via collaboration platforms and regular (online) meetings. The OST employees are regularly informed about the progress of the project via the web-based Confluence tool as part of the OST's internal communication concept. 7. Compliance Compliance for the sub-project guarantees its lawful implementation. Particular cornerstones for this are: Compliance with cantonal requirements and laws Protection needs analysis in accordance with the requirements for information protection and data protection Consideration of the requirements for diversity 8. Placing the order The program committee approved the project order at its meeting on November 6, 2020. With this resolution, the present sub-project contract is considered approved. Doc: ITBO_OST_TPA3_V9.0 page 27