Discover the world of generative AI. The participants will learn what is generative AI and what the basic implementations of Generative AI they can deal with and they will acquire hands-on experience on how to solve their problems, develop new business ideas and perform proper decision-making using these tools.
Course Overview Table
| Chapter | Details |
| Partner | University “St. Kliment Ohridski” – Bitola, Faculty of Information and Communication Technologies |
| Title | Generative AI Implementation – new business ideas source |
| Service | Course |
| Target Group | Employees in SME’s, Startups, Public Administration, Logistics, Industry, private or public organizations and all other individuals in need to learn Generative AI implementation basics |
| Format | In-Person Training |
| Focused on Key Technologies | Artificial Intelligence, Generative AI implementation |
| Status | Ready to offer |
| Stakeholders from SME/PA Side | Organizations seeking to implement innovations and improve decision-making through the implementation of generative AI in the working process. |
| Requirements for Participation | Basic computer literacy; interest in Generative AI implementation |
| Estimated Duration | 16 hours |
Description of the Course
The course “Generative Artificial Intelligence – new business ideas source” will provide learners with the basics of Generative of Artificial Intelligence (GAI) and its implementation. The course will provide the participants with a basic understanding of the practical impact GAI can have on their organization, and how professionals can apply GAI in their own organizations. This course helps learners to recognize the opportunities and threats GAI could present for their industries. The course is designed to focus on
Generative AI as the forefront of the AI revolution, with immense ability to create new content (text, images, audio, video, etc.) that didn’t exist before which opens vast possibilities for a business to start leveraging generative models to transform traditional industries and offer new services.
The course is structured over four days with equally distributed load:
Module 1: Introduction to Generative Artificial Intelligence
What is GAI and how it generates content (text, images, code)
Types of GAI tools like like GPT-4, Grok, Cortana, DALL·E, Stable Diffusion, Codex, …
Dive into the fundamentals of machine learning and neural networks to better understand how generative models work. Ethical use of GAI.
Module 2: Applications of GAI with Existing Tools.
Review of real-world examples with platforms that offer APIs or pre-trained models, like OpenAI, Hugging Face, or DeepAI. Work with text-to-image models (e.g., DALL·E) or text generation models (e.g., GPT-4) to understand their capabilities.
Module 3: Create an MVP (Minimum Viable Product):
Identify a specific problem and create a prototype using GAI. For example, an MVP for an AI-powered developing audio snippets from text inputs, generating sounds that correspond to visual content based on user preferences.
Module 4: Implement Use Case:
Understand the target audience and assess their needs. Look at competitors, existing solutions, and market gaps to shape the business model. Gather feedback from potential customers and iterate on the product. Once the prototype is ready, test it with early users, refine it based on feedback, and gradually scale the business. Use the AI-generated content to grow the marketing efforts of the participant’s organization.
GAI is a rapidly growing field with huge potential for new business ideas. By learning this technology, participants can build unique services that meet the needs of both businesses and individuals.
Upon completion of the course, learners will be able to: understand core concepts of GAI, and diffusion models, and their applications in text, image, audio, and video generation, to gain hands-on experience with tools to build, train, and fine-tune generative models and to apply GAI to real-world problems, such as content creation, data augmentation, or creative design, through projects or case studies. They will learn to design generative architectures and understand the ethical implications of GAI, such as deepfakes, misinformation, and bias amplification, and learn strategies for responsible AI development.
Additional Course Information
| Category | Details |
|
Developed skills |
Participants will acquire knowledge and skills, including: |
| Skill 1: Understanding core concepts and their applications in text, image, audio, and video generation.
Skill 2: Technical Proficiency: Gain hands-on experience with tools to build, train, and fine-tune generative models. Skill 3: Model Design and Evaluation: Learn to design generative architectures, optimize hyperparameters, and evaluate model performance. Skill 4: Data Handling Skills: Develop skills in curating and preprocessing datasets for training robust generative models, including addressing biases and ensuring data quality. Skill 5: Ethical Awareness: Understand the ethical implications of GAI, such as deepfakes, misinformation, and bias amplification, and learn strategies for responsible AI development. Skill 6: Practical Application: Apply GAI to real-world problems, such as content creation, data augmentation, or creative design, through projects or case studies |
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| Learning MethodsUsed | Lectures, hands-on exercises, group discussions |
| References/Resources | Case studies in practical implementation, official documentation for GAI |
| Overview Slides | Supporting slides and materials will be provided on the course platform |