AI for Application Programmers

Artificial Intelligence (AI) is currently one of the fastest-growing fields in technology. For an application programmer without deep knowledge in AI, it's important to know that AI now offers many tools and libraries that allow easy integration into applications without the need for a profound understanding of algorithms. These tools, often provided as cloud services by major tech companies, enable developers to implement features like image recognition, text analysis, or recommendation systems with minimal effort. Additionally, there are many available resources, courses, and tutorials to help programmers quickly grasp the basics of AI.

Target Audience:

• Developers, especially those working on the backend of applications.
• IT architects.

Course Objectives:

The seminar will provide fundamental information about AI so that even a programmer without specific knowledge in this field can receive guidance on how and why to implement AI elements into various applications. The seminar will showcase basic possibilities of some commercially available systems, demonstrate their deployment through simple examples, cover basic elements of AI architecture, and provide resources for further education in this field.

Agenda:

How AI Works

• Overview from 10,000 feet – basic principles, architecture.
• Non-technical aspects of AI (ethics, security, etc.).
• What AI roughly can and cannot do.
• Basic overview of cognitive function groups.

Models

• AI models – basic types.
• Model architecture.
• Model training.
• Hugging Face – explanation and usage.

Open Source

• TXTAI as an example – explaining basic principles of AI applications with AI SaaS overview.
• Architecture for integrating SaaS into corporate environments.
• OpenAI (ChatGPT)
  • Offerings.
  • Customization.
  • Document embedding.
  • Plugin development.
  • Cost considerations/tracking/cost limitations.

Google Bard.

Azure Cognitive Services

• Speech.
• Vision.
• Decision.
• Examples.
• Cost considerations.

IBM Watson Assistant.

• Practical Examples  - Integrating AI into application environments.

GitHub Copilot.

Determining viewer characteristics on a video panel and influencing video content.

Integrating enterprise chat with cognitive services.

Discussion and Project Solutions. Participants discuss and seek solutions for ongoing projects.

Participant Prerequisites:

• Solid practical knowledge in at least one of the following programming languages:
  • Python (most examples will be in this environment).
  • Node.js (JavaScript).
  • Java/Kotlin.
  • C#.
• Knowledge of GIT services.
• Own laptop/computer with the development environment installed for one of the above programming languages.
• Basic knowledge of system integration through REST APIs.