During the talk, we'll dive into the historical context of Generative AI and examine their challenges. From legal compliance to fairness, transparency, security, and accountability, we'll discuss strategies for implementing Responsible AI principles.
It's important to note that the landscape for AI-driven products is still evolving, and there are no established best practices. The legislative framework surrounding these models remains uncertain, making it even more vital to engage in discussions…

Are Large Language Models (LLMs) sophisticated pattern matchers ('parrots') without understanding or potential prodigies that eventually surpass human intelligence? Drawing insights from both camps, we attempt to reconcile these perspectives, examines the current state of LLMs, their potential trajectories, and the profound impact these developments have on how we engineer software in the years to come.

Target Audience: Developers and Architects
Prerequisites: A basic understanding of Large…

Artificial Intelligence (AI) has become integral to software development, automating complex tasks and shaping this field's future. However, it also comes with challenges. In this talk, we explore how AI impacts current software development and possibilities for the future. We'll delve into AI language models in programming, discussing pros, cons and challenges. This talk, tailored to both supporters and skeptics of AI in software development, doesn't shy away from discussing the ethical…

Tauchen Sie in die faszinierende Welt von ChatGPT ein! Dabei werden die Sprecher auf die Grundlagen von GPT und auf die besonderen Fähigkeiten von einem der aktuell leistungsfähigsten textbasierten Dialogsysteme eingehen.
Ein wichtiger Aspekt ist darüber hinaus, das Verständnis für Prompt Engineering zu fördern. Durch die Optimierung von Prompts lässt sich die Qualität der Antworten von ChatGPT drastisch verbessern. Wir behandeln einige bewährte Verfahren für die Konstruktion von Prompts, wie…

Great engineers often use back-of-the-envelope calculations to estimate resources and costs. This practice is equally beneficial in Machine Learning Engineering, aiding in confirming the feasibility and value of an ML project. In my talk, I'll introduce a collaborative design toolkit for ML projects. It includes Machine Learning Canvas and MLOps Stack Canvas to identify ML use cases and perform initial prototyping, thus ensuring a business problem can be effectively solved within reasonable cost…