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 that shape responsible AI practices.

Target Audience: Decision Makers, Developers, Managers, Everyone - AI-driven products require cross-functional teams
Prerequisites: None
Level: Basic

Extended Abstract:
Foundation models like GPT-4, BERT, or DALL-E 2 are remarkable in their versatility, trained on vast datasets using self-supervised learning. However, the adaptability of these models brings forth ethical, socio-technical, and legal questions that demand responsible development and deployment.
During the talk, we'll delve into the history of AI to better understand the evolution of generative models. We'll explore strategies for implementing Responsible AI principles, tackling issues such as legal compliance, fairness, transparency, security, accountability and their broader impact on society.
It's important to note that there are currently no established best practices for AI-driven products, and the legislative landscape surrounding them remains unclear. This underscores the significance of our discussion as we collectively navigate this emerging field.

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 Language Models is helpful but not required
Level: Basic

Extended Abstract:
Large Language Models (LLMs) are complex 'black box' systems. Their capabilities remain largely mysterious, only beginning to be understood through interaction and experimentation. While these models occasionally yield surprisingly accurate responses, they also exhibit shocking, elementary mistakes and limitations, creating more confusion than clarity.
When seeking expert insights, we find two diverging perspectives. On one side, we have thinkers like Noam Chomsky and AI experts such as Yann LeCun, who view LLMs as stochastic 'parrots' — sophisticated pattern matchers without true comprehension.
In contrast, AI pioneers like Geoffrey Hinton and Ilya Sutskever see LLMs as potential 'prodigies' — AI systems capable of eventually surpassing human intelligence and visionaries like Yuval Noah Harari that view LLMs as substantial societal threats.
Regardless of whether we see LLMs as 'parrots' or 'prodigies', they undeniable are catalyzing a paradigm shift in software engineering, broadening horizons, and pushing the boundaries of the field.
What are the theories underpinning these experts' views? Can their perspectives be reconciled, and what can we learn for the future of software engineering?
To answer these questions, we examine the current capabilities and developments of LLMs and explore their potential trajectories.