Ethics in AI: A Challenging Task

In the first part we cover current specific challenges: (1) discrimination (e.g., facial recognition, justice, sharing economy, language models); (2) phrenology (e.g., biometric based predictions); (3) unfair digital commerce (e.g., exposure and popularity bias); and (4) stupid models (e.g., Signal, minimal adversarial AI). These examples do have a personal bias but set the context for the second part where we address four generic challenges: (1) too many principles (e.g., principles vs. techniques), (2) cultural differences (e.g., Christian vs. Muslim); (3) regulation (e.g., privacy, antitrust) and (4) our cognitive biases. We finish discussing what we can do to address these challenges in the near future.

Introduction to Self-Driving Cars

Autonomous driving is one of the greatest challenges of our times. Big companies, researchers, and designers of modern cities are drawn towards autonomous vehicles. In this presentation, we’ll initially go through the basic principles that apply in each stage of autonomous driving and then we’ll have a look at NVIDIA’s autonomous driving model. By using Udacity’s open-source simulator as a guide, we will describe the stages of training an autonomous driving system, while applying the deep neural network architecture as proposed by NVIDIA.

Intro to Databases

Databases are one of the longer lasting and most widespread ways to store data in a computer. The growing complexity of information made the formal design and modeling of the databases mandatory. This workshop will introduce participants to database design techniques, as well as data structuring and access. The hands-on Read more…