INTRODUCTION

• 1. Artificial Intelligence has become a hot topic due to major advances in the field. However, many people participate in the debate without having the necessary understanding of the subject. In this chapter, we will explain some basic concepts of AI that may be useful for legal scholars and practitioners. It will provide readers with the necessary background to fully understand the impact of AI on law.

First, we provide a clear definition of AI and discuss the Turing Test. This test was a first controversial attempt to measure machine intelligence (part 2). We then focus on the working of AI. We consider two main AI approaches, namely knowledge-based and data-based learning. The latter is gaining importance every day, mainly due to the massive production of data by the Internet of Things (IoT). Machine learning (ML) can be considered the core of the data-based approach. One very popular ML method is the artificial neural network (ANN), which is described as well. We briefly discuss how it works and focus on its evolution into deep learning (DL). This evolution results from the increased data production and computing power. While DL has been a quantum leap for AI, it also has some drawbacks. These will be covered as well (part 3). AI has several sub-disciplines, many of which rely on ML. We briefly discuss search algorithms, computer vision, natural language processing (NLP), speech processing and agents (part 4). Having touched upon the foundations of AI, we subsequently focus on the wide range of areas and fields in which AI is already used. We discuss the current state of the art and expected evolutions in transportation, robotics, healthcare, education, public safety and security, art and entertainment, and law. We also look at the more distant future of AI (part 5). We conclude this chapter with some considerations regarding the ethical and safety aspects of AI (part 6).

DEFINING AND MEASURING AI

DEFINITION: WHAT IS AI?

• 2. Nowadays, AI is ubiquitous in the news. This results in a huge number of articles and (academic) papers on this technology. These texts are usually either overly optimistic or pessimistic1 regarding the possibilities as well as the dangers and challenges of AI.