Artificial Intelligence (AI) aims to build machines that are able to behave in ways we associate with human activity: perceiving and analysing our environment, taking decisions, communicating and learning. There are various approaches to achieving this. The most well-known, and arguably most advanced, is machine learning (ML), which itself has various broad approaches.
To mention just two approaches, in supervised learning algorithms make associations between a given input and the desired output by learning on training sets comprising many correct input/output pairs. In reinforcement learning, the ML algorithm repeatedly chooses from a given set of actions in order to maximise a reward function which should lead it to the desired result. A typical example is learning to play a game such as Go, chess or video games, where the reward function is increasing the score or winning the game. Reinforcement learning is considered to be a promising strategy to address complex real-world problems.
Machine learning algorithms have passed a number of impressive milestones in recent years. They identified objects by vision better than humans in 2015.1 The following year, they beat a Go champion and started playing complex video games.2 Autonomous cars have driven tens of millions of kilometres with very few accidents.3 Deep learning algorithms have become extraordinarily adept at mimicking traditionally human activities such as language processing and artistic creation.4 This rapid and impressive progress is primarily due to the increasing amount of available data and computing power. However, many applications require even more sophisticated skills, such as the ability to make sensible decisions in highly uncertain environments; transparency and traceability; the ability to combine data from highly heterogenous sources, and long-term memory and the inclusion of context.
Selection of GESDA best reads and key reports
There are several large-scale efforts to map the state of the art of artificial intelligence and to predict its evolution. Stanford’s “One Hundred Year Study on Artificial Intelligence” produces a summary of the major technological trends and applications by domains as well as legal, ethical and policy issues every five years.5 The “20-Year Community Roadmap for Artificial Intelligence Research in the US” from the Association for the Advancement of AI (AAAI) proposes detailed research roadmaps and recommendations about research infrastructures and education.6 The yearly State of AI Report summarises the main developments of AI of the past year in the field of research, industry and politics as well as education and experts.7 Other roadmaps focus on the opportunities and challenges of integrating AI in government, society and industry from European8 and Chinese9 perspectives.