Artificial intelligence (AI) represents emerging technology with beneficial potential for the maritime domain, to contain all natural and manmade features, events, or activities appertaining to the seas, oceans or other navigable waterways. It is not a single technology but a continuum of capabilities designed to synergize computational processing power with human creativity. This chapter introduces key AI concepts, including but not limited to, algorithms, reinforcement learning, deep learning and artificial general intelligence. Science has not yet achieved sentient machines, and fully autonomous vessels may not become commonplace for a number of years; nevertheless, current AI technologies offer risk-reduction methodologies to human-crewed vessels operating in dynamic and often dangerous conditions. In general, AI can enhance compliance with the law of the sea and reduce marine casualties. Specifically, this chapter proposes that AI technologies should be adopted to facilitate safer navigation through improved hydrographic services and AI-supported decision-making for vessel masters and human crews at sea.

The chapter on visual models discusses basic ways that scientists create visual representations of their data, including charts and graphs, in order to understand their data better. Like all models, visual models are a simplified version of reality. Two of the visual models discussed in this chapter are the frequency table and histogram. The histogram, in particular, is useful in the shape of the distribution of data, skewness, kurtosis, and the number of peaks. Other visual models in the social sciences include frequency polygons, bar graphs, stem-and-leaf plots, line graphs, pie charts, and scatterplots. All of these visual models help researchers understand their data in different ways, though none is perfect for all situations. Modern technology has resulted in the creation of new ways to visualize data. These methods are more complex, but they provide data analysts with new insights into their data. The incorporation of geographic data, animations, and interactive tools give people more options than ever existed in previous eras.

Graphs are a powerful and concise way to communicate information. Representing data from an experiment in the form of an x-y graph allows relationships to be examined, scatter in data to be assessed and allows for the rapid identification of special or unusual features. A well laid out graph containing all the components discussed in this chapter can act as a 'one stop' summary of a whole experiment. Someone studying an account of an experiment will often examine the graph(s) included in the account first to gain an overall picture of the outcome of an experiment. The importance of graphs, therefore, cannot be overstated as they so often play a central role in the communication of the key findings of an experiment. This chapter contains many examples of graphs and includes exercises and end of chapter problems which reinforce the graph-plotting principles.