Setting the stage for the machine intelligence era in marine science
By: Oxford Academic
Tags: Oxford University Press, Marine Science, Artificial Intelligence, Machine Learning

A new themed set published in ICES Journal of Marine Science explores the application of artificial intelligence and machine learning in marine science. The following excerpt is from the introduction to the themed set:

Artificial intelligence (AI) is increasingly being applied to all kinds of data. Some applications of AI are face recognition systems, natural language processing (e.g. speech recognition, language understanding, language generation, and language translation), disease detection systems, video surveillance, quality inspection in manufacturing, product design and creation, robotics, and self-driving cars (Dargan et al., 2019). It is accurate to say that AI is now everywhere, from our smartphones, to web-browser, to cars.

Machine learning (ML), which is a subfield of AI, implements dynamic models resulting in data-driven decisions. ML techniques can be applied to high-dimensional (Fan et al., 2009), nonlinear, complex, and big data. Further, the ML approach is effective even in cases where the data are noisy (e.g. Frenay and Verleysen, 2014; Xiao et al., 2015) or some identification labels are missing (McKnight et al., 2007; Aste et al., 2015). ML is also able to address the small sample size problem: so-called zero or few-shot learning (Huo et al., 2019). What makes ML most appealing is its capacity to handle problems that are impossible or too challenging for traditional approaches, which require many people and considerable time and resources to produce the desired accuracy. In other words, ML provides not only effective solutions, robustness, and accuracy but also efficiency as it can rapidly process huge amounts of data.

Deep learning (DL), inspired by the structure and function of the human brain, is a subfield of ML that involves the use of artificial neural networks (ANNs). ANN can take several forms, including recurrent neural networks (Hochreiter and Schmidhuber, 1997) and convolutional neural networks (CNNs) (Krizhevsky et al., 2012). Although ANNs are not new, their wide use only became practical after the development of massively parallel graphical processing units (GPUs). GPUs provide computation power and fast processing so that DL architectures running on GPUs can analyse huge amounts of data quickly and efficiently. In 2012, Krizhevsky et al.(2012) proved that CNNs can achieve a high level of accuracy in image classification. The success of CNNs has been extended to other computer vision tasks, for example object localization (Ren et al., 2015; Redmon et al., 2016), semantic segmentation (Long et al., 2015; Badrinarayanan et al., 2017), natural language processing for speech recognition (Hinton et al., 2012), machine translation (Sutskever et al., 2014), optical character recognition (Goodfellow et al., 2014), face recognition and verification (Taigman et al., 2014), object recognition (Xiao et al., 2015), and so forth. All of these will, in due course, be applied to data analysis in many branches of research, including marine science.

Studying and sustainably managing marine ecosystems presents special challenges because they are three dimensional, expansive, very dynamic, and complex. These characteristics require data collection over a wide range of spatiotemporal scales, which has been a major challenge (Godø et al., 2014; Janzen et al., 2019). Rapid progress in sensors, information, and communication technologies now allows marine scientists to collect large volumes of data at ever lower cost.

Studying and sustainably managing marine ecosystems presents special challenges because they are three dimensional, expansive, very dynamic, and complex.

Moored buoys support long-term monitoring and high resolution measurements of physical, chemical, and biological variables, as well as acoustics, at fixed locations and transmit their data in real-time via satellite uplink or cabled connection to shore (e.g. Aguzzi et al., 2015; Van Engeland et al., 2019). However, they are limited to monitoring depths from the seabed to the ocean surface. Sophisticated and heavily instrumented towed observation platforms, and autonomous drones, are collecting large volumes of data of many types (e.g. De Robertis et al., 2019; Lombard et al., 2019; Verfuss et al., 2019). However, the capacity of human experts to filter, curate, and analyse all of these data is limited. This is where ML and AI will be making greater-and-greater contributions as methods improve and are implemented more broadly.

ML can be applied to automate various routine tasks in marine science. The prediction of ocean weather, for example detecting sea surface temperature (Tanaka et al., 2004; Wu et al., 2006), habitat modelling (Krasnopolsky, 2009; Thessen, 2016), modelling monsoons (Cavazos et al., 2002), forecasting sea level fluctuations (Makarynskyy et al., 2004), wind and wave modelling (Forget et al., 2015; James et al., 2018), and the detection of acute situations, for example oil spill and other point sources of pollution (Kubat et al., 1998) are just some of the applications. Continuous underwater video and acoustic surveillance systems are rapidly developing tools to monitor marine life while computer vision and ML techniques contribute by automatically analysing the massive data streams from these platforms (e.g. Fisher et al., 2016). These data can already be used to extract higher-level interpretations by automatically detecting and tracking fish underwater (Spampinato et al., 2008), identifying fish species (Joly et al., 2015; Siddiqui et al., 2018; Villon et al., 2018; Allken et al., 2019), and estimating swimming trajectories and speeds (Beyan et al., 2018). Eventually, it will be possible to use time series of these data streams to assess changes in species abundance and distribution, environmental change, predator–prey relationships, and more (Fisher et al., 2016). Baited cameras and camera traps allow data to be collected without disturbing animals, which produces high volumes of images that can be analysed by using DL techniques (e.g. Tabak et al., 2019). There is also great potential to apply DL to automatic fish identification, counting, and sizing on fishing vessels (e.g. Bartholomew et al., 2018).

In this context, the objective of this themed set of articles was to bring together contributions on the broad theme of the applications of AI, ML, DL, and advanced data systems (e.g. block chains) to research, monitoring and management of marine organisms and ecosystems.

The articles that appear in this themed set, and the many relevant articles that they cite, demonstrate that AI is already a very helpful tool in a wide variety of applications in marine science.