AI To Harness Oceanography’s Potential
One thing that dominates the surface of the earth is the ocean. From regulating our climate, securing transportation of goods across nations, from minerals to polymetallic nodules, harnessing clean energy sources to deep research, it holds numerous potentials that are yet to be harnessed. The United Nations has declared 2021 to 2030 – a Decade of Ocean Science for Sustainable Development to support efforts to reverse the trend of declining ocean health and bring ocean stakeholders worldwide together behind a collective structure to work for ocean sustainability. Enhancing ocean understanding to tap potential In a recent research by the University of Bath, two AI algorithms — Latent Variable Gaussian Process (LVGP) model and Probabilistic Principal Component Analysis (PPCA) were used to understand the sonar echoes in the ocean. The research aimed at observing the changes that can happen with sonar echoes at different depths, salinity, and temperature. The algorithms were capable of classifying underwater environments from simulated sonar measurements with an average accuracy of more than 90%. The application of artificial intelligence, machine learning algorithms, and smart robots seems to be the perfect combination in the future to come. Deep-sea mining and deep-sea research without disturbing the life beneath seem difficult a few years before, but not anymore. With the application of these latest technologies, oceanographers can create accurate cartography, understand the impact of climate change, species status, salinity, and gather a large amount of data to explore the areas left behind. The study of life underwater seems exciting, but it is rather difficult to collect and process data. The Autosub2KUI, an autonomous underwater vehicle, will exploit the latest Marine Autonomous Systems (MAS) jointly developed by the National Oceanography Centre of the UK along with its industry partners at the Marine Robotics Innovation Centre. This AUV, equipped with the finest sonars and a camera system, is capable of operating even under ice. The data thus collected will be analysed through deep-learning algorithms to infer patterns associated with life in deep waters. Mapping of ocean topography and potential resources extraction depends on accurate information of the location as well as the volume present. BioCam – the 3D visual imaging technology equipped with a high-sensitivity detector can provide colour images of benthic life forms and identify hydro-thermal vents and topography of the ocean floor. The images can help us precisely capture the impact of increasing ocean temperature and acidification on various planktonic lifestyles, including corals. Moreover, it will help countries to go for mining with a sufficient database. The presence of many diverse plankton species with similar features presents a difficult task to classify and study particular species. Coming to the rescue, Convolutional Neural Networks (CNNs) – the weight-sharing network based on image convolution are used to categorise the image class. The computational efficiency, and the self-learning capability of CNNs, provides efficient and robust performance in the processing of images. The convolution result contains a convolution kernel and output. The kernel matches the image features and can be activated for amplification, and the output can be used for image classification. Image courtesy: Simplilearn In addition to the same, image-based explainable AI methods could help us understand the parts of the observed organism, which helps determine their identity. ML algorithms can further help establish the connection between the images of planktons taken with the genomic data to help us understand the genetic variations and genes responsible for their shapes. Developing new ports, building energy infrastructures like off-shore wind farms, and opening new shipping lanes are a must for future growth. However, sound can travel at least four to five times faster in the water when compared to its speed in the air. Any construction or container movement that produces sound is responsible for ‘acoustic pollution’, which is rarely talked about. This leads to many marine species getting impacted, including whales, and dolphins because of their sensitivity to high sound levels. ML techniques detect marine animals present in the proximity on a real-time basis; thus, it will provide decision-makers with the data to find a balance in advance. Take, for example, the image by the Sinay – a maritime data solution startup. Wrapping Up Technological development in the field of artificial intelligence, intelligent robotics, and machine learning can be deployed to harness ocean potential without impacting their sustainability. AI can correct our knowledge gaps, create awareness, and can help us to course-correct our set of actions before it’s too late.
Adopting AI technologies to tap oceanic potential in a sustainable manner can open gates for further development.
Adopting AI technologies to tap oceanic potential in a sustainable manner can open gates for further development.
["AI (Artificial Intelligence)", "Convolutional Neural Networks", "Machine Learning", "Robotics", "sustainability"]
["Go", "artificial intelligence", "machine learning", "TPU", "AI", "sustainability", "neural network", "ML", "Machine Learning", "Robotics", "Convolutional Neural Networks", "RAG", "Aim", "R", "AI (Artificial Intelligence)"]
["AI", "artificial intelligence", "machine learning", "ML", "neural network", "Aim", "RAG", "TPU", "R", "Go"]
Kumar Gandharv, PGD in English Journalism (IIMC, Delhi), is setting out on a journey as a tech Journalist at AIM. A keen observer of National and IR-related news.