One of the world's most endangered whales was spotted swimming with a newborn in the waters off South Carolina's Hilton Head island. A drone camera captured footage of a 50-ton North Atlantic right whale and her calf frolicking about four miles from shore. According to the boat captain who spotted the pair on Friday, the mother was 50 feet long and the calf was close to 15 feet. The North Atlantic right whale is among the rarest of marine mammals, with less than 400 left in the world. Collisions with boats and entanglement in lobster nets are the main reason they are critically endangered.
Fish farmers in Norway are using AI models designed to cut costs and improve the efficiency of their efforts to raise salmon, one of the country's major exports, thanks to efforts of the Norwegian Open AI Lab. The efforts are part of a growing trend to apply AI automation to aquaculture, which is the farming of fish, crustaceans, mollusks, aquatic plants, algae and other organisms. The AI models are designed to optimize feeding, keep the fish clean and healthy, and help companies make better decisions regarding farm operations, according to an account in WSJ Pro. The Norwegian Open AI Lab is run by Norwegian telecommunications carrier Telenor AS A, which along with other companies, provides technology services such as testing of 5G mobile connectivity, to salmon farms. Salmon exports in 2019 totaled some $11.3 billion, according to the Norwegian Seafood Council.
Japanese fish industries are starting to use artificial intelligence to select high-quality fish at markets and find good fishing grounds, areas where they have traditionally relied largely on experience and intuition. AI tools are drawing attention because they can easily replicate proficient skills, including those needed to evaluate tuna quality and determine good spots to catch saury. When judging the quality of fish, buyers look at how fresh and firm the meat is and how much fat it puts on. "You need over 10 years of experience" to acquire an excellent eye, a fish market worker said. Advertising giant Dentsu Inc. and others jointly developed and put into practical use a smartphone app that enables users to easily pick out delicious tuna.
It's therefore little wonder that we're witnessing changes to the industry. These include moving fish farms into deeper waters – less accessible locations that will require technology to manage. Also, as aquaculture becomes more competitive, it's not just about practicing sustainability, it's about being able to prove it.
Recent advances in time series classification have largely focused on methods that either employ deep learning or utilize other machine learning models for feature extraction. Though successful, their power often comes at the requirement of computational complexity. In this paper, we introduce GeoStat representations for time series. GeoStat representations are based off of a generalization of recent methods for trajectory classification, and summarize the information of a time series in terms of comprehensive statistics of (possibly windowed) distributions of easy to compute differential geometric quantities, requiring no dynamic time warping. The features used are intuitive and require minimal parameter tuning. We perform an exhaustive evaluation of GeoStat on a number of real datasets, showing that simple KNN and SVM classifiers trained on these representations exhibit surprising performance relative to modern single model methods requiring significant computational power, achieving state of the art results in many cases. In particular, we show that this methodology achieves good performance on a challenging dataset involving the classification of fishing vessels, where our methods achieve good performance relative to the state of the art despite only having access to approximately two percent of the dataset used in training and evaluating this state of the art.
In the midst of crises such as Hurricane Laura, police brutality and a global health pandemic, agencies like the National Oceanic and Atmospheric Administration (NOAA) are especially attentive to building resilience. At GovLoop's online training Thursday, NOAA Fisheries' Chief Information Officer (CIO) Roy Varghese shared five pillars around how the agency reimagined IT resilience. They include smart cloud adoption, data, artificial intelligence (AI) and machine learning (ML), partnerships, and recruiting, retaining and reskilling the workforce. Of these pillars, Varghese said that talent -- the people -- are at the center of building resilience. "IT resiliency, from my perspective, comes from our people," Varghese said.
A burst of technology in the 1960s--the Green Revolution--raised agricultural output significantly across developing economies. Since then, rising incomes have boosted protein consumption worldwide, and elevated new challenges: greenhouse-gas emissions from agriculture are increasing (more than a fifth of all emissions worldwide), while a host of practices, from waste to overfishing, threaten the sustainability of food supplies. The COVID-19 pandemic has brought these concerns to the fore: the disease has disrupted supply chains and demand, perversely increasing the amount of food waste in farms and fields while threatening food security for many. As agriculture gradually regains its footing, participants and stakeholders should be casting an eye ahead, to safeguarding food supplies against the potentially greater and more disruptive effects of climate change. Once again, innovation and advanced technologies could make a powerful contribution to secure and sustainable food production. For example, digital and biotechnologies could improve the health of ruminant livestock, requiring fewer methane-producing animals to meet the world's protein needs. Genetic technologies could play a supporting role by enabling the breeding of animals that produce less methane. Meanwhile, AI and sensors could help food processors sort better and slash waste, and other smart technologies could identify inedible by-products for reprocessing. Data and advanced analytics also could help authorities better monitor and manage the seas to limit overfishing--while enabling boat crews to target and find fish with less effort and waste.
As organizations gain more experience deploying machine learning (ML) and artificial intelligence (AI) across different parts of the business, they're discovering new and interesting ways to use the technology. Typical use cases include established applications such as personalization, fraud detection, and speech recognition. But there's much more to explore. "The cloud enables extremely low-cost compute and storage, which opens up opportunities for more modeling," says Sri Elaprolu, senior leader, Amazon Machine Learning Solutions Lab. "There's lots of innovation yet to happen. We are barely scratching the surface."
The National Oceanic and Atmospheric Administration wants to leverage machine learning to improve its ability to verify marine species. NOAA's Fisheries Sampling Branch is responsible for monitoring and observing fishing in the Northeast and mid-Atlantic. Over the last 25 years, FSB has worked to make this identification easier for its observers -- who started out taking pictures of samples of frozen fish with film cameras, noting the location and focusing on the unique characteristics that allow differentiation of each fish species, such as whole body, gills or fins. The images were submitted within 48 hours and verified by other observers. Currently, images -- which still vary greatly in quality due to different cameras, photographic conditions and characteristics of the species -- are upload to the Fish House, a web-based user interface for a Oracle relational database that is used for species verification.
Endangered sperm whales are less likely to forage for food at dawn in some areas of the Mediterranean, underwater robotic equipment has revealed. Unmanned underwater robots equipped with acoustic monitors recorded the sperm whale sounds over several months and thousands of miles of ocean. Sperm whales emit distinct'clicks' to sense objects from reflected sound waves – a process called echo-location – and social interaction purposes. The recordings confirmed the whales' widespread presence in the north-western Mediterranean Sea – especially in the Gulf of Lion, just of the south coast of France. However, in the Gulf of Lion, click recordings showed a clear pattern of decreased foraging efforts, indicated by fewer clicks, at dawn.