A drone flying over a Florida beach Saturday caught on camera a struggle between a fisherman and a massive hammerhead shark. Curtis Williams, a drone operator, captured the shark fighting against the man. At certain points in the video, the man can be seen just inches away from the massive shark as a crowd gathers around. The Florida Fish and Wildlife Commission urges fishermen to "minimize fight time" with prohibited sharks by using a certain kind of tackle that helps to release the sharks.
A fisherman got one magnificent catch during the Fourth of July weekend when he reeled in a massive hammerhead shark off Florida's Panama City Beach -- and it was all captured in a drone video. Curtis Williams was flying his drone over Panama City Beach on Saturday when he saw the fisherman attempting to capture the shark. "Vacationing at beach and was going to video a nice pleasant sunset. The unidentified fisherman later released the shark back into the ocean.
Just as automation and machine learning have given internet companies detailed records and predictions of how users behave online, they can potentially enable scientists and government agencies to build similarly detailed models of the world's fisheries. "Today it's estimated that what's called illegal, unreported, and unregulated fishing costs the region between a half billion dollars and $1.5 billion a year," Zimring says. The Nature Conservancy is working with governments in the region, including in Palau, Micronesia, the Marshall Islands, and the Solomon Islands, to implement alternative monitoring programs, capturing video footage of fishing vessels instead of placing observers on each boat. To make analyzing the footage more feasible, The Nature Conservancy is investigating ways to use machine learning techniques to process that video material.
Deep learning models, trained by using a large set of labeled data and neural network architectures that contain many layers, routinely achieve impressive accuracy. The article explains how Neurala's technology will assist Air Shepherd's analysts in identifying animals, poachers, and vehicles from the terabytes of data created by the drones' video feeds. BeeScanning is a smartphone app that uses deep learning to analyze images of bee colonies to determine if they are infected by varroa mites. The Nature Conservancy's Indonesia Fisheries program is working with 2 technology companies that use machine learning to sort and recycle cell phones to develop a prototype, called Fishface, that applies this same technology to species identification for fish.
FORTALEZA, BRAZIL – Researchers in Brazil are experimenting with a new treatment for severe burns using the skin of tilapia fish, an unorthodox procedure they say can ease the pain of victims and cut medical costs. Related Image Doctors wrap a child's burnt skin with sterilized tilapia fish skin at Dr. Jose Frota Institute in the northeastern costal city of Fortaleza, Brazil, May 3, 2017. A tilapia fish and tilapia fish skins are displayed in Jaguaribara, Brazil, April 26, 2017. After about 10 days, doctors remove the bandage.
Google partnered with nonprofits to found Global Fishing Watch to detect illegal fishing activity using satellite data in near real time. The algorithm for detecting apparent fishing activity uses AIS data from roughly 35,000 fishing vessels worldwide (out of the roughly 200,000 vessels on the seas in the course of a year). The system captures the navigation pattern information of these 35,000 vessels with 22 million data points per day allowing deep learning algorithms to learn and then detect patterns that indicate fishing. Satellite intelligence startup Orbital Insight partnered with Global Forest Watch to detect illegal logging and other causes of global forest degradation.
Before you see data -- whether you are a baby learning a language or a scientist analyzing some data -- you start with a lot of uncertainty and then as you have more and more data you have more and more certainty," Ghahramani said. One of the center's area of study is trust and transparency around AI, while other areas of focus include policy, security and the impacts that AI could have on personhood. Adrian Weller, a senior researcher on Ghahramani's team and the trust and transparency research leader at the Leverhulme Centre for the Future of Intelligence, explained that AI systems based on machine learning use processes to arrive at decisions that do not mimic the "rational decision-making pathways" that humans comprehend. But by providing a means for making AI functions more transparent, commercial users of AI tools and their consumers could better understand how it works, determine its trustworthiness, and decide whether it is likely to meet the company's or its customer's needs.
While we love to hate Apple's virtual assistant, Siri, she has been hailed a hero by some fishermen in Florida, who claim that it saved their lives. Though it seems like Siri is incapable of doing anything well aside from dishing out sass, the assistant is actually pretty good at making hands-free phone calls. Though simple, this feature did save lives this weekend. Here's why that's a big problem Three boaters ran into rough seas on Saturday morning while fishing four miles off of the coast of Key Biscayne, and Siri came to their rescue, according to Fox 4.
TripAdvisor brings to you a fun, new feature we have been testing and is now available on Facebook Messenger. With this new feature now you can send TripAdvisor a message via Facebook Messenger asking where to stay, eat, or what to do anytime, anywhere. TripAdvisor's bot will eventually learn what you like in the perfect trip. No problem – just log on to Facebook Messenger and simply send the TripAdvisor bot a message asking for sushi restaurants nearby your current location.
The premise behind Google's Cloud Machine Learning Engine and TensorFlow technologies is to democratize access to machine learning tools and technologies. Here are seven companies that implemented Google's machine learning tools to solve problems in their business. The deep learning tools used for fraud monitoring achieved 80-90% accuracy in detecting fraud and alerting the company to take action. As a food quality and safety company in Japan, Kewpie's problem was properly detecting defective potato cubes that would be used in a stew.