For nearly as long as surfing has existed, surfers have been obsessed with the search for the perfect wave. While this hunt has taken surfers from tropical coastlines to icebergs, these days that search may take place closer to home. That is, at least, the vision presented by developers and boosters in the growing industry of surf pools, spurred by advances in wave-generating technology that have finally created artificial waves surfers actually want to ride. But there's a problem: some of these pools are in drought-ridden areas, and face fierce local opposition. At the core of these fights is a question that's also at the heart of the sport: What is the cost of finding, or now creating, the perfect wave--and who will have to bear it?

In the present time, all know about World Wide Web and work over the Internet daily. In this paper, we introduce the search engines working for keywords that are entered by users to find something. The search engine uses different search algorithms for convenient results for providing to the net surfer. Net surfers go with the top search results but how did the results of web pages get higher ranks over search engines? how the search engine got that all the web pages in the database? This paper gives the answers to all these kinds of basic questions. Web crawlers working for search engines and robot exclusion protocol rules for web crawlers are also addressed in this research paper. Webmaster uses different restriction facts in robot.txt file to instruct web crawler, some basic formats of robot.txt are also mentioned in this paper.

Data selection in instruction tuning emerges as a pivotal process for acquiring high-quality data and training instruction-following large language models (LLMs), but it is still a new and unexplored research area for vision-language models (VLMs). Existing data selection approaches on LLMs either rely on single unreliable scores, or use downstream tasks for selection, which is time-consuming and can lead to potential over-fitting on the chosen evaluation datasets. To address this challenge, we introduce a novel dataset selection method, Self-Filter, that utilizes the VLM itself as a filter. This approach is inspired by the observation that VLMs benefit from training with the most challenging instructions. Self-Filter operates in two stages. In the first stage, we devise a scoring network to evaluate the difficulty of training instructions, which is co-trained with the VLM. In the second stage, we use the trained score net to measure the difficulty of each instruction, select the most challenging samples, and penalize similar samples to encourage diversity. Comprehensive experiments on LLaVA and MiniGPT-4 show that Self-Filter can reach better results compared to full data settings with merely about 15% samples, and can achieve superior performance against competitive baselines.

Surfers at California's San Onofre Beach were seen surrounded by great white sharks while out enjoying the surf. Recently published footage from one of California's most popular surf beaches shows at least four sharks swimming beneath the waters as surfers nonchalantly chase waves. Photographer Kevin Christopherson captured the group of aquatic predators via drone camera over San Onofre State Beach in San Diego County, California. Surfers in the drone camera footage seem unaware of, or unconcerned about, the group of sharks just beneath their boards. "I counted 4 maybe 5 total great white sharks, and it didn't stop anyone from catching some waves!"

Around 3.7 million Australians have been unintentionally caught in an ocean rip. For the unprepared it can be a harrowing experience--but for experienced surfers, rips are a handy way to ride through the whitewash and out to the break. We're not surfers, but we work at Australia's national science agency in "strategic foresight," which you can think of as the study of the currents taking the world into the future. These currents are a bit like ocean rips: they present risks for those who don't understand them, but opportunities for societies, organizations and people who are prepared. We call the biggest currents "megatrends", and in a new report we examine the megatrends that will shape the next 20 years.

It might not be obvious from the TV coverage, but the Tokyo 2020 Olympics (which of course are being held in 2021) are infused with big data and AI to an extent never before experienced in an Olympic games. It's been 53 years since the Olympics officially adopted electronic time-keeping equipment to track racers in Olympic events. Omega's Magic Eye camera, which debuted in 1948, gave us the first of many "photo-finish" for track events, and was soon adopted in other events too. Now the technology is going up a notch in the Tokyo 2020 Olympics (which perhaps should have been called the 2021 games), and Omega is behind much of it. For example, Omega, which is the official timekeeper for 35 Olympic sports, is using cameras equipped with computer vision capabilities to track the movement of beach volleyball players, as well as the ball.

The Living Planet Index produced by WWF estimates that wildlife population sizes have dropped by 68 per cent since 1970. The charity advocates the use of artificial intelligence (AI) as a tool of conservation technology to monitor and curb this alarming rate of decline. One of the most useful applications is in acoustic monitoring, recording the sounds of wildlife ecosystems on weatherproof sensors. Many animals, from birds and bats to mammals and even invertebrates, use sound for communication, navigation and territorial defence, providing reams of rich data on how a species population is doing. AI provides a fast and cost-effective way to analyse hours of recordings for patterns of behaviour.

Artificial intelligence may seem futuristic, but smart brands are already focusing on creating practical, measurable consumer-facing applications with the technology. Brand and agency leaders are using this new technology for everything from shifting how media dollars are deployed, to customer service, to using artificial intelligence for content creation. Competitors enter and stakes rise. And consumers expect more from the companies they buy from. It is not about just being the most convenient option, or the cheapest option.

SYDNEY – High-tech shark-spotting drones are patrolling dozens of Australian beaches this summer to quickly identify underwater predators and deliver safety devices to swimmers and surfers faster than traditional lifesavers. As hundreds of people lined up in the early morning sun to take part in a recent ocean swimming race at Bilgola Beach north of Sydney, they did so in the knowledge the ocean had been scanned to keep them safe. "I think it is really awesome," 20-year-old competitor Ali Smith said. "It is cool to see technology and ocean swimming getting together, and hopefully more people will feel safer and get involved." The drones being used are top notch.

Beachgoers in Australia can be a little less panicky about shark attacks this summer, because artificial intelligence-equipped drones will be monitoring the water for big scary fish. Developed by researchers at University of Technology Sydney, the AI system--dubbed Sharkspotter--can identify sharks and notify beachgoers when they're nearby. An Australian drone company called Westpac teamed up with the school to outfit its battery-powered Little Ripper Lifesaver unmanned helicopters with the technology in an effort to, hopefully, offer swimmers and surfers better protection. The AI-equipped drones will patrol "many main beaches in Australia" this summer, the school said in a news release. "The system will give an overhead warning to swimmers/surfers when a shark or a potential risk is detected, using an on-board megaphone attached to the drones," Professor Michael Blumenstein, head of the UTS School of Software, said in a statement, adding that the system "will create a positive impact for the public, making beach recreation much safer." Sharkspotter uses "cutting-edge deep neural networks and image processing techniques" to examine live video feeds in real time and detect the presence of sharks, and distinguish them from other marine life and objects, the school said.