In the US alone, the American Trucking Association estimates there are more than 3.5 million truck drivers on the roads, with nearly 8 million people employed across the wider industry. Census Bureau statistics show that trucking is the most common job in 29 US states, ahead of farming, teaching and secretarial positions.
In June 2020, the Californian company OpenAI announced GPT-2's upgrade to GPT-3, a language model based on artificial intelligence and deep learning with cognitive capabilities. It is a technology that has generated great expectations and that has been presented as the most important and useful advance in AI in recent years. OpenAI is a non-profit company founded by Elon Musk, co-founder and director of Tesla and SpaceX, which was born with the aim of researching and democratizing access to General Artificial Intelligence. Originally, it was a non-profit organization. However, in 2020, it became a company and partnered with Microsoft in order to achieve new advances, both in the field of language with GPT-3 models, and in the field of robotics and vision.
Can a machine powered by artificial intelligence (AI) successfully persuade an audience in debate with a human? Researchers at IBM Research in Haifa, Israel, think so. They describe the results of an experiment in which a machine engaged in live debate with a person. Audiences rated the quality of the speeches they heard, and ranked the automated debater's performance as being very close to that of humans. Such an achievement is a striking demonstration of how far AI has come in mimicking human-level language use (N.
Unexpected problems during completion create costs that can cause a well to be outside its planned authorization for expenditure, even uneconomic. These problems range from experiencing abnormally high pressures during treatment to casing failures. The authors of the complete paper use machine-learning methods combined with geomechanical, wellbore-trajectory, and completion data sets to develop models that predict which stages will experience difficulties during completion. The operator's acreage is in the southeastern portion of the Midland Basin. In this area of the basin, the Wolfcamp B and C intervals often contain a significant amount of slope sediments and carbonate debris flows because of the proximity of the eastern shelf.
Facial recognition is problematic for humans. When it works, it invades privacy and eases us into a surveillance state. When it doesn't work, people have been falsely arrested by police. For bears, it's all good – and facial recognition is now being used to help research, monitor and protect the animals using a neural network-based system called BearID. Normally, that requires methodically examining photographs or physically tagging the animal, as the University of Victoria researcher's work on grizzly behaviour requires being able to pinpoint a specific individual.
ADELAIDE, AUSTRALIA--Cosmos Magazine reports that Daryl Wesley of Flinders University and Mimal and Marrku Traditional Owners of the Wilton River area used machine learning to analyze changes in rock art styles in northern Australia's Arnhem Land. The computer was supplied with information of more than 1,000 types of objects and a mathematical model to determine how similar two images are to one another. The model was then applied to images of the rock art. "One amazing outcome is that the machine learning approach ordered the styles in the same chronology that archaeologists have ordered them in by inspecting which appear on top of which," said team member Jarrad Kowlessar of Flinders University. Styles of artwork that are closer to each other in age are also closer to each other in appearance, he explained.
The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrally training their ML models or inference purposes. To overcome these challenges, distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges, thus reducing the communication overhead and latency as well as improving data privacy. However, deploying distributed learning over wireless networks faces several challenges including the uncertain wireless environment, limited wireless resources (e.g., transmit power and radio spectrum), and hardware resources. This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks.
David Tejeda helps deliver food and drinks to tables at a small restaurant in Dallas. Sometimes he lends a hand at a restaurant in Los Angeles too. Tejeda does all this from his home in Belmont, California, by tracking the movements and vital signs of robots that roam around each establishment, bringing dishes from kitchen to table, and carrying back dirty dishes. Sometimes he needs to help a lost robot reorient itself. "Sometimes it's human error, someone moving the robot or something," Tejeda says.
Researchers at the University of Barcelona have developed an open access, deep learning-based web app that will enable the detection and quantification of floating plastics in the sea with a reliability of over 80%. Floating sea macro-litter is a threat to the conservation of marine ecosystems worldwide. According to UNESCO, plastic debris causes the deaths of more than a million seabirds every year, as well as more than 100,000 marine mammals. Eroded fragments, known as micro-plastics, are now prevalent across the food chain. The largest density of floating litter is found in the great ocean gyres (systems of circular currents) with litter being caught and spun in these vast cycles.
According to OpenAI, more than 300 applications are using GPT-3, which is part of a field called natural language processing. An average of 4.5 billion words are written per day. Some say the quality of GPT-3's text is as good as that written by humans. What follows is GPT-3's response to topics in general investing. MarketWatch: "How to invest in cryptocurrencies by GPT-3."