According to the institute, its forecasting solution will help farmers deal with crop diseases in a timely manner and curb overuse of pesticides, which is rampant due to the lack of accurate information about the extent of crop infection. IIIT Naya Raipur's forecasting solution uses drones to monitor crops and capture live images if it detects any issues in them. The images are then sent from the drone in real time to the institute's servers, where an image classification model based on convolutional neural networks (CNN) is used to identify the disease and insects that are affecting it. CNNs are AI algorithms commonly used for image and video recognition. They can process an image, assign importance to its various attributes, and differentiate one image from another.
According to the World Health Organization (WHO), the world is going hungry. WHO data shows that in 2018, the most recent year for which data is available, 820 million people lacked enough food to eat, an increase of nine million people over the year before. Hunger kills plenty of people worldwide. It also impacts those who survive, causing serious childhood development issues like stunting, where children are too short for their age, and wasting, where they're too thin for their age. The explosion in our planet's population is a major factor in there not being enough food to go around.
Metaverse is one of the hottest buzzwords of the moment. It's basically a virtual world created by combining different technologies, including virtual and augmented reality. While it doesn't technically exist yet, companies like Facebook hope the metaverse will become a place where we go to meet, work, play, study and shop. This'extended reality' is predicted to be the next evolution of the internet and will blur the lines between physical and digital life. Think in-game purchases, where computer gamers can buy virtual goods and services using real money. Jobs in the metaverse might include personalised avatar creator or metaverse research scientist.
Modern neural language models widely used in tasks across NLP risk memorizing sensitive information from their training data. As models continue to scale up in parameters, training data, and compute, understanding memorization in language models is both important from a learning-theoretical point of view, and is practically crucial in real world applications. An open question in previous studies of memorization in language models is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing "common" memorization such as familiar phrases, public knowledge or templated texts. In this paper, we provide a principled perspective inspired by a taxonomy of human memory in Psychology. From this perspective, we formulate a notion of counterfactual memorization, which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We further estimate the influence of each training example on the validation set and on generated texts, and show that this can provide direct evidence of the source of memorization at test time.
The future of computer vision and machine learning can be seen trundling at about 1 mile per hour at a lettuce field in the Salinas Valley of California. In certain fields, a tractor is pulling a highly specialized robot called the "lettuce bot." The robot, made by Blue River Technology, contains enough smarts to differentiate the weeds from the budding lettuce plants and then kill those weeds with an injection of fertilizer. The result is a weed-free field without the use of expensive and harmful pesticides -- making Blue River's robot a threat to the $31-billion pesticide business and a friend of organic farmers. The startup, founded in 2011, on Monday said it has raised $3.1 million in a Series A round led by Khosla Ventures.
The nutrient content of our vegetables is down 40% over the last two decades and our soil health is suffering due to increasingly harsh herbicide use, according to Carbon Robotics founder Paul Mikesell. And farmers are increasingly concerned about the long-term health impacts of continually spraying chemicals on their fields. But not weeding will cost half your crop, killing profitability. A self-driving farm robot that kills 100,000 weeds an hour ... by laser. "We wanted [to] figure out if there's a better way we could do this."
Machine learning can pinpoint "genes of importance" that help crops to grow with less fertilizer, according to a new study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture. Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins and pathogen exposure--which in turn would inform crop improvement, disease prognosis, epidemiology and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge.
Machine learning can pinpoint "genes of importance" that help crops to grow with less fertilizer, according to a new study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture. Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins, and pathogen exposure-;which in turn would inform crop improvement, disease prognosis, epidemiology, and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge. In the Nature Communications study, NYU researchers and collaborators in the U.S. and Taiwan tackled this challenge using machine learning, a type of artificial intelligence used to detect patterns in data.
In today's fast-paced world of city living and stressful work-life imbalances, especially on the (hopefully) tail-end of a year of pandemic quarantine measures, many young workers are yearning to get closer to nature and family. In the face of re-emerging commutes and the push-and-pull of back-to-the-office versus hybrid or fully-remote working, many young robots would rather ditch the status quo and return to the countryside to scratch a living from the land like their ancestors before them. And they'll bring lasers, too. Of course, we're not talking about the weary office drones being herded back to the office after a year of blissfully working at home, but of robots armed with deep learning computer vision systems and precision actuators for a new breed of farming automation. This new breed of automated agriculture promises to decrease inputs and the side-effects of modern agriculture, while helping farmers deal with everything from labor shortages to climate change.