It's rare to see tech headlines about agriculture, but the field (pardon the pun) is often at the forefront of implementing new technology Perhaps no recent tech development has had a greater impact on the industry than smart technology, and this IoT data is being used to improve operations across nearly all modern farming operations around the globe. Here are a few examples. Farmers were among the first to adopt GPS technology; John Deere was the first tractor manufacturer to implement GPS technologies in the early 1990s, and farmers quickly began using GPS assistance and even automated steering to reduce user errors. GPS technology can be combined with sensor data to create ultra-precise maps of varying factors. Knowing how soil quality varies across large plots of land, for example, can help farmers know which areas need which type of fertilizers.
SINGAPORE/KUALA LUMPUR - The stench of curdled milk wafted from a shipping container of waste at Malaysia's Port Klang as Environment Minister Yeo Bee Yin told a group of journalists in May she would send the maggot-infested rubbish back where it came from. Yeo was voicing a concern that has spread across Southeast Asia, fueling a media storm over the dumping of rich countries' unwanted waste. About 5.8 million tons of trash was exported between January and November last year, led by shipments from the U.S., Japan and Germany, according to Greenpeace. Now governments across Asia are saying no to the imports, which for decades fed mills that recycled waste plastic. As more and more waste came, the importing countries faced a mounting problem of how to deal with tainted garbage that couldn't be easily recycled.
Most attempts at giving robots muscles tend to be heavy, slow or both. Scientists might finally have a solution that's both light and nimble, though. They've developed fibers that can serve as artificial muscles for robots while remaining light, responsive and powerful. They bonded two polymers with very different thermal expansion rates (a cyclic copolymer elastomer and a thermoplastic polyethylene) that reacts with a strong pulling force when subjected to even slight changes in heat. They're so strong that just one fiber can lift up to 650 times its weight, and response times can be measured in milliseconds.
Over the last 15 years there has been a surge in the use of machine learning to gain materials chemistry insights. These methods use existing data (largely computed with ab-initio methods) to train statistical models that can make useful predictions about whether chemical compounds will be stable, and the properties they are likely to exhibit. However, a large majority of the knowledge the scientific community has generated to date is recorded as "unstructured" text, and has therefore been largely inaccessible to machine-learning and statistical analysis. In recent years however, the Natural Language Processing (NLP) research community has made great progress on methods to computationally parse and learn from unstructured text. In our paper, we show how the application of an unsupervised NLP model can capture information from the materials chemistry literature in a way that also uncovers latent knowledge previously unknown to the research community.
An engineer working for Japanese carmaker Nissan has built a robot to help farmers reduce the use of herbicides and pesticides on their rice crops. The compact robot, called Aigamo, is designed to mimic the natural use of ducks that paddle around in flooded paddy fields. Ducks have been used as natural weed repellents for centuries to tear them up and feed on insects, with their manure even acting as an additional fertiliser. As it glides through the water, two mechanisms on the bottom muddy the water to prevent weeds from getting enough sunlight to grow. The technique was used in the late 20th century with live ducks, called'aigamo,' which would paddle the water with the same results and eat any insects they found along the way.
During a wide-ranging discussion at Amazon's re:MARS conference in Las Vegas, Naveen Rao, corporate vice president and general manager of AI at Intel, spoke about machine learning's rapid progress and the fields it might transform, in addition to the steps he believes must be taken to ensure it's not abused. Rao compared the advent of modern AI approaches with the iPhone. Like the iPhone, he said, machine learning -- a technique underlying systems from Amazon's Alexa to Google Lens -- wasn't the first form of AI, but it was nonetheless "exciting" and "consequential." He characterizes the coming AI revolution as the single largest transition the human species has ever encountered. "Few people anticipated the big-picture changes that smartphones would bring. No one foresaw that smartphones could make our work day substantially longer because we'd never get away from email," he said.
Much of modern-day agriculture is dominated by monoculture, the practice of producing a single crop on a large swath of land. This approach makes it easier for farmers to manage their fields with tractors and other basic automated tools, but it also strips the soil of nutrients and reduces its productivity. As a result, many farmers rely heavily on nitrogen-based fertilizers, which can convert into nitrous oxide, a greenhouse gas 300 times more potent than carbon dioxide. Robots run on machine-learning software could help farmers manage a mix of crops more effectively at scale, while algorithms could help farmers predict what crops to plant when, regenerating the health of their land and reducing the need for fertilizers.
Traditionally, supercomputer performance is measured using the High-Performance Linpack (HPL) benchmark, which is the basis for the Top500 list that biannually ranks world's fastest supercomputers. The Linpack benchmark tests a supercomputer's ability to conduct high-performance tasks (like simulations) that use double-precision math. On June's Top500 list, announced Monday, Summit's 148 Linpack petaflops land it first place by a comfortable margin. Using that same machine configuration, Oak Ridge National Laboratory (ORNL) and Nvidia have tested Summit on HPL-AI and gotten a result of 445 petaflops. While the HPL benchmark tests supercomputers' performance in double-precision math, AI is a rapidly growing use case for supercomputers -- and most AI models use mixed-precision math.
"Zero Hunger" is one of the 17 UN SDGs expected to be achieved by 2030. According to the United Nations, up to 80% of food consumed in most developing countries are produced by smallholder farmers who, however, account for approximately 50% of the 815 million people suffering from hunger worldwide. At the Summit's session on AI and Agriculture, Justin Gong, Co-founder and Vice President of XAG, together with other panel experts from Microsoft, Tata Group and Connecterra has proposed projects and initiatives to exploit new possibilities of AI technology to improve food security and end hunger. Artificial Intelligence, through continuously analysing massive data related to climate, lands, crop growing, etc., while automatically designing and optimising algorithms for decision-making, can help farmers diagnose plant diseases, predict natural disasters and employ appropriate resources to close the yield gap. At XAG, AI-powered intelligent devices such as drones and sensors have been leveraged to establish digital farming infrastructure in rural areas and enable precision agriculture which, for example, accurately target pesticides, seeds, fertilisers and water to wherever it is needed.
The field of artificial intelligence (AI) is growing at a rapid pace, developing algorithms and automated machines that show promise in making the workplace more efficient and less biased. Many of us already interact with artificial intelligence in our daily lives, often without even realizing it--it's responsible for everything from credit score calculators to search engine results to what we see on social media.1 Likewise, organizations have introduced AI into many work processes, especially recruiting and talent-management functions. In many cases, algorithms sort through numerous factors to profile people and make predictions about them. AI hiring and talent-management systems have the potential to move the needle on gender equality in workplaces by using more objective criteria in recruiting and promoting talent.2