Vegebot, a vegetable-picking robot, uses machine learning to identify and harvest a commonplace, but challenging, agricultural crop. A team at the University of Cambridge initially trained Vegebot to recognize and harvest iceberg lettuce in the laboratory. It has now been successfully tested in a variety of field conditions in cooperation with G's Growers, a local fruit and vegetable co-operative. Although the prototype is nowhere near as fast or efficient as a human worker, it demonstrates how the use of robotics in agriculture might be expanded, even for crops like iceberg lettuce which are particularly challenging to harvest mechanically. The researchers published their results in The Journal of Field Robotics.
With open source and third-party tools to guide them, businesses should be able to incorporate artificial intelligence into their work without having to start from scratch, right? Not if the industry in question has no data, as is the case with parts of the food industry. AgShift CEO Miku Jha has a mantra to sum up the problem: "Older is the industry, tougher is the battle." "No matter how deep your bank account is, if you wanted to bring AI to solve the issues in the food supply chain, you will have to start from the absolute bottom. Because no one has images stored anywhere, anywhere in any facility," Jha said at VentureBeat's Transform 2019 conference.
The'Vegebot', developed by a team at the University of Cambridge, was initially trained to recognise and harvest iceberg lettuce in a lab setting. It has now been successfully tested in a variety of field conditions in cooperation with G's Growers, a local fruit and vegetable co-operative. Although the prototype is nowhere near as fast or efficient as a human worker, it demonstrates how the use of robotics in agriculture might be expanded, even for crops like iceberg lettuce which are particularly challenging to harvest mechanically. The results are published in The Journal of Field Robotics. Crops such as potatoes and wheat have been harvested mechanically at scale for decades, but many other crops have to date resisted automation.
Around the world, an industry has emerged around automating food service through robotics, raising questions about job security and mass unemployment while also prompting praise for streamlining and innovation. In the epicenter of Silicon Valley, where innovation is exalted beyond all else, this industry has played out in various forms, from cafes, burger shops and pizza delivery to odd vending machines. Man cannot survive on bread alone, the saying goes, but in the Bay Area, a woman could conceivably sustain herself on a varied menu of foodstuffs that had not passed the hand of man in preparation at all that day. And that woman is me. I began my day with a coffee at CafeX, where I met Francisco, the dancing and spinning robotic arm.
Recently, I crossed paths at an airport with a Midwestern brewmaster who shared that he was ready to retire, but simply couldn't. There was no one to take his place who could brew the company's trademark recipes for beer. This is not an uncommon business problem. Semiconductor companies report that their master materials engineers, who could work around a material shortage and still come up with an effective product, are retiring. It's creating a know-how gap that might leave the next materials shortage unsolved, since newer employees lack the know-how and experience.
Characterized by a fusion of technologies that blur the lines between the physical and digital, the Fourth Industrial Revolution is spreading across the manufacturing world. As a component of this revolution, a growing number of suppliers are using augmented reality (A.R.) to improve operations in workforce training and equipment maintenance. A.R. is a technologically enhanced version of reality created by using technology to overlay digital information on an image of something being viewed through a device, such as smart goggles or a smartphone camera. The goggles are often voice-controlled, leaving wearers with both hands free. Statista estimates the A.R. market was worth $5.91 billion in 2018 and that it will reach more than $198.7 billion by 2025.
American tech stars such as Oracle founder Larry Ellison, Amazon boss Jeff Bezos or the investor legend Peter Thiel do not believe in such humility, but instead, let billions of dollars go to decipher the mystery of why people age and die. If the three business leaders blindly believed the computer, there would be a more pleasant way for them to at least postpone death: drink more champagne. Because the analysis of many data and influencing factors provides the clear connection that with increasing champagne consumption the life expectancy rises. Although it is not well known how much Dom Pérignon the billionaires drink per week. What is certain, however, is that each of them ignores this connection.
Arla Foods has developed a new artificial intelligence tool to better predict their milk intake from farmer-owners. This means that 200 million kilos of milk can now be utilised better each year making Arla's value chain even more sustainable. Every year, Arla collects around 13 billion kilos of milk from their 10,300 farmer-owners across Northern Europe. To transform as much as possible into healthy and more sustainable dairy products, the cooperative is keeping a close eye on emerging technologies that can make the dairy production even more efficient. Most recently, Arla has developed a tool that uses artificial intelligence (AI) to predict how much milk 1,5 million cows will produce in the future.
In this May 22, 2019, photo, a customer waits for a coffee in front of a robot named b;eat after placing an order at a cafe in Seoul, South Korea. SEOUL, South Korea – Are robot baristas the future of South Korea's vibrant coffee culture? The company now has 45 robot-equipped outlets in shopping malls, company cafeterias, schools and an airport. Coffee is just one of many industries that could be transformed by automated services in this tech-forward nation, a notion both exciting and worrisome as jobs become scarcer. South Korean industries, including restaurants, convenience stores, supermarkets, banks and manufacturers, are relying increasingly on robots and other automation.
This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer's utility is additive in the different categories. Her preferences about product attributes as well as her price sensitivity vary across products and are in general correlated across products. We build on techniques from the machine learning literature on probabilistic models of matrix factorization, extending the methods to account for time-varying product attributes and products going out of stock. We evaluate the performance of the model using held-out data from weeks with price changes or out of stock products. We show that our model improves over traditional modeling approaches that consider each category in isolation. One source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data. Using held-out data, we show that our model can accurately distinguish which consumers are most price sensitive to a given product. We consider counterfactuals such as personally targeted price discounts, showing that using a richer model such as the one we propose substantially increases the benefits of personalization in discounts.