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The grocery chain and agency 360i recently launched a Twitter account called Chefbot that can identify up to three foods in user-submitted images and search a database of recipes for the best match. The makers claim the bot is trained to recognize 2,000 ingredients and pulls from a log of around 20,000 recipes. While the tool is fairly rudimentary for now, Kroger has big plans to grow it into something more sophisticated over time, along with 360i and tech partners Coffee Labs and Clarifai. The creators plan to incorporate every interaction the bot has into its training data in order to make future iterations more accurate at identifying foods and customizing recipes. "Over time, Chefbot will continuously learn and improve, and we're excited about how the experience will evolve and can eventually integrate into Kroger's mobile app," Menno Kluin, chief creative officer at 360i, said.
Prime Day has come early with some amazing deals. Purchases you make through our links may earn us a commission. Amazon's long-awaited Prime Day will be here before you know it (it's October 13 and October 14 if you haven't already set your alarms) but if you're anything like us, and the anticipation is proving too much to bear, we're going to let you in a little secret: You don't have to wait for the big day to arrive. The mega-retailer has already released a slew of amazing deals ahead of schedule, and we've sorted through them all to bring you the best of the best. From a pair of true wireless earbuds that's sure to knock your socks off to a slow cooker that will make holiday cooking a breeze, these are the best early Prime Day deals you can shop this weekend.
Foodborne illness afflicts 48 million people annually in the U.S. alone. Over 128,000 are hospitalized and 3,000 die from the infection. While preventable with proper food safety practices, the traditional restaurant inspection process has limited impact given the predictability and low frequency of inspections, and the dynamic nature of the kitchen environment. Despite this reality, the inspection process has remained largely unchanged for decades. CDC has even identified food safety as one of seven "winnable battles"; however, progress to date has been limited.
The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.
One of the greatest challenges faced by users who are visually impaired is identifying packaged foods, both in a grocery store and also in their kitchen cupboard at home. This is because many foods share the same packaging, such as boxes, tins, bottles and jars, and only differ in the text and imagery printed on the label. However, the ubiquity of smart mobile devices provides an opportunity to address such challenges using machine learning (ML). In recent years, there have been significant improvements in the accuracy of on-device neural networks for various perception tasks. When coupled with the increased computing power in modern smartphones, it is now possible for many vision tasks to yield high performance while running entirely on a mobile device.
Google's AI can now identify food in the supermarket, in a move designed to help the visually impaired. It is part of Google's Lookout app, which aims to help those with low or no vision identify things around them. A new update has added the ability for a computer voice to say aloud what food it thinks a person is holding based on its visual appearance. One UK blindness charity welcomed the move, saying it could help boost blind people's independence. Google says the feature will "be able to distinguish between a can of corn and a can of green beans".
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
Building rich machine learning datasets in a scalable manner often necessitates a crowd-sourced data collection pipeline. In this work, we use human studies to investigate the consequences of employing such a pipeline, focusing on the popular ImageNet dataset. We study how specific design choices in the ImageNet creation process impact the fidelity of the resulting dataset---including the introduction of biases that state-of-the-art models exploit. Our analysis pinpoints how a noisy data collection pipeline can lead to a systematic misalignment between the resulting benchmark and the real-world task it serves as a proxy for. Finally, our findings emphasize the need to augment our current model training and evaluation toolkit to take such misalignments into account. To facilitate further research, we release our refined ImageNet annotations at https://github.com/MadryLab/ImageNetMultiLabel.
SodaStream, an Israeli manufacturer of fizzy drink devices, gained visibility in the U.S. and Europe as a healthy and environment friendly alternative to carbonated giants like Coca Cola. But soon after relocating from a controversial site in the occupied West Bank to a new facility in southern Israel, executives realised that the company is facing a new challenge: streamlining operations in order to stay competitive with low-cost manufacturer rivals from China while quenching a fast-growing thirst for its bubbly beverages. To rein in costs and make SodaStream's four manufacturing lines more efficient, executives decided to automate assembly lines with robots, computerise production, and connect all manufacturing processes under one control system. The multi-year project was aimed at boosting output to keep pace with 30 percent yearly sales surges, while utilising artificial intelligence, machine learning and cloud computing to get a better handle on optimising production. "We continued to grow rapidly and were packed with endless employees. The dining room was full. The production side was full. We knew that we wouldn't be able to allow ourselves to keep operating the same way… whether in terms of space, efficiency, or in terms of costs," said Kfir Suissa, chief operation officer at SodaStream, which was acquired by PepsiCo in 2018 for US$3.2 billion.