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Can robots make food service safer for workers?


Health care workers are not the only unwilling essential services frontline workers at increased risk of COVID-19. According to the Washington Post on April 12, "At least 41 grocery workers have died of the coronavirus and thousands more have tested positive in recent weeks". At the same time, grocery stores are seeing a surge in demand and are currently hiring. The food industry is also seeing increasing adoption of robots in both the back end supply chain and in the food retail and food service sectors. "Grocery workers are risking their safety, often for poverty-level wages, so the rest of us can shelter in place," said John Logan, director of labor and employment studies at San Francisco State University. "The only way the rest of us are able to stay home is because they're willing to go to work."

Billionaire used facial recognition app Clearview AI to get intel on his daughter's date

Daily Mail - Science & tech

A billionaire supermarket tycoon used the creepy facial recognition app Clearview AI to investigate his daughter's date as she dined in a Manhattan restaurant. Gristedes Food CEO John Catsimatidis, 71, spotted his daughter, Andrea, on a date with an unfamiliar man while dining at Cipriani Downtown, an Italian restaurant in the SoHo neighbourhood of Lower Manhattan, in the October of 2018. Enlisting a waiter to secure a photo of the mystery individual, Mr Catsimatidis ran the image through the Clearview AI facial recognition system, which cross referenced the image with photos scraped from sites like Facebook and Instagram. The app produced a selection of other images of the man, along with the websites on which they appeared. The billionaire was able to discover that his daughter's date was a venture capitalist who hailed from San Francisco, California -- information he proceeded to text to Ms Catsimatidis from across the restaurant.

FoodTech Revolution: Automation, Delivery And Convenience - Disruption Hub


With the world's population on track to exceed nine billion by 2050, it's crunch time for a solution to sustainable food production. However, undermining efforts to feed the planet at every step are our attitudes to waste. Around a third of the total food produced in the world currently gets thrown away, with citizens of rich countries by far the worst offenders. For businesses in the food industry, fluctuating customer demand and inadequate access to data make it difficult to get quantities right. Innovating the supply chain is a clear solution to improving waste and delivering the kind of food services that consumers want.

Machine Learning and AI in Food Industry: Solutions and Potential


Artificial Intelligence and Machine Learning solutions offer large possibilities to optimize and automate processes, save costs and make less human error possible for many industries. Food and Beverage is not an exception, where it can be beneficially applied in restaurants, bar and cafe businesses as well as in food manufacturing. These two segments have common use cases where AI in the food industry can be applied, as well as different ones, which is linked to different problems that must be solved. Knowing what goods to manufacture in large amounts or what dishes are the best choice to include in your restaurant menu is the key to increase earnings. Often customers' and market demands are changing very fast and so it is even more important to be one step ahead to take measures in time.

Unsupervised Multi-Document Opinion Summarization as Copycat-Review Generation Machine Learning

Summarization of opinions is the process of automatically creating text summaries that reflect subjective information expressed in input documents, such as product reviews. While most previous research in opinion summarization has focused on the extractive setting, i.e. selecting fragments of the input documents to produce a summary, we let the model generate novel sentences and hence produce fluent text. Supervised abstractive summarization methods typically rely on large quantities of document-summary pairs which are expensive to acquire. In contrast, we consider the unsupervised setting, in other words, we do not use any summaries in training. We define a generative model for a multi-product review collection. Intuitively, we want to design such a model that, when generating a new review given a set of other reviews of the product, we can control the `amount of novelty' going into the new review or, equivalently, vary the degree of deviation from the input reviews. At test time, when generating summaries, we force the novelty to be minimal, and produce a text reflecting consensus opinions. We capture this intuition by defining a hierarchical variational autoencoder model. Both individual reviews and products they correspond to are associated with stochastic latent codes, and the review generator ('decoder') has direct access to the text of input reviews through the pointer-generator mechanism. In experiments on Amazon and Yelp data, we show that in this model by setting at test time the review's latent code to its mean, we produce fluent and coherent summaries.

Would You Like Fries With That? McDonald's Already Knows the Answer


But in the coming years, the company's machine learning technology could change how consumers decide what to eat -- and, in a potentially ominous development for their waistlines, make them eat more. So far, the technological advances can be experienced mostly at the chain's thousands of drive-throughs, where for years menu boards have displayed a familiar array of McDonald's favorites: Big Macs, Quarter Pounders, Chicken McNuggets. Now, the chain has digital boards programmed to market that food more strategically, taking into account such factors as the time of day, the weather, the popularity of certain menu items and the length of the wait. On a hot afternoon, for example, the board might promote soda rather than coffee. At the conclusion of every transaction, screens now display a list of recommendations, nudging customers to order more.

The present and future of food tech investment opportunity – TechCrunch


There is no bigger industry on our planet than food and agriculture, with a consistent, loyal customer base of 7 billion. In fact, the World Bank estimates that food and agriculture comprise about 10% of the global GDP, meaning that, food and agriculture would be valued at about $8 trillion globally based on the projected global GDP of $88 trillion for 2019. On the food front, a record $1.71 trillion was spent on food and beverages in 2018 at grocery stores and other retailers and away-from-home meals and snacks in the United States alone. During the same year, 9.7% of Americans' disposable personal income was spent on food -- 5% at home and 4.7% away from home -- a percentage that has remained steady amidst economic changes over the past 20 years. However, despite a stalwart customer base, the food industry is facing unprecedented challenges in production, demand and regulations stemming from consumer trends.

Would You Like Fries With That? McDonald's Already Knows the Answer


As the evolution of the McDonald's drive-through shows, the internet shopping experience, with its recommendation algorithms and personalization, is increasingly shaping the world of brick-and-mortar retail, as restaurants, clothing stores, supermarkets and other businesses use new technology to collect consumer data and then deploy that information to encourage more spending. At some stores, Bluetooth devices now track shoppers' movements, allowing companies to send texts and emails recommending products that customers lingered over but did not buy. And a number of retailers are experimenting with facial-recognition tools and other technologies -- sometimes known as "offline cookies" -- that allow businesses to gather information about customers even when they are away from their computers. In the restaurant world, the increasingly popular food-delivery apps have produced a slew of customer data. But much of that information is controlled by third-party technology companies rather than by the restaurants themselves, underlining the importance of tech expertise as the industry grows more competitive.

McDonald's acquires AI tech company Apprente


Technology is becoming an increasingly important investment for the fast food chain, especially since it can help improve drive-thru times and labor costs, two areas the company has been working to improve. McDonald's previously said it was testing voice-activated drive-thrus and must have liked the results to pursue an acquisition. It is also testing automated deep-fryers that cut down on labor in the kitchen. With this latest acquisition, McDonald's is securing its place as a tech leader within the fast food space. It previously bought Dynamic Yield for $300 million earlier this year and has since deployed the company's decision technology at the drive-thru at 8,000 restaurants in the U.S. and plans to reach just about all drive-thrus in the U.S. and Australia by the end of the year.

McDonald's (MCD) Acquires Startup to Add Artificial Intelligence to Its Drive-Through Restaurants - Crypto Mak


McDonald's (MCD) is one of the oldest and most popular fast food restaurant chains in the world and as with any other establishments that have been around as long, the company is constantly seeking ways to improve its service and the experience it gives customers. The 79-year-old restaurant has now announced the acquisition of Apprente, in a bid to improve its service using automated voice commands. Founded in 2017, Apprente is a Silicon Valley startup which focuses on the use of artificial intelligence to analyze and improve orders placed via a drive-through. Proper use of this technology could significantly improve McDonald's service by reducing service time considerably. If all goes according to plan, McDonald's hopes to also apply Apprente's technology to orders placed using the company's mobile app as well as its order kiosks.