McDonald’s Is Testing Automated Drive-thru Ordering at 10 Chicago Restaurants

Amelia Lucas wrote . . . . . . . . .

At 10 McDonald’s locations in Chicago, workers aren’t taking down customers’ drive-thru orders for McNuggets and french fries — a computer is, CEO Chris Kempczinski said Wednesday.

Kempczinski said the restaurants using the voice-ordering technology are seeing about 85% order accuracy. Only about a fifth of orders need to be a taken by a human at those locations, he said, speaking at Alliance Bernstein’s Strategic Decisions conference.

Over the last decade, restaurants have been leaning more into technology to improve the customer experience and help save on labor. In 2019, under former CEO Steve Easterbrook, McDonald’s went on a spending spree, snapping up restaurant tech. One of those acquisitions was Apprente, which uses artificial intelligence software to take drive-thru orders. Financial terms of the deal were not disclosed.

Kempczinski said the technology will likely take more than one or two years to implement.

“Now there’s a big leap from going to 10 restaurants in Chicago to 14,000 restaurants across the U.S., with an infinite number of promo permutations, menu permutations, dialect permutations, weather — and on and on and on,” he said.

Another challenge has been training restaurant workers to stop themselves from jumping in to help.

McDonald’s has also been looking into automating more of the kitchen, such as its fryers and grills, Kempczinski said. He added, however, that that technology likely won’t roll out within the next five years, even though it’s possible now.

“The level of investment that would be required, the cost of investment, we’re nowhere near to what the breakeven would need to be from the labor cost standpoint to make that a good business decision for franchisees to do,” Kempczinski said.

And because restaurant technology is moving so fast, Kempczinski said, McDonald’s won’t always be able to drive innovation itself or even keep up. The company’s current strategy is to wait until there are opportunities that specifically work for it.

“If we do acquisitions, it will be for a short period of time, bring it in house, jumpstart it, turbo it and then spin it back out and find a partner that will work and scale it for us,” he said.

Source: CNBC

Japanese Companies Go High-tech in the Battle Against Food Waste

Tetsushi Kajimoto wrote . . . . . . . . .

Japanese companies are ramping up the use of artificial intelligence and other advanced technology to reduce waste and cut costs in the pandemic, and looking to score some sustainability points along the way.

Disposing of Japan’s more than 6 million tonnes in food waste costs the world’s No.3 economy some 2 trillion yen ($19 billion) a year, government data shows. With the highest food waste per capita in Asia, the Japanese government has enacted a new law to halve such costs from 2000 levels by 2030, pushing companies to find solutions.

Convenience store chain Lawson Inc has started using AI from U.S. firm DataRobot, which estimates how much product on shelves, from onigiri rice balls to egg and tuna sandwiches, may go unsold or fall short of demand.

Lawson aims to bring down overstock by 30% in places where it has been rolled out, and wants to halve food waste at all of its stores in 2030 compared with 2018.

Disposal of food waste is the biggest cost for Lawson’s franchise owners after labour costs.

Drinks maker Suntory Beverage & Food Ltd is experimenting with another AI product from Fujitsu Ltd to try to determine if goods such as bottles of oolong tea and mineral water have been damaged in shipping.

Until now, that’s been a time-consuming human endeavour. With the new AI, Suntory hopes to gauge when a damaged box is just that, or when the contents themselves have been damaged and need to be returned.

Suntory aims to reduce the return of goods by 30-50% and cut the cost of food waste and develop a common standard system that can be shared by other food makers and shipping firms.

SUSTAINABLE DEVELOPMENT GOALS

Japan’s notoriously fussy shoppers are showing signs of getting on board, especially as the coronavirus pandemic hits incomes.

Tatsuya Sekito launched Kuradashi, an e-commerce firm dealing in unsold foods at a discount, in 2014 after seeing massive amounts of waste from food processors while working for a Japanese trading firm in China.

The online business is now thriving due partly to a jump in demand for low-priced unsold foods as consumers became more cost conscious amid the COVID-19 pandemic.

“Sales grew 2.5 times last year from a year before, while the amount of food waste has doubled since the coronavirus cut off food supply chain,” Sekito told Reuters.

Kuradashi has a network of 800 companies, including Meiji Holdings Co, Kagome Co and Lotte Foods Co, who sell it a total 50,000 items including packs of instant curry, smoothies and high-quality nori.

“Japanese shoppers tend to be picky but we attract customers by offering not just a sale but a chance to donate a portion of purchases to a charity, raising awareness about social issues,” Sekito said.

Membership numbers jumped to 180,000 in 2021 from 80,000 in 2019.

Others have also joined forces with food firms in developing new technological platform to cut food waste as part of global efforts to meet sustainable development goals (SDGs).

NEC Corp is using AI that can not only analyse data such as weather, calendar and customers’ trends in estimating demand but also give reasoning behind its analysis.

NEC has deployed the technology to some major retailers and food makers, helping them reduce costs by 15%-75%.

NEC hopes to share and process data through a common platform among makers, retailers and logistics, to reduce mismatches in supply chains.

“Reducing food waste is not our ultimate goal,” said Ryoichi Morita, senior manager overseeing NEC’s digital integration.

“Eventually, we hope it can lead to resolve other business challenges such as minimizing costs, fixing labour shortages, streamlining inventory, orders and logistics.”

Source: Reuters

Artificial Intelligence to Take Drive-thru Orders at Englewood Restaurant in the U.S.

Can I take your order?

It won’t be a human taking your order at the Lee’s Famous Recipe restaurant in Englewood for much longer.

Beginning Monday, the restaurant will be using artificial intelligence to take orders of customers passing through the drive-thru.

The Hi Auto technology will be used after it was developed with speech enhancement software that eliminates background noise and is able to accurately recognize a person’s voice.

“The Artificial Intelligence (AI) order taker will greet the customer, take their order and enter the order directly into the register system,” said Andrea Newport, spokesperson for the restaurant. “Employees in the restaurant will be able to listen to every transaction through existing headsets and intervene in case an issue arises during the order process.”

Far Hills Development, LLC, which operates the Englewood location and 12 other locations around the Miami Valley, said they believe the technology will improve service times and alleviate staffing challenges that have impacted restaurants during COVID-19.

The technology also can be scaled to include video and recognize license plates and greet regular customers by name and know their favorite menu items, Newport said.

Source: WHIO

Bringing AI and Robots to Daily Life including Housekeeping

Samsung has long been at the forefront of AI and robotics innovation, leveraging its seven global AI research centers to advance technology. By bringing AI to its products, Samsung is creating new home experiences from washing machines that optimize water usage, detergents, and wash cycles, to TVs with a Quantum AI Processor that can upscale HD content into pristine 8K resolution.

The major technologies Samsung introduced recently include the following:

JetBot 90 AI+:

Coming to the US 1H 2021, this new vacuum cleaner uses object recognition technology to identify and classify objects to decide the best cleaning path. LiDAR and 3D sensors allow JetBot 90 AI+ to avoid cables and small objects, while still cleaning hard-to-reach corners in your home. Also outfitted with a camera, JetBot90 AI+ is integrated with the SmartThings5 app to assist you with home monitoring.

Samsung Bot™ Care6:

The latest development in Samsung’s growing robotics lineup, Samsung Bot™ Care is designed use AI to recognize and respond to your behavior. It will be able to act as both a robotic assistant and companion, helping to take care of the details in your life. It will also learn your schedule and habits and send you reminders to help guide you throughout your busy day.

Samsung Bot™ Handy7:

Also in development, Samsung Bot™ Handy will rely on advanced AI to recognize and pick up objects of varying sizes, shapes and weights, becoming an extension of you and helping you with work around the house. Samsung Bot™ Handy will be able to tell the difference between the material composition of various objects, utilizing the appropriate amount of force to grab and move around household items and objects, working as your trusted partner to help with house chores like cleaning up messy rooms or sorting out the dishes after a meal.

Source: Samsung

Using Artificial Intelligence to Find New Uses for Existing Medications

Emily Caldwell wrote . . . . . . . . .

Scientists have developed a machine-learning method that crunches massive amounts of data to help determine which existing medications could improve outcomes in diseases for which they are not prescribed.

The intent of this work is to speed up drug repurposing, which is not a new concept – think Botox injections, first approved to treat crossed eyes and now a migraine treatment and top cosmetic strategy to reduce the appearance of wrinkles.

But getting to those new uses typically involves a mix of serendipity and time-consuming and expensive randomized clinical trials to ensure that a drug deemed effective for one disorder will be useful as a treatment for something else.

The Ohio State University researchers created a framework that combines enormous patient care-related datasets with high-powered computation to arrive at repurposed drug candidates and the estimated effects of those existing medications on a defined set of outcomes.

Though this study focused on proposed repurposing of drugs to prevent heart failure and stroke in patients with coronary artery disease, the framework is flexible – and could be applied to most diseases.

“This work shows how artificial intelligence can be used to ‘test’ a drug on a patient, and speed up hypothesis generation and potentially speed up a clinical trial,” said senior author Ping Zhang, assistant professor of computer science and engineering and biomedical informatics at Ohio State. “But we will never replace the physician – drug decisions will always be made by clinicians.”

The research is published in Nature Machine Intelligence.

Drug repurposing is an attractive pursuit because it could lower the risk associated with safety testing of new medications and dramatically reduce the time it takes to get a drug into the marketplace for clinical use.

Randomized clinical trials are the gold standard for determining a drug’s effectiveness against a disease, but Zhang noted that machine learning can account for hundreds – or thousands – of human differences within a large population that could influence how medicine works in the body. These factors, or confounders, ranging from age, sex and race to disease severity and the presence of other illnesses, function as parameters in the deep learning computer algorithm on which the framework is based.

That information comes from “real-world evidence,” which is longitudinal observational data about millions of patients captured by electronic medical records or insurance claims and prescription data.

“Real-world data has so many confounders. This is the reason we have to introduce the deep learning algorithm, which can handle multiple parameters,” said Zhang, who leads the Artificial Intelligence in Medicine Lab and is a core faculty member in the Translational Data Analytics Institute at Ohio State. “If we have hundreds or thousands of confounders, no human being can work with that. So we have to use artificial intelligence to solve the problem.

“We are the first team to introduce use of the deep learning algorithm to handle the real-world data, control for multiple confounders, and emulate clinical trials.”

The research team used insurance claims data on nearly 1.2 million heart-disease patients, which provided information on their assigned treatment, disease outcomes and various values for potential confounders. The deep learning algorithm also has the power to take into account the passage of time in each patient’s experience – for every visit, prescription and diagnostic test. The model input for drugs is based on their active ingredients.

Applying what is called causal inference theory, the researchers categorized, for the purposes of this analysis, the active drug and placebo patient groups that would be found in a clinical trial. The model tracked patients for two years – and compared their disease status at that end point to whether or not they took medications, which drugs they took and when they started the regimen.

“With causal inference, we can address the problem of having multiple treatments. We don’t answer whether drug A or drug B works for this disease or not, but figure out which treatment will have the better performance,” Zhang said.

Their hypothesis: that the model would identify drugs that could lower the risk for heart failure and stroke in coronary artery disease patients.

The model yielded nine drugs considered likely to provide those therapeutic benefits, three of which are currently in use – meaning the analysis identified six candidates for drug repurposing. Among other findings, the analysis suggested that a diabetes medication, metformin, and escitalopram, used to treat depression and anxiety, could lower risk for heart failure and stroke in the model patient population. As it turns out, both of those drugs are currently being tested for their effectiveness against heart disease.

Zhang stressed that what the team found in this case study is less important than how they got there.

“My motivation is applying this, along with other experts, to find drugs for diseases without any current treatment. This is very flexible, and we can adjust case-by-case,” he said. “The general model could be applied to any disease if you can define the disease outcome.”

Source: The Ohio State University