The future is in a Shell gas station in the sleepy Silicon Valley suburb of Campbell, Calif.
Customers who walk into the service station's adjacent convenience store to buy Kettle Chips and Arizona Ice Tees never deal with human cashiers. Instead, technology is the shopkeeper.
The store, owned by the chain Loop, officially opened in December after a few months of testing. AiFi, the startup that created the software for managing the store, is pitching its technology as a way to help retailers remain competitive by converting their brick-and-mortar shops into cashierless markets.
For many people, filing insurance claims means tedious paperwork and stressful phone calls. It doesn’t have to be that way—and, soon, it won’t. The Next Normal looks at how telematics and artificial intelligence will enable faster, easier claims.
The machine learning model, which Freeport developed with McKinsey, the consultancy, uses data from sensors around the mine and suggests new ways to improve the performance of its crushers and processing mills, according to the company.
The system found that the mine was producing seven distinct types of ore and that the processing method, which involves the use of large flotation tanks, could be adjusted to recover more copper by adjusting the PH level.
The machine-learning programme is one of three major initiatives Freeport says will boost copper production by 30 per cent.
Indians stockpile more of the precious metal than citizens of any other country, and their hoard is seven times the amount of gold mined globally in 2018. Now, Bangalore-based Rupeek is giving the centuries-old business of gold lending an internet-aided makeover to help Indians cash in on the 25,000 tons of the metal the World Gold Council estimates they’ve stashed away. Backed by two of Silicon Valley’s most powerful venture capital firms, Sequoia Capital and Accel, the startup allows people to borrow money against their hoard, often built up from wedding gifts or inherited from their families. “Indians have a mind-boggling $1 trillion worth of gold sitting in their homes, several times the gold in Fort Knox,” says Sumit Maniyar, Rupeek’s founder and chief executive officer.
A motorcycle-borne loan agent from the startup arrived within the hour. He used a computer vision-aided testing kit to appraise the metal’s purity. A team 600 miles away crunched data to complete the background check and process the loan electronically. About 30 minutes later, 200,000 rupees ($2,800) had been deposited in Mhatre’s bank account by one of Rupeek’s partners, an Indian private lender called Federal Bank. The startup’s 24/7 control room turned on the risk-monitoring system to track the agent, ensuring he securely deposited the jewelry at the bank’s nearest vault.
While Aker BP got the help it needed from a small Austin, Texas-based AI software firm, SparkCognition, some bigger oil-and-gas companies are working with giants in the tech industry. Exxon Mobil Corp. in February started a partnership with Microsoft Corp. to deploy AI programs to optimize its operations in the Permian, or West Texas Basin. The oil giant also recently installed an AI program to gain insights from data coming from millions of sensors that monitor its global refineries. Total SA, meanwhile, is linking up with Google Inc. to better interpret seismic data, and is set to increase its investment in AI to squeeze more hydrocarbons from existing assets.
The WSJ has broken down some of the technology several retailers are trying out in response to the Amazon Go concept - see my blog post in Jan 2018
How many are experimenting?
"A global survey of about 400 retailers conducted in June by research and advisory firm International Data Corp. found that 28% are testing or piloting cashierless systems, said Leslie Hand, vice president of IDC’s Retail Insights division."
In 2017, Ping An, China’s second-largest insurer and its biggest non-state-owned company by revenue, rolled out a “Superfast Onsite Investigation” system—enabling policyholders to submit claims by simply opening a smartphone app and answering a few questions. But the app’s niftiest feature offers the option to not even wait for an inspector. Instead, customers can snap photos of a damaged vehicle and send them to a Ping An computer, which can respond with a repair estimate in three minutes or less. If the customer accepts the estimate, then wancheng! (“Done!”) Ping An can transfer funds immediately.
Last year, Ping An’s customers used this feature to settle 7.3 million claims, or 62% of the total. The service saves the company more than $750 million each year by reducing bogus claims and human error. But its simplicity belies the extraordinary sophistication of the artificial intelligence and data-processing operations that make it possible.
Ant Financial’s Alipay and Tencent’s WeChat have changed the way many people live their financial lives. They are one-stop shops that enable half a billion Chinese to access a dizzying array of services, from payments, loans, investments, and credit scores to taxi rides, travel bookings, and social media.Because so much is sold via these apps, Alibaba and Tencent know the health (or lack thereof) of many small businesses across China. As a result, they can lend to companies that banks might consider too risky. Likewise, people with no traditional credit score can get cheap loans because Ant Financial has their payment and purchase history.
The doors opened, and with that, Nordstrom finally had a horse in New York City’s high-end retail race, competing with stores like Saks Fifth Avenue, Bloomingdale’s, and Bergdorf Goodman.
But the 47,000-square-foot emporium is also a sophisticated shopping-tech laboratory—one of several where Nordstrom is urgently experimenting with ways to make upscale, high-touch retail more competitive in an e-commerce-driven era. The mazelike layout, spread over three floors, showcases subtle but important technological enhancements. Some are super-practical—such as self-service bins where shoppers can drop off returns (including online purchases) and get credit almost immediately. Others have a wow factor: In the suits section, customers can see how bespoke jackets would look on life-size avatars of themselves, projected on screens, a digital take on the adage, “Measure twice, cut once.” And yet others pamper you: If you’ve “reserved” a product online to try on before you buy, there’ll be a fitting room waiting for you—with your name on the door.
BusinessWeek has a cover story on Palantir including this on its Foundry product
“Palantir’s newest product, Foundry, aims to finally break through the profitability barrier with more automation and less need for on-site engineers. Airbus SE, the big European plane maker, uses Foundry to crunch airline data about specific onboard components to track usage and maintenance and anticipate repair problems. Merck KGaA, the pharmaceutical giant, has a long-term Palantir contract to use Foundry in drug development and supply chain management.”
Why are Palantir products so complex?
A personal opinion from Ethan Bond at Quora because the tools are used in highly sensitive applications.
“Let’s take the example of a minor feature I’ve been building out recently: commenting. This is exactly what it sounds like. We want to allow users to comment on datasets and analysis artifacts to collaborate with each other. We want them to be able to @mention other users to invite them to take a look at some analysis.
Sounds easy, right?
A few issues: Let’s say a user mentions another user in a comment on a dataset. We can’t notify recipients about datasets that they don’t have access to, as it leaks information about the existence of the dataset. We also can’t tell the commenter exactly who has what amount of access to the dataset, because it’d leak privilege information about the recipient. Now let’s assume the recipient does have access. We’d like her to receive an email about this comment. But that email can’t contain any information about the contents of the dataset because data can’t be sent over email. So now you might get an ambiguous “X User commented on Y Dataset.” If a user is getting too many notifications, she might want to unsubscribe – but for security reasons, in order to do that she’d likely have to log in through her employer’s network which you often can’t do (easily) from your phone. To alleviate this we might want to batch the notifications, but given the nature of our clients, some notifications are truly urgent and we don’t have a way to automatically and reliably differentiate urgent from non-urgent notifications. Now let’s say the commenter’s company is merging with another company, and as part of the merge, both userbases (Company A and Company B) will have access to the same datasets, but they won’t have the ability to know that the other company’s userbase exists until after the merger is completed.
A bit more complicated than your standard commenting feature, eh?”
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