DiDi partners with SoftBank in Japan for platform services for taxi industry, launches open new …

A key element in DiDi’s strategy is building a smart transportation ecosystem to capture future opportunities, with global expansion through alliances with regional partners. Source: DiDi. Click to enlarge.

From its founding as a taxi-hailing business, DiDi has been building up a world-leading one-stop transportation platform since 2012. The company continues to apply its big data capabilities to increase taxi drivers’ work efficiency and income. With 2 million taxi drivers connected to the app, DiDi is now the world’s leading online platform for taxi-hailing. In 2017, taxi drivers completed 1.1 billion rides on DiDi. DiDi is also working closely with taxi companies to help them build intelligent IT and driver management systems. Currently DiDi has established partnerships with about 500 taxi operators in China.

Separately, in Beijing, DiDi launched its car-sharing platform. DiDi is partnering with automakers, new energy transportation infrastructure operators and after-sales service providers to build an open new energy car-sharing system. The network of strategic partners includes 12 top automakers including BAIC BJEV, BYD, Chang’an Automobile Group, Chery Automobile Group, Dongfeng Passenger Vehicle, First Auto Works, Geely Auto, Hawtai Motor, JAC Motors, KIA Motors, Renault-Nissan-Mitsubishi, and Zotye Auto.

According to a study by GM Insights, the global car-sharing market is expected to grow 34% annually from 2017 to 2024, while the annual growth rate in China will exceed 40%. The first generation of large-scale, new energy car-sharing platforms are expected to materialize in core emerging countries such as China.

DiDi hopes to leverage on its AI strengths and national network to empower the entire automotive industry chain. The company’s data analytics capabilities enable smarter network management based on dynamic understanding of user distribution and attributes. Under the partnership, DiDi will open its platform to automakers’ own sharing services. The platform will introduce to individuals and corporate partners not only diversified models from automakers, but also auto-related finance and insurance services.

In addition to automakers, DiDi will also work closely with other car-sharing services, rental companies, infrastructure operators and after-sales service providers. As of August 2017, DiDi—which acquired Uber China in 2016—had built investment and technology partnerships with seven leading rideshare companies of the world, including Lyft, Grab, Ola, Uber, 99, Taxify and Careem.

DiDi believes the new program will reduce cost and enhance efficiency for the entire industry chain by integrating resources from cars, capital, parking spaces, charging points and refueling stations, to auto-maintenance and repair services in a new, open ecosystem of collaboration.


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Cyberwarfare is taking to the skies, aboard drones

Uber uses a master algorithm to determine how much money its drivers make—and women are ending up with less.

The gap: In a study released today of over 1.8 million drivers on the platform, women were found to earn $1.24 per hour less than men. Women also earned $130 less per week on average, in part because they tend to drive fewer hours.

The cause: The study, which was carried out by researchers at Stanford and Uber and has not undergone peer review, attributed the difference in pay to fact that male Uber drivers:

—Are more likely to drive in higher-paying locations

—Drive faster

—Take on trips with shorter distances to the rider

—Chose to drive longer trips

All of these are variables in the formula Uber uses to calculate driver wages, and the study showed they all tilted in men’s favor (the study claims men earn $21.28 an hour, on average). Women also have higher turnover on the platform, and more experienced drivers tend to get higher pay.

Though it wasn’t covered in the study, one reason women may avoid higher-paying areas is that they don’t feel safe—they may opt not to drive late at night in certain places, for instance, or stay away from neighborhoods that are considered dangerous.

Closing the gap: The study shows there’s a persistent disparity in pay by gender, and Uber may have a hard time fixing it. Stanford economist Rebecca Diamond, one of the paper’s coauthors, says the researchers considered recommending taking speed out of the equation, for example. But as she says, “both riders and drivers would prefer to arrive at the destination sooner.”

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Breakfast Briefing: Alibaba’s “Moonshots”, Uber Makes Peace and Fatter Children Worldwide

North Korea’s Cyber Capability

It has been confirmed that North Korean hackers stole US-South Korea military plans last year.

Editor’s Remarks: A South Korean lawmaker announced that the cyber attack, which occurred last year, led to North Korea obtaining copies of a plan hatched by the US and South Korea to take out Kim Jong-un. While North Korea is commonly thought of as technologically backward, the fact that the rogue state was able to access highly-classified documents reveals that they have a strong capacity for cyber warfare. Recent North Korean hacks having included attacks on bitcoin exchanges following the recent imposition of fresh UN sanctions upon the nation. Evidently, North Korea’s cyber potential has significantly improved in the four short years since the infamous attack on Sony for its production of “The Interview”.

Madrid Demands Clarity from Catalonia

Prime Minister Mariano Rajoy declared that the regional government must decide whether or not to declare independence.

Editor’s Remarks: After Catalan President Carles Puigdemont suspended the region’s declaration of independence on Tuesday, Madrid has stated it requires clarity on the matter. Article 155 of the Spanish constitution gives Madrid the power to remove Catalonian autonomy and seize control of the region, though this has never before been exercised. It appears that Puigdemont has deliberately obfuscated his government’s official position, preferring to speak in ambiguous terms about independence in recent days, in order to avoid Rajoy from triggering Article 155 and plunging the nation deeper into a constitutional crisis.

Childhood Obesity up 1000% in 40 Years

A study by the World Health Organisation and Imperial College examined the BMI data for 31.5m children.

Editor’s Remarks: As economies have grown more affluent, the number of obese children has increased to 124m worldwide, meaning that the number of overweight children in the world is about to overtake the number of underweight ones. The world’s highest child obesity rates are found in Polynesia and Micronesia, while in the US has around 20% of girls and boys are obese. Meanwhile, obesity levels are significantly lower in western Europe, where around 7-10% of children are classed as obese.

Uber Puts its Cards on the Table

The beleaguered ride-hailing giant said it would ‘exert more control’ over drivers if UK law changed.

Editor’s Remarks: Following Uber’s ban in London, company representatives said that if the UK required its drivers to be classed as employees with benefits, sweeping internal changes would be made. The comments have been interpreted as a sign that Uber may alter its labour model in the UK following the recent regulatory scrutiny. As such, Uber could become more akin to a private-hire car service, which dictate where and when drivers operate. However, since many of Uber’s UK drivers say that the main boon of working for the company is flexibility, they might not welcome the suggestions.

Alibaba Sets aside $15bn for “Moonshots”

The Chinese e-commerce giant will double R&D to double down on artificial intelligence (AI) and quantum computing.

Editor’s Remarks: The company’s R&D spend will increase to $15bn over the next three years in order to drive its business through the development of next-generation technology. The new plans will add 100 scientists housed in cutting-edge research laboratories, where they will focus help Alibaba keep up with its rivals Tencent and Amazon, to its existing network of 25,000 engineers. Furthermore, the company’s renewed efforts reflect Chinese state policy, which sets out the ambition that the country should aspire to become a world leader in AI.


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A Top Data Scientist at Uber Has Left the Company

Laszlo Korsos, a technologist developing Uber’s pricing systems, has left his position at the ride-hailing giant.

On Monday, Korsos joined hedge fund Citadel as a managing director and chief data officer. He had been a lead data scientist at Uber for three years.

“I am very excited about joining Citadel and helping the firm develop innovative ways to strategically manage the massive flow of Big Data that forms an important component of our investment strategies,” he told Fortune in a statement.

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Korsos began his career at The Blackstone Group as a summer analyst and went on to roles at Nuveen Investments and Goldman Sachs. He also previously worked as a consultant at Citadel Investment Group in Chicago for about a year.

He found out about Uber’s data scientist opportunity while climbing Mount Aconcagua, in Argentina, with a venture capitalist. “After seeing the opportunity to grow the mathematical technology across the company, I jumped at the chance to join the team,” he said in a 2014 interview.

In April of 2014, he joined Uber, where he founded and led its optimization and economics quant team. The team helped develop Uber’s pricing system technology–such as setting and optimizing fares–for projects including UberPool and UberEats.

Ken Griffin, the founder and CEO of Citadel, told Fortune in a statement: “Our ability to leverage big data effectively in our investment processes is critical to our success as a firm. Building on our current foundation, we are pleased to have a talent of Laszlo’s caliber joining our team.”


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Uber Unveils New Privacy Tool That Protects Individual User Data

Photo: Getty

You might think of Uber as a ride-hailing company or a lawsuit-ridden self-driving car developer, but at its core, Uber is a big data company. It has to constantly crunch location coordinates, traffic data, payment information, and tax rates—and putting all that data in Uber’s hands sometimes makes users nervous.

But now Uber is debuting a differential privacy tool that it will use to analyze its vast data stores. Differential privacy allows for analysis of large data sets without revealing the identity of any individual included in the data, and is used by companies like Apple and Google to gain insight from user data without compromising privacy. Uber’s new tool will let its data analysts know the likely privacy implications of any queries they make on Uber’s data before they make them.


“Effectively, it’s a way to take a look at queries and decide how sensitive the resulting data is from that query without having to run the query,” Uber’s manager of privacy engineering Menotti Minutillo told Gizmodo.

Here’s how it’ll work: Imagine Uber data analysts want to figure out what the average distance is for a ride in San Francisco. They’ll need to query large swathes of data about rides in the city, but pulling that thread could expose lots of information about individual riders and drivers. Differential privacy scrambles the data and injects noise, making it impossible to trace trip information back to a particular user.


But some queries are more sensitive than others, and therefore require more noise. “The average trip distance in a smaller city with far fewer trips is more influenced by a single trip and may require more noise to provide the same degree of privacy. Differential privacy defines the precise amount of noise required given the sensitivity,” Katie Tezapsidis, an Uber software engineer on the privacy team, explained in a blog post announcing the change.


In order to calculate that sensitivity, Uber partnered with a team of security researchers from the University of California, Berkeley. The researchers worked for over a year to come up with the calculation technique, nicknamed Elastic Sensitivity, which Uber is releasing today as an open-source tool.

Elastic Sensitivity will make it possible for analysts at Uber—and elsewhere—to quickly adapt differential privacy standards to a variety of queries. Previously, an analyst would have queried a database and then tried to weed out sensitive or unnecessary data after the fact. Now, data will come out clean.

“Our team is very, very interested in providing the tools and platforms so people can do their job in a privacy-appropriate way,” Minutillo said. The tool will be able to make suggestions about how much noise should be added in order to preserve privacy, or whether the query should be run at all. “In cases where you have a legitimate use—you need to retrieve data to do analysis—this is an additional layer of protection,” Minutillo added. “We can feel comfortable that the analyst can still get results that are correct, and reduce the risk of singling out any individual that’s in that set.”