Alibaba to Challenge Amazon with a Cloud Service Push in Europe

Alibaba Group Holding Ltd. is in talks with BT Group PLC about a cloud services partnership as the Chinese internet giant challenges Amazon.com Inc.’s dominance in Europe. An agreement between Alibaba and the IT consulting unit of Britain’s former phone monopoly could be similar to Alibaba’s existing arrangement with Vodafone Group Plc in Germany, according […]

Alibaba Group Holding Ltd. is in talks with BT Group PLC about a cloud services partnership as the Chinese internet giant challenges Amazon.com Inc.’s dominance in Europe.

An agreement between Alibaba and the IT consulting unit of Britain’s former phone monopoly could be similar to Alibaba’s existing arrangement with Vodafone Group Plc in Germany, according to a person familiar with the matter, who asked not to be identified as the talks are private.

A BT spokeswoman confirmed by email that the U.K. telecom company is in talks with Alibaba Cloud and declined to give details. A spokesman for Alibaba declined to comment.

Started in 2009, Alibaba Cloud has expanded fast beyond China in a direct challenge to Amazon Web Services, the e-commerce giant’s division that dominates cloud computing. Alibaba Cloud is now the fourth-biggest global provider of cloud infrastructure and related services, behind Amazon, Microsoft Corp. and Alphabet Inc.’s Google, according to a report last month by Synergy Research Group.

Europe has become key to Alibaba Cloud’s success outside China, with prospects in the U.S. made murky by President Donald Trump’s America First agenda. Alibaba has pulled back in the U.S. just as tensions between America and China have escalated under Trump.

Alibaba started the German partnership with Vodafone in 2016. The Hangzhou, China-based company put its first European data center in Frankfurt, allowing Vodafone to resell Alibaba Cloud services such as data storage and analytics. Last week, Alibaba Cloud moved into France, agreeing to work with transport and communications company Bollore SA in cloud computing, big data and artificial intelligence.

Telecom dilemma

BT’s talks with Alibaba underscore a dilemma for the telecom industry. As big tech companies and consulting firms muscle in on their business installing and maintaining IT networks for large corporations, they must choose whether to resist them, or accept their help and decide which to ally with.

BT Global Services has struck up partnerships with Amazon, Microsoft and Cisco Systems Inc., while Spain’s Telefonica SA works with Amazon. In Germany, while Deutsche Telekom AG’s T-Systems has partners including China’s Huawei Technologies Co. and Cisco, it has structured its public cloud offering as an alternative to U.S. giants Amazon and Google—touting its ability to keep data within Germany where there are strict data-protection laws, 100% out of reach of U.S. authorities.

A deal with Alibaba could bolster BT’s cloud computing and big data skills as clients shift more of their IT capacity offsite to cut costs.

BT is undertaking a digital overhaul of its Global Services business in a restructuring involving thousands of job cuts after revenue at the division fell 9% last year. The poor performance of Global Services and the ouster last month of BT CEO Gavin Patterson have fueled speculation among some analysts that BT may sell the division. Still, the unit is seen by some investors as critical for BT’s relationships with multinational clients.

Read the source article in Digital Commerce 360.

Tale of Two Amazons: Average Pay is $28.5K; Software Engineers Earn North of $100K

Amazon disclosed in a filing Wednesday (April 19) that the median pay for its employees was $28,446 in 2017. Put another way: half of Amazon’s employees earned less than that amount. In the same year, Amazon created more than 130,000 jobs, many of them for AI scientists. The underwhelming figure was made public as part of a new rule put into […]

Amazon disclosed in a filing Wednesday (April 19) that the median pay for its employees was $28,446 in 2017. Put another way: half of Amazon’s employees earned less than that amount. In the same year, Amazon created more than 130,000 jobs, many of them for AI scientists.

The underwhelming figure was made public as part of a new rule put into effect this year by the Securities and Exchange Commission requiring companies to disclose the pay ratio between their CEOs and overall employees.

Jeff Bezos, Amazon’s CEO and the world’s richest man, received a total compensation of about $1.68 million last year — or 59 times the median Amazon employee compensation.

Call it a tale of two Amazons: those who work in technical roles and those who work in warehouses and grocery stores.

Amazon said in a statement provided to CNN that the median pay includes “global, full and part-time” employees across “every area of the company.”

“In every country and every sector where we employee people, we offer highly competitive wage and benefits such as company stock, health insurance and retirement savings, innovative parental leave, and training for in-demand jobs through our Career Choice program,” the company said.

Amazon now has more than half a million employees worldwide, thanks in large part to a heavy investment in fulfillment centers and its $13.7 billion acquisition of Whole Foods, which had about 87,000 employees when the deal was announced.

Bezos said in a letter to shareholders Wednesday that Amazon created more than 130,000 jobs last year alone, not counting acquisitions. Those new jobs “cover a wide range of professions, from artificial intelligence scientists to packaging specialists to fulfillment center associates,” he wrote.

But the artificial intelligence scientists are bound to make substantially more than the fulfillment center workers.

The average salary for software engineers at Amazon is north of $100,000, according to data from PayScale, a salary comparison service.

By comparison, a full-time warehouse associate at one of Amazon’s fulfillment centers in New Jersey could make as much as $13.85 per hour, according to a current job posting. That would come out to about the same as last year’s median pay.

Read the source article at CNNtech.com.

Inside Amazon’s Artificial Intelligence Flywheel

IN EARLY 2014, Srikanth Thirumalai met with Amazon CEO Jeff Bezos. Thirumalai, a computer scientist who’d left IBM in 2005 to head Amazon’s recommendations team, had come to propose a sweeping new plan for incorporating the latest advances in artificial intelligence into his division. He arrived armed with a “six-pager.” Bezos had long ago decreed that […]

IN EARLY 2014, Srikanth Thirumalai met with Amazon CEO Jeff Bezos. Thirumalai, a computer scientist who’d left IBM in 2005 to head Amazon’s recommendations team, had come to propose a sweeping new plan for incorporating the latest advances in artificial intelligence into his division.

He arrived armed with a “six-pager.” Bezos had long ago decreed that products and services proposed to him must be limited to that length, and include a speculative press release describing the finished product, service, or initiative. Now Bezos was leaning on his deputies to transform the company into an AI powerhouse. Amazon’s product recommendations had been infused with AI since the company’s very early days, as had areas as disparate as its shipping schedules and the robots zipping around its warehouses. But in recent years, there has been a revolution in the field; machine learning has become much more effective, especially in a supercharged form known as deep learning. It has led to dramatic gains in computer vision, speech, and natural language processing.

In the early part of this decade, Amazon had yet to significantly tap these advances, but it recognized the need was urgent. This era’s most critical competition would be in AI—Google, Facebook, Apple, and Microsoft were betting their companies on it—and Amazon was falling behind. “We went out to every [team] leader, to basically say, ‘How can you use these techniques and embed them into your own businesses?’” says David Limp, Amazon’s VP of devices and services.

Thirumalai took that to heart, and came to Bezos for his annual planning meeting with ideas on how to be more aggressive in machine learning. But he felt it might be too risky to wholly rebuild the existing system, fine-tuned over 20 years, with machine-learning techniques that worked best in the unrelated domains of image and voice recognition. “No one had really applied deep learning to the recommendations problem and blown us away with amazingly better results,” he says. “So it required a leap of faith on our part.” Thirumalai wasn’t quite ready—but Bezos wanted more. So Thirumalai shared his edgier option of using deep learning to revamp the way recommendations worked. It would require skills that his team didn’t possess, tools that hadn’t been created, and algorithms that no one had thought of yet. Bezos loved it (though it isn’t clear whether he greeted it with his trademark hyena-esque laugh), so Thirumalai rewrote his press release and went to work.

Thirumalai was only one of a procession of company leaders who trekked to Bezos a few years ago with six-pagers in hand. The ideas they proposed involved completely different products with different sets of customers. But each essentially envisioned a variation of Thirumalai’s approach: transforming part of Amazon with advanced machine learning. Some of them involved rethinking current projects, like the company’s robotics efforts and its huge data-center business, Amazon Web Services (AWS). Others would create entirely new businesses, like a voice-based home appliance that would become the Echo.

Read the source article in Wired.

Google Cloud Platform cuts the price of GPUs by up to 36 percent

Google has announced lower prices for the use of Nvidia’s Tesla GPUs through its Compute Engine by up to 36 percent. In U.S. regions, using the somewhat older K80 GPUs will now cost $0.45 per hour while using the newer and more powerful P100 machines will cost $1.46 per hour (all with per-second billing). The company is also dropping the […]

Google has announced lower prices for the use of Nvidia’s Tesla GPUs through its Compute Engine by up to 36 percent. In U.S. regions, using the somewhat older K80 GPUs will now cost $0.45 per hour while using the newer and more powerful P100 machines will cost $1.46 per hour (all with per-second billing).

The company is also dropping the prices for preemptible local SSDs by almost 40 percent. “Preemptible local SSDs” refers to local SSDs attached to Google’s preemptible VMs. You can’t attach GPUs to preemptible instances, though, so this is a nice little bonus announcement — but it isn’t going to directly benefit GPU users.

As for the new GPU pricing, it’s clear that Google is aiming this feature at developers who want to run their own machine learning workloads on its cloud, though there also are a number of other applications — including physical simulations and molecular modeling — that greatly benefit from the hundreds of cores that are now available on these GPUs. The P100, which is officially still in beta on the Google Cloud Platform, features 3594 cores, for example.

Developers can attach up to four P100 and eight K80 dies to each instance. Like regular VMs, GPU users will also receive sustained-use discounts, though most users probably don’t keep their GPUs running for a full month.

It’s hard not to see this announcement in the light of AWS’s upcoming annual developer conference, which will take over most of Las Vegas’s hotel conference space next week. AWS is expected to make a number of AI and machine learning announcements, and chances are we’ll see some price cuts from AWS, too.

Read the source article at TechCrunch.

The future of getting dressed: AI, VR and smart fabrics

Cher Horowitz’s closet from the film “Clueless” had a futuristic computer system that helped her put together outfits. Back in 1995, the concept teased what it might be like to get dressed in the future. Technology has evolved a lot since then, but closets have been largely untouched by innovation. Now, that’s starting to change. […]

Cher Horowitz’s closet from the film “Clueless” had a futuristic computer system that helped her put together outfits. Back in 1995, the concept teased what it might be like to get dressed in the future.

Technology has evolved a lot since then, but closets have been largely untouched by innovation.

Now, that’s starting to change.

“If algorithms do their job well, people will spend less time thinking about what to wear,” said Ranjitha Kumar, an assistant professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign.

From artificial intelligence and gadgets to smart fabrics and virtual reality, technology is poised to breathe innovation into not only how we dress but how we shop.

The most recognizable example is Amazon’s Echo Look, which received significant buzz when it was announced earlier this year. The gadget ($200) serves as a style assistant to help you decide what to wear.

Like Amazon’s other smart speakers, the Echo Look will tell you the weather or play music. But the oval-shaped product also has a voice-controlled camera for taking photos of you in various outfits. It works alongside an app.

After snapping photos of you in two outfits in front of the device, its built-in Style Check tool decides which one is best. It leans on a combination of machine learning technology and human opinion.

Amazon’s “fashion specialists” train the software to be a judge of style. The automated results consider “fit, color, styling, seasons and current trends.” It’ll also suggest similar styles to buy from various brands. Through testing, we found that the suggestions can be hit or miss.

“The brand selection is pretty limited, and while the Echo Look may help you decide between two looks, it can’t take into account the total context of where you’re going,” said personal stylist and creative director Taylor Okata.

Okata, whose clients include E! and SELF Magazine, doesn’t consider the technology a threat to his work: “There’s just that interpersonal communication that it just doesn’t have.”

Meanwhile, retail experts say the Echo Look’s success will depend on if it adds more value than just asking a friend for fashion advice.

Sucharita Mulpuru, an analyst at research firm Forrester, said those buying the device are early adopters — and it lacks widespread appeal.

“It’s such a foreign concept to rely on a device to tell you what to wear,” she told CNN Tech.

Read the source article at CNNTech.com.

An ecosystem perspective on Microsoft’s acquisition of LinkedIn

Ecosystem view based on investment styles



Providing a perspective on this ecosystem must be seen in the context of a number of limitations. Business ecosystems are formed based on basic connections between human and organizational relationships. Some of these relationships are physical and others are inferred for example, an organization employs a CEO, CFO, etc and operates primarily at a location and sells a number of products and services to a defined market. Other relationships include sub-organizations, funding and venture related interactions.

Investment styles vary widely, but they all have venture activity and seed funding projects. The ecosystem is tightly integrated with competitive forces creating tight boundaries across services and products offered. If Microsoft wants to get access to the wider Salesforce market, it needed to find the primary data play and enable the core products across their platforms.

Microsoft market performance



Microsoft had a rocky few years under Balmer, but Satya’s focus brought it back on track. He took over the reigns in 2014 and started acquiring key organizations to bolster the core future focus of Microsoft as a key digital player in the cloud.


LinkedIn’s acquisition might not have made sense initially, but the competitive ecosystem showed that primary data is a key competitive force. Google, Apple, etc all focused on owning primary client data and leveraged is across their products. Salesforce already had well developed models as their dominance continued.

Competitive graph approach

The result is an overlapping graph that enables multiple services beyond CRM with primary data. But, not just any data – LinkedIn is the most widely used business network available today. Google’s knowledge graph, Facebook’s social graph and LinkedIn’s economic graph have all created an entirely new way of looking at the world. It’s not that network theory is new, but that these companies have socialized the use of network data across many different constructs. Google has both primary data and links to the world’s knowledge, Facebook has the same and LinkedIn captured a key portion of the incredibly important world.



Looking at all inferences across the ecosystem, it became clear that LinkedIn was a lone player amongst many dominant multi-service strategic technology companies. To further explore and exploit the value created by the vast LinkedIn network it needed to move into a different competitive category. This move will create market pressures where Salesforce and Microsoft ended up fighting over LinkedIn.

Bottom Line

The combination of the new Microsoft strategy and the ability to compete with other big players like Amazon AWS and Google Cloud will propel them into a new competitive space.