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Archivo del Autor: Belen De Leon

Uber loses London license over safety concerns, eBay sells StubHub video – CNET

In today’s top stories, Transport for London has blocked Uber from renewing its license over safety risks, eBay sells StubHub to Viagogo and Boston Dynamics robots are tested by Massachusetts Police.
Source: CNET

3 new Apple Maps features you should try now — and how to use them – CNET

These new features help turn Apple Maps from a laughable Google Maps replacement to an app that can stand on its own.
Source: CNET

Costco's best Black Friday 2019 deals: Big savings on laptops from Dell and Lenovo, and more – CNET

Costco is already offering Black Friday deals, with more to come later on. We’ve picked out the top options.
Source: CNET

New smartphone figures highlight continued struggles to grow market

In some corners, the smartphone market is showing its first signs of life in some time.

Recent figures from Canalys indicate a small but notable uptick in the European market as shipments grew 3%, year-over-year in Q3.

The analyst firm put global growth at 1% globally in another recent report. Generally, such numbers wouldn’t warrant much celebration, but the way the market has been going, most manufacturers will take what they can get.

New numbers out this morning from Gartner paint a less rosy picture, with sales numbers declining 0.4%. It’s not a huge discrepancy between shipping and sales figures, but it’s the difference between being in the red and being in the black for the quarter.


Source: TechCrunch

Leavy.co, the app for millennials who want to rent out their room while travelling, discloses $14M funding

Leavy.co, the Paris-born startup that offers a travel app for millennials to help them travel more without getting into further debt, has quietly raised $14 million in funding.

The investment — which is pegged as a seed round and actually closed in January! — is led by Dutch investor Prime Ventures, with participation from angel investor Dominique Vidal (who is also a partner at Index Ventures). Pieter Welten, a partner at Prime Ventures, has taken a seat on Leavy’s board.

Founded in 2017 by CEO Aziza Chaouachi, who at the time was studying law and traveling a lot, and later joined by co-founders Yassine Ben Romdhane (COO) and Mario Moinet (Chief Strategy Officer), the Leavy.co app is described as a “travel community and marketplace” that wants to help millennials travel (more) for less.

At the heart of its offering is a way for travellers (dubbed “Happy Leavers”) to rent out their room or apartment when they are away to help fund their trip. Other members (dubbed “Hosts on Demand”) then get paid to act as a local host and manage the Leavy booking. The idea, explained Chaouachi on a call yesterday evening, is to scale the community model that she first developed informally amongst friends and via her use of Airbnb when she was a student.

The killer feature — and undoubtedly where things get more interesting — is that Leavy gives members cash up front when they make their space available prior to traveling, regardless of whether or not a booking takes place. The exact price offered is dynamic and factors in various data, including how much notice is given.

Chaouachi declined to talk about the range of margin Leavy expects to generate, but, unlike most marketplaces, the startup is taking on most of the risk. If it fails to rent out a space while a member is away, it loses money. On the other hand, if its algorithm works well, it should be quite lucrative.

However, Chaouachi stressed that its algorithm and everything the startup does is being optimised for member satisfaction. If the pricing doesn’t work for all sides of the marketplace — renters, guests and hosts — Leavy’s network will stop growing and that’s the bottom line for a community-driven business that is incredibly reliant on network effects.

“We are the first pure-scale marketplace that takes a risk for its users,” she told me in an email prior to our call. “As network orchestrators, we generate profit with dynamic pricing. Our tech is obsessed with finding the optimal point of satisfaction for every user”.

Additionally, the Leavy.co app rewards members with travel credit — called Leavy Coins – when they invite friends to join and share travel tips and recommendations, or when posting photos to the app. They can then spend Leavy Coins on various travel-related products.

More broadly, the problem that Chaouachi is passionate about solving is that millennials are typically saddled with debt, often through buying an education or high accommodation costs, and that the desire and need to travel can often push them into further debt.

“As millennials, we go broke the second we decide to get an education or a credit card,” she says. “Buying a house like our parents is not an option, so we crave that weekend trip to Lisbon to lift our spirits before we go back to work on Monday. Once our outrageous rents have been paid out, it is the only air we can afford and — most of all — post about. The travel industry is the second-fastest-growing sector… and it is so at our expense, since we are its largest spenders”.

To that end, Leavy.co says its community network has grown to more than 65,000 millennials, of whom 60% are women. The young company already has 100 employees across 6 markets, with offices in Paris, Amsterdam, London, Madrid, Rome, and Lisbon. The plan is to open up in the U.S. by the end of this year.

“We are a brand new actor within the travel space, the next generation of OTA (online travel agency),” adds Chaouachi. “But unlike other OTAs such as traditional search engines like Booking.com, or short-term rental platforms like Airbnb, we don’t offer yet another item the Instagram-generation will never be able to buy: we actually boost their buying power instead”.


Source: TechCrunch

Web inventor Berners-Lee wants to save us from a digital dystopia

First he created the web. Now Tim Berners-Lee is creating a movement to save it. His Contract for the Web aims take it to a better place and stop the world from sliding into a “digital dystopia.”
Source: Digital trends

India’s electric bike rental startup Yulu inks strategic partnership with Bajaj Auto, raises $8M

Yulu, a Bangalore-based electric bike sharing platform that maintains a partnership with Uber, said today it has won the backing of one of the country’s largest automakers.

The two-year-old startup said it has entered into a strategic partnership with Bajaj Auto, which has also funded Yulu’s $8 million Series A financing round. As part of the partnership, Bajaj will co-design and manufacture future generation of Yulu two-wheelers, Amit Gupta, cofounder and chief executive of Yulu, told TechCrunch in an interview.

Yulu, which operates in Bengaluru and recently entered portions of New Delhi and Mumbai, has raised about $18.5 million from VCs to date, he said. Yulu has also raised money in debt, but it declined to reveal the figure.

The startup maintains over 3,000 electric bikes on its platform. A customer, who does not need a driving license, can rent the bike through Yulu’s app for 14 cents, pay 14 cents for each hour of usage and then park it at the nearest zone.

Gupta said Yulu plans to have 100,000 two-wheelers in its fleet by end of next year. And that’s where its partnership with Bajaj Auto would come in handy. The startup currently relies on its Chinese original design manufacturer partners to build its bikes. But Bajaj Auto, which has decades of experience building two-wheelers in the nation, will be taking care of the manufacturing from here, he said.

“They clearly have much better understanding of the Indian context,” he said. Bajaj Auto announced last month that it was reviving a decades old, sleeper hit scooter model Chetak, only this time it would run on electric. “They know the micro-mobility and share-mobility spaces. The partnership is bringing together the combined learning of building Yulu network and operational learning of Chetak,” he said.

In a statement, Rajiv Bajaj, Managing Director of Bajaj, said, “in Yulu we find an experienced and committed partner with robust achievement of success metrics in a very short time. And this is why we decided to partner with them in their journey of bringing Yulu service to every neighborhood of Urban India.”

Yulu is also expanding its presence quickly in the nation. In Delhi, it has secured the permission to offer electric bikes at 250 subway stations. “We are already servicing in nine of those,” said Gupta, who also co-founded advertising tech giant InMobi .

“We work through clusters. So we deploy about 1000 vehicles, and set up 200 to 300 parking stations and 25 to 30 charging stations. We have been able to replicate this cluster model in many places,” he said.

These bikes can ride as fast as 25 kmph (15.5 mph), and cover 60 kms (37.2 miles) in one charging cycle. The startup works with mom and pop stores and individuals to expand its parking and charging stations. “It’s very economical,” Gupta said. Yulu also has an army of workers who swap the used battery with a freshly charged one, he said.

The market of two-wheelers has grown in India despite the proliferation of taxi services in the country in recent years. With major cities in India grappling with ever growing traffic congestions, the future of two-wheelers seems brighter than ever. Gupta said Yulu’s partnership with Uber has chugged along smoothly and they may extend it soon.

Ride-hailing giant Ola has invested $100 million in scooter rental startup Vogo. Ola has also invested heavily in two-wheeler category. In September, it said its two-wheeler business — Ola Bike — was already operational in 150 Indian cities and towns and projected it would hit 450 cities and towns by next year. Both Yulu and Vogo compete with Bounce, which is also based in Bangalore. Ather Energy, another Bangalore-based startup that manufactures electric-scooters, raised $51 million in May.


Source: TechCrunch

HP knocks $250 off of its premium Chromebook x360 for Black Friday

HP is knocking $250 off of one of the best Chrome OS computers available on the market, the Chromebook x360 ahead of Black Friday.
Source: Digital trends

Alibaba’s shares climb almost 8% in their first morning of trading on the Hong Kong stock exchange

Alibaba share price increased as much as 7.7% during its first morning of trading on the Hong Kong Stock Exchange. Soon after the market opened, the shares climbed from their listing price of HKD $176 (a 2.9% discount from their closing price on the New York Stock Exchange on Tuesday) to HKD $189.50.

Each of Alibaba’s American depositary receipts on the NYSE is equivalent to about eight Hong Kong shares. Alibaba issued 500 million new ordinary shares for the secondary offering, plus an overallotment option for 75 million shares that will allow it to raise even more money if exercised. Its Hong Kong shares are trading under the ticker number 9988, a play on the words for “long-term prosperity” in Chinese.

Alibaba’s debut on the New York Stock Exchange in 2014 raised a total of $25 billion, making it the largest public offering in history. The company had initially considered holding its IPO in Hong Kong, but at the time, its stock exchange did not allow dual-class shares, a structure often used by tech startups because it allows holders of one class of shares to have more voting rights than common shareholders, ensuring companies continue to have control even after they go public.

Last year, the Hong Kong Stock Exchange changed its rules to accommodate dual-class share, enabling tech companies, including Meituan and Xiaomi, to debut there.

In a press statement, Alibaba CEO Daniel Zhang said “As a result of the continuous innovation and changes to the Hong Kong capital market, we are able to realize what we regrettably missed out on five years ago. Today, we realized what we said then: ‘When conditions allow, we will come back to Hong Kong.’”

Listing on Hong Kong will also make it easier for more Chinese investors to buy and sell Alibaba shares, once it is included in the Stock Connect, a collaboration between the Hong Kong, Shanghai and Shenzhen stock exchanges.

This is not the first time Alibaba has had a presence on the Hong Kong stock market. In 2007, its B2B e-commerce platform, Alibaba.com, went public there, before the company took the unit private again in 2012.

Alibaba’s Hong Kong debut comes after months of tumultuous pro-democracy demonstrations (the stock exchange has stayed stable despite the protests), and the day after more than half the 452 seats up for vote in local district council elections flipped from pro-Beijing to pro-democracy candidates. Demonstrators have called for more transparency from the government and police, and the election results send a clear signal about public sentiment to chief executive Carrie Lam.


Source: TechCrunch

Inside the Instagram AI that fills Explore with fresh, juicy content

Instagram has posted an article describing the behind-the-scenes machinery that fills the Explore tab in Instagram with new, interesting stuff every time you open it. It’s a bit technical, so here are five takeaways.

Even Instagram and Facebook have limited resources

Unlike the feed, which some still would prefer was simply chronological, the Explore tab needs to be algorithmically driven. But understanding what’s happening on an image-based social network and recommending new content to people is a problem that’s exactly as hard as you make it.

If these companies had infinite processing power and time, they’d probably come at the question of Explore a bit differently. But as it is they need to serve hundreds of millions of people on short notice and with merely enormous computing resources. I think they put this at the top of the post so people don’t wonder why they’re cutting corners.

It’s also easier to experiment and iterate when you can change stuff and see results quickly, they point out.

It’s all about the account, not the post

So much is posted to Instagram that it would be pretty much impossible to keep track of every photo individually, for recommendation purposes anyway. It’s simpler and more efficient to track accounts, since accounts tend to have themes or topics, from a broader one like “travel” to something highly specific, like especially round seals.

While liking one post from an account doesn’t necessarily mean you’ll like everything else from that account, it is a good indicator that you’re at least interested in the theme of that account. Even if it was this particular post of this particular cat that you wanted to heart because it reminds you of old Mittens, if you’re liking pictures from an account that mostly posts cats, that’s valuable information.

Complex habits inform the algorithm

Notably it isn’t just image features that Instagram uses to figure out what accounts are topically linked, though of course that kind of thing can be detected too. They also use your behavior.

For instance, when you like several posts in a row, they’re more likely to be linked in some way even if Instagram’s algorithms can’t quite see it:

If an individual interacts with a sequence of accounts in the same session, it’s more likely to be topically coherent compared with a random sequence of accounts from the diverse range of Instagram accounts. This helps us identify topically similar accounts.

People just tend to look into stuff that way, going from one travel-focused account to the next, or focusing on animals because they need a pick-me up. All that information gets sucked up by the algorithm and inspected for relevance. Of course deliberate actions like “see fewer posts like this” and blocking accounts has a lot of weight as well.

From “seed accounts” to a top 25

The process of getting from a couple billion posts to just two dozen can be pretty difficult, but you can cut the problem down to manageable size by limiting the Explore tab to accounts linked in some way to accounts the user has already liked or saved posts from. These are called “seed accounts” because everything else in the process really grows out of them.

Because of how the machine learning system represents accounts and their topics inside itself, it’s super easy for it to find a couple hundred similar accounts.

Imagine if you know someone likes a particular reddish-orange marble and you need to find some more like it. If you just dip your hand into a sack of marbles you’re unlikely to find one quickly. Even if you pour them out on the floor you’ll still have to hunt around for a bit. But if you’ve already organized them by color, all you have to do is reach into the general vicinity of the marble they like and you’re almost guaranteed to pick a winner.

The machine learning model does that by giving all these accounts a sort of location in a virtual space, and the closer two are in that space, the closer they are topically.

So the really hard part of paring down a set of billions to a set of hundreds is basically already accomplished by the way the accounts are classified.

From there Instagram does three passes with neural networks of increasing complexity.

First, slightly confusingly, is a simpler, combined version of the next two processes, which takes it from 500 to 150 accounts. This is a little weird, but think about it this way: This neural network has seen steps 2 and 3 happen many times and has a pretty good idea of what they do. Sort of like if you’d seen cookies get made enough times that you could guess at a recipe. You’d probably get close, but you also wouldn’t want to publish it to like a hundred million people. So this step just gets the obvious stuff right.

Second is a computationally cheap neural network that uses way more signals than the simple topical similarity mentioned above. Here’s where your individual likes come into play, as well as the deeper data about accounts. You like travel, sure, but in particular you like couples traveling — both things the marble-sorting algorithm above can help with. Other parameters, like a post’s general popularity, or actually its being different from the other posts in the mix, figure in as well. That skims another 100 off the top, leaving 50.

Third is a computationally expensive version of the above, which does another pass on those 50 and cuts them in half, basically by looking closer and taking the time to include, perhaps, a thousand data points each rather than a hundred.

I guess that was kind of long for a “takeaway.” Don’t worry, the next one is quick.

And of course, no �

“We want to make sure the content we recommend is both safe and appropriate for a global community of many ages on Explore,” they write. “Using a variety of signals, we filter out content we can identify as not being eligible to be recommended.”

So now you know why you don’t get any of that in Explore.


Source: TechCrunch

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