Daily Archives: April 1, 2018

No more DACA deals!


Check out @realDonaldTrump’s Tweet: https://twitter.com/realDonaldTrump/status/980443810529533952?s=09

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Blessed Easter to all of hour followers: Christus resurrexit! Resurrexit vere! Gospel of the Day (Mark 16:1-7) Holy Saturday (Vigil Mass)


Blessed Easter to all of hour followers:
Christus resurrexit! Resurrexit vere!

Gospel of the Day (Mark 16:1-7)
Holy Saturday (Vigil Mass)

When the sabbath was over, Mary Magdalene, Mary, the mother of James, and Salome bought spices so that they might go and anoint him.
Very early when the sun had risen, on the first day of the week, they came to the tomb.
They were saying to one another, “Who will roll back the stone for us from the entrance to the tomb?”
When they looked up, they saw that the stone had been rolled back; it was very large.
On entering the tomb they saw a young man sitting on the right side, clothed in a white robe, and they were utterly amazed.
He said to them, “Do not be amazed! You seek Jesus of Nazareth, the crucified. He has been raised; he is not here. Behold the place where they laid him.
But go and tell his disciples and Peter, ‘He is going before you to Galilee; there you will see him, as he told you.'”
https://www.vaticannews.va/en/word-of-the-day/2018/03/31.html

China says space station burns up over South Pacific | Reuters


https://www.reuters.com/article/us-china-space/china-tiangong-1-to-re-enter-atmosphere-on-monday-morning-space-authority-idUSKCN1H81JD
China says space station burns up over South Pacific
Reuters Editorial

SHANGHAI (Reuters) – China’s Tiangong-1 space station re-entered the earth’s atmosphere and burnt up over the middle of the South Pacific on Monday, the Chinese space authority said.

FILE PHOTO: A model of the Tiangong-1 space lab module (L), the Shenzhou-9 manned spacecraft (R) and three Chinese astronauts is displayed during a news conference at Jiuquan Satellite Launch Center, in Gansu province, China June 15, 2012. REUTERS/Jason Lee/File Photo

FILE PHOTO: A model of the Tiangong-1 space lab module (L), the Shenzhou-9 manned spacecraft (R) and three Chinese astronauts is displayed during a news conference at Jiuquan Satellite Launch Center, in Gansu province, China June 15, 2012. REUTERS/Jason Lee/File Photo

The craft re-entered the atmosphere around 8:15 a.m. Beijing time (0015GMT) and the “vast majority” of it had burnt up upon re-entry, the authority said in a brief statement on its website.

It had said shortly before that it was expected to re-enter off the Brazilian coast in the South Atlantic near the cities of Sao Paulo and Rio de Janeiro.

Beijing said on Friday it was unlikely any large pieces would reach the ground.

The 10.4-metre-long (34.1-foot) Tiangong-1, or “Heavenly Palace 1”, was launched in 2011 to carry out docking and orbit experiments as part of China’s ambitious space program, which aims to place a permanent station in orbit by 2023.

It was originally planned to be decommissioned in 2013 but its mission was repeatedly extended.

China had said its re-entry would occur in late 2017 but that process was delayed, leading some experts to suggest the space laboratory was out of control.

The Chinese tabloid Global Times said on Monday worldwide media hype about the re-entry reflected overseas “envy” of China’s space industry.

“It’s normal for spacecraft to re-enter the atmosphere, yet Tiangong-1 received so much attention partly because some Western countries are trying to hype and sling mud at China’s fast-growing aerospace industry,” it said.

Reporting by David Stanway and Wang Jing; Additional reporting by Ben Blanchard in BEIJING; Editing by Paul Tait

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A Space Shuttle on the Streets of Los Angeles – The Atlantic


https://www.theatlantic.com/photo/2012/10/a-space-shuttle-on-the-streets-of-los-angeles/100386/

Watch “”It Never Rains In Southern California” w/Lyrics- Albert Hammond” on YouTube


Watch “Scott McKenzie – San Francisco [HD]” on YouTube


Watch “MELANIE SAFKA Dust in the Wind” on YouTube


Watch “MGMT – Time to Pretend (lyrics)” on YouTube


Watch “Inside Space Shuttle Columbia STS 107 during the Accident All hell breaks loose” on YouTube


Watch “Everybody Knows – Sigrid – From Justice League Original Motion Picture Soundtrack (official video)” on YouTube


Watch “The Jungle Book – I wanna be like you w/lyrics” on YouTube


Watch “Fiddler on the roof – If I were a rich man (with subtitles)” on YouTube


Israel rejects UN and EU calls for inquiry into Gaza bloodshed


https://www.theguardian.com/world/2018/apr/01/israel-rejects-un-and-eu-calls-for-inquiry-into-gaza-bloodshed
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Israel rejects UN and EU calls for inquiry into Gaza bloodshed
Defence minister says soldiers ‘did what had to be done’ after protests turned violent

Oliver Holmes and Hazem Balousha in Gaza City
Sun 1 Apr 2018 12.51 EDT Last modified on Sun 1 Apr 2018 14.59 EDT
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Palestinian protesters demonstrate during clashes with Israeli security forces near the border

Israel’s defence minister has rejected United Nations and European Union calls for an investigation into the killing of more than a dozen Palestinians by the military during demonstrations on the Gaza frontier.

Gaza’s coastal enclave has been shaken by the bloodiest episode in years after protests advertised as peaceful sit-ins turned violent, with Israeli troops firing rounds of live ammunition at crowds of stone-throwers.

Hospitals in Gaza have recorded hundreds of emergency admissions from the protest, and doctors have said most were for gunshot wounds.

The UN secretary general, António Guterres, and the EU’s top diplomat, Federica Mogherini, called for independent inquiries into the bloodshed, which left 16 people dead.

But the Israeli defence minister, Avigdor Lieberman, told Israel’s public radio on Sunday that there will not be an inquiry. “From the standpoint of the [Israeli Defence Force] soldiers, they did what had to be done,” he said. “I think that all of our troops deserve a commendation.”

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Israel has accused Gaza’s rulers, Hamas, of using “violent riots to camouflage terror”. It also pointed to an attempted gun attack on Friday against soldiers along the border.

Israeli army spokespeople have said claims by the Gaza health ministry that more than 750 people were wounded by live fire are exaggerated.

At the Gaza Strip’s main Shifa hospital, the digital registry of A&E admissions on Friday, seen by the Guardian, showed that from 8.45am until the end of the day, 275 people from the protest arrived. It did not specify injuries, but doctors said most had gunshot wounds to the legs.

A clerk said a further eight patients were transferred from surrounding clinics to Shifa’s operating theatres. Surgeons said many patients had large exit wounds.

On Sunday a 23-year-old man, Adam Abu Ghanima, said he had just driven to the hospital from a demonstration, which was smaller than Friday’s. His kneecap had been pierced and blood soaked the sheets of the bed where he lay.

He said he had planned to place a Palestinian flag near the frontier. “I was right next to the Israeli soldiers. Before they shot me, they fired warning shots in the air,” he said. But he kept going, he added, “to bring Jerusalem back”.

Another man said he had been shot trying to lift a Palestinian flag that had fallen over on the Gazan side.

Doctors said most of those admitted since Friday had been discharged, but some awaiting surgery lay in beds surrounded by friends and families.

Ibrahim Fathi Hasna, 22, said he and another man who had wire cutters and a Molotov cocktail had managed to cut through a fence at a protest on Saturday to breach an Israeli-controlled area. They were both shot.

Hasna said he crawled back, eyes filled with teargas, until he was hoisted into an ambulance. The other man was hit in the back, he said, and he was unsure of his condition. Asked why he had wanted to cross the fence, he replied: “I just wanted to be there.”

The Great March of Return is a planned six-week demonstration calling for refugees and their descendants to be allowed back to their family homes in Israel. Backed by Hamas and other militant and political Palestinian factions, larger gatherings are expected every Friday, the holy day for Muslims.

Israel did not specify exact orders to troops, but a spokesperson said anyone approaching the “hostile border” was a potential threat. “People coming towards the fence, attempting to penetrate and break into the fence, damaging the infrastructure or using that area as a staging ground could potentially be shot,” said Lt Col Peter Lerner, of the Israel Defense Forces.

On Sunday Turkey’s president, Recep Tayyip Erdoğan, called Israel’s prime minister, Benjamin Netanyahu, a “terrorist”. Netanyahu tweeted that the Israeli army “will not be lectured by those who have indiscriminately bombed civilian populations for years”, referring to Turkey.

Gaza has been blockaded for a decade by Israel and Egypt, which tightly control goods and people entering the 140 sq mile area.

The demonstrations in Gaza appeared to be split in two, with women and children staying hundreds of metres from the perimeter fence, protesting in a festival-like atmosphere. Groups of mostly young men headed closer to throw rocks and light bottles of petrol. There have been no reports of Israeli casualties.

Israel said 10 of the dead belonged to Hamas. Hamas said five members of its armed wing who participated in the protest were killed.

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How Cambridge Analytica’s Facebook targeting model really worked | Ars Technica


https://arstechnica.com/tech-policy/2018/04/how-cambridge-analyticas-facebook-targeting-model-really-worked/
Ars Technica
How Cambridge Analytica’s Facebook targeting model really worked
Ars Staff 04/1/2018 12:00 pm Categories: Policy View non-AMP version at arstechnica.com

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The researcher whose work is at the center of the uproar over Cambridge Analytica’s Facebook data analysis and political advertising has revealed that his method worked much like the one Netflix uses to recommend movies.

In an email to me, Cambridge University scholar Aleksandr Kogan explained how his statistical model processed Facebook data for Cambridge Analytica. He claims it works about as well as more traditional voter-targeting methods based on demographics like race, age, and gender.

If confirmed, Kogan’s account would mean the digital modeling Cambridge Analytica used was hardly the virtual crystal ball a few have claimed. Yet the numbers Kogan provides also show what is—and isn’t—actually possible by combining personal data with machine learning for political ends.

Regarding one key public concern, though, Kogan’s numbers suggest that information on users’ personalities or “psychographics” was just a modest part of how the model targeted citizens. It was not a personality model strictly speaking, but rather one that boiled down demographics, social influences, personality, and everything else into a big correlated lump. This soak-up-all-the-correlation-and-call-it-personality approach seems to have created a valuable campaign tool, even if the product being sold wasn’t quite as it was billed.

The promise of personality targeting

In the wake of the revelations that Trump campaign consultants Cambridge Analytica used data from 50 million Facebook users to target digital political advertising during the 2016 US presidential election, Facebook has lost billions in stock market value, governments on both sides of the Atlantic have opened investigations, and a nascent social movement is calling on users to #DeleteFacebook.

But a key question has remained unanswered: Was Cambridge Analytica really able to effectively target campaign messages to citizens based on their personality characteristics—or even their “inner demons,” as a company whistleblower alleged?

If anyone would know what Cambridge Analytica did with its massive trove of Facebook data, it would be Aleksandr Kogan and Joseph Chancellor. It was their startup, Global Science Research, that collected profile information from 270,000 Facebook users and tens of millions of their friends using a personality test app called “thisisyourdigitallife.”

Part of my own research focuses on understanding machine learning methods, and my forthcoming book discusses how digital firms use recommendation models to build audiences. I had a hunch about how Kogan and Chancellor’s model worked.

So I emailed Kogan to ask. Kogan is still a researcher at Cambridge University; his collaborator Chancellor now works at Facebook. In a remarkable display of academic courtesy, Kogan answered.

His response requires some unpacking, and some background.

From the Netflix Prize to “psychometrics”

Back in 2006, when it was still a DVD-by-mail company, Netflix offered a reward of $1 million to anyone who developed a better way to make predictions about users’ movie rankings than the company already had. A surprise top competitor was an independent software developer using the pseudonym Simon Funk, whose basic approach was ultimately incorporated into all the top teams’ entries. Funk adapted a technique called “singular value decomposition,” condensing users’ ratings of movies into a series of factors or components—essentially a set of inferred categories, ranked by importance. As Funk explained in a blog post,

“So, for instance, a category might represent action movies, with movies with a lot of action at the top, and slow movies at the bottom, and correspondingly users who like action movies at the top, and those who prefer slow movies at the bottom.”
Factors are artificial categories, which are not always like the kind of categories humans would come up with. The most important factor in Funk’s early Netflix model was defined by users who loved films like “Pearl Harbor” and “The Wedding Planner” while also hating movies like “Lost in Translation” or “Eternal Sunshine of the Spotless Mind.” His model showed how machine learning can find correlations among groups of people, and groups of movies, that humans themselves would never spot.

Funk’s general approach used the 50 or 100 most important factors for both users and movies to make a decent guess at how every user would rate every movie. This method, often called dimensionality reduction or matrix factorization, was not new. Political science researchers had shown that similar techniques using roll-call vote data could predict the votes of members of Congress with 90 percent accuracy. In psychology the “Big Five” model had also been used to predict behavior by clustering together personality questions that tended to be answered similarly.

Still, Funk’s model was a big advance: It allowed the technique to work well with huge data sets, even those with lots of missing data—like the Netflix dataset, where a typical user rated only few dozen films out of the thousands in the company’s library. More than a decade after the Netflix Prize contest ended, SVD-based methods, or related models for implicit data, are still the tool of choice for many websites to predict what users will read, watch, or buy.

These models can predict other things, too.

Facebook knows if you are a Republican

In 2013, Cambridge University researchers Michal Kosinski, David Stillwell and Thore Graepel published an article on the predictive power of Facebook data, using information gathered through an online personality test. Their initial analysis was nearly identical to that used on the Netflix Prize, using SVD to categorize both users and things they “liked” into the top 100 factors.

The paper showed that a factor model made with users’ Facebook “likes” alone was 95 percent accurate at distinguishing between black and white respondents, 93 percent accurate at distinguishing men from women, and 88 percent accurate at distinguishing people who identified as gay men from men who identified as straight. It could even correctly distinguish Republicans from Democrats 85 percent of the time. It was also useful, though not as accurate, for predicting users’ scores on the “Big Five” personality test.

There was public outcry in response; within weeks Facebook had made users’ likes private by default.

Kogan and Chancellor, also Cambridge University researchers at the time, were starting to use Facebook data for election targeting as part of a collaboration with Cambridge Analytica’s parent firm SCL. Kogan invited Kosinski and Stillwell to join his project, but it didn’t work out. Kosinski reportedly suspected Kogan and Chancellor might have reverse-engineered the Facebook “likes” model for Cambridge Analytica. Kogan denied this, saying his project “built all our models using our own data, collected using our own software.”

What did Kogan and Chancellor actually do?

As I followed the developments in the story, it became clear Kogan and Chancellor had indeed collected plenty of their own data through the thisisyourdigitallife app. They certainly could have built a predictive SVD model like that featured in Kosinski and Stillwell’s published research.

So I emailed Kogan to ask if that was what he had done. Somewhat to my surprise, he wrote back.

“We didn’t exactly use SVD,” he wrote, noting that SVD can struggle when some users have many more “likes” than others. Instead, Kogan explained, “The technique was something we actually developed ourselves … It’s not something that is in the public domain.” Without going into details, Kogan described their method as “a multi-step co-occurrence approach.”

However, his message went on to confirm that his approach was indeed similar to SVD or other matrix factorization methods, like in the Netflix Prize competition, and the Kosinki-Stillwell-Graepel Facebook model. Dimensionality reduction of Facebook data was the core of his model.

How accurate was it?

Kogan suggested the exact model used didn’t matter much, though—what matters was the accuracy of its predictions. According to Kogan, the “correlation between predicted and actual scores … was around [30 percent] for all the personality dimensions.” By comparison, a person’s previous Big Five scores are about 70 to 80 percent accurate in predicting their scores when they retake the test.

Kogan’s accuracy claims cannot be independently verified, of course. And anyone in the midst of such a high-profile scandal might have incentive to understate his or her contribution. In his appearance on CNN, Kogan explained to a increasingly incredulous Anderson Cooper that the models had actually not worked very well.

Scientist: Didn’t know data used to target voters
In fact, the accuracy Kogan claims seems a bit low, but plausible. Kosinski, Stillwell, and Graepel reported comparable or slightly better results, as have several other academic studies using digital footprints to predict personality (though some of those studies had more data than just Facebook “likes”). It is surprising that Kogan and Chancellor would go to the trouble of designing their own proprietary model if off-the-shelf solutions would seem to be just as accurate.

Importantly, though, the model’s accuracy on personality scores allows comparisons of Kogan’s results with other research. Published models with equivalent accuracy in predicting personality are all much more accurate at guessing demographics and political variables.

For instance, the similar Kosinski-Stillwell-Graepel SVD model was 85 percent accurate in guessing party affiliation, even without using any profile information other than likes. Kogan’s model had similar or better accuracy. Adding even a small amount of information about friends or users’ demographics would likely boost this accuracy above 90 percent. Guesses about gender, race, sexual orientation and other characteristics would probably be more than 90 percent accurate too.

Critically, these guesses would be especially good for the most active Facebook users—the people the model was primarily used to target. Users with less activity to analyze are likely not on Facebook much anyway.

When psychographics is mostly demographics

Knowing how the model is built helps explain Cambridge Analytica’s apparently contradictory statements about the role—or lack thereof—that personality profiling and psychographics played in its modeling. They’re all technically consistent with what Kogan describes.

A model like Kogan’s would give estimates for every variable available on any group of users. That means it would automatically estimate the Big Five personality scores for every voter. But these personality scores are the output of the model, not the input. All the model knows is that certain Facebook likes, and certain users, tend to be grouped together.

With this model, Cambridge Analytica could say that it was identifying people with low openness to experience and high neuroticism. But the same model, with the exact same predictions for every user, could just as accurately claim to be identifying less educated older Republican men.

Kogan’s information also helps clarify the confusion about whether Cambridge Analytica actually deleted its trove of Facebook data, when models built from the data seem to still be circulating, and even being developed further. The whole point of a dimension reduction model is to mathematically represent the data in simpler form. It’s as if Cambridge Analytica took a very high-resolution photograph, resized it to be smaller, and then deleted the original. The photo still exists—and as long as Cambridge Analytica’s models exist, the data effectively does too.

The Conversation
This article was originally published on The Conversation. View article comments

London murder rate overtakes New York for first time ever after spate of fatal stabbings and shootings


https://www.standard.co.uk/news/crime/london-murder-rate-overtakes-new-york-for-first-time-ever-after-spate-of-fatal-stabbings-and-a3803566.html

News › Crime
London murder rate overtakes New York for first time ever after spate of fatal stabbings and shootings
MARTIN COULTER
4 hours ago

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London’s murder rate has overtaken New York City’s for the first time ever, according to a new report.

February marked the first month the UK capital saw more murders than New York, with 15 dead (nine aged 30 or younger).

According to the report in the Sunday Times, London also suffered 22 fatal stabbings and shootings in March, higher than the 21 in the Big Apple.

Both cities have similarly sized populations of around 8.5m people. New York City’s murder rate has decreased by around 87 per cent since the 1990s.

pa-35062815.jpg
A Police officers outside a house on Earls Court Road (PA Wire/PA Images)
Meanwhile, London’s has grown by nearly 40 per cent in just three years, not including deaths caused by terrorist attacks.

On Saturday a murder probe was launched after a 36-year-old woman was killed in what is believed to be the 30th incident of fatal knife crime in the capital this year.

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The death came just hours after a man 23-year-old man died after being stabbed in the neck in Plumstead, south-east London on Thursday evening.

Jacob Whittingham, charity head of programmes for Fight for Peace, told the paper: “What’s scary about London is the randomness of the crime.

“With young people in London, you have no idea if and when you may be the victim of a violent crime — that’s why they feel the need to carry weapons.”

Britain’s most senior police officer recently said social media was partially to blame for the soaring rate of knife crime in the UK.

Met Commissioner Cressida Dick said websites and mobile phone applications such as YouTube, Snapchat and Instagram were partially to blame for the bloodshed.

Fatal stabbings in England and Wales are now at their highest levels since 2011, rising by 12 per cent in the year ending December 2017.

More about: | New York City | New York | London | knife crime | Plumstead | Sunday Times
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