Facebook taps London police to track terror livestreams

Assistant Commissioner of the Metropolitan Police Neil Basu speaks to the media after a car crashed outside the Houses of Parliament in Westminster, London, last year. (Reuters)
Updated 17 September 2019

Facebook taps London police to track terror livestreams

  • London’s Metropolitan Police said the initiative will see it start providing Facebook in October with footage of training by its forearms command unit
  • The Christchurch attack images were broadcast live for 17 minutes — and remained online for a further 12 minutes — before Facebook was alerted by a user and took it down

LONDON: Facebook on Tuesday teamed up with the London police to help its artificial intelligence tools track livestreams of terror attacks such as the New Zealand mosque massacre.
In March, a self-professed white supremacist used a head-mounted camera to broadcast live footage on Facebook of him attacking two mosques in the city of Christchurch.
Facebook and platforms such as YouTube came under intense criticism for initially failing to detect the broadcast and then struggling to take down its uploads that proliferated online.
New Zealand’s Jacinda Ardern and other world leaders in May launched a “Christchurch Call to Action” against online extremism — a campaign Facebook and other major platforms quickly joined later that month.
The California-based social media behemoth said Tuesday it was in the process of updating its policies for dealing with extremism and online hate.
“Some of these changes predate the tragic terrorist attack in Christchurch, New Zealand, but that attack, and the global response to it in the form of the Christchurch Call to Action, has strongly influenced the recent updates to our policies and their enforcement.”
London’s Metropolitan Police said the initiative will see it start providing Facebook in October with footage of training by its forearms command unit.
The videos will be captured on body cameras provided by Facebook that officers wear during exercises.
This will help Facebook “capture the volume of images needed to train our machine learning tools,” the company said.
“This will mean our AI tools will be able to more accurately and rapidly identify real life first person shooter incidents and remove them from our platform.”
The London police said its footage will be combined with video Facebook is already using from law enforcement agencies in the United States.
This technology will “also significantly help prevent the glorification of such acts and the promotion of the toxic ideologies that drive them,” Britain’s Special Operations assistant commissioner Neil Basu said.
The Metropolitan Police said Facebook decided to ask London for help because it has created the world’s first counter-terror Internet response team focused on online hate.
The speed with which the videos spread and Facebook’s initial inability to track them all down redoubled public and government scrutiny of the world’s biggest social media company.
The Christchurch images were broadcast live for 17 minutes — and remained online for a further 12 minutes — before Facebook was alerted by a user and took it down.
Yet millions of upload and shares continued to spread online for days.
Facebook on Tuesday defended its track record but conceded that “bad actors will continue to try to get around our systems.”
It reported banning 200 white supremacist organizations and removing 26 million “pieces of content” or terrorist organization such as the Islamic State.
Facebook said Tuesday that it was also expanding to Australia and Indonesia a US program in which users who search for extremist content on the platform are directed to a special support group.
The US group was “founded by former violent extremists that provides crisis intervention, education, support groups and outreach,” Facebook said.


Facebook researchers use maths for better translations

Updated 13 October 2019

Facebook researchers use maths for better translations

  • Facebook researchers say rendering words into figures and exploiting mathematical similarities between languages is a promising avenue
  • Allowing as many people as possible worldwide to communicate is not just an altruistic goal, but also good business

PARIS: Designers of machine translation tools still mostly rely on dictionaries to make a foreign language understandable. But now there is a new way: numbers.

Facebook researchers say rendering words into figures and exploiting mathematical similarities between languages is a promising avenue — even if a universal communicator a la Star Trek remains a distant dream.

Powerful automatic translation is a big priority for Internet giants. Allowing as many people as possible worldwide to communicate is not just an altruistic goal, but also good business.

Facebook, Google and Microsoft as well as Russia’s Yandex, China’s Baidu and others are constantly seeking to improve their translation tools.

Facebook has artificial intelligence experts on the job at one of its research labs in Paris. Up to 200 languages are currently used on Facebook, said Antoine Bordes, European co-director of fundamental AI research for the social network.

Automatic translation is currently based on having large databases of identical texts in both languages to work from. But for many language pairs there just aren’t enough such parallel texts.

That’s why researchers have been looking for another method, like the system developed by Facebook which creates a mathematical representation for words.

Each word becomes a “vector” in a space of several hundred dimensions. Words that have close associations in the spoken language also find themselves close to each other in this vector space.

“For example, if you take the words ‘cat’ and ‘dog’, semantically, they are words that describe a similar thing, so they will be extremely close together physically” in the vector space, said Guillaume Lample, one of the system’s designers.

“If you take words like Madrid, London, Paris, which are European capital cities, it’s the same idea.”

These language maps can then be linked to one another using algorithms — at first roughly, but eventually becoming more refined, until entire phrases can be matched without too many errors.

Lample said results are already promising. For the language pair of English-Romanian, Facebook’s current machine translation system is “equal or maybe a bit worse” than the word vector system, said Lample.

But for the rarer language pair of English-Urdu, where Facebook’s traditional system doesn’t have many bilingual texts to reference, the word vector system is already superior, he said.

But could the method allow translation from, say, Basque into the language of an Amazonian tribe? In theory, yes, said Lample, but in practice a large body of written texts are needed to map the language, something lacking in Amazonian tribal languages.

“If you have just tens of thousands of phrases, it won’t work. You need several hundreds of thousands,” he said.

Experts at France’s CNRS national scientific center said the approach Lample has taken for Facebook could produce useful results, even if it doesn’t result in perfect translations.

Thierry Poibeau of CNRS’s Lattice laboratory, which also does research into machine translation, called the word vector approach “a conceptual revolution.”

He said “translating without parallel data” — dictionaries or versions of the same documents in both languages — “is something of the Holy Grail” of machine translation.

“But the question is what level of performance can be expected” from the word vector method, said Poibeau. The method “can give an idea of the original text” but the capability for a good translation every time remains unproven.

Francois Yvon, a researcher at CNRS’s Computer Science Laboratory for Mechanics and Engineering Sciences, said “the linking of languages is much more difficult” when they are far removed from one another.

“The manner of denoting concepts in Chinese is completely different from French,” he added.
However even imperfect translations can be useful, said Yvon, and could prove sufficient to track hate speech, a major priority for Facebook.