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Facebook says AI detects 94.7% of hate speech removed from platform

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The Facebook logo is displayed during the F8 Facebook Developers conference on April 30, 2019 in San Jose, California.

Justin Sullivan | Getty Images

Facebook announced Thursday that artificial intelligence software now detects 94.7% of the hate speech that gets removed from its platform.

Mike Schroepfer, Facebook’s chief technology officer, revealed the figure in a blog post, adding that it is up from 80.5% a year ago and just 24% in 2017. The figure was also shared in Facebook’s latest Community Standards Enforcement Report.

Social media firms such as Facebook, Twitter and TikTok have been criticized for failing to keep hate speech, such as racial slurs and religious attacks, off their platforms.

The companies employ thousands of content moderators around the world to police the posts, photos and videos that get shared on their platforms. On Wednesday, more than 200 Facebook moderators said in an open letter to CEO Mark Zuckerberg that the company has risked their lives by forcing them back to the office during the coronavirus pandemic.

But humans alone aren’t enough and the tech giants have become increasingly reliant on a field of AI known as machine learning, whereby algorithms improve automatically through experience.

“A central focus of Facebook’s AI efforts is deploying cutting-edge machine learning technology to protect people from harmful content,” said Schroepfer.

“With billions of people using our platforms, we rely on AI to scale our content review work and automate decisions when possible,” he added. “Our goal is to spot hate speech, misinformation, and other forms of policy-violating content quickly and accurately, for every form of content, and for every language and community around the world.”

But Facebook’s AI software still struggles to spot some pieces of content that break the rules. It finds it harder, for example, to grasp the intended meaning of images that have text overlaid, and it doesn’t always get sarcasm or slang. In many of these instances, humans would quickly be able to determine if the content in question violates Facebook’s policies.

Facebook said it has recently deployed two new AI technologies to help it combat these challenges. The first is called a “Reinforced Integrity Optimizer,” which learns from real online examples and metrics instead of an offline dataset. The second is an AI architecture called “Linformer,” which allows Facebook to use complex language understanding models that were previously too large and “unwieldly” to work at scale.

“We now use RIO and Linformer in production to analyze Facebook and Instagram content in different regions around the world,” said Schroepfer.

Facebook said it has also developed a new tool to detect deepfakes (computer-generated videos made to look real) and made some improvements to an existing system called SimSearchNet, which is an image-matching tool designed to spot misinformation on its platform.

“Taken together, all these innovations mean our AI systems have a deeper, broader understanding of content,” said Schroepfer. “They are more attuned to things people share on our platforms right now, so they can adapt quicker when a new meme or photo emerges and spreads.”

Schroepfer noted the challenges Facebook faces are “complex, nuanced, and rapidly evolving,” adding that misclassifying content as hate speech or misinformation can “hamper people’s ability to express themselves.”


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Facebook acquires Kustomer, a CRM start-up

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Mark Zuckerberg, chief executive officer and founder of Facebook Inc., arrives for a House Financial Services Committee hearing in Washington, D.C., U.S., on Wednesday, Oct. 23, 2019.

Andrew Harrer | Bloomberg | Getty Images

Facebook on Monday announced the acquisition of Kustomer, a customer relationship management start-up, for an undisclosed sum.

CRM tools help clients manage their communications with customers via phone, email, text or direct messages in specific apps, such as WhatsApp or Messenger.

The acquisition of a business-software company like Kustomer is unusual for Facebook. In the past, Facebook has primarily bought consumer-centric companies, like gif catalog company Giphy in May and Spanish cloud video gaming company PlayGiga in December 2019. These acquisitions usually serve as the foundation for building features for Facebook users.

By bringing Kustomer into the fold, Facebook will providing small businesses that use its service to advertise and sell goods more features to close sales through the social network’s services. This should seemingly lead these businesses to spend more on Facebook advertisements. That’s key for the company, which makes nearly 99% of its revenue from advertising.

Kustomer is based in New York and was founded in 2015. The company has raised approximately $173.5 million, according to Crunchbase.


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Scottish homes to be first in world to use 100% green hydrogen | Environment

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Hundreds of homes in Scotland will soon become the first in the world to use 100% green hydrogen to heat their properties and cook their meals as part of a new trial that could help households across the country replace fossil fuel gas.

Some 300 homes in Fife will be fitted with free hydrogen boilers, heaters and cooking appliances to be used for more than four years in the largest test of whether zero carbon hydrogen, made using renewable energy and water, could help meet Britain’s climate goals.

They will begin to receive green gas from the end of 2022, at no extra charge, and up to 1,000 homes could be included if the first phase of the trial is completed successfully.

The trial has the backing of the energy regulator, Ofgem, which has awarded £18m to SGN to develop the pioneering project. The grant is part of a funding competition which supports innovation to help prepare Britain’s energy grids for a low-carbon future. The Scottish government will support the project with a grant of £6.9m.

Ofgem’s £56m funding pot will also support a £12.7m project from National Grid to carry out “offline” hydrogen trials, using old gas grid pipes, to test the safety of transporting hydrogen gas across the country.

Green hydrogen is a central part of the government’s plan to wean Britain off fossil fuels because it can be used in the same ways as fossil fuel gas but produces no carbon emissions. This is particularly important for central heating, which makes up almost a third of the UK’s greenhouse gas emissions because 85% of homes use a gas boiler.

Antony Green, the head of National Grid’s hydrogen project, said: “If we truly want to reach a net zero decarbonised future, we need to replace methane with green alternatives like hydrogen.

“Sectors such as heat are difficult to decarbonise, and the importance of the gas networks to the UK’s current energy supply means projects like this are crucial if we are to deliver low carbon energy, reliably and safely to all consumers.”

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Ofgem’s remaining funds in its annual network innovation competition will be awarded to three pioneer projects which aim to use new technology to improve power substations, stabilise voltage control systems and strengthen electricity transmission towers.

Jonathan Brearley, the chief executive of Ofgem, said: “The winning projects were those which showed the most potential to make the game-changing leaps in technology we need to build a greener, fairer energy system at the lowest cost to consumers.”

Kwasi Kwarteng, the energy minister, said the UK “must continue driving forward” the new low-carbon technologies which will be needed to meet the government’s “bold ambition for a green industrial revolution”.


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DeepMind AI cracks 50-year-old problem of protein folding | DeepMind

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Having risen to fame on its superhuman performance at playing games, the artificial intelligence group DeepMind has cracked a serious scientific problem that has stumped researchers for half a century.

With its latest AI program, AlphaFold, the company and research lab showed it can predict how proteins fold into 3D shapes, a fiendishly complex process that is fundamental to understanding the biological machinery of life.

Independent scientists said the breakthrough would help researchers tease apart the mechanisms that drive some diseases and pave the way for designer medicines, more nutritious crops and “green enzymes” that can break down plastic pollution.

DeepMind said it had started work with a handful of scientific groups and would focus initially on malaria, sleeping sickness and leishmaniasis, a parasitic disease.

“It marks an exciting moment for the field,” said Demis Hassabis, DeepMind’s founder and CEO. “These algorithms are now becoming mature enough and powerful enough to be applicable to really challenging scientific problems.”

Venki Ramakrishnan, the president of the Royal Society, called the work “a stunning advance” that had occurred “decades before many people in the field would have predicted”.

DeepMind is best known for its run of human-trouncing programs that achieved supremacy in Chess, Go, Starcraft II and old-school Atari classics. But superhuman gameplay was never the primary aim. Instead, games provided a training ground for programs that, once powerful enough, would be unleashed on real-world problems.

Protein folding has been a grand challenge in biology for 50 years. An arcane form of molecular origami, its importance is hard to overstate. Most biological processes revolve around proteins and a protein’s shape determines its function. When researchers know how a protein folds up, they can start to uncover what it does. How insulin controls sugar levels in the blood and how antibodies fight coronavirus are both determined by protein structure.

Scientists have identified more than 200m proteins but structures are known for only a fraction of them. Traditionally, the shapes are discovered through meticulous lab work that can take years. And while computer scientists have made headway on the problem, inferring the structure from a protein’s makeup is no easy task. Proteins are chains of amino acids that can twist and bend into a mind-boggling variety of shapes: a googol cubed, or 1 followed by 300 zeroes.

To learn how proteins fold, researchers at DeepMind trained their algorithm on a public database containing about 170,000 protein sequences and their shapes. Running on the equivalent of 100 to 200 graphics processing units – by modern standards, a modest amount of computing power – the training took a few weeks.

DeepMind put AlphaFold through its paces by entering it for a protein olympics known as Casp, the Critical Assessment of Protein Structure Prediction, which takes place every two years. Entrants to the international competition are given the amino acid sequences for about 100 proteins and challenged to work them out. The results from teams that use computers are compared with those based on lab work.

AlphaFold not only outperformed other computer programs but reached an accuracy comparable to the laborious and time-consuming lab-based methods. When ranked across all proteins analysed, AlphaFold had a median score of 92.5 out of 100, with 90 being the equivalent to experimental methods. For the hardest proteins, the median score fell, but only marginally to 87.

Hassabis said DeepMind had started work on how to give researchers access to AlphaFold to help with scientific research. Andrei Lupas, the director of the Max Planck Institute for Developmental Biology in Tübingen, Germany, said he had already used the program to solve a protein structure that scientists had been stuck on for a decade.

Janet Thornton, a director emeritus of EMBL’s European Bioinformatics Institute near Cambridge, who was not involved in the work, said she was excited to hear the results. “This is a problem that I was beginning to think would not get solved in my lifetime,” she said. “Knowing these structures will really help us to understand how human beings operate and function, how we work.”

John Jumper, a researcher on the team at DeepMind, said: “We really didn’t know until we saw the Casp results how far we had pushed the field.” It is not the end of the work, however. Future research will focus on how proteins combine to form larger “complexes” and how they interact with other molecules in living organisms.


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