The 25-year-old Noah Green, who rammed his car into the North Barricade on Friday, killing one Capitol Police officer and wounding another, reportedly called himself a Nation of Islam follower. DC officials said that the attack does not appear to be terrorism-related, but the investigation is ongoing.
The suspect in Friday’s attack on Capitol Police reportedly suffered mental issues, alleging that US government agencies were spying on him, several media reports suggest.
According to Metropolitan Police, just after 1:00 pm EST, a man, later identified as a 25-year-old from Indiana named Noah Green, rammed his blue sedan into two Capitol Police officers and then hit the north barricade barrier. After that, he exited the vehicle with a knife in his hand, but was shot dead by police.
Both The New York Times and NBC News report, citing Green’s Facebook posts (his account has apparently been taken down), that the suspect was a supporter of Louis Farrakhan, the leader of the Nation of Islam (NOI), a Black religious movement that, in fact, has little to do with traditional teachings of Islam.
The Southern Poverty Law Center has characterized the NOI as a “black supremacist hate group” over its members making anti-Semitic and homophobic statements.
According to The Hill, Green shared confessions on social media about how “tough” his life has been in recent years, including losing his job over apparent mental issues. “I have been tried with some of the biggest, unimaginable tests in my life. I am currently now unemployed, after I left my job, partly due to afflictions”, the Capitol attacker reportedly posted.
Just two hours before the attack, according to CNN, the suspect posted a video on his Instagram account with a caption that said: “The US Government is the #1 enemy of Black people!” Another post on Green’s Instagram account reportedly claimed that Farrakhan had saved him “after the terrible afflictions I have suffered presumably by the CIA and FBI, government agencies of the United States of America.”
A tow truck tows away the car used to ram a police barricade outside the U.S. Capitol building on Capitol Hill in Washington, U.S. April 2, 2021.
Capitol Police confirmed the identity of one of the two police officers, who died from his injuries after the attack, as 18-year veteran William Evans. President Joe Biden in a statement expressed condolences and said he ordered the White House flag to be lowered at half-mast.
Officials have yet to determine the exact motive behind the attack. Metropolitan Police Chief Robert Contee said in the press conference that the attack does not appear to be terrorism-related, but the investigation is ongoing and investigators will continue to see if there are any links to terrorism.
That’s why a team at the Mila-Quebec Artificial Intelligence Institute, led by Professor Yoshua Bengio, wants to bring it home – right to your doorstep in fact. His team has developed a tool that makes it possible to visualize the effects of floods, wildfires and smog anywhere in the world. Their simulation does this by making use of a generative adversarial network (GAN), a type of machine-learning algorithm. GANs can also produce things such as deepfake images, which are digitally composed of millions of images to create realistic photos of something (or someone) new.
For two years, 30 scientists have worked on the project, which is named after thispersondoesnotexist.com, a website portfolio of deepfake faces. Bengio’s version is called “This Climate Does Not Exist.” All a user has to do is type in an address or select a marker on Google Street View, and then indicate what kind of catastrophe they want to see: flood, wildfire or smog. The algorithm works its magic and returns the image with the requested effect. These images are not intended to be an accurate portrayal of what would happen at each specific location if no action on climate change is taken, but rather are a recreation of the worst possible effects in the scenario of the user’s choice.
The realism is particularly striking in the flooding option, which was the most difficult for Bengio’s team to produce. The algorithm takes the location proposed by the user, automatically places a layer of water on it and then adapts it to the environment of the image itself. The result is hyperrealistic.
“One of the most important challenges has been getting the algorithm to simulate flooding in a wide variety of images,” explains Alex Hernandez-Garcia, one of the project’s lead researchers. “One module of the algorithm is in charge of detecting which parts of the image should be covered with water and another module is in charge of generating the water texture by incorporating the context of the image, for example, the reflection of buildings. Finally, these results are combined to generate the final image.”
To detect which parts to cover with water and which to leave unscathed, Hernandez-Garcia and his colleagues combined several artificial intelligence (AI) and machine-learning techniques. “We generated a virtual city that allowed us to make a series of images with and without water. We also adjusted an algorithm that was able to make good predictions in that virtual world, detecting the different parts of a scene: the ground, cars, buildings, trees, people and so on,” he explained. “However, the algorithm must be able to make good predictions based on real images [those from Google Street View].” For the latter, they used generative adversarial networks.
The process is completed in a few seconds, and before displaying the image to the user some information is provided about the causes and consequences of the selected weather phenomenon, and its relationship to climate change. For example, if a flood is chosen, it indicates that flash floods kill about 5,000 people a year, that sea levels are expected to rise by two meters by the end of the century and that this major disruption to the planet will forever alter the lives of at least one billion people by the end of 2050. “If we do nothing, soon we will face major climate catastrophes,” says Professor Bengio, the institute’s scientific director. “This website makes the risks of climate change much more real and personal to people,” he argues.
Generative adversarial networks
The quality of AI took a giant leap forward about a decade ago with the emergence and consolidation of machine learning and deep learning. These techniques are based on training a machine so that it is capable of performing complex tasks after reaching certain conclusions on its own. For example, if you want the algorithm to distinguish between blueberry muffins and chihuahuas, the programmer will feed it a series of examples of each category, followed by thousands of images that are not pre-sorted. The machine will establish which is which, and when it gets it wrong and is made aware of the error, will refine its criteria.
Bengio won the 2018 Turing Award, considered the Nobel Prize of computer science, along with Geoffrey Hinton and Yann LeCun, for their contribution to the development of neural networks. This is a further step in machine learning that attempts to mimic the functioning of the human brain: applying several simultaneous layers of processing to increase performance. Neural networks are behind the most complex classification systems, such as voice assistants or advanced prediction models.
Generative adversarial networks (GANs) go even further. They were invented at the Mila-Quebec Artificial Intelligence Institute in 2014 and are capable of generating new content that looks faultlessly real to the human eye. GANs are behind the increasingly sophisticated deepfake videos of Tom Cruise or Donald Trump now circulating online, in which politicians or celebrities say or act in whichever way their creator likes. They work thanks to competition between two neural networks: one tries to produce images that are as realistic as possible and the other tries to detect whether they are real or a fabrication. This tension is replicated thousands or millions of times and during this process, the generating network learns to create more and more successful images. When the first network succeeds in fooling the second, we have a winning image. From there, a perfectly rendered image of New York City’s Times Square inundated by flooding is just a click away.
The Quebec lab is now using a new type of GAN they have developed to generate the climate change images seen on their website. “In general, the limited availability of images and the need to adapt the algorithm to a multitude of situations have been the main technical challenges we have faced,” says Hernandez-Garcia.
Hit by new US sanctions and the collapse of the banking system in neighbouring Lebanon, cash-strapped Damascus is relying increasingly on unorthodox methods for raising funds – money either pocketed by officials in Damascus for their own personal wealth, or put towards the 10-year-old war effort.
Researchers analysed hundreds of UN contracts to procure goods and services for people living in government-held areas of Syria, where more than 90% of the population are living in poverty since the Syrian pound, or lira, crashed last year.
While the central bank’s official exchange rate has improved this year to SYP2,500 to the US dollar, the black market rate is SYP3,500. Legitimate traders and consumers prefer to use the black market rate, as they receive more Syrian pounds for foreign currency.
Since the UN is forced by the Syrian government to use the official rate, half of foreign aid money exchanged into Syrian pounds in 2020, when the rates were hugely divergent, was lost after being exchanged at the lower, official rate.
“This shows an incredibly systematic way of diverting aid before it even has a chance to be implemented or used on the ground,” said Natasha Hall, of the CSIS, a Washington-based thinktank that helped compile the research.
“If the goal of sanctions overall is to deprive the regime of the resources to commit acts of violence against civilians and the goal of humanitarian aid is to reach people in need then we have this instance … where aid is at complete contradiction to those two stated goals.”
After 10 years of civil war in Syria, international donor fatigue, already seen in decreasing aid pledges, has turned to more overt political re-engagement with Assad’s regime.
Without the US playing a strong role in finding a political solution in Syria, which Washington still publicly advocates, Arab nations – including the US-allied Jordan, the United Arab Emirates, Saudi Arabia and Egypt – have recently restarted diplomatic talks, reopened borders for trade and signalled renewing economic cooperation.
If salaries, cash-aid programmes and other funding streams not made public were included, the bank could be making hundreds of millions of dollars, according to researchers.
The funding has been channelled through various UN agencies – the Office for the Coordination of Humanitarian Affairs (OCHA); the World Food Programme; the UN Development Programme; the UNHCR; the Food and Agriculture Organisation; and Unicef.
The UN’s financial tracking system told the researchers it did not monitor the amount of money exchanged into Syrian pounds as “tracking such information was beyond the scope of their mission”.
Hall said there was a “reticence” about investigating how much aid had been diverted. She said donors were well aware of the problem. “I think it is about [them] choosing certain battles to fight. It’s just not clear to me that any battles are being fought when it comes to aid in Syrian government-held areas today,” she said.
“There’s really no way for us, as independent consultants, to know the full extent of how aid is spent inside the country … We just wanted to flag that, even through this limited portal to understanding how much is spent, it’s already tens of millions of dollars which is hoarded.”
She believes the UN should negotiate a preferential exchange rate with the Syrian government – – to at least reduce the amount siphoned off.
Sara Kayyali, of HRW, said “there was no due diligence in terms of human rights” within UN procurement to avoid bankrolling Syria.
“This should be a wake-up call to the UN … they need to revise the way they provide aid and revise how they consider their obligations to respect human rights in light of this, because it’s difficult to justify this idea that hundreds of millions of dollars are going to an abusive state apparatus,” she said.
Danielle Moylan, a spokesperson for the UN agencies mentioned, said: “The UN welcomes all independent scrutiny of humanitarian operations in Syria. Our foremost priority has, and always will be, assisting the people in need in Syria, guided by humanitarian principles, accountability to the affected populations, transparency, efficiency and effectiveness.
“The majority of UN’s procurement for our humanitarian response in Syria is made in international and regional markets and therefore not affected by the Syrian exchange rate. Otherwise, as is the case in any country, the UN in Syria is required to use the official exchange rate,” Moylan said.
“In the past, the UN and humanitarian partners have negotiated a ‘preferential’ exchange rate for humanitarian operations [and] continues to engage the Central Bank of Syria on the issue of ‘preferential’ exchange rates.”
The European Parliament announced that Kremlin critic Alexei Navalny has won the Sakharov Prize for defending human rights. The parliament’s president David Sassoli wrote on Twitter: “Alexei Navalny is the winner of this year’s #SakharovPrize. He has fought tirelessly against the corruption of Vladimir Putin’s regime. This cost him his liberty and nearly his life. Today’s prize recognises his immense bravery and we reiterate our call for his immediate release.”