He thought back to reports from just a few hours earlier, when the Ukrainian surveillance team said they were tracking Tank and had intelligence that the suspect had been at home recently. None of it seemed believable.
Five individuals were detained in Ukraine on that night, but when it came to Tank, who police alleged was in charge of the operation, they left empty-handed. And none of the five people arrested in Ukraine stayed in custody for long.
Somehow, the operation in Ukraine—a two-year international effort to catch the biggest cybercriminals on the FBI’s radar—had gone sideways. Tank had slipped away while under SBU surveillance, while the other major players deftly avoided serious consequences for their crimes. Craig and his team were livid.
But if the situation in Ukraine was frustrating, things were even worse in Russia, where the FBI had no one on the ground. Trust between the Americans and Russians had never been very strong. Early in the investigation, the Russians had waved the FBI off Slavik’s identity.
“They try to push you off target,” Craig says. “But we play those games knowing what’s going to happen. We’re very loose with what we send them anyway, and even if you know something, you try to push it to them to see if they’ll cooperate. And when they don’t—oh, no surprise.”
Even so, while the raids happened in Donetsk, the Americans hoped they would get a call from Russia about an FSB raid on the residence of Aqua, the money launderer Maksim Yakubets. Instead, there was silence.
The operation had its successes—dozens of lower-level operators were arrested across Ukraine, the United States, and the United Kingdom, including some of Tank’s personal friends who helped move stolen money out of England. But a maddening mixture of corruption, rivalry, and stonewalling had left Operation Trident Breach without its top targets.
“It came down to D-Day, and we got ghosted,” Craig says. “The SBU tried to communicate with [the Russians]. The FBI was making phone calls to the embassy in Moscow. It was complete silence. We ended up doing the operation anyway, without the FSB. It was months of silence. Nothing.”
Not everyone in the SBU drives a BMW.
After the raids, some Ukrainian officials, who were unhappy with the corruption and leaks happening within the country’s security services, concluded that the 2010 Donetsk raid against Tank and the Jabber Zeus crew failed because of a tip from a corrupt SBU officer named Alexander Khodakovsky.
At the time, Khodakovsky was the chief of an SBU SWAT unit in Donetsk known as Alpha team. It was the same group that led the raids for Trident Breach. He also helped coordinate law enforcement across the region, which allowed him to tell suspects in advance to prepare for searches or destroy evidence, according to the former SBU officer who spoke to MIT Technology Review anonymously.
When Russia and Ukraine went to war in 2014, Khodakovsky defected. He became a leader in the self-proclaimed Donetsk People’s Republic, which NATO says receives financial and military aid from Moscow.
The problem wasn’t just one corrupt officer, though. The Ukrainian investigation into—and legal proceedings against—Tank and his crew continued after the raids. But they were carefully handled to make sure he stayed free, the former SBU officer explains.
“Through his corrupt links among SBU management, Tank arranged that all further legal proceedings against him were conducted by the SBU Donetsk field office instead of SBU HQ in Kyiv, and eventually managed to have the case discontinued there,” the former officer says. The SBU, FBI, and FSB did not respond to requests for comment.
Tank, it emerged, was deeply entangled with Ukrainian officials linked to Russia’s government—including Ukraine’s former president Viktor Yanukovych, who was ousted in 2014.
Yanukovych’s youngest son, Viktor Jr., was the godfather to Tank’s daughter. Yanukovych Jr. died in 2015 when his Volkswagen minivan fell through the ice on a lake in Russia, and his father remains in exile there after being convicted of treason by a Ukrainian court.
When Yanukovych fled east, Tank moved west to Kyiv, where he is believed to represent some of the former president’s interests, along with his own business ventures.
“Through this association with the president’s family, Tank managed to develop corrupt links into the top tiers of Ukrainian government, including law enforcement,” the SBU officer explains.
Ever since Yanukovych was deposed, Ukraine’s new leadership has turned more decisively toward the West.
“The reality is corruption is a major challenge to stopping cybercrime, and it can go up pretty high,” Passwaters says. “But after more than 10 years working with Ukrainians to combat cybercrime, I can say there are plenty of really good people in the trenches silently working on the right side of this fight. They are key.”
Warmer relations with Washington were a major catalyst for the ongoing war in eastern Ukraine. Now, as Kyiv tries to join NATO, one of the conditions of membership is eliminating corruption. The country has lately cooperated with Americans on cybercrime investigations to a degree that would have been unimaginable in 2010. But corruption is still widespread.
“Ukraine overall is more active in combating cybercrime in recent years,” says the former SBU officer. “But only when we see criminals really getting punished would I say that the situation has changed at its root. Now, very often we see public relations stunts that do not result in cybercriminals’ ceasing their activities. Announcing some takedowns, conducting some searches, but then releasing everyone involved and letting them continue operating is not a proper way of tackling cybercrime.”
And Tank’s links to power have not gone away. Enmeshed with the powerful Yanukovych family, which is itself closely aligned with Russia, he remains free.
A looming threat
On June 23, FSB chief Alexander Bortnikov was quoted as saying his agency would work with the Americans to track down criminal hackers. It didn’t take long for two particular Russian names to come up.
Even after the 2010 raids took down a big chunk of his business, Bogachev continued to be a prominent cybercrime entrepreneur. He put together a new crime ring called the Business Club; it soon grew into a behemoth, stealing more than $100 million that was divided among its members. The group moved from hacking bank accounts to deploying some of the first modern ransomware, with a tool called CryptoLocker, by 2013. Once again, Bogachev was at the center of the evolution of a new kind of cybercrime.
Around the same time, researchers from the Dutch cybersecurity firm Fox-IT who were looking closely at Bogachev’s malware saw that it was not just attacking targets at random. The malware was also quietly looking for information on military services, intelligence agencies, and police in countries including Georgia, Turkey, Syria, and Ukraine—close neighbors and geopolitical rivals to Russia. It became clear that he wasn’t just working from inside Russia, but his malware actually hunted for intelligence on Moscow’s behalf.
How AI is reinventing what computers are
Fall 2021: the season of pumpkins, pecan pies, and peachy new phones. Every year, right on cue, Apple, Samsung, Google, and others drop their latest releases. These fixtures in the consumer tech calendar no longer inspire the surprise and wonder of those heady early days. But behind all the marketing glitz, there’s something remarkable going on.
Google’s latest offering, the Pixel 6, is the first phone to have a separate chip dedicated to AI that sits alongside its standard processor. And the chip that runs the iPhone has for the last couple of years contained what Apple calls a “neural engine,” also dedicated to AI. Both chips are better suited to the types of computations involved in training and running machine-learning models on our devices, such as the AI that powers your camera. Almost without our noticing, AI has become part of our day-to-day lives. And it’s changing how we think about computing.
What does that mean? Well, computers haven’t changed much in 40 or 50 years. They’re smaller and faster, but they’re still boxes with processors that run instructions from humans. AI changes that on at least three fronts: how computers are made, how they’re programmed, and how they’re used. Ultimately, it will change what they are for.
“The core of computing is changing from number-crunching to decision-making,” says Pradeep Dubey, director of the parallel computing lab at Intel. Or, as MIT CSAIL director Daniela Rus puts it, AI is freeing computers from their boxes.
More haste, less speed
The first change concerns how computers—and the chips that control them—are made. Traditional computing gains came as machines got faster at carrying out one calculation after another. For decades the world benefited from chip speed-ups that came with metronomic regularity as chipmakers kept up with Moore’s Law.
But the deep-learning models that make current AI applications work require a different approach: they need vast numbers of less precise calculations to be carried out all at the same time. That means a new type of chip is required: one that can move data around as quickly as possible, making sure it’s available when and where it’s needed. When deep learning exploded onto the scene a decade or so ago, there were already specialty computer chips available that were pretty good at this: graphics processing units, or GPUs, which were designed to display an entire screenful of pixels dozens of times a second.
Anything can become a computer. Indeed, most household objects, from toothbrushes to light switches to doorbells, already come in a smart version.
Now chipmakers like Intel and Arm and Nvidia, which supplied many of the first GPUs, are pivoting to make hardware tailored specifically for AI. Google and Facebook are also forcing their way into this industry for the first time, in a race to find an AI edge through hardware.
For example, the chip inside the Pixel 6 is a new mobile version of Google’s tensor processing unit, or TPU. Unlike traditional chips, which are geared toward ultrafast, precise calculations, TPUs are designed for the high-volume but low-precision calculations required by neural networks. Google has used these chips in-house since 2015: they process people’s photos and natural-language search queries. Google’s sister company DeepMind uses them to train its AIs.
In the last couple of years, Google has made TPUs available to other companies, and these chips—as well as similar ones being developed by others—are becoming the default inside the world’s data centers.
AI is even helping to design its own computing infrastructure. In 2020, Google used a reinforcement-learning algorithm—a type of AI that learns how to solve a task through trial and error—to design the layout of a new TPU. The AI eventually came up with strange new designs that no human would think of—but they worked. This kind of AI could one day develop better, more efficient chips.
Show, don’t tell
The second change concerns how computers are told what to do. For the past 40 years we have been programming computers; for the next 40 we will be training them, says Chris Bishop, head of Microsoft Research in the UK.
Traditionally, to get a computer to do something like recognize speech or identify objects in an image, programmers first had to come up with rules for the computer.
With machine learning, programmers no longer write rules. Instead, they create a neural network that learns those rules for itself. It’s a fundamentally different way of thinking.
Decarbonizing industries with connectivity and 5G
The United Nations Intergovernmental Panel on Climate Change’s sixth climate change report—an aggregated assessment of scientific research prepared by some 300 scientists across 66 countries—has served as the loudest and clearest wake-up call to date on the global warming crisis. The panel unequivocally attributes the increase in the earth’s temperature—it has risen by 1.1 °C since the Industrial Revolution—to human activity. Without substantial and immediate reductions in carbon dioxide and other greenhouse gas emissions, temperatures will rise between 1.5 °C and 2 °C before the end of the century. That, the panel posits, will lead all of humanity to a “greater risk of passing through ‘tipping points,’ thresholds beyond which certain impacts can no longer be avoided even if temperatures are brought back down later on.”
Corporations and industries must therefore redouble their greenhouse gas emissions reduction and removal efforts with speed and precision—but to do this, they must also commit to deep operational and organizational transformation. Cellular infrastructure, particularly 5G, is one of the many digital tools and technology-enabled processes organizations have at their disposal to accelerate decarbonization efforts.
5G and other cellular technology can enable increasingly interconnected supply chains and networks, improve data sharing, optimize systems, and increase operational efficiency. These capabilities could soon contribute to an exponential acceleration of global efforts to reduce carbon emissions.
Industries such as energy, manufacturing, and transportation could have the biggest impact on decarbonization efforts through the use of 5G, as they are some of the biggest greenhouse-gas-emitting industries, and all rely on connectivity to link to one another through communications network infrastructure.
The higher performance and improved efficiency of 5G—which delivers higher multi-gigabit peak data speeds, ultra-low latency, increased reliability, and increased network capacity—could help businesses and public infrastructure providers focus on business transformation and reduction of harmful emissions. This requires effective digital management and monitoring of distributed operations with resilience and analytic insight. 5G will help factories, logistics networks, power companies, and others operate more efficiently, more consciously, and more purposely in line with their explicit sustainability objectives through better insight and more powerful network configurations.
This report, “Decarbonizing industries with connectivity & 5G,” argues that the capabilities enabled by broadband cellular connectivity primarily, though not exclusively, through 5G network infrastructure are a unique, powerful, and immediate enabler of carbon reduction efforts. They have the potential to create a transformational acceleration of decarbonization efforts, as increasingly interconnected supply chains, transportation, and energy networks share data to increase efficiency and productivity, hence optimizing systems for lower carbon emissions.
Surgeons have successfully tested a pig’s kidney in a human patient
The reception: The research was conducted last month and is yet to be peer reviewed or published in a journal, but external experts say it represents a major advance. “There is no doubt that this is a highly significant breakthrough,” says Darren K. Griffin, a professor of genetics at the University of Kent, UK. “The research team were cautious, using a patient who had suffered brain death, attaching the kidney to the outside of the body, and closely monitoring for only a limited amount of time. There is thus a long way to go and much to discover,” he added.
“This is a huge breakthrough. It’s a big, big deal,” Dorry Segev, a professor of transplant surgery at Johns Hopkins School of Medicine who was not involved in the research, told the New York Times. However, he added, “we need to know more about the longevity of the organ.”
The background: In recent years, research has increasingly zeroed in on pigs as the most promising avenue to help address the shortage of organs for transplant, but it has faced a number of obstacles, most prominently the fact that a sugar in pig cells triggers an aggressive rejection response in humans.
The researchers got around this by genetically altering the donor pig to knock out the gene encoding the sugar molecule that causes the rejection response. The pig was genetically engineered by Revivicor, one of several biotech companies working to develop pig organs to transplant into humans.
The big prize: There is a dire need for more kidneys. More than 100,000 people in the US are currently waiting for a kidney transplant, and 13 die of them every day, according to the National Kidney Foundation. Genetically engineered pigs could offer a crucial lifeline for these people, if the approach tested at NYU Langone can work for much longer periods.