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Why then attack on the Capitol was inevitable

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Why then attack on the Capitol was inevitable


Maybe you saw this coming nearly a decade ago, when #YourSlipIsShowing laid bare how racist Twitter users were impersonating Black women on the internet. Maybe, for you, it was during Gamergate, the online abuse campaign targeting women in the industry. Or maybe it was the mass shooting in Christchurch, when a gunman steeped in the culture of 8chan livestreamed himself murdering dozens of people. 

Maybe it was when you, or your friend, or your community, became the target of an extremist online mob, and you saw online anger become real world danger and harm. 

Or maybe what happened on Wednesday, when a rabble of internet-fuelled Trump supporters invaded the Capitol, came as a surprise.

For weeks they had been planning their action in plain sight on the internet—but they have been showing you who they are for years. The level of shock you feel right now about the power and danger of online extremism depends on whether you were paying attention. 

The consequences of inaction

The mob who tried to block Congress from confirming Joe Biden’s presidential victory  showed how the stupidity and danger of the far-right internet could come into the real world again, but this time it struck at the center of the US government. Neo-nazi streamers weren’t just inside the Capitol, they were putting on a show for audiences of tens of thousands of people who egged them on in the chats. The mob was having fun doing memes in the halls of American democracy as a woman—a Trump supporter whose social media history shows her devotion to QAnon—was killed trying to break into Congressional offices.

The past year, especially since the pandemic, has been one giant demonstration of the consequences of inaction; the consequences of ignoring the many, many people who have been begging social media companies to take the meme-making extremists and conspiracy theorists that have thrived on their platforms seriously. 

Facebook and Twitter acted to slow the rise of QAnon over the summer, but only after the pro-Trump conspiracy theory was able to grow relatively unrestricted there for three years. Account bans and algorithm tweaks have long been too little, too late to deal with racists, extremists and conspiracy theorists, and they have rarely addressed the fact that these powerful systems were working exactly as intended.    

I spoke with a small handful of the people who could have told you this was coming about this for a story in October. Researchers, technologists, and activists told me that major social media companies have, for the entirety of their history, chosen to do nothing, or to act only after their platforms cause abuse and harm. 

Ariel Waldman tried to get Twitter to meaningfully address abuse there in 2008. Researchers like Shafiqah Hudson, I’Nasah Crockett, and Shireen Mitchell have tracked exactly how harassment works and finds an audience on these platforms for years. Whitney Phillips talked about how she’s haunted by laughter—not just from other people, but also her own—back in the earliest days of her research into online culture and trolling, when overwhelmingly white researchers and personalities treated the extremists among them as edgy curiosities.

Many, many people who have been begging social media companies to take the meme-making extremists and conspiracy theorists seriously.

Ellen Pao, who briefly served as CEO of Reddit in 2014 and stepped down after introducing the platform’s first anti-harassment policy, was astonished that Reddit had only banned r/The_Donald in June 2020, after evidence had built for years to show that the popular pro-Trump message board served as an organizing space for extremiss and a channel for mob abuse. Of course, by the time it was banned, many of its users had already migrated away from Reddit to TheDonald.win, an independent forum created by the same people who ran the previous version. Its pages were filled with dozens of calls for violence ahead of Wednesday’s rally-turned-attempted-coup. 

Banning Trump doesn’t solve the issue

Facebook, Twitter, and YouTube didn’t create conspiracy thinking, or extremist ideologies, of course. Nor did they invent the idea of dangerous personality cults. But these platforms have—by design—handed those groups the mechanisms to reach much larger audiences much faster, and to recruit and radicalize new converts, even at the expense of the people and communities those ideologies target for abuse. And crucially, even when it was clear what was happening, they chose the minimal amount of change—or decided not to intervene at all. 

In the wake of the attempted coup on the Capitol building, people are again looking at the major social media companies to see how they respond. The focus is on Trump’s personal accounts, which he used to encourage supporters to descend on DC and then praised them when they did. Will he be banned from Twitter? There are compelling arguments for why he should. 

But as heavy and consequential as that would be, it’s also, in other ways… not. Abuse, harassment, conspiracy thinking, and racism will still be able to benefit from social media companies that remain interested in only acting when it’s too late, even without Trump retweeting them and egging them on. 

Facebook has banned Trump indefinitely, and also increased the extent of their moderation of groups, where a lot of conspiracy-fueled activity lives. These changes are good, but again, not new: people have told Facebook about this for years; Facebook employees have told Facebook about this for years. Groups were instrumental in organizing Stop the Steal protests in the days after the election, and before that, in anti-mask protests, and before that in spreading fake news, and before that in as a central space for anti-vaccine misinformation. None of this is new. 

There are only so many ways to say that more people should have listened. If you’re paying attention now, maybe you’ll finally start hearing what they say. 

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Rediscover trust in cybersecurity

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Rediscover trust in cybersecurity


The world has changed dramatically in a short amount of time—changing the world of work along with it. The new hybrid remote and in-office work world has ramifications for tech—specifically cybersecurity—and signals that it’s time to acknowledge just how intertwined humans and technology truly are.

Enabling a fast-paced, cloud-powered collaboration culture is critical to rapidly growing companies, positioning them to out innovate, outperform, and outsmart their competitors. Achieving this level of digital velocity, however, comes with a rapidly growing cybersecurity challenge that is often overlooked or deprioritized : insider risk, when a team member accidentally—or not—shares data or files outside of trusted parties. Ignoring the intrinsic link between employee productivity and insider risk can impact both an organizations’ competitive position and its bottom line. 

You can’t treat employees the same way you treat nation-state hackers

Insider risk includes any user-driven data exposure event—security, compliance or competitive in nature—that jeopardizes the financial, reputational or operational well-being of a company and its employees, customers, and partners. Thousands of user-driven data exposure and exfiltration events occur daily, stemming from accidental user error, employee negligence, or malicious users intending to do harm to the organization. Many users create insider risk accidentally, simply by making decisions based on time and reward, sharing and collaborating with the goal of increasing their productivity. Other users create risk due to negligence, and some have malicious intentions, like an employee stealing company data to bring to a competitor. 

From a cybersecurity perspective, organizations need to treat insider risk differently than external threats. With threats like hackers, malware, and nation-state threat actors, the intent is clear—it’s malicious. But the intent of employees creating insider risk is not always clear—even if the impact is the same. Employees can leak data by accident or due to negligence. Fully accepting this truth requires a mindset shift for security teams that have historically operated with a bunker mentality—under siege from the outside, holding their cards close to the vest so the enemy doesn’t gain insight into their defenses to use against them. Employees are not the adversaries of a security team or a company—in fact, they should be seen as allies in combating insider risk.

Transparency feeds trust: Building a foundation for training

All companies want to keep their crown jewels—source code, product designs, customer lists—from ending up in the wrong hands. Imagine the financial, reputational, and operational risk that could come from material data being leaked before an IPO, acquisition, or earnings call. Employees play a pivotal role in preventing data leaks, and there are two crucial elements to turning employees into insider risk allies: transparency and training. 

Transparency may feel at odds with cybersecurity. For cybersecurity teams that operate with an adversarial mindset appropriate for external threats, it can be challenging to approach internal threats differently. Transparency is all about building trust on both sides. Employees want to feel that their organization trusts them to use data wisely. Security teams should always start from a place of trust, assuming the majority of employees’ actions have positive intent. But, as the saying goes in cybersecurity, it’s important to “trust, but verify.” 

Monitoring is a critical part of managing insider risk, and organizations should be transparent about this. CCTV cameras are not hidden in public spaces. In fact, they are often accompanied by signs announcing surveillance in the area. Leadership should make it clear to employees that their data movements are being monitored—but that their privacy is still respected. There is a big difference between monitoring data movement and reading all employee emails.

Transparency builds trust—and with that foundation, an organization can focus on mitigating risk by changing user behavior through training. At the moment, security education and awareness programs are niche. Phishing training is likely the first thing that comes to mind due to the success it’s had moving the needle and getting employees to think before they click. Outside of phishing, there is not much training for users to understand what, exactly, they should and shouldn’t be doing.

For a start, many employees don’t even know where their organizations stand. What applications are they allowed to use? What are the rules of engagement for those apps if they want to use them to share files? What data can they use? Are they entitled to that data? Does the organization even care? Cybersecurity teams deal with a lot of noise made by employees doing things they shouldn’t. What if you could cut down that noise just by answering these questions?

Training employees should be both proactive and responsive. Proactively, in order to change employee behavior, organizations should provide both long- and short-form training modules to instruct and remind users of best behaviors. Additionally, organizations should respond with a micro-learning approach using bite-sized videos designed to address highly specific situations. The security team needs to take a page from marketing, focusing on repetitive messages delivered to the right people at the right time. 

Once business leaders understand that insider risk is not just a cybersecurity issue, but one that is intimately intertwined with an organization’s culture and has a significant impact on the business, they will be in a better position to out-innovate, outperform, and outsmart their competitors. In today’s hybrid remote and in-office work world, the human element that exists within technology has never been more significant.That’s why transparency and training are essential to keep data from leaking outside the organization. 

This content was produced by Code42. It was not written by MIT Technology Review’s editorial staff.

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How AI is reinventing what computers are

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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. 

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Decarbonizing industries with connectivity and 5G

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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.

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