Instead of trying to work through these issues at the national level, the sequencing contracts allow individual public health agencies to request the names and contact information of people who have tested positive for variants of concern. But that just pushes the same problems of data ownership down the chain.
“Some states are very good and want to know a lot about variants that are circulating in their state,” says LabCorp’s Brian Krueger. “The other states are not.”
Public health epidemiologists often have little experience with bioinformatics, using software to analyze large datasets like genomic sequences. Only a few agencies have preexisting sequencing programs; even if they did, having each jurisdiction analyze just a small slice of the data set undercuts how much knowledge can be gleaned about real-world behavior.
Getting around those issues—making it easier to connect sequences and clinical metadata on a large scale—would require more than just root-and-branch reform of privacy regulations, however. It would mean a reorganization of the entire health care system in the US, where each of the 64 public health agencies operate as fiefdoms, and there is no centralization of information or power.
“Metadata is the single biggest uncracked nut,” says Jonathan Quick, managing director of pandemic response, preparedness, and prevention at the Rockefeller Foundation. (The Rockefeller Foundation helps fund coverage at MIT Technology Review, although it has no editorial oversight.) Because it’s so hard for public health experts to put together big enough data sets to really understand real-world variant behavior, our understanding has to come from vaccine manufacturers and hospitals that add sequencing to their own clinical trials, he says.
It’s frustrating to him that so many huge data sets of useful information already exist in electronic medical records, immunization registries, and other sources but can’t easily be used.
“There’s a whole lot more that could be learned, and learned faster, without the shackles we put on the use of that data,” says Quick. “We can’t just rely on the vaccine companies to do surveillance.”
Boosting state-level bioinformatics
If public health labs are expected to focus more on tracking and understanding variants on their own, they’ll need all the help they can get. Doing something about variants case by case, after all, is a public health job, while doing something about variants on a policy level is a political one.
Public health labs generally use genomics to expose otherwise hidden information about outbreaks, or as part of track-and-trace efforts. In the past, sequencing has been used to connect E. coli outbreaks to specific farms, identify and interrupt chains of HIV transmission, isolate US Ebola cases, and follow annual flu patterns.
Even those with well-established programs tend to use genomics sparingly. The cost of sequencing has dropped dramatically over the last decade, but the process is still not cheap, particularly for cash-strapped state and local health departments. The machines themselves cost hundreds of thousands of dollars to buy, and more to run: Illumina, one of the biggest makers of sequencing equipment, says labs spend an average of $1.2 million annually on supplies for each of its machines.
Health agencies don’t just need money; they also need expertise. Surveillance requires highly trained bioinformaticians to turn a sequence’s long strings of letters into useful information, as well as people to explain the results to officials, and convince them to turn any lessons learned into policy.
Fortunately, the OAMD has been working to support state and local health departments as they try to understand their sequencing data, employing regional bioinformaticians to consult with public health officers and facilitating agencies’ efforts to share their experiences.
It is also pouring hundreds of millions into building and supporting those agencies’ own sequencing programs—not just for covid, but for all pathogens.
But many of those agencies are facing pressure to sequence as many covid genomes as possible. Without a cohesive strategy for collecting and analyzing data, it’s unclear how much utility those programs will have.
“We’ll miss a ton of opportunities if we just give health departments money to set up programs without having a federal strategy so that everyone knows what they’re doing,” says Warmbrod.
Initial visions, usurped
Mark Pandori is director of the Nevada state public health laboratory, one of the programs OAMD supports. He has been a strong proponent of genomic surveillance for years. Before moving to Reno, he ran the public health lab in Alameda County, California, where he helped pioneer a program using sequencing to track how infections were being passed around hospitals.
Turning sequences into usable data is the biggest challenge for public health genomics programs, he says.
“The CDC can say, ‘Go buy a bunch of sequencing equipment, do a whole bunch of sequencing.’ But it doesn’t do anything unless the consumers of that data know how to use it, and know how to apply it,” he says. “I’m talking to you about the robotics we need to get things sequenced every day, but health departments just need a simple way to know if cases are related.”
When it comes to variants, public health labs are under many of the same pressures the CDC faces: everyone wants to know what variants are circulating, whether or not they can do anything with the information.
Pandori launched his covid sequencing program hoping to cut down on the labor needed to investigate potential covid outbreaks, quickly identifying whether cases caught near each other were related or coincidental.
His lab was the first in North America to identify a patient reinfected with covid-19, and later found the B.1.351 variant in a hospitalized man who had just come back from South Africa. With rapid contact tracing, the health department was able to prevent it from spreading.
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.
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.