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Police in Ogden, Utah and small cities around the US are using these surveillance technologies

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Police in Ogden, Utah and small cities around the US are using these surveillance technologies


One afternoon, I accompanied Heather West, the detective who’d been perusing gray pickups in the license-plate database, and Josh Terry, the analyst who’d spotted the kidnapper with the Cowboys jacket, to fly a drone over a park abutting a city-owned golf course on the edge of town. West was at the controls; Terry followed the drone’s path in the sky and maintained “situational awareness” for the crew; another detective focused on the iPad showing what the drone was seeing, as opposed to where and how it was flying. 

Of all the gadgets under the hood at the real time crime center, drones may well be the most tightly regulated, subject to safety (but not privacy) regulations and review by the Federal Aviation Administration. In Ogden, neighbor to a large Air Force base, these rules are compounded by flight restrictions covering most of the city. The police department had to obtain waivers to get its drones off the ground; it took two years to develop policies and get the necessary approvals to start making flights. 

Joshua Terry, an analyst who does much of the real time crime center’s mapping work, with a drone.

NIKI CHAN WYLIE

The police department purchased its drones with a mind to managing large public events or complex incidents like hostage situations. But, as Dave Weloth soon found, “the more we use our drones, the more use cases we find.” At the real time crime center, Terry, who has a master’s in geographic information technology, had given me a tour of the city with images gathered on recent drone flights, clicking through to cloud-shaped splotches, assembled from the drone’s composite photographs, that dotted the map of Ogden. 

Above 21st Street and Washington, he zoomed in on the site of a fatal crash caused by a motorcycle running a red light. A bloody sheet covered the driver’s body, legs splayed on the pavement, surrounded by a ring of fire trucks. Within minutes, the drone’s cameras had scanned the scene and created a 3D model accurate to a centimeter, replacing the complex choreography of place markers and fixed cameras on the ground that sometimes leave major intersections closed for hours after a deadly collision.

No one seemed to give much thought to the fact that quietly, people who were homeless had become the sight most frequently captured by the police department’s drone program.

When the region was hit by a powerful windstorm last September, Terry flew a drone over massive piles of downed trees and brush collected by the city. When county officials saw the resulting volumetric analysis—12,938 cubic yards—that would be submitted as part of a claim to the Federal Emergency Management Agency, they asked the police department to perform the same service for two neighboring towns. Ogden drones have also been used to pinpoint hot spots after wildland fires, locate missing persons, and fly “overwatch” for SWAT team raids.

This flight was more routine. When I pulled into the parking lot, two officers from Ogden’s community policing unit looked on as West steered the craft over a dense stand of Gambel oak and then hovered over a triangular log fort on a hillside a couple of hundred yards away. Though they’d never encountered people on drone sweeps through the area, trash and makeshift structures were commonplace. Once the RTCC pinpointed the location of any encampments, the community service officers would go in on foot to get a closer look. “We get a lot of positive feedback from runners, hikers,” one officer explained. After one recent visit to a camp near a pond on 21st Street, he and the county social service workers who accompanied him found housing for two people they’d met there. When clearing camps, police also “try and connect [people] with services they need,” Weloth said. The department recently hired a full-time homeless outreach coordinator to help. “We can’t police ourselves out of this problem,” he said, comparing the department’s efforts to keep new camps from springing up to “pushing water uphill.”

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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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Surgeons have successfully tested a pig’s kidney in a human patient

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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.

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