Existing on-premises and centralized cloud infrastructure can’t support the vast computing needs of these powerful applications, which require low latency—or data-transfer delay—to smoothly transport and get real-time access to data. To reduce latency
and bandwidth use, as well as rein in costs, computing power and processes must be closer to the physical location of the data. The solution? Move computing power to local infrastructure at the “edge” of the network, rather than relying on distant
A whopping 90% of industrial enterprises will use edge computing technology by 2022, according to Frost & Sullivan, while a recent IDC report (registration required) found that 40% of all organizations will invest in edge computing over the next year. “Edge computing is necessary
to enable the next-generation industrial revolution,” says Bike Xie, vice president of engineering at AI technology vendor Kneron. The future of AI and other automation technologies depends on the decentralized edge, he explains, whether it is
by connecting internet-of-things and other devices to distributed network nodes or implementing AI-enabled chips that can build algorithmic models autonomously.
“Edge computing is complementary to the cloud,” Xie says. “Like cloud, edge technology enables applications manufacturers need to both gain and apply the data-driven knowledge that will power smart factories and products.”
Manufacturing moves to the edge
The move toward edge computing is the result of a sea change in manufacturing over the past two decades. Manufacturers, whether they make industrial products, electronic equipment, or consumer goods, have transitioned slowly but steadily to increased
automation and self-monitoring of systems and processes to drive greater efficiency in producing products, maintaining equipment, and optimizing every link in the supply chain.
As manufacturers implement more sensor-based, automation-driven devices, they also produce more data than ever before. But often, data sets from sensor-based devices to centralized systems can quickly grow unwieldy, slowing down automation and making real-time
Edge computing allows manufacturers to make flexible choices about processing data to eliminate time lags and decrease bandwidth use, as well as about which data can be destroyed right after it is processed, says Xie. “Manufacturers can process data quickly
at the edge if data transmission to the cloud is a bottleneck, or move certain data to the cloud if latency and bandwidth are not an issue.” Not only does processing data closer to where it’s used save bandwidth and reduce costs, he adds,
but data is more secure because it’s processed right away.
IDC predicts that by 2023 more than 50% of new enterprise IT infrastructure deployed will be at the edge rather than in corporate data centers, up from less than 10% in 2020.
An example of toggling from cloud to edge comes from Paul Savill, senior vice president for product management and services at Lumen, a technology company that offers an edge computing platform.
Lumen recently did an installation at a newly built, million-square-foot factory. Robotic systems from about 50 different manufacturers rely on edge computing “because they needed to be within 5 milliseconds of latency to accurately control the robotics,” Savill says.
The deployment provides secure connectivity from the edge applications to the robotics manufacturers’ data centers, “where they collect information on a real-time basis.”
But for long-term storage of data and for machine-learning and analytics applications—all that goes in the public cloud, says Savill. Other, larger workloads are processed in big data centers “with vast computational power” that can process enormous sums of data quickly.
“That chain from the public cloud to the edge compute to on-premises is very important,” says Savill. “It gives customers the ability to leverage the latest advanced technologies in a way that saves them money and drives tremendous efficiency.”
Yann LeCun has a bold new vision for the future of AI
Melanie Mitchell, an AI researcher at the Santa Fe Institute, is also excited to see a whole new approach. “We really haven’t seen this coming out of the deep-learning community so much,” she says. She also agrees with LeCun that large language models cannot be the whole story. “They lack memory and internal models of the world that are actually really important,” she says.
Natasha Jaques, a researcher at Google Brain, thinks that language models should still play a role, however. It’s odd for language to be entirely missing from LeCun’s proposals, she says: “We know that large language models are super effective and bake in a bunch of human knowledge.”
Jaques, who works on ways to get AIs to share information and abilities with each other, points out that humans don’t have to have direct experience of something to learn about it. We can change our behavior simply by being told something, such as not to touch a hot pan. “How do I update this world model that Yann is proposing if I don’t have language?” she asks.
There’s another issue, too. If they were to work, LeCun’s ideas would create a powerful technology that could be as transformative as the internet. And yet his proposal doesn’t discuss how his model’s behavior and motivations would be controlled, or who would control them. This is a weird omission, says Abhishek Gupta, the founder of the Montreal AI Ethics Institute and a responsible-AI expert at Boston Consulting Group.
“We should think more about what it takes for AI to function well in a society, and that requires thinking about ethical behavior, amongst other things,” says Gupta.
Yet Jaques notes that LeCun’s proposals are still very much ideas rather than practical applications. Mitchell says the same: “There’s certainly little risk of this becoming a human-level intelligence anytime soon.”
LeCun would agree. His aim is to sow the seeds of a new approach in the hope that others build on it. “This is something that is going to take a lot of effort from a lot of people,” he says. “I’m putting this out there because I think ultimately this is the way to go.” If nothing else, he wants to convince people that large language models and reinforcement learning are not the only ways forward.
“I hate to see people wasting their time,” he says.
The Download: Yann LeCun’s AI vision, and smart cities’ unfulfilled promises
“We’re addicted to being on Facebook.”
—Jordi Berbera, who runs a pizza stand in Mexico City, tells Rest of World why he has turned to selling his wares through the social network instead of through more conventional food delivery apps.
The big story
“Am I going crazy or am I being stalked?” Inside the disturbing online world of gangstalking
Jenny’s story is not linear, the way that we like stories to be. She was born in Baltimore in 1975 and had a happy, healthy childhood—her younger brother Danny fondly recalls the treasure hunts she would orchestrate. In her late teens, she developed anorexia and depression and was hospitalized for a month. Despite her struggles, she graduated high school and was accepted into a prestigious liberal arts college.
There, things went downhill again. Among other issues, chronic fatigue led her to drop out. When she was 25 she flipped that car on Florida’s Sunshine Skyway Bridge in an apparent suicide attempt. At 30, after experiencing delusions that she was pregnant, she was diagnosed with schizophrenia. She was hospitalized for half a year and began treatment, regularly receiving shots of an antipsychotic drug. “It was like having my older sister back again,” Danny says.
On July 17, 2017, Jenny jumped from the tenth floor of a parking garage at Tampa International Airport. After her death, her family searched her hotel room and her apartment, but the 42-year-old didn’t leave a note. “We wanted to find a reason for why she did this,” Danny says. And so, a week after his sister’s death, Danny—a certified ethical hacker—decided to look for answers on Jenny’s computer. He found she had subscribed to hundreds of gangstalking groups across Facebook, Twitter, and Reddit; online communities where self-described “targeted individuals” say they are being monitored, harassed, and stalked 24/7 by governments and other organizations—and the internet legitimizes them. Read the full story.
The US Supreme Court has overturned Roe v. Wade. What does that mean?
Access to legal abortion is now subject to state laws, allowing each state to decide whether to ban, restrict or allow abortion. Some parts of the country are much stricter than others—Arkansas, Oklahoma and Kentucky are among the 13 states with trigger laws that immediately made abortion illegal in the aftermath of the ruling. In total, around half of states are likely to either ban or limit access to the procedure, with many of them refusing to make exceptions, even in pregnancies involving rape, incest and fetuses with genetic abnormalities. Many specialized abortion clinics may be forced to close their doors in the next few days and weeks.
While overturning Roe v Wade will not spell an end to abortion in the US, it’s likely to lower its rates, and force those seeking them to obtain them using different methods. People living in states that ban or heavily restrict abortions may consider travelling to other areas that will continue to allow them, although crossing state lines can be time-consuming and prohibitively expensive for many people facing financial hardship.
The likelihood that anti-abortion activists will use surveillance and data collection to track and identify people seeking abortions is also higher following the decision. This information could be used to criminalize them, making it particularly dangerous for those leaving home to cross state lines.
Vigilante volunteers already stake out abortion clinics in states including Mississippi, Florida and North Carolina, filming people’s arrival on cameras and recording details about them and their cars. While they deny the data is used to harass or contact people seeking abortions, experts are concerned that footage filmed of clients arriving and leaving clinics could be exploited to target and harm them, particularly if law enforcement agencies or private groups were to use facial recognition to identify them.
Another option is to order so-called abortion pills to discreetly end a pregnancy at home. The pills, which are safe and widely prescribed by doctors, are significantly less expensive than surgical procedures, and already account for the majority of abortions in the US.