Edge computing is about processing data as close to the source as possible, which reduces both latency and bandwidth use. This concept is seen as critical for furthering the Internet of Things and for driving the development of autonomous vehicles.
What is edge computing?
Edge computing is a decentralized approach to computing applied to networks (the opposite to cloud computing’s centralized approach). The concept relates to how a network stores its information. In edge computing, most data on a network is moved away from physical computers. For businesses, data is moved onto a private server.
Edge computing is especially useful in cases where a lot of data is generated. The approach allows for the successful triage of data locally so that some of it is processed locally, reducing the backhaul traffic to the central data repository. This is very useful in cases where many devices are connected together, as with the Internet of Things.
Edge computing helps to make the Industrial Internet of Things possible. This is an area of great value. McKinsey & Co. calculate that the Industrial Internet of Things will generate $7.5 trillion in value by 2025. The advantages here are to connect people to machine data that accelerate digital industrial transformation.
How can edge computing benefit business?
The advantages of edge computing are that it takes less time to move data and there are fewer are less hardware limitations and that hardware limitations are easily addressed. With conventional storage systems, hardware is normally required, and this can create a bottleneck that places a restriction on how much memory can be moved at any time point. The use of hardware also leads to slower data transfer speeds.
Furthermore, the costs of operating and maintaining the hardware are relatively more expensive.
Security is also stronger with edge computing, making edge computing systems harder for hackers to penetrate. This is because data is continually moving between network modes.
When data are moved throughout a network, they go through different security layers to ensure hackers cannot get into the system, but edge computing goes beyond this. More security layers are used because, instead of the data moving between the network nodes, the data moves from the Internet into the servers and onto the nodes. This provides an opportunity for creating additional firewalls and antivirus scans.
How are businesses using edge computing?
Businesses can derive many advantages from the edge computing concept. The edge process enables analytics and data gathering to occur at the source of the data. This enables companies to leverage resources from devices that are not necessarily continuously connected to a network like laptops, smartphones, tablets and sensors.
Autonomous vehicles and edge computing
Among the more specific examples is autonomous car technology. These are, in a sense, datacenters on wheels, and here edge computing plays a key role. To collect the high volumes of data, edge computing provides an advantage. In terms of data, Intel estimates that autonomous cars, with their many on-vehicle sensors, generate over 40 terabytes of data for each eight hours of driving. Given that this level of data cannot be easily sent to a cloud (and this also presents a safety risk in terms of delayed reactions), the use of edge computing becomes a necessity.
Security cameras and edge computing
A second example is with security systems. If a large complex is served by dozens of high-definition Internet of Things video cameras where data is continuously streaming that signal to a cloud server, these systems can be slow to respond. This is especially so if the security protocol is designed to respond to motion-detection. This set-up places a major strain on the building’s Internet infrastructure, with a high proportion of the bandwidth becoming consumed by a high volume of video footage.
With the edge concept, each camera would have an independent internal computer to run the motion-detecting application and then sent footage to the cloud server as needed. This improves efficiency and lowers bandwidth use.
Fleet management and edge computing
Edge computing also helps to improve the efficiency of fleet management. While a large volume of key performance data needs to be collected – wheels, brakes, battery, electrical – where such data requires a response, such as a potential brake failure, then some of this data needs to be collected and stored locally on the edge in order to minimize the risk of vehicle breakdown or accident.
An example of edge computing applied to fleet management is with trailer temperature. With most fleet monitoring systems, only temperature readings that are outside of a set range are reported back to fleet managers through telematics. The fleet manager then needs to assess whether or not there is a problem. However, with edge analytics, temperature readings can be analyzed onboard a vehicle and notified to the driver, empowering the driver to take steps to mitigate the temperature fluctuation.
AI will fuel the next wave of digital transformation in Asia
From the recently-wrapped Milken Institute Asia Summit in Singapore, president of Asia and corporate vice president at Microsoft, Ralph Haupter, spoke to Bloomberg Markets: Asia on how Artificial Intelligence will continue to disrupt the technology space and drive growth on the continent.
As it stands, an increasing number of reports are showing the importance of AI on growth on a global scale:
- AI could contribute an additional $15.7 trillion to the global economy by 2030 (PwC)
- The technology represents a potential impact on GDP of 26.1 percent in China (PwC)
- 28 percent of businesses are already realizing tangible returns on their AI implementation (AI Business)
“We need to understand that AI is the next accelerator for digital transformational companies,” explained Haupter. “We did a study here in Asia and it turns out that companies really think AI will drive double on innovation and double on productivity. That’s pretty impactful.”
The study referenced by Haupter was released earlier this year, showing that AI will accelerate the rate of innovation and employee productivity improvements to nearly double in Asia Pacific by 2021. Furthermore, only 41 percent of organizations in the region have embarked on the AI journey.
Speaking to Bloomberg, Haupter cited one success story: Narayana Health in India, which uses AI visual recognition with its X-Rays. “The quality is better, the cost is down, scale is higher — that’s what technology is about. It makes me excited.”
The urgency of re-skilling
Of course, a significant touchpoint when discussing the important and rise of AI on growth, is the prioritization of reskilling workers.
A recent IBM Institute for Business Value study found that “as many as 120 million workers in the world’s 12 largest economies may need to be retrained or reskilled over the next three years as a result of the advent of artificial intelligence (AI) and automation.”
In his interview, Haupter is quick to point out that AI “is something that is augmenting us as human beings, and not replacing us,” emphasizing that reskilling is a clear goal on the agenda.
DX Journal covers the impact of digital transformation (DX) initiatives worldwide across multiple industries.
Robot delivery: Bots will be bringing parcels to your home
Ford, FedEx and Amazon are each at an advanced stage with autonomous robot delivery vehicles, designed to bring packages to the doors of businesses and homes. Several successful pilots have been completed.
Each robot looks different but the objective is similar — getting a package to a customer using an autonomous machine. The aim of these new robot delivery tools is to boost efficiency and eliminate the need to pay people to carry out the final part of the delivery process.
Ford / Agility Robotics
Ford, more commonly associated with cars and trucks, is partnering with legged locomotion specialist Agility Robotics to assess how self-driving car deliveries can be improved. The project objective is to ensure self-driving vehicles can accomplish something that’s been very difficult to accomplish: carrying out the last step of the delivery, from the car to the recipient’s front door.
The two companies hope the answer is a two-legged robot called “Digit”.
Digit has been designed to approximate the look and walk of a human. The robot is constructed from lightweight material and it is capable of lifting packages that weigh up to 40 pounds. In tests, Digit has been shown to be capable of going up and down stairs and to negotiate uneven terrain, thanks to the use of LiDAR and stereo cameras.
The courier delivery services company FedEx is developing an autonomous delivery robot designed to assist retailers make same-day and last-mile deliveries to their customers. The device is called the FedEx SameDay Bot, and the aim is to deliver packages by bot directly to customers’ homes or businesses the same day. The device has been developed in collaboration with DEKA Development & Research Corp., run by Dean Kamen, the inventor of the Segway.
The FedEx device is the most adventurous of the three, in that it will cross roads and is destined to cover longer distances. The interaction with roads is supported by machine-learning algorithms to help the robot to detect and avoid obstacles, plot a safe path, and to follow road and safety rules.
Amazon’s autonomous delivery robots are about to begin rolling out on California sidewalks. Amazon Scout will begin with delivering packages to the company’s Prime customers residing in Southern California. The new Amazon device will work during daylight hours, providing small and medium-sized packages to customers. The Amazon Scout is a six-wheeled electric-powered vehicle around the size of a small cooler. In terms of movement, the Scout rolls along sidewalks at what’s described as a walking pace.
Amazon began testing out the Scout in January 2019, running a pilot program using six machines to deliver packages in Snohomish County, Washington. Vice president of Amazon Scout Sean Scott said: “We developed Amazon Scout at our research and development lab in Seattle, ensuring the devices can safely and efficiently navigate around pets, pedestrians and anything else in their path.”
Following the success of the pilot — where the Scout autonomously navigated the various obstacles commonly found in residential neighborhoods like trashcans, skateboards, lawn chairs, the occasional snow blower and more — the device is ready for a wider launch.
The wider launch will feature a small number of Amazon Scout devices, delivering Monday through Friday, during daylight hours in the Irvine area of California, according to Smart2Zero. Customers will order items as they would normally, but in some cases their Amazon packages will be delivered by an Amazon Scout. To make sure things go smoothly, each Scout will initially be accompanied by a human “Amazon Scout Ambassador.”
Amazon adds fear detection and age ranges to its facial-recognition tech as the Border Patrol looks to award a $950 million contract
- Amazon Web Services has added several new features to its facial-recognition technology, Rekognition.
- This includes expanded age-recognition capabilities and the new ability to recognize fear.
- Rekognition is a controversial technology and has been the subject of much criticism and protests — from both inside and outside Amazon.
- These new features drew some flack from commenters on Twitter.
- Meanwhile, the US Customers and Border Patrol is looking for quotes on a sweeping new border protection system that includes more facial-recognition tech.
Amazon Web Services has expanded the capabilities of its controversial facial-recognition technology called Rekognition.
It now better detects more age ranges and it can also detect fear, the company announced in a blog post on Monday.
The company explained (emphasis ours):
“Today, we are launching accuracy and functionality improvements to our face analysis features. Face analysis generates metadata about detected faces in the form of gender, age range, emotions, attributes such as ‘Smile’, face pose, face image quality and face landmarks. With this release, we have further improved the accuracy of gender identification. In addition, we have improved accuracy for emotion detection (for all 7 emotions: ‘Happy’, ‘Sad’, ‘Angry’, ‘Surprised’, ‘Disgusted’, ‘Calm’ and ‘Confused’) and added a new emotion: ‘Fear’.Lastly, we have improved age range estimation accuracy; you also get narrower age ranges across most age groups.”
Earlier this month AWS also announced that Rekognition can now detect violent content such as blood, wounds, weapons, self-injury, corpses, as well as sexually explicit content.
But it was the news of more age ranges and fear detection that was met with comments on Twitter.
Just last month several protesters interrupted Amazon AWS CTO Werner Vogels during a keynote speech at an AWS conference in New York.
They were protesting AWS’s work with the U.S. Immigration and Customs Enforcement (ICE) and the family separation policy at the Southern Border. Amazon hasn’t acknowledged whether ICE uses its Rekognition technology, but the company did meet with ICE officials to pitch its facial-recognition tech, among other AWS services, as revealed by emails between Amazon and various government officials obtained by the American Civil Liberties Union Foundations.
Amazon’s Rekognition has come under fire from a wide range of groups who want the company to stop selling it to law enforcement agencies. In April, AI experts penned an open letter to Amazon about it. Civil rights group have protested it. 100 Amazon employees sent a letter to management last year asking the company to stop selling Rekognition to law enforcement. Another 500 signed a letter this year asking Amazon to stop working with ICE altogether.
“AWS comes under fire for Rekognition sales to the federal government, who in turn is building concentration camps for children, and AWS’s response is to improve ‘age range estimation’ and ‘fear detection’ in the service? Are you f– KIDDING ME?!” tweeted Corey Quinn from the Duckbill Group, a consultant that helps companies manage their AWS bill. Quinn also hosts theScreaming in the Cloud podcast.
Another developer tweeted, “In 25 years we’re going to be talking about how AWS handled this situation in the same way we talk about how IBM enabled the holocaust. Every engineer and ML researcher who worked on this should be ashamed of themselves.”
The CBP is looking to buy more facial-recognition tech
Meanwhile, the U.S. Customs and Border Protection (CBP), a sister agency to ICE, has put out a new request for quotes on a sweeping new border-security system that includes expanded use of facial-recognition technology.
“Integration of facial recognition technologies is intended throughout all passenger applications,” the RFQ documents say.
The CBP already uses facial recognition at various airports, such as in Mexico City, where it matches passenger’s faces with photos taken from their passports or other government documents, it says.
And the CBP uses other biometric information, such as taking fingerprints of people at the border if it suspects that they are entering the country illegally, it says.
“CBP’s future vision for biometric exit is to build the technology nationwide using cloud computing,” the agency wrote in a 2017 article about the use of facial recognition and finger-print tech.
This new contract for new border security technologies is expected to begin in early 2020 and could be worth $950 million over its lifespan, according to the RFQ documents.
This article was originally published on Business Insider. Copyright 2019.
Manufacturing2 months ago
IoT + AI = Operations Intelligence: A new equation for a new world of data
Manufacturing1 month ago
IoT + Digital Twin = Operations Intelligence: An Equation that Delivers Useful What-If Scenarios
Manufacturing1 month ago
What you need to know if you’re attending AVEVA World Summit
Manufacturing3 weeks ago
IoT + big data analytics = operations intelligence: An equation that draws a better picture
Financial Services2 months ago
Measures to put the digital transformation of banks back on track