What is neuromorphic computing?

As the name suggests, neuromorphic computing uses a model that's inspired by the workings of the brain.

The brain makes a really appealing model for computing: unlike most supercomputers, which fill rooms, the brain is compact, fitting neatly in something the size of, well... your head. 

Brains also need far less energy than most supercomputers: your brain uses about 20 watts, whereas the Fugaku supercomputer needs 28 megawatts -- or to put it another way, a brain needs about 0.00007% of Fugaku's power supply. While supercomputers need elaborate cooling systems, the brain sits in a bony housing that keeps it neatly at 37°C. 

True, supercomputers make specific calculations at great speed, but the brain wins on adaptability. It can write poetry, pick a familiar face out of a crowd in a flash, drive a car, learn a new language, take good decisions and bad, and so much more. And with traditional models of computing struggling, harnessing techniques used by our brains could be the key to vastly more powerful computers in the future.

Why do we need neuromorphic systems?

Most hardware today is based on the von Neumann architecture, which separates out memory and computing. Because von Neumann chips have to shuttle information back and forth between the memory and CPU, they waste time (computations are held back by the speed of the bus between the compute and memory) and energy -- a problem known as the von Neumann bottleneck.

By cramming more transistors onto these von Neumann processors, chipmakers have for a long time been able to keep adding to the amount of computing power on a chip, following Moore's Law. But problems with shrinking transistors any further, their energy requirements, and the heat they throw out mean without a change in chip fundamentals, that won't go on for much longer.

As time goes on, von Neumann architectures will make it harder and harder to deliver the increases in compute power that we need.

To keep up, a new type of non-von Neumann architecture will be needed: a neuromorphic architecture. Quantum computing and neuromorphic systems have both been claimed as the solution, and it's neuromorphic computing, brain-inspired computing, that's likely to be commercialised sooner. 

As well as potentially overcoming the von Neumann bottleneck, a neuromorphic computer could channel the brain's workings to address other problems. While von Neumann systems are largely serial, brains use massively parallel computing. Brains are also more fault-tolerant than computers -- both advantages researchers are hoping to model within neuromorphic systems.

First, to understand neuromorphic technology it make sense to take a quick look at how the brain works. 

Messages are carried to and from the brain via neurons, a type of nerve cell. If you step on a pin, pain receptors in the skin of your foot pick up the damage, and trigger something known as an action potential -- basically, a signal to activate -- in the neurone that's connected to the foot. The action potential causes the neuron to release chemicals across a gap called a synapse, which happens across many neurons until the message reaches the brain. Your brain then registers the pain, at which point messages are sent from neuron to neuron until the signal reaches your leg muscles -- and you move your foot.

An action potential can be triggered by either lots of inputs at once (spatial), or input that builds up over time (temporal). These techniques, plus the huge interconnectivity of synapses -- one synapse might be connected to 10,000 others -- means the brain can transfer information quickly and efficiently.

Neuromorphic computing models the way the brain works through spiking neural networks. Conventional computing is based on transistors that are either on or off, one or zero. Spiking neural networks can convey information in both the same temporal and spatial way as the brain can and so produce more than one of two outputs. Neuromorphic systems can be either digital or analogue, with the part of synapses played by either software or memristors.

Memristors could also come in handy in modelling another useful element of the brain: synapses' ability to store information as well as transmitting it. Memristors can store a range of values, rather than just the traditional one and zero, allowing it to mimic the way the strength of a connection between two synapses can vary. Changing those weights in artificial synapses in neuromorphic computing is one way to allow the brain-based systems to learn.

Along with memristive technologies, including phase change memory, resistive RAM, spin-transfer torque magnetic RAM, and conductive bridge RAM, researchers are also looking for other new ways to model the brain's synapse, such as using quantum dots and graphene.

What uses could neuromorphic systems be put to?

For compute heavy tasks, edge devices like smartphones currently have to hand off processing to a cloud-based system, which processes the query and feeds the answer back to the device. With neuromorphic systems, that query wouldn't have to be shunted back and forth, it could be conducted within the device itself. 

But perhaps the biggest driving force for investments in neuromorphic computing is the promise it holds for AI.

Current generation AI tends to be heavily rules-based, trained on datasets until it learns to generate a particular outcome. But that's not how the human brain works: our grey matter is much more comfortable with ambiguity and flexibility.

It's hoped that the next generation of artificial intelligence could deal with a few more brain-like problems, including constraint satisfaction, where a system has to find the optimum solution to a problem with a lot of restrictions. 

Neuromorphic systems are also likely to help develop better AIs as they're more comfortable with other types of problems like probabilistic computing, where systems have to cope with noisy and uncertain data. There are also others, such as causality and non-linear thinking, which are relatively immature in neuromorphic computing systems, but once they're more established, they could vastly expand the uses AIs could be put to.

Are there neuromorphic computer systems available today?

Yep, academics, startups and some of tech's big names are already making and using neuromorphic systems.

Intel has a neuromorphic chip, called Loihi, and has used 64 of them to make an 8 million synapse system called Pohoiki Beach, comprising 8 million neurones (it's expecting that to reach 100 million neurones in the near future). At the moment, Loihi chips are being used by researchers, including at the Telluride Neuromorphic Cognition Engineering Workshop, where they're being used in the creation of artificial skin and in the development of powered prosthetic limbs.

IBM also has its own neuromorphic system, TrueNorth, launched in 2014 and last seen with 64 million neurones and 16 billion synapses. While IBM has been comparatively quiet on how TrueNorth is developing, it did recently announce a partnership with the US Air Force Research Laboratory to create a 'neuromorphic supercomputer' known as Blue Raven. While the lab is still exploring uses for the technology, one option could be creating smarter, lighter, less energy-demanding drones.

Neuromorphic computing started off in a research lab (Carver Mead's at Cal-tech) and some of the best known are still in academic institutions. The EU-funded Human Brain Project (HBP), a 10-year project that's been running since 2013, was set up to advance understanding of the brain through six areas of research, including neuromorphic computing.

The HBP has led to two major neuromorphic initiatives, SpiNNaker and BrainScaleS. In 2018, a million-core SpiNNaker system went live, the largest neuromorphic supercomputer at the time, and the university hopes to eventually scale it up to model one million neurones. BrainScaleS has similar aims as SpiNNaker, and its architecture is now on its second generation, BrainScaleS-2.

What are the challenges to using neuromorphic systems?

Shifting from von Neumann to neuromorphic computing isn't going to come without substantial challenges.

Computing norms -- how data is encoded and processed, for example -- have all grown up around the von Neumann model, and so will need to be reworked for a world where neuromorphic computing is more common. One example is dealing with visual input: conventional systems understand them as a series of individual frames, while a neuromorphic processor would encode such information as changes in a visual field over time. 

Programming languages will also need to be rewritten from the ground up, too. There are challenges on the hardware side: new generations of memory, storage and sensor tech will need to be created to take full advantage of neuromorphic devices.  

Neuromorphic technology could even need a fundamental change in how the hardware and software is developed, because of the integration between different elements in neuromorphic hardware, such as the integration between memory and processing.

Do we know enough about the brain to start making brain-like computers?

One side effect of the increasing momentum behind neuromorphic computing is likely to be improvements in neuroscience: as researchers start to try to recreate our grey matter in electronics, they may learn more about the brain's inner workings that help biologists learn more about the brain.

And similarly, the more we learn about the human brain, the more avenues are likely to open up for neuromorphic computing researchers. For example, glial cells -- the brain's support cells -- don't figure highly in most neuromorphic designs, but as more information comes to light about how these cells are involved in information processing, computer scientists are starting to examine whether they should figure in neuromorphic designs too.

And of course, one of the more interesting questions about the increasingly sophisticated work to model the human brain in silicon is whether researchers may eventually end up recreating -- or creating -- consciousness in machines.

https://www.zdnet.com/

Intel Machine Programming Tool Detects Bugs in Code

Intel unveiled ControlFlag – a machine programming research system that can autonomously detect errors in code. Even in its infancy, this novel, self-supervised system shows promise as a powerful productivity tool to assist software developers with the labor-intensive task of debugging. In preliminary tests, ControlFlag trained and learned novel defects on over 1 billion unlabeled lines of production-quality code.

In a world increasingly run by software, developers continue to spend a disproportionate amount of time fixing bugs rather than coding. It’s estimated that of the $1.25 trillion that software development costs the IT industry every year, 50 percent is spent debugging code1.

Debugging is expected to take an even bigger toll on developers and the industry at large. As we progress into an era of heterogenous architectures — one defined by a mix of purpose-built processors to manage the massive sea of data available today — the software required to manage these systems becomes increasingly complex, creating a higher likelihood for bugs. In addition, it is becoming difficult to find software programmers who have the expertise to correctly, efficiently and securely program across diverse hardware, which introduces another opportunity for new and harder-to-spot errors in code.

When fully realized, ControlFlag could help alleviate this challenge by automating the tedious parts of software development, such as testing, monitoring and debugging. This would not only enable developers to do their jobs more efficiently and free up more time for creativity, but it would also address one of the biggest price tags in software development today.

How It Works: ControlFlag’s bug detection capabilities are enabled by machine programming, a fusion of machine learning, formal methods, programming languages, compilers and computer systems.

ControlFlag specifically operates through a capability known as anomaly detection. As humans existing in the natural world, there are certain patterns we learn to consider “normal” through observation. Similarly, ControlFlag learns from verified examples to detect normal coding patterns, identifying anomalies in code that are likely to cause a bug. Moreover, ControlFlag can detect these anomalies regardless of programming language.

A key benefit of ControlFlag’s unsupervised approach to pattern recognition is that it can intrinsically learn to adapt to a developer’s style. With limited inputs for the control tools that the program should be evaluating, ControlFlag can identify stylistic variations in programming language, similar to the way that readers recognize the differences between full words or using contractions in English.

The tool learns to identify and tag these stylistic choices and can customize error identification and solution recommendations based on its insights, which minimizes ControlFlag’s characterizations of code in error that may simply be a stylistic deviation between two developer teams.

Intel has even started evaluating using ControlFlag internally to identify bugs in its own software and firmware product development. It is a key element of Intel’s Rapid Analysis for Developers project, which aims to accelerate velocity by providing expert assistance.

https://newsroom.intel.com/

With COVID-19 hanging on, migration to the cloud accelerates

With the COVID-19 pandemic showing no signs of abating, migration to the cloud is expected to accelerate as enterprises choose to let someone else worry about their server gear.

In its global IT outlook for 2021 and beyond, IDC predicts the continued migration of enterprise IT equipment out of on-premises data centers and into data centers operated by cloud service providers (such as AWS and Microsoft) and colocation specialists (such as Equinix and Digital Realty).

The research firm expects that by the end of 2021, 80% of enterprises will put a mechanism in place to shift to cloud-centric infrastructure and applications twice as fast as before the pandemic. CIOs must accelerate the transition to a cloud-centric IT model to maintain competitive parity and to make the organization more digitally resilient, the firm said.

"The COVID-19 pandemic highlighted that the ability to rapidly adapt and respond to unplanned/foreseen business disruptions will be a clearer determiner of success in our increasingly digitalized economy," said Rick Villars, IDC group vice president for worldwide research, in a statement. "A large percentage of a future enterprise's revenue depends upon the responsiveness, scalability, and resiliency of its infrastructure, applications, and data resources."

In this new normal, the most important thing enterprises can do is seek opportunities to leverage new technologies to take advantage of competitive/industry disruptions and extend capabilities for business acceleration.

Additional IDC predictions include:

Edge becomes a top priority: Reactions to changed workforce and operations practices during the pandemic will be the dominant accelerators for 80% of edge-driven investments and business model changes in most industries through 2023.

The intelligent digital workspace: By 2023, 75% of global 2000 companies will commit to providing technical parity to a workforce that is hybrid by design rather than by circumstance, enabling them to work together separately and in real time.

The pandemic's IT legacy: Through 2023, coping with technical debt accumulated during the pandemic will shadow 70% of CIOs, causing financial stress, inertial drag on IT agility, and "forced march" migrations to the cloud.

Resiliency is central to the next normal: In 2022, enterprises focused on digital resiliency will adapt to disruption and extend services to respond to new conditions 50% faster than ones fixated on restoring existing business/IT resiliency levels.

A shift towards autonomous IT operations: Thanks to AI/ML advances in analytics, an emerging cloud ecosystem will be the underlying platform for all IT and business automation initiatives by 2023.

Opportunistic AI expansion: By 2023, one quarter of global 2000 companies will acquire at least one AI software start-up to ensure ownership of differentiated skills and IP out of competitive necessity.

Relationships are under review: By 2024, 80% of enterprises will overhaul relationships with suppliers, providers, and partners to better execute digital strategies.

Sustainability becomes a factor: By 2025, 90% of global 2000 companies will mandate reusable materials in IT hardware supply chains, carbon neutrality targets for providers' facilities, and lower energy use as prerequisites for doing business.

People still matter: Through 2023, half of enterprises' hybrid workforce and business automation efforts will be delayed or will fail outright due to underinvestment in building IT/Sec/DevOps teams with the right tools/skills. Enterprises will turn to new ways to find the talent they need.

https://www.networkworld.com/

Machine learning in network management has promise, challenges

 As part of the trend toward more automation and intelligence in enterprise networks, artificial intelligence and machine learning are increasingly in-demand because the ability to programmatically identify problems with the network and provide instant diagnosis of complex problems is a powerful one.

Applying AI and ML to network management can enable the consolidation of input from multiple management platforms for central analysis. Rather than IT staff manually combing through reports from diverse devices and applications, machine learning can make quick, automated diagnoses of problems.

Gartner senior director and analyst Josh Chessman laid out the problem for the IT worker that machine learning is designed to solve: “I’ve got all these monitoring tools, and they’re all telling me something’s wrong, but they’re not telling me where it is. The biggest strength with this stuff today is that it can identify ‘you’ve got 26 events from seven different tools, and they’re all about a network problem.’”

It’s difficult to say how rapidly enterprises are buying AI and ML systems, but analysts say adoption is in the early stages.

One sticking point is confusion about what, exactly, AI and ML mean. Those imagining AI as being able to effortlessly identify attempted intruders, and to analyze and optimize traffic flows will be disappointed. The use of the term AI to describe what’s really happening with new network management tools is something of an overstatement, according to Mark Leary, research director at IDC.

“Vendors, when they talk about their AI/ML capabilities, if you get an honest read from them, they’re talking about machine learning, not AI,” he said.

There isn’t a hard-and-fast definitional split between the two terms. Broadly, they both describe the same concept—algorithms that can read data from multiple sources and adjust their outputs accordingly. AI is most accurately applied to more robust expressions of that idea than to a system that can identify the source of a specific problem in an enterprise computing network, according to experts.

“We’re probably overusing the term AI, because some of these things, like predictive maintenance, have been in the field for a while now,” said Jagjeet Gill, a principal in Deloitte’s strategy practice.

Another sticking point for a lot of ML systems is cross-compatibility. Much of what’s on the market currently takes the form of a vendor adding a new feature to one of its existing products. That’s handy for all-Cisco shops, for example, but can be a problem in a multi-vendor environment. “A lot of vendors are adding AIops because it’s kind of a buzzword,” said Chessman. “It doesn’t give you a lot of visibility into products from other vendors.”

There are vendor-agnostic ML systems for network management out there—Moogsoft and BigPanda are two of the bigger names in the field—but it’s more common to find ML features bundled with specific vendors’ products. “So take Netscout. They’ve got some ML, and it does a good job, but it’s focused on Netscout [products],” Chessman said.

Regardless of the hurdles the technology has to overcome, ML is likely to make many IT professionals’ jobs a lot easier, according to Peter Suh, the head of Accenture’s North American network practice. “Having those types of tools and solutions is going to be good,” he said. “It’ll help you walk through what’s going on on the network at any given time.”

While it’s also a potential step in the direction of full network automation, it might also result in the loss of jobs for IT staff, that’s not likely to happen in the immediate future, according to Gartner’s Chessman. What’s more probable is that ML will help free up IT staff to work on more revenue-generating activities, rather than putting out fires, he said. “Full automation is still years and years away.”

https://www.networkworld.com/

IBM to open-source space junk collision avoidance

Space is already a pretty messy place, with tens of thousands of manmade objects, the majority of them unpowered, hurdling around the planet. As space exploration ramps up on the heels of privatization and aided by miniaturization, the debris field is only going to grow.

That's a pretty big problem. So-called man-made anthropogenic space objects (ASOs) travel at speeds up to 8,000 meters per second, meaning a collision involving even a tiny fragment and a satellite or crewed vehicle could be devastating.

All of this makes it extremely important for space agencies and private space companies to be able to anticipate the trajectories of manmade objects long before launch and to plan accordingly. Unfortunately, that's not very easy to do, and as the quantity of space junk increases, it's only going to get more difficult.

Enter the Space Situational Awareness (SSA) project, an open-source venture between IBM and Dr. Moriba Jah at the University of Texas at Austin to determine where ASOs are (orbit determination) and where they will be in the future (orbit prediction).

Some explanation is required here. Current methods for orbit prediction rely on physics-based models that in turn require extremely precise information about ASOs. The problem is that the location data available about ASOs comes from terrestrial-based sensors and tends to be imperfect. Factors like space weather further complicated the picture.

The idea behind SSA is that machine learning can create models that learn when physical models incorrectly predict an ASO's future location. Physics models, according to this strategy, are plenty good when it comes to orbital dynamics, but to maximize effectiveness they need to learn how and when they get it wrong and to account for that variability.

The data used for the project comes from United States Strategic Command (USSTRATCOM) via the space-track.org website. The team used the IBM Cloud Bare Metal Server with 16 Intel Xeon Processors, 120 GB RAM, and two Nvidia Tesla V100 GPUs (each with 16GB of RAM) to run the physical models to predict the orbits of all ASOs in low earth orbit and train ML models to learn the physics model error. As a result, the team was able to predict the future orbits of the ASOs. 

https://www.zdnet.com/

Microsoft's Underwater Data Center Makes Environmental Strides

At the beginning of this summer, with no fanfare and little publicity, Redmond, Wash.-based Microsoft hauled its shipping container-sized underwater data center, consisting of 864 servers at a depth of 117 feet, from the seabed off the coast of the Orkney Islands to the northeast of Scotland. 

Microsoft’s Project Natick

The experiment, called Project Natick, aimed to find out whether it would be economical and better for the environment to place data centers under water. The first conclusions from the project are starting to trickle in and they appear to be positive.

The retrieval of the center represented the final phase of a years-long effort, which was itself Phase 2 of a wider project that started in 2015 off the west coast of America where the company sank a data center to the seabed for 105 days to find out if computing was possible underwater given the extreme environment.

The team hypothesized that a sealed container on the ocean floor could provide ways to improve the overall reliability of data centers. On land, corrosion from oxygen and humidity, temperature fluctuations and bumps and jostles from people who replace broken components are all variables that can contribute to equipment failure.

The Northern Isles experiment, according to the company, has confirmed its hypothesis, which could have major implications for data centers on land.

Lessons learned from Project Natick are also informing Microsoft’s data center sustainability strategy around energy, waste and water, said Ben Cutler, a project manager in Microsoft’s Special Projects research group who leads Project Natick.

What is more, he added, the proven reliability of underwater data centers has prompted discussions with a Microsoft team in Azure that’s looking to serve customers who need to deploy and operate tactical and critical data centers anywhere in the world. “We are populating the globe with edge devices, large and small,” said William Chappell, vice president of mission systems for Azure said in a statement about the project. “To learn how to make data centers reliable enough not to need human touch is a dream of ours.”

The Underwater Advantage

Without really understanding the science behind data centers, it is easy to see the attraction. Apart from the fact that more than half the world’s population lives within 120 miles of the coast, the temperature of the water, which should keep the centers cool, makes for energy-efficient data centers that can use heat exchange plumbing in much the same way submarines do.

There is also the advantage of geography. As the location of data becomes increasingly important for regulators, locating data centers inside geographical boundaries’ around coasts will be easier than on land and will solve that problem. By placing them in the waters off big cities it will make information retrieval and use of the web and video streaming quicker. “We are now at the point of trying to harness what we have done as opposed to feeling the need to go and prove out some more,” Cutler said in a statement. “We have done what we need to do. Natick is a key building block for the company to use if it is appropriate.”*

Early conversations are already taking place about the potential future of Project Natick centered on how to scale up underwater data centers to power the full suite of Microsoft Azure cloud services, which may require linking together a dozen or more vessels the size of the Northern Isles. But it is a major step forward and could see more centers under the sea.

According to an Energy Innovation paper earlier this year on average, servers and cooling systems account for the greatest shares of direct electricity use in data centers at 43% of consumed power each, followed by storage drives and network devices.

Energy Innovation is a San Francisco-based nonpartisan energy and environmental policy firm. It delivers research and original analysis to policymakers to help them make informed choices on energy policy.

The substantial electricity use of data centers also gives rise to concerns over their carbon dioxide emissions. However, it is not yet possible to accurately estimate total CO2 emissions, due to a lack of data on the locations of the vast majority of global data centers and the emissions intensities, according to EI, but it is likely to be substantial. However, this is not the first time companies have tried to make data centers more energy efficient.

Increasing Demand for Energy

Randy Cozzens is EVP and head of energy, utilities, and chemicals at New York City-based Capgemini North America. He points out that resolving this is a key issue for enterprises given that they play an integral role in the evolving digital society, with an increasing consumer demand and appetite for an always on, no latency internet experience.

The increase in demand over the past 20 years naturally led to an increase in data centers and the added energy they use to operate. However, there have been active initiatives implemented to make data centers more energy efficient, he said. These include energy monitoring software, power-saving modes, server cooling systems, and virtualization. In addition, the rapid shift for many organizations to move their data to the cloud will potentially reduce the world's overall energy emission from data centers. “Cloud data centers are typically built to use less energy than standard data centers and are incentivized to reduce energy outputs by operating on solar or wind power,” he said.

“As IT continues to prioritize sustainable initiatives within its data centers, and as pivots to the cloud increase across industry sectors, the amount of energy being used by the world's data centers has the potential to decrease and become less of a threat to the environment.”

Competitive Edge

The problem is only going to get worse too, Akram TariqKhan of India-based ecommerce site YourLibaas, told us. With the demand for cloud computing increasing at an exponential pace, they are going to be disastrous for the environment. “As a heavy user of cloud servers, I can share how we end up buying excessive capacity only because the fierce competition has ended up the industry offering dirt-cheap prices,” he said.

“This leads to a spiral effect with unused capacity leading to an increased negative impact on the environment. Amazon's spot instances offer unused capacity at cheaper prices for temporary projects attempting to resolve this issue.”

Data Center Advantage

Tina Nikolovska is founder of TeamStage points to four different ways that data centers can help environment, ways that are mentioned every time this subject comes up. Globally, they can be summarized as reducing carbon footprint as one of the critical reasons in favor of digital workplaces.

1. No Commuting Carbon Footprint

There are enormous amounts of money and gas spent on commuting — whether using public transportation or self-owned cars. With less commuting, less pollution gets released into the atmosphere. If we are speaking about the trend of digitalization of workspaces, we can significantly reduce vehicle-produced gas emissions.

2. Digitalized Data Means No Paper

The only logical trend to follow remote work is the no-paper policy. Keeping data in print is unnecessary and obsolete, with the expanding cloud storage market and improved cybersecurity. Again, the global effect of paper reduction can be huge for ecology.

3. Less General Waste

General waste, including single-use items that offices abundantly waste, like coffee cups, water cups, paper towels, straws, etc., can be dramatically reduced by adopting the digital workspace. But it is more than general waste; it is the amounts of working clothes, shoes, make-up, and grooming products that are generously spent, with markets grossing billions of dollars on (often non-ethical) production. This is not to say that everyone will lock behind their door and reappear in a few years, looking like cavemen (and women). 

4. Working From Home

Working from home makes another type of consumer, where less is spent on small, single-use items and pleasures, and more on long-term comfort items. Think about the millions of dollars spent on coffees and lunches, and how most people are now cooking their food and preparing home-made coffee. It seems that the trend is to invest in a better coffee machine (that lasts for years) and drink the coffee from a regular, washable cup, instead of a plastic one. Multiply that with millions of workers around the globe, and the result is significantly reduced waste.

Digital Workplace Data Centers

So why does any of this matter? Boston-based Brightcove’s CEO, Jeff Ray, believes that this could be a tipping point issue for technology. “We are living through a pivotal moment in history where the world is undergoing a rapid digital transformation. 2020 has become video’s evolutionary moment and streaming over OTT devices is one area where we will continue to see growth," he said. "Consumers and businesses — many of whom are facing economic challenges due to the pandemic — are seeing the value of cutting the cable cord and subscribing to streaming services instead." That means data, and lots of it.

If video is increasingly important for companies as it is the main way to connect with their audiences right now, not having the capability to store the data it contains will hinder the development of digital workplaces. Not having high touch moments with clients means companies could be more creative and interact in new ways to build the future.

Even with the return of live sports and greater easing of restrictions in Q3, there is no going back to our previous consumption and working habits. This digital transformation will result in an evolution of how we do business and video will become an inherent part of workflows now and in the future.

"Forward thinking companies know the future of work involves a hybrid model," continued Ray. "Physical events will have to have a digital experience to reach attendees that are unable to attend in person. Hybrid events increase ROI and expand reach. It is no longer an either or, it is both. Companies have seen the vast opportunities ahead with virtual connection, there is no going back. If companies are not embracing video now, in the future they will have bigger problems than today. Don’t get left behind.”

https://www.cmswire.com/

Edge computing: The next generation of innovation

Like other hot new areas of enterprise tech, edge computing is a broad architectural concept rather than a specific set of solutions. Primarily, edge computing is applied to low-latency situations where compute power must be close to the action, whether that activity is industrial IoT robots flinging widgets or sensors continuously taking the temperature of vaccines in production. The research firm Frost & Sullivan predicts that by 2022, 90 percent of industrial enterprises will employ edge computing.

Edge computing is a form of distributed computing that extends beyond the data center mothership. When you think about it, how else should enterprises invest in the future? Yes, we know that a big chunk of that investment will go to the big public cloud providers – but hardware and software that enterprises own and operate isn’t going away. So why not physically distribute it where the business needs it most?

Augmenting the operational systems of a company’s business on location – where manufacturing or healthcare or logistical operations reside – using the awesome power of modern servers can deliver all kinds of business value. Typically, edge computing nodes collect gobs of data from instrumented operational systems, process it, and send only the results to the mothership, vastly reducing data transmission costs. Embedded in those results are opportunities for process improvement, supply chain optimization, predictive analytics, and more.

CIO, Computerworld, CSO, InfoWorld, and Network World have joined forces to examine edge computing from five different perspectives. These articles help demonstrate that this emerging, complex area is attracting some of the most intriguing new thinking and technology development today.

The many sides of the edge

Edge computing may be relatively new on the scene, but it’s already having a transformational impact. In “4 essential edge-computing use cases,” Network World’s Ann Bednarz unpacks four examples that highlight the immediate, practical benefits of edge computing, beginning with an activity about as old-school as it gets: freight train inspection. Automation via digital cameras and onsite image processing not only vastly reduces inspection time and cost, but also helps improve safety by enabling problems to be identified faster. Bednarz goes on to pinpoint edge computing benefits in the hotel, retail, and mining industries.

CIO contributing editor Stacy Collett trains her sights on the gulf between IT and those in OT (operational technology) who concern themselves with core, industry-specific systems – and how best to bridge that gap. Her article “Edge computing’s epic turf war” illustrates that improving communication between IT and OT, and in some cases forming hybrid IT/OT groups, can eliminate redundancies and spark creative new initiatives.

One frequent objection on the OT side of the house is that IoT and edge computing expose industrial systems to unprecedented risk of malicious attack. CSO contributing writer Bob Violino addresses that problem in “Securing the edge: 5 best practices.” One key recommendation is to implement zero trust security, which mandates persistent authentication and micro-segmentation, so a successful attack in one part of an organization can be isolated rather than spreading to critical systems.

Computerworld contributing writer Keith Shaw examines the role of 5G in “Edge computing and 5G give business apps a boost.” One of 5G’s big selling points is its low latency, a useful attribute for connecting IoT devices. But as IDC research director Dave McCarthy explains in the article, the reduction in latency won’t help you when you’re connecting to a far-flung data center. On the other hand, if you deploy “edge computing into the 5G network, it minimizes this physical distance, greatly improving response times,” he says.

In case you’re wondering, the hyperscale cloud providers aren’t taking this edge stuff lying down. In “Amazon, Google, and Microsoft take their clouds to the edge,” InfoWorld contributing editor Isaac Sacolick digs into the early-stage edge computing offerings now available from the big three, including mini-clouds deployed in various localities as well as their exiting on-prem offerings (such as AWS Outposts or Azure Stack) that are fully managed by the provider. Sacolick writes that “the unique benefit public cloud edge computing offers is the ability to extend underlying cloud architecture and services.”

The crazy variety of edge computing offerings and use cases covers such a wide range, it begins to sound like, well, computing. As many have noted, the “big cloud” model is reminiscent of the old mainframe days, when customers tapped into centralized compute and storage through terminals rather than browsers. Edge computing recognizes that not everything can or should be centralized. And the inventive variations on that simple notion are playing a key role in shaping the next generation of computing.

https://www.networkworld.com/