New AI tool tracks evolution of COVID-19 conspiracy theories on social media

 A new machine-learning program accurately identifies COVID-19-related conspiracy theories on social media and models how they evolved over time—a tool that could someday help public health officials combat misinformation online.

“A lot of machine-learning studies related to misinformation on social media focus on identifying different kinds of conspiracy theories,” said Courtney Shelley, a postdoctoral researcher in the Information Systems and Modeling Group at Los Alamos National Laboratory and co-author of the study that was published last week in the Journal of Medical Internet Research.

“Instead, we wanted to create a more cohesive understanding of how misinformation changes as it spreads. Because people tend to believe the first message they encounter, public health officials could someday monitor which conspiracy theories are gaining traction on social media and craft factual public information campaigns to preempt widespread acceptance of falsehoods.”

The study, titled “Thought I’d Share First,” used publicly available, anonymized Twitter data to characterize four COVID-19 conspiracy theory themes and provide context for each through the first five months of the pandemic.

The four themes the study examined were that 5G cell towers spread the virus; that the Bill and Melinda Gates Foundation engineered or has otherwise malicious intent related to COVID-19; that the virus was bioengineered or was developed in a laboratory; and that the COVID-19 vaccines, which were then all still in development, would be dangerous.

“We began with a dataset of approximately 1.8 million tweets that contained COVID-19 keywords or were from health-related Twitter accounts,” said Dax Gerts, a computer scientist also in Los Alamos’ Information Systems and Modeling Group and the study’s co-author. “From this body of data, we identified subsets that matched the four conspiracy theories using pattern filtering, and hand labeled several hundred tweets in each conspiracy theory category to construct training sets.”

Using the data collected for each of the four theories, the team built random forest machine-learning, or artificial intelligence (AI), models that categorized tweets as COVID-19 misinformation or not.

“This allowed us to observe the way individuals talk about these conspiracy theories on social media, and observe changes over time,” said Gerts.

The study showed that misinformation tweets contain more negative sentiment when compared to factual tweets and that conspiracy theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events.

For example, Bill Gates participated in a Reddit “Ask Me Anything” in March 2020, which highlighted Gates-funded research to develop injectable invisible ink that could be used to record vaccinations. Immediately after, there was an increase in the prominence of words associated with vaccine-averse conspiracy theories suggesting the COVID-19 vaccine would secretly microchip individuals for population control.

Furthermore, the study found that a supervised learning technique could be used to automatically identify conspiracy theories, and that an unsupervised learning approach (dynamic topic modeling) could be used to explore changes in word importance among topics within each theory.

“It’s important for public health officials to know how conspiracy theories are evolving and gaining traction over time,” said Shelley. “If not, they run the risk of inadvertently publicizing conspiracy theories that might otherwise ‘die on the vine.’ So, knowing how conspiracy theories are changing and perhaps incorporating other theories or real-world events is important when strategizing how to counter them with factual public information campaigns.”

https://www.lanl.gov/discover/news-release-archive/2021/April/0419-ai-tool-tracks-conspiracy-theories.php

How the data-center workforce is evolving

The COVID-19 pandemic has profoundly affected many areas of IT, including the data center, where changes to the infrastructure--particularly adoption of cloud services--are bringing about the need for new skill sets among workers who staff them.

Perhaps no technology industry benefitted more from the pandemic than cloud computing; the location independence of cloud services makes them ideal for a world where the majority of line-of-business as well as IT workers are no longer in the office.

But does that mean businesses will rely on infrastructure as a service (IaaS) and no longer need their own on-premises data centers and data-center IT teams? Analysts and futurists have been asking this question for about a decade, but now cloud, already strong before the pandemic, has gone through an inflection point and brought new immediacy to the issue.

The answer is that data centers are not going anywhere anytime soon, but they will look fundamentally different. That's good news for people currently working in data centers and those considering careers there, because adoption of cloud and other changes will create a wave of new opportunities.

Uptime Institute predicts that data-center staff requirements will grow globally from about 2 million full-time employees in 2019 to nearly 2.3 million by 2025. Growth in expected demand will mainly come from cloud and colocation data centers. Enterprise data centers will continue to employ a large number of staff, but cloud data-center staff will outnumber enterprise data-center staff after 2025, Uptime says.

On the hiring side, finding the right talent remains difficult for many organizations. In 2020, 50% of data center owners or operators globally reported having difficulty finding qualified candidates for open jobs, compared to 38% in 2018, according to Uptime Institute.

For IT pros looking to be part of the new data center, here are some of the top roles and in-demand skills to develop.

Technical architect

The role of the technical architect has grown in importance because applications are no longer deployed in technology silos. In the past, each application had its own servers, storage, and security. Modern data centers are built on disaggregated infrastructure where resources are shared across multiple applications.

This requires new infrastructure design skills to ensure application performance remains high as the underlying technology is being shared across a broad set of applications. And it requires high-level domain knowledge of network, storage, servers, virtualization, and other infrastructure.

Data-center architect

The challenging job of data-center architect requires specific knowledge of the physical data center--an understanding of power, cooling, real estate, cost structure, and other factors essential to designing data centers. Architects help determine the layout of the facility as well as its physical security. The internal design involving racks, flooring and wiring is also part of this role. If done poorly, the job can have an enormous negative impact on the workflows of the technical staff.

Cloud management

There is no single cloud provider, and an emerging and continually evolving enterprise role is selecting and managing cloud services--private, public and hybrid. The attributes of cloud providers vary, with some being strong in specific regions while others may be better suited than competitors to provide specific services, for example. In some cases, third-party cloud services are inappropriate, making private cloud the best answer, as is often the case when strict data privacy is called for.

Cloud services need to be constantly monitored and optimized to ensure businesses are not overspending in some areas and underspending in others. At the same time, cost optimization cannot be allowed to result in performance issues. This role requires the skills to properly evaluate cloud offerings and provide ongoing management.

AI and ML

Data volumes are now massive and getting larger by the day, and with the rise of edge computing, more data will reside in more places. Artificial intelligence and machine learning are required to facilitate effective data management. There's a wide range of jobs in this area across the spectrum of the AI lifecycle, including training AI systems, modeling, programming and providing human-in-the-loop participation to ensure AI goals are being met.

Data analytics

The future data center will be driven by analyzing massive amounts of data. Expect this trend to continue as more data is being generated by IoT endpoints, video systems, robots--almost everything we do. Data-center operations teams will make critical decisions based on the analysis of this data. Businesses today have a shortage of people with analytic skills, particularly those who understand how to use AI/ML to accelerate the analysis.

Software skills

Many IT engineers, particularly those who work with network infrastructure, are hardware centric. Sure, they may know how to hunt and peck on a command-line interface, but that's not really a software skill. Most network engineers have never executed even basic software functions like making an API call. Using APIs can make many tasks much easier than trying to write a script to parse a CLI.

Not all network engineers need to become programmers--although those who want to should focus on languages such as Python and Ruby--but all should become software power-users and understand how to use APIs and SDKs to perform administrative tasks. All modern network infrastructure has been designed to be managed through APIs, many of them cloud based. The days of being a CLI jockey are over, and an unwillingness to admit it is the biggest threat to today's data-center engineers.

Data-center security

There are multiple avenues for jobs in data-center security, given that this discipline refers to both physical and cyber security. Data centers house sensitive and proprietary data, and breaches can have disastrous consequences for an organization. Physical security was once done with badge readers and keypads, but there has been a wealth of innovation, including AI-enabled cameras, fingerprint scanners, iris readers and facial-recognition systems. This promises to be an exciting area to work in over the next decade.   

Cyber security has also evolved as security-information and event-management tools transition to ML-based systems that enable security professionals to see things they never could before. Also, many advanced organizations are adopting zero-trust models to isolate application traffic from other systems. Through the use of microsegmentation, secure zones can be created, minimizing the "blast radius" of a breach.

Data-center networking

The role of the network in the data center has changed significantly over the past decade. The traditional multi-tier architectures that were optimized for North-South traffic flows have shifted to leaf-spine networks that are designed for higher volumes of East-West traffic. Also, software-defined networking (SDN) systems are being used to provision virtual-fabric overlays of the physical underlay. This brings greater automation, traffic visibility, and cost effectiveness to the data-center network.

Network engineers who work in data centers need to become familiar with new concepts associated with network fabrics such as Linux-based operating systems, open-source network platforms, VxLAN tunnels and Ethernet VPNs. These all increase the scalability, elasticity and resiliency of the network while simplifying network operations. Also, most data-center platforms are now open by design, making vendor interoperability much easier and breaking the lock-in customers experienced in the past.

Another aspect of data center networking that's changed is cloud connectivity. Historically, network engineers were concerned with the network inside the data center, which is a highly controlled environment.

The rise of cloud and edge computing dictates that the network extend outside the physical confines of customer premises, across the wide area to the cloud provider. It's imperative that the network function as if it is a single, continuous fabric across all cloud locations. There are a number of ways to do this, including SD-WAN, SASE and direct cloud connects.

Jobs outside the data center

What, if any, are the jobs for data center professionals if they want to transition out of that environment but make use of their current skills? Unfortunately, those skills don't translate well. You don't see many mainframe engineers or PBX administrators around anymore.

However, while the future lies with the jobs outlined here, it will take a long time for legacy data centers to transition. After all, businesses do often adopt an "if it ain't broke, don't fix it" mentality when it comes to mission-critical systems. So for those unable or unwilling to reskill, it may be necessary to seek employers in verticals that tend to be on the slower end of technology adoption--state and local government, regional banks, and specialty retail are some examples.

The future of data centers lies in distributed clouds, and that changes the skiill sets needed to run them. Data centers are certainly not going away, but they will look much different in the future, and that should be exciting for all.

https://www.networkworld.com/

Top metrics for effective multicloud management

When it comes to effectively managing a multicloud environment, there are a ton of network and application metrics that enterprise customers should be watching.

Among enterprises, the trend is toward multicloud environments, which can include workloads running on-premises and in public clouds run by multiple cloud providers such as AWS, Microsoft Azure, IBM/Red Hat, Google Cloud Platform and others. Gartner predicts by 2021, more than 75% of midsize and large organizations will have adopted some form of a multicloud and/or hybrid IT strategy. Likewise, IDC predicts that by 2022, more than 90% of enterprises worldwide will be relying on a mix of on-premises/dedicated private clouds, multiple public clouds, and legacy platforms to meet their infrastructure needs.

"As enterprises increasingly embrace multicloud, they must consider what KPIs [key performance indicators] will best measure their success in managing multicloud environments," said Briana Frank, director of product management with IBM Cloud.

"There's a variety of KPIs that can help evaluate success, including financial metrics, such as cost, return on investment, and rate of change in the reduction of total cost of ownership," Frank said. "However, enterprises should go beyond financial metrics alone and also consider measures of other critical causes for concern, such as downtime caused by outages and security breaches," Frank added. 

Useful KPIs in a multicloud setting include those that measure costs and billing, network and application performance, said Roy Ritthaler, vice president of product marketing in VMware's cloud management business unit.

Financial KPIs

Optimizing multicloud costs and eliminating waste are key goals for IT and lines of business, and metrics should include budget tracking and detailed spend analysis, Ritthaler said.

"They should also provide capabilities to uncover hidden costs, flag anomalies, reallocate cloud spend for showback and chargeback, and provide proactive recommendations to purchase and exchange reservations and savings plans," Ritthaler said.

Common multicloud KPIs include looking at the cost of all untagged resources, such as databases that might be using resources. Tagged resources can include myriad items such as the owner of a resource, the environment its operating in, and project name, for example. The idea is to most effectively identify, manage and support resources.

Other financial KPIs include a look at the percentage of infrastructure running on demand and the percentage of total bill charged back, Ritthaler said.

Security and network KPIs

Visualizing the security posture of the connectivity in a multicloud network is absolutely necessary, as there are too many components to consider individually, Ritthaler said.

"Identifying bad actors or bad security posture is handled in a single tool for monitoring and guarding," Ritthaler said. "Capabilities include monitoring traffic to detect vulnerabilities and to design app-centric security and generating recommended firewall rules to implement application security."

Some common KPIs include measuring security incidents per month by team, the number of security lapses, and the time to remediate security violations measured in hours, Ritthaler said.

Security is critical, but so it getting a handle on access policies, managing QoS, and providing consistent measuring capabilities across such a diverse group of systems, said Nabil Bukhari, chief technology officer with Extreme Networks. "Applications are the star of the multicloud show, but getting a handle on network performance metrics – like latency, packet loss– is important too," Bukhari said.

Additional network-related KPIs include measuring response time, bandwidth usage and throughput.

Network monitoring must include end-to-end network visibility across physical and virtual environments, and it's built from flow-based traffic monitoring using NetFlow, sFlow and SNMP device monitoring, Ritthaler said.

Application performance KPIs

On the applications side, each application will have its own performance metrics to monitor, and it's important to have tools that can tie these applications to the infrastructure that they're running on, to deliver an end-to-end picture of the infrastructure.

"Many organizations already have multiple application performance monitoring tools. However, these tools do not provide the end-to-end visibility that you need to troubleshoot over different teams," Ritthaler said. "Being able to consolidate those tools into a single solution brings teams together when problems occur."

Ritthaler said some common application KPIs include user experience measurements from APM (application performance management) packages; configured versus used resource consumption (such as CPU and memory); response times; and connection counts.

"Applications tend to spread out over multiple clouds; the path of least resistance will typically be taken. Documentation will fall behind, and this is where automatic discovery is necessary," Ritthaler said. "Application discovery works over the multicloud architecture to make organizations aware of where their applications are running, who they are talking to, what application dependencies there are, and how they are secured."

Compliance KPIs

Compliance is another area that requires management attention, experts say.  

"Compliance KPIs require continuously inspecting cloud-resource configurations and benchmarking them against cloud, industry and custom security and compliance standards. It should track compliance score, violations, and resolution progress," Ritthaler said. 

https://www.networkworld.com/

Customers need to safeguard the health of hybrid and public cloud services using logs and metrics to proactively monitor and troubleshoot issues with native cloud services as they occur and suggest remediation steps, Ritthaler said.

Scientists count elephants from space with satellites and computer smarts

 People like to talk about landmarks that can be "seen from space," from the pyramids to the Great Wall of China. But what about something much smaller, like elephants? They can be spotted, too, with the help of satellites and an algorithm trained to look for them.

A team led by researchers with the University of Oxford and the University of Bath in the UK developed a method for counting African elephants using imagery from Maxar satellites, opening up a new way to monitor vulnerable and endangered animals.

"For the first time, scientists have successfully used satellite cameras coupled with deep learning to count animals in complex geographical landscapes," said the University of Bath in a statement Tuesday.

The satellite images could offer an effective alternative to surveillance done by humans in aircraft, which can be an expensive and challenging way of counting elephants. 

The space method has "comparable accuracy to human detection capabilities," according to a Maxar statement. Satellites can also easily cover a tremendous amount of ground. 

The research team published a paper on the elephant detection work in the journal Remote Sensing in Ecology and Conservation in late December.  

Researchers have used satellites for wildlife monitoring projects before, as when NASA located a secret penguin colony. Satellites have been used to collect data on whales, which are fairly easy to spot against blue water. What makes the elephant project so innovative is that the method can pick out elephants from a diverse landscape of grass and woodlands.   

There are an estimated 40,000 to 50,000 African elephants left in the wild and they are listed as "vulnerable" on the IUCN Red List of Threatened Species. The population is under pressure from habitat loss and poaching.

"Accurate monitoring is essential if we're to save the species," said University of Bath computer scientist Olga Isupov, creator of the algorithm that detects the elephants. "We need to know where the animals are and how many there are." 

The team hopes the system and will be adaptable for smaller animals as satellite resolution continues to improve. It might not be ready to track mice just yet, but elephants are an excellent start.

https://www.cnet.com/

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/