Edge compute and edge networking is gaining rapid traction. The emergence of millions of IoT sensors and devices is producing enormous amounts of critical data, forcing businesses to move heuristical services closer to where the data is being generated. The ability to capture more data is being enabled by technology evolutions like 5G, Wifi6, and expansion of national fiber networks.
Workloads are becoming more distributed to support new generation apps and broader use of Machine Learning and AI to help make real-time decisions. Ultimately, these services connect back to larger scale data centers, but the immediate data capture and connectivity happens very near the end sensor or application. According to IDC, by 2023 more than 50% of new enterprise IT infrastructure deployed will be at the edge. Data can be lost forever at the edge, making solid architecture to enable edge technologies important considerations.
The edge is critical to locally managing immediate data and controls, but the delivery from the edge to the core (and back to the edge) will be just as critical. Network connectivity will need to be scalable at massive amounts that were inconceivable three to five years ago. The ability to bring analytical data and content back and forth from large core data centers to the near-edge or far-edge will require 100 Gbps (if not Tbps) or more of backbone capability.
That is more difficult than it sounds. In fact, it’s really complicated and confusing for the average enterprise. Add the challenges of getting the data from the edge to the core and back again and you find that a complex ecosystem of multiple players are required to make an edge deployment successful. There’s also a consideration of how latency impacts applications; what is close enough or not? If only 2-3% of edge data needs to be stored, how do you decide what data is important?
Emerging edge applications are very latency sensitive. These decisions need to be made locally to save time, for example, to turn off manufacturing equipment due to a safety issue that could injure an employee. Or process audio data within a city to detect a gun shot and vector video cameras and emergency responders quickly, gathering critical data at the scene. Or perhaps a manufacturing or logistics service is building a local cost effective 5G network to enable it’s IoT services, without the additional expense of paying national 5G public providers.
Continue reading: https://datacenterfrontier.com/edge-to-core-networking-is-critical-for-iot-devices-ai-and-automation/

It’s fair to say the permissionless crypto world seemed to do all the running in the blockchain space in 2021. But that doesn’t mean those of us working to bring advanced cryptographic techniques to the world of business were resting.
Indeed, and despite the ongoing challenges of the pandemic, 2021 was a year of significant progress for the digitisation of capital markets. 2022 will be another unprecedented year in financial technology – and the following three trends look set to continue reshaping the landscape for market participants, governments, regulators, and infrastructure providers over the next 12 months.
1. The quest for trust will dominate the digital realm
We never think about it in our day to day lives but the ability to develop trust in each other in the real world is what has, uniquely, unleashed humanity’s potential. Trust is the cornerstone of human civilisation.
And as more of our personal and professional lives move into the digital realm, the sheer lack of trust in the digital realm is a trillion-dollar problem the industry must tackle in 2022.
Trust allows us to do things that would be almost impossible if we had to verify everything for ourselves. Can you imagine, for example, aviation if you couldn’t trust the airline’s safety engineers? And how often have you relied on a trusted brand when searching for a meal in an unfamiliar location? Imagine if you had to check the ingredients yourself before tucking in! Put simply: if we can trust, we don’t have to verify. 
Continue reading: https://www.forbes.com/sites/richardgendalbrown/2022/01/10/three-predictions-for-the-year-ahead-in-digital-finance/?sh=57ab0901128e

Switching from traditional base-and-rover surveying to drone surveying is one of the most effective ways to upgrade your construction workflow, setting you and your team up to make better, data-driven decisions.
Drones are more efficient, shaving days or weeks off typical surveying time. Since today’s drones are cheaper and simpler to use than traditional surveying equipment, you’ll be able to capture onsite data more effectively without exorbitantly expensive surveying equipment. Drone imaging allows you to collect and analyze data whenever you need, and with the right processing software and ground control, you can achieve survey-grade accuracy throughout your site.
One of the biggest perceived obstacles to starting a drone program is airspace and flight regulations. These regulations are actually easy to follow, and by understanding a few key laws and processes, you’ll be collecting survey-grade data with drones in no time! To that end, this article will cover the following:

• What makes something a drone?
• Registering your drone
• Getting licensed to fly
• Licensing resources (study guides, where to take the test)
• Understanding where you can fly
• Airspace restrictions
• Special waivers

Commercial drone regulations
A drone is a type of aircraft, albeit a tiny one. All unmanned autonomous vehicles (UAVs) are regulated by a country’s aviation authority, and rules around how, when, and if you can fly differ by country.
Each country and region has different UAV rules and regulations, with Australia and the EU introducing new regulations in 2021. For locations in the Asia Pacific region, refer to the International Civil Aviation Organization’s guidelines.
Continue reading: https://www.forconstructionpros.com/construction-technology/article/21992842/propeller-aero-regulations-to-know-before-starting-a-construction-drone-program

Synthetic Data Will Be a Requirement to Build the Metaverse 
Yashar Behzadi, CEO and founder, Synthesis AI
“The metaverse cannot be built without the use of synthetic data.
“To recreate reality as a digital twin, it’s necessary to deeply understand humans, objects, 3D environments and their interactions with one another. Creating these AI capabilities requires tremendous amounts of high-quality labeled 3D data, which is impossible for humans to label. We are incapable of labeling distance in 3D space, inferring material properties or labeling light sources needed to recreate spaces in high-fidelity. 
“Synthetic data built using a combination of generative AI models and visual effects (VFX) technologies will be a key enabler of the AI models required to power new metaverse applications.”
AI Introduces Software Development Teams to the Age of Augmented Analytics
Florian Schouten, vice president of product management, Digital.ai
“AI’s next shining moment will be empowering humans with data-driven recommendations for business decisions, across industries, in the form of augmented analytics. 
“With an increased emphasis on governance and risk, we are going to see AI predict risk around software release schedules and tell companies why that release is at risk, providing deeper insights and allowing companies to avoid detrimental errors like the ones Facebook and Twitch could not.”
Model Evaluation and Tuning Goes Mainstream
Wilson Pang, CTO, Appen    
“In 2022, the need for regular model evaluation and tuning becomes AI program table stakes. Machine learning models are dynamic, they can’t be deployed and forgotten. ML models in production need to be updated and retrained based on a variety of factors, including the ongoing results, as well as changes in infrastructure, data sources and business models. 
“The awareness of the need to regularly evaluate models took a huge leap in 2021. According to the Appen State of AI report, 87% of organizations update their models at least quarterly, up from 80% last year, with 57% updating their models at least monthly; 91% of large organizations update their models at least quarterly, and organizations that use external data providers are most likely to update their models at least monthly. 
“In 2022, given the overall maturing of the AI industry, Gartner found that at least 40% already had some form of AI program last year. Enterprises will shift focus from implementation to optimization, resulting in increased reliance on model evaluation tuning and solutions and the vendors that can assist in this process.”
Continue reading: https://www.iotworldtoday.com/2022/01/10/ai-predictions-2022-the-expert-views/

As companies navigate the unpredictability of COVID-19 and finance continues an accelerated phase with Big Data and predictive analytics, AI is making its case for inclusion in business forecasting.
Like CRMs, which have evolved from being a "nice to have" to a "need to have" over time, forecasting is becoming essential to a company's bottom line. It's what helps a business cover operating expenses, buy more inventory, market new products and attract new investments. But traditional models can no longer keep up with changing macroeconomic conditions, and AI-based models add a level of predictability and adaptability that is now crucial.
Traditional forecasting models fall short
On any given day, traditional forecasting might achieve 75% accuracy (compared to AI's potential to reach much higher). But why is this method more limiting? Traditional models lack adaptability. They can only take in a limited amount of data and variables compared to AI and machine learning models, which can take unlimited internal and external data sets.
Traditional forecasting also requires an extended amount of time to evaluate projections versus actuals and adjust assumptions. The assumptions these models use are often high-level metrics that don't tie into the operational realities of the business. AI, however, offers deeper granularity and updates its forecasts at a daily rate, allowing businesses to react and adjust much faster.
Unfortunately, many companies avoid AI as a mere "nice to have," and many that adopt AI abandon their models or don't realize its full ROI. As the AI/Data/BI Product Director at Paro, where we've operationalized AI into our forecasting models, I have come away with some important lessons for implementing AI forecasting and bringing it to its full potential.
Lesson one: Go granular
Many companies currently use a top-down approach for forecasting, in which they focus their model on top-line financial projections. I recommend doing the opposite.
Instead, each company project should get its own AI forecast. The sum of those forecasts equals the top-line performance. This approach increases forecast accuracy and makes it easier to identify a culprit when performance doesn't meet expectations.
Continue reading: https://www.cfodive.com/spons/demystifying-ai-forecasting-why-and-how-your-business-should-implement-sma/616563/

The new year is finally upon us, and here at WEMP, we have been reflecting upon the last year, as another unprecedented 12 months have gone before us. Although 2021 kicked off with hopes that the pandemic was on its way out, the recent emergence of the omicron variant and lockdowns around the globe have once again shown the world that we never know what the future may hold.
While the age of the pandemic continues to set new precedents for the healthcare landscape, simultaneously, so has the dawn of DeFi. As people stayed locked down at home, the retail investing trend took the world by storm, and the new era of cryptocurrency has increasingly gained momentum throughout the last year, with Bitcoin, Ethereum, and Dogecoin all making consistent headlines.
In addition to providing financial security for those living in governments with financial control, the emergence of cryptocurrency trading has been a welcome new form of income in a time where mass lay offs occurred and economic uncertainty ravaged the globe. The digital asset has been thrust into the spotlight over the past year, creating a frenzy of trading activity.
Continue reading: https://cryptodaily.co.uk/2022/01/women-in-crypto-a-powerful-blend

Blockchain is set to improve the efficiency and security of procurement companies in all industries — while adding value for their customers. 
Are you ready to be blockchain-ready in your supply chain?
Cryptocurrencies are one of the most famous examples of blockchain potential. It is the technology drawing attention from outside finance.
Blockchain is Transforming Supply Chain Management 
The decentralized ledger is the core technology of blockchain. It records and protects transactions shared between multiple parties. 
Cryptocurrencies use blockchain such as Bitcoin, Ethereum, and Dogecoin to allow unlimited anonymous parties to transact without an intermediary.
Supply chain management allows known parties to transact directly, while increasing security, contract compliance, and reducing costs. Supply chain blockchains “tokenize” a variety of transaction-related data. This creates unique and easily verifiable identifiers such as purchase orders, inventory units, and bills of lading.
Continue reading: https://readwrite.com/2022/01/10/blockchain-where-are-we-going/

Many people have experienced ordering fast food in a drive-thru and opening the bag later to find their French fries are missing, or their burgers are covered in ketchup they did not want. Artificial intelligence (AI) may make ordering food through a talking box easier, faster and more accurate.
You may not have to wait long for AI to take your next drive-thru order. Checkers & Rally's are joining McDonald's MCD -1.1% on the list of fast food restaurants that are testing voice-ordering bots in their drive-thrus. Through a partnership with Presto, the company plans to install the AI-based voice assistant in 267 restaurants. For now, only the corporate-owned restaurants will get the new technology.
The Bot Will Take Your Order Now
Presto’s AI voice assistant automates speech recognition in restaurants and can be used in drive-thrus, kiosks, pay-at-table systems and other places. Presto shares that it has an accuracy of over 95% and improves labor productivity by as much as three times. The conversational AI technology can greet customers, take orders, transfer orders to point of sale (POS) systems and do other functions.
Continue reading: https://www.forbes.com/sites/lanabandoim/2022/01/10/will-artificial-intelligence-be-taking-your-next-drive-thru-order/?sh=2b6a74471bc0

A chronic shortage of workers has more employers turning to artificial intelligence to supercharge recruiting efforts, seeking an edge in an increasingly pitched battle to fill job openings.
By automating previously manual tasks—like pre-screening job applicants for basic qualifications, checking for professional credentials and licenses, or scheduling follow-up interviews—employers hope to streamline the hiring process and scoop up available workers before competitors move in.
Artificial-intelligence capabilities, like conversational AI software, can speed up the early back-and-forth emails, texts and other communications with applicants and quickly get strong candidates in front of recruiters. Other AI-enabled tools are being used to accelerate the employee onboarding process, getting new hires oriented, trained and set up with computers, business apps and corporate email accounts.
Trucking company U.S. Xpress Enterprises Inc. uses conversational AI software to handle most of the early stages of the hiring process, including text exchanges with job applicants, said Amanda Thompson, the Chattanooga, Tenn.-based business’s chief people officer. When job seekers submit an application via a mobile device, the AI tool automatically replies with a series of preliminary questions, she said.
Continue reading: https://www.wsj.com/articles/employers-investors-take-notice-of-ai-tools-to-speed-job-recruitment-11641599629?mod=business_minor_pos10

When “The Matrix” movie first aired in 1999, it showed us an extreme version of artificial intelligence and the power of data that in many ways seemed impossible. More than 20 years later, the latest installment has been released into a world where the perception and adoption of AI and data has drastically changed.
In 2022, AI and data are no longer “nice-to-haves” or unworkable ambitions. For years, organizations have accepted the realization that each of these capabilities is essential to gain an advantage and grow -- and the proof is clear. Consumers are increasingly comfortable and confident with AI-enabled interactions, enterprises are pushing through common barriers to scale their AI programs, and companies are shifting away from gut-feel and intuition to relying squarely on data-driven decision making.
With several high priority use cases creating prime opportunities for adoption, here are four AI and data trends I expect to see take shape this year: 
Continue reading: https://www.informationweek.com/big-data/ai-and-data-this-year-bigger-bolder-and-business-focused

From chatbots to robots and data-driven algorithms, adoption of artificial intelligence has been for years. But the pandemic took AI to the level, with 52% of companies accelerating their AI adoption plans because of COVID.
What used to be seen as niche is now widespread, with innovative AI applications across all industries. 86% of companies say AI became a mainstream technology in 2021—and it shows no signs of stopping. 
AI transforms customer and employee experiences, increases productivity and efficiency, lowers costs, improves safety and helps the environment. Here are five fresh examples of AI in action: 
1 . REX Real Estate leverages AI for fast listings
Real estate is known for red tape and plenty of paperwork. REX Real Estate leverages AI to create a faster and more efficient process for listing and selling homes. Users enter their address and basic home features, including the number of bedrooms and bathrooms. The Instant List feature’s algorithm then considers the home’s age, size and location to automatically recommend a list price and provide an estimate of how long it will take to sell. Instant List also populates the listing agreement and sends it to a local agent for signature. Automating the real estate listing process moves transactions along faster and leads to homes spending less time on the market. 
Continue reading: https://www.forbes.com/sites/blakemorgan/2022/01/10/5-fresh-examples-of-ai/?sh=388b64765736

Since ancient times, technology has allowed society to defy the limits of human possibility. Especially with regards to modern healthcare, the world has harnessed technology to make incredibly innovative outcomes possible.
One such example is drone technology— although originally envisioned for aerial and military use, innovators have found a way to embrace this technology for another crucial application: augmenting healthcare delivery.  
Drones have displayed incredible promise in various aspects of clinical care. One of the most profound applications has been the use of drones for telehealth and virtual care, as envisioned by University of Cincinnati researchers last year. The value impact of this is momentous, as it provides an opportunity to expand care to otherwise hard-to-reach patients and potentially resolve the significant access-to-care issues plaguing modern day healthcare.
Additionally, drones are also being utilized worldwide for delivery of healthcare supplies and goods. I wrote last year about how drones are being used for delivery of Covid-19 vaccines, which is an incredibly innovative solution to the seemingly daunting task of deciphering vaccine delivery logistics.
Continue reading: https://www.forbes.com/sites/saibala/2022/01/09/drones-may-become-the-next-big-thing-in-healthcare-delivery/?sh=7aed3fd11e9b

If you’re pressed for time, here’s a one-word executive summary of where hybrid cloud is headed in 2022: Everywhere.
That declaration requires only modest exaggeration. Roughly half (48 percent) of respondents in O’Reilly’s 2021 Cloud Adoption Survey plan to migrate 50 percent or more of their applications to a cloud in the coming year. The same survey found a healthy mix of public cloud (67 percent), private cloud (45 percent), and traditional on-premises infrastructure (55 percent) already in use.
Meanwhile, 38 percent of the organizations included in  Red Hat's 2021 Global Tech Outlook already had a hybrid cloud or multi-cloud strategy in place. The report shows clear growth ahead in 2022, and more organizations plan to use three or more clouds than ever.
There’s a visible relationship between hybrid cloud and digital transformation, and they’re playing in concert with each other. One of the Red Hat report’s six key conclusions speaks to this: “Digital transformation initiatives continue to progress, and many companies accelerated their transformation plans this year,” the report says. “In these plans to innovate, security remains a top challenge and priority, and hybrid cloud environments continue to grow.”
The reward for a CIO’s strategic vision and their team’s hard work to this point is that the rest of the organization is essentially saying: “More, please.” Hybrid cloud – along with other transformative technologies like containers and AI/ML – will fuel the next phases of growth.
“Expect greater demand for faster innovation and digital transformation to deliver business growth across the enterprise,” Kaushik De, VP, GTM lead for custom software development and cloud CoE lead, Capgemini Americas. “Companies should anticipate accelerated business processes and the need to quickly demonstrate the business value of cloud solutions.”
Predicting “everywhere” as the strategic direction for hybrid cloud isn’t much in the way of hyperbole. We can certainly dive into more specifics, however. Here are four hybrid cloud trends to watch in 2022.
1. Hybrid cloud goes to the edge
Hybrid cloud definitions vary but typically refer to some combination of public cloud, private cloud, and/or bare-metal infrastructure.
That’s historically accurate. The gist of NIST’s decade-old definition of the hybrid deployment model is based on the (still relevant) use case of “bursting” from your data center to a public cloud to handle capacity surges. But the basic definitions are now expanding to reflect the emerging realities of hybrid cloud architecture, which is not actually limited to your datacenter and cloud platforms of choice.
"A hybrid cloud extends all the way out to the edge of a computing infrastructure where data is collected and acted upon."
“Hybrid cloud is no longer just about public clouds and clouds hosted by an organization in its own datacenter,” says Gordon Haff, technology evangelist at Red Hat. “A hybrid cloud extends all the way out to the edge of a computing infrastructure where data is collected and acted upon. This is really today’s hybrid cloud story.”
Put another way, the paths of two major enterprise IT trends are merging. Hybrid cloud is fundamentally about running your workloads in the best possible environments; edge computing is about bringing the environment to the workload and its associated data, or at least as close as possible. It’s a match made in IT heaven.
“Hybrid cloud goes beyond a choice of different isolated centralized sets of computing resources, which sometimes go by the multi-cloud moniker – though it’s often hard to distinguish the hybrid cloud and multi-cloud terms these days,” Haff says. “Rather, it envisions architectures where computing happens where it’s appropriate and data flows where it’s best handled.”
Continue reading: https://enterprisersproject.com/article/2022/1/hybrid-cloud-4-trends-watch-2022

In the 2010s, organizations became keenly aware that data would become the critical ingredient in driving competitive advantage, differentiation and growth. But to this day, putting data to work remains a difficult challenge for many if not most organizations.
As the cloud matures, it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible. We’ve also seen better tooling in the form of data workflows, streaming, machine intelligence and artificial intelligence, developer tools, security, observability, automation, new databases and the like. These innovations accelerate data proficiency but at the same time add complexity for practitioners. Data lakes, data hubs, data warehouses, data marts, data fabrics, data meshes, data catalogs and data oceans are forming, evolving and exploding onto the scene.
In an effort to bring perspective to this sea of optionality, we’ve brought together some of the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond.
In this Breaking Analysis, we’ll review the predictions from six of the best analysts in data and data management who will present and discuss their top predictions and trends for 2022 and the first half of this decade.
Continue reading: https://siliconangle.com/2022/01/09/analyst-predictions-2022-future-data-management/

When it comes to the most popular technological trends of the 21st century, blockchain seems to be cementing its status as one of the frontrunners. The whole concept of the blockchain was first developed in order to support Bitcoin, a kind of cryptocurrency. However, the blockchain has grown to become the primary source of power for numerous cryptocurrencies and could potentially be the central hub for the future of all online transactions. Many developers are working hard on integrating the concept of the blockchain into various fields such as medicine, art, fashion, and finance. In this article, we are going to go over what the blockchain is, how it works, and how it can be applied to everyday life.
What is the Blockchain?
Think of the blockchain as a digital ledger that takes note of different transactions that are conducted via the internet. This ledger is maintained by a network of different computers, thereby making it difficult to hack or manipulate. Since the responsibility of maintaining the ledger is spread out across computers all over the world, any kind of currency that is built on blockchain technology is often referred to as decentralized finance. With standard fiat currency that continues to be the mainstream financial language of most people today, money is typically regulated by governing bodies and centralized agencies. However, with blockchain technology, all financial transactions that take place are more democratic in nature since everyone plays a part in securing the digital ledger without the intervention or oversight of a government, bank, or any kind of authoritative body.
Continue reading: https://www.jpost.com/special-content/what-is-the-blockchain-and-how-does-it-work-691970

Automated vehicles
Automated transport is, undoubtedly, the most discussed and most hyped artificial intelligence (AI) application in reference to public transport. The true automated vehicle (AV) evangelist predicts a future where mass transit as we know it no longer exists: people may have stopped owning vehicles, but they will travel autonomously in private pods, not following preset routes and not sharing with random strangers. A complete change from bus, tram and rail services as we currently know them.  
This future scenario is mercifully unlikely to be realized. It depends on a level of affordability for AVs which seems unrealistic, particularly when we consider the vehicle and infrastructure components that will be necessary to deliver an efficient and safe on-demand AV service to every citizen. It also ignores the issue of congestion; if it unexpectedly became affordable to provide door‑to-door individual AV services for all, the resulting congestion would be as bad as what we see now on our networks, and may even be worse. It is true that, in the pure AV scenario, on-street parking disappears, freeing up more roadspace for moving vehicles. But it is also true that we have no reason to believe that the law of induced demand does not apply to AVs, and an affordable and comfortable (which, when discussing private car versus public transport, often means private and safe from required social interaction with strangers) on‑demand AV service should be expected to induce extra trips. Mass transit will, therefore, remain an essential component of a livable city, and we include less dense urban areas in this. 
An evolution, not a revolution
Moving on from the AV scenario, AI actually has a lot to offer to improve public transport in many different ways. Not through a revolution – as with AVs – but through an evolution such as we are already familiar with: from tickets issued from a roll of paper, through to magnetic stripe paper tickets, smartcards and contactless; from route and timetable planning with pen and ink, to using PCs, through to the use of dedicated software. AI is just the latest new tool being added to these chains of improvement, and AI itself is only in its infancy in terms of capability and application.
Continue reading: https://www.intelligenttransport.com/transport-articles/131855/artificial-intelligence-public-transport/

The US state of Michigan and Canadian province of Ontario have announced a new collaboration to study commercial beyond-line-of-sight drone skyways in three proposed areas, including an international cross-border connection.
The project will investigate using unmanned aerial systems (UAS), or drones, in operations like just-in-time delivery, medical transport, and other small-scale deployments. The information gathered from this feasibility study will be used to further decision-making in preparing for the future of advanced air mobility in North America.
Announcing the project yesterday, Michigan Governor Gretchen Whitmer said, “Cross-border partnership is critical across all dimensions of mobility, including aerial systems. Michigan and Ontario have a rich history of partnering on groundbreaking innovations and this project by Air Space Link continues that tradition.
“Considering the density of auto suppliers, logistic companies, technology startups, and consumers in the region, it is a natural fit to test this cutting-edge aerial technology here. The vital research could lead to faster product deliveries and reduced supply chain disruptions in the future, helping us grow Michigan’s economy and put Michiganders first.”
The project will be carried out by the Michigan Department of Transportation (MDOT) and the Michigan Aeronautics Commission (MAC) and the State of Michigan, with support from the Michigan Office of Future Mobility and Electrification (OFME), the Michigan Economic Development Corp., and the Government of Ontario, through the Ontario Vehicle Innovation Network (OVIN),
“Ontario has a strong and strategically important economic connection with the State of Michigan,” said Ontario Premier Doug Ford. “We welcome this opportunity to further deepen that relationship and foster new jobs and economic opportunities in developing industries now and well into the future.”
Continue reading: https://www.aerospacetestinginternational.com/news/drones-air-taxis/michigan-and-ontario-investigate-creation-of-drone-skyways.html

It’s been four days since a winter storm pounded Virginia with up to 11 inches of snow and left tens of thousands of residents without power. Crews at utility company Dominion Energy have been working tirelessly ever since to ensure a swift response to the outages. But with heavy, wet snow knocking down trees and making some areas especially inaccessible by road, drones have played a critical role in giving linemen a head-start on understanding what they’re dealing with.
“Our crews are working as quickly as they can to safely navigate icy roadways, road closures, downed trees, and tree limbs. In some localities, the damage is so severe that some areas are not even accessible by foot, in those cases, we are using drones to assess the damage,” Dominion Energy said in a statement Tuesday.
As of today, nearly 90% of Dominion users who lost power have got their lights back on. But officials are quick to point out that such rapid progress on restoration would not have been possible without the use of drones.
“It’s quicker, it’s safer, and it gets you access to areas that you might not otherwise get to until further into the restoration effort,” Kevin Curtis, Dominion’s vice president of electric transmission, explains why the company uses drones to deal with power outages.
Continue reading: https://dronedj.com/2022/01/06/drones-fix-power-outage-virginia/

Blockchain solutions would create a more secure and efficient way for the government to operate while giving citizens more peace of mind that their data is secure.
Last year the world produced 2.5 quintillion data bytes daily, and as that number continues to grow, the need to find cutting edge ways to protect, secure, and verify this data has become paramount. During that year, cybercriminals committed one of the worst cyber-espionage incidents against the United States when it hacked thousands of organizations around the world, including branches of the U.S. government. Blockchain technology can help prevent these types of incursions into sensitive and vital data.
Governments need to reinvest in ways to better handle that data within their own networks and the data generated by its citizens. To do that, there needs to be a major investment in blockchain technology. Due to its decentralization, paired with cryptography, blockchain reduces redundancy, streamlines workflows, increases auditability, and ensures better security protocols for data integrity. Therefore, it’s a more efficient way for governments to reduce bureaucracy and regain the trust of its citizens during major events like elections and identification.
Continue reading: https://www.rtinsights.com/implementing-blockchain-to-revolutionize-government-work/

Blockchain technology and NFTs aren’t going anywhere, and some big brands have already dipped their toes in and demonstrated that they can generate real engagement.
Marketers are always in search of new strategies and technologies to gain a competitive advantage, so it’s not surprising that in outlooks for the upcoming year, there’s been a lot of talk about how NFTs and blockchain technology can give brands a lift. The digital landscape has expanded to include 3D AR and VR environments. Meanwhile, Meta (the company formerly known as Facebook) has gotten out front with the concept of a connected metaverse.
When weighing all of the options that appear on the horizon, marketers should set clear goals and expectations instead of chasing the next flashy gimmick. But they also shouldn’t miss out on the transition to blockchain and virtual environments going on right now in some marketing channels.
Blockchain’s benefits for identity
“The industry has known for a while that blockchain’s transparent, decentralized nature, and immutability have valuable applications in preventing ad fraud and securing the ad supply chain,” said Mel Bessaha, Senior Vice President of Demand for video technology company Connatix. “Those same benefits also deliver great value for brands that are dedicated to building strong first-party data strategies. Those strategies will naturally require as much first-party data as possible.”
Continue reading: https://martech.org/how-marketers-will-use-blockchain-technology-and-nfts-in-2022-for-identity-branding-and-engagement/

With the use of drones on the increase in the power sector and other areas and the demand for longer ranges and flight times, they are an obvious target for the use of hydrogen as a fuel as a lighter – and potentially cleaner – alternative to the standard lithium-ion battery power.
The first hydrogen-powered drone flights of which we are aware took place back during 2020 by the multinational Doosan in South Korea, with the delivery of facemasks to remote islands and over two-hour flight-time tests monitoring gas pipelines over distances of up to 80km.
Subsequently, the company has evolved its hydrogen-powered drones, it’s first the DS30 built for endurance and the smaller general purpose DT30, to second generation models. Features of the DS30W include improved wind resistance – it has been demonstrated in winds as strong as 12m/s – while the DT30N offer an extended flight time of 2.5 hours and increased 5kg payload.
With the recent launch of the DS30W in Europe, Doosan is eyeing its use out in the North Sea.
In partnership with the Dutch Maritime Emerging Technologies Innovation Park (METIP) and the operator DroneQ Robotics, the companies intend to develop applications for hydrogen drones through demonstration and validation of use cases and pilots in areas such as inspections and goods delivery in the maritime and offshore energy industries in the North Sea.
“The second-generation hydrogen drone from Doosan Mobility International has a flight time of two hours and is resistant to adverse weather conditions, making it ideal for complex maritime applications such as inspecting offshore installations or performing search and rescue missions,” states John Troch, co-founder of the central Netherlands based DroneQ.
First flight in Japan
Now the Singapore headquartered H3 Dynamics has also joined the hydrogen drone race, with the first test flights in Japan, with approval from the country’s Ministry of Economy, Trade and Industry (METI).
The drone was equipped with H3 Dynamics’ AEROSTAK hydrogen fuel cell system and integration support was provided by Toyota Tsusho Nexty Electronics, a car electronics subsidiary of the Toyota Group.
With the flight demonstration under its belt, H3 Dynamics, which recently closed a $26 million series B financing, anticipates that its success opens the way towards wider adoption of hydrogen in air mobility applications.
Continue reading: https://www.powerengineeringint.com/smart-grid-td/drone-technology/hydrogen-powered-drones-hold-potential-for-offshore-energy/

Across the country, police departments are using myriad means and resources at their disposal to stock up on drones. According to the most recent tally on the Atlas of Surveillance (a project of EFF and the University of Nevada), at least 1,172 police departments nationwide are using drones. And over time, we can expect more law enforcement agencies to deploy them. A flood of COVID relief money, civil asset forfeiture money, federal grants, or military surplus transfers enable more departments to acquire these flying spies.
But how are police departments using them?
A new law in Minnesota mandates the yearly release of information related to police use of drones, and gives us a partial window into how law enforcement use them on a daily basis. The 2021 report released by the Minnesota Bureau of Criminal Apprehension documents use of drones in the state during the year 2020.
According to the report, 93 law enforcement agencies from across the state deployed drones 1,171 times in 2020—with an accumulative price tag of almost $1 million. The report shows that the vast majority of the drone deployments are not used for the public safety disasters that so many departments use to justify drone use. Rather, almost half (506) were just for the purpose of “training officers.” Other uses included information collection based on reasonable suspicion of unspecified crimes (185), requests from other government agencies unrelated to law enforcement (41), road crash investigation (39), and preparation for and monitoring of public events (6 and 12, respectively). There were zero deployments to counter the risk of terrorism.  Police deployed drones 352 times in the aftermath of an “emergency” and 27 times for “disaster” response.
This data isn’t terribly surprising. After all, we’ve spent years seeing police drones being deployed in more and more mundane policing situations and in punitive ways.
After the New York City Police Department accused one racial justice activist, Derrick Ingram, of injuring an officer’s ears by speaking too loudly through his megaphone at a protest, police flew drones by his apartment window—a clear act of intimidation. The government also flew surveillance drones over multiple protests against police racism and violence during the summer of 2020. When police fly drones over a crowd of protestors, they chill free speech and political expression through fear of reprisal and retribution from police. Police could easily apply face surveillance technology to footage collected by a surveillance drone that passed over a crowd, creating a preliminary list of everyone that attended that day’s protest.
Continue reading: https://www.eff.org/deeplinks/2022/01/how-are-police-using-drones

Artificial intelligence is all about productivity, right? A.I.-based software programs, such as Hubstaff, record keyboard strokes, mouse movements, and the websites employees visit when they work. Time Doctor uses webcams to shoot videos and pictures of users’ screens at periodic intervals to check whether employees are at their computers. Isaak monitors employee interactions, combining its data with personnel records to identify the employees that are most collaborative. And Enable measures the time employees take to complete tasks, suggests ways they can speed up, and assigns productivity scores. Managers can use the grades to identify employees who are worth retaining—and those who aren’t. 
Whatever you may privately feel about the technology, A.I. is here to stay. But have business leaders developed the right attitude to productivity in the A.I. Age? 
Machine productivity, measured by the quantitative output produced every minute, hour, or day, can be mechanically monitored and managed. But we humans aren’t exactly machines. Science shows that we work best when we mix work with breaks, and that we’re most motivated when we enjoy ownership and independence. These “human” factors demand a nuanced perspective on productivity that digital technologies may not yet possess. Still, shunning the technology isn’t the answer; used in the right way, A.I. can benefit both employees and businesses, as a recent BCG–MIT research project has shown. The key is for companies to ensure that the productivity of both humans and A.I. rises as they work together, and that they bring out the best in each other. 
Continue reading: https://fortune.com/2022/01/07/artificial-intelligence-ai-productivity-challenges-human-element/

Artificial intelligence is all about productivity, right? A.I.-based software programs, such as Hubstaff, record keyboard strokes, mouse movements, and the websites employees visit when they work. Time Doctor uses webcams to shoot videos and pictures of users’ screens at periodic intervals to check whether employees are at their computers. Isaak monitors employee interactions, combining its data with personnel records to identify the employees that are most collaborative. And Enaible measures the time employees take to complete tasks, suggest ways they can speed up, and assigns productivity scores. Managers can use the grades to identify employees who are worth retaining—and those who aren’t. 
Whatever you may privately feel about the technology, A.I. is here to stay. But have business leaders developed the right attitude to productivity in the A.I. Age? 
Machine productivity, measured by the quantitative output produced every minute, hour, or day, can be mechanically monitored and managed. But we humans aren’t exactly machines. Science shows that we work best when we mix work with breaks, and that we’re most motivated when we enjoy ownership and independence. These “human” factors demand a nuanced perspective on productivity that digital technologies may not yet possess. Still, shunning the technology isn’t the answer; used in the right way, A.I. can benefit both employees and businesses, as a recent BCG–MIT research project has shown. The key is for companies to ensure that the productivity of both humans and A.I. rises as they work together, and that they bring out the best in each other. 
Sadly, companies, especially those that have started using A.I. in the workplace, tend to focus only on the technology and almost forget about the human aspects while trying to optimize productivity. CEOs, we believe, must tackle four challenges to get the most out of combining employees and A.I. 
Continue reading: https://fortune.com/2022/01/07/artificial-intelligence-ai-productivity-challenges-human-element/

Edge computing networks prioritize distributed infrastructure and micro data centers that bring processing and storage of digital resources to global end-users. But how should organizations at large approach edge network deployment?

While the last decade has seen the widespread adoption of cloud computing — which relies on centrally hosted and managed data centers — edge computing provides enterprise organizations with the methods needed to keep pace with data speeds and serve a global audience. With clients and end users top of mind, edge networks help reduce latency, speed delivery of resources, and improve the customer experience.
This article looks at what to consider when adding edge network infrastructure, edge computing hardware traits, what edge solutions offer, and the top edge service vendors.
How to Build an Edge Computing Network
Comprehensive edge computing networks require a long-term strategy with adequate resources, partners, and implementation. With a proper inventory of distributed network infrastructure, organizations can evaluate target locations for potential edge networks, find the appropriate hardware and software needs, and continually develop their newest network segment.
1. Evaluate Edge Needs, Resources, and Objectives
Organizations interested in deploying edge computing networks must first know their intended audience, which is existing or potential clients benefiting from the additional infrastructure. Knowledge of customer requirements like global geographic locations, concentrated service regions, or content delivery speeds can help drive decision-making around an edge investment.
Establishing an inventory of existing data centers and distributed network infrastructure — including IoT devices and sensors — is essential to developing new edge networks. The next step is finding edge computing solutions that are flexible enough to integrate with legacy systems and the latest products.
Read more: Edge Cache: What Is Edge Caching? How Does it Work? 
2. Select and Deploy Edge Computing Solutions
Edge computing companies are the vendors offering organizations the hardware, networking machinery, processors, colocation data center contracts, and innovative edge technologies needed to establish an edge network.
Enterprise organizations with existing global infrastructure and networks will have a far easier time adding edge devices and transforming legacy networks to serve physically close end users.
Alternatively, startups and smaller companies will need to obtain the required infrastructure and solutions provided by edge computing providers to create edge networks. Because of the sensitive nature of uptime and reliable service, organizations must conduct due diligence in selecting adequate solutions.
3. Maintain, Secure, and Optimize Edge Infrastructure
Once established, edge computing networks require ongoing maintenance and performance tuning like any other IT network. Administrators can dive into edge-specific traffic, evaluate potential problems, and remediate as needed to optimize the distributed network. 
Organizations adopting additional edge networks isolated by long geographic distances must develop a strategy that accounts for and centrally maintains an array of remote networks.
Continue reading: https://www.serverwatch.com/guides/build-edge-network/