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AI

Spell unveils deep learning operations platform to cut AI training costs

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Spell today unveiled an operations platform that provides the tooling needed to train AI models based on deep learning algorithms.

The platforms currently employed to train AI models are optimized for machine learning algorithms. AI models based on deep learning algorithms require their own deep learning operations (DLOps) platform, Spell head of marketing Tim Negris told VentureBeat.

The Spell platform automates the entire deep learning workflow using tools the company developed in the course of helping organizations build and train AI models for computer vision and speech recognition applications that require deep learning algorithms.

Deep roots

Deep learning algorithms trace their lineage back to neural networks in a field of machine learning that structures algorithms in layers to create a neural network that can learn and make intelligent decisions on its own. The artifacts and models that are created using deep learning algorithms, however, don’t lend themselves to the same platforms used to manage machine learning operations (MLOps), Negris said.

An AI model based on deep learning algorithms can require tracking and managing hundreds of experiments with thousands of parameters spanning large numbers of graphical processor units (GPUs), Negris noted. The Spell platform specifically addresses the need to manage, automate, orchestrate, document, optimize, deploy, and monitor deep learning models throughout their entire lifecycle, he said. “Data science teams need to be able to explain and reproduce deep learning results,” Negris added.

While most existing MLOps platforms are not well suited to managing deep learning algorithms, Negris said the Spell platform can also be employed to manage AI models based on machine learning algorithms. Spell does not provide any tools to manage the lifecycle of those models, but data science teams can add their own third-party framework for managing them to the Spell platform.

The Spell platform also reduces cost by automatically invoking spot instances that cloud service providers make available for a finite amount of time whenever feasible, Negris said. That capability can reduce the total cost of training an AI model by as much as 66%, he added. That’s significant because the cost of training AI models based on deep learning algorithms can in some cases reach millions of dollars.

A hybrid approach

In time, most AI applications will be constructed using a mix of machine and deep learning algorithms. In fact, as the building of AI models using machine learning algorithms becomes more automated, many data science teams will spend more of their time constructing increasingly complex AI models based on deep learning algorithms. The cost of building AI models based on deep learning algorithms should also steadily decline as GPUs deployed in an on-premises IT environment or accessed via a cloud service become more affordable.

In the meantime, Negris said that while the workflows for building AI models will converge, it’s unlikely traditional approaches to managing application development processes based on DevOps platforms will be extended to incorporate AI models. The continuous retraining of AI models that are subject to drift does not lend itself to the more linear processes that are employed today to build and deploy traditional applications, he said.

Nevertheless, all the AI models being trained eventually need to find their way into an application deployed in a production environment. The challenge many organizations face today is aligning the rate at which AI models are developed with the faster pace at which applications are now deployed and updated.

One way or another, it’s only a matter of time before every application — to varying degrees — incorporates one or more AI models. The issue going forward is finding a way to reduce the level of friction that occurs whenever an AI model needs to be deployed within an application.

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AI

How AI can simplify, streamline, and enhance supply chain operations

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Global economic activity is picking up now that pandemic-related restrictions are easing. But this return to normal has not been without hiccups, notably including supply chains emerging from virtual shutdowns.

However, few organizations are eager to revert to the manual-driven operational frameworks of the previous decade. The shift to advanced automation and artificial intelligence in the management layer was already underway before the pandemic hit, and there are signs this change is accelerating the drive to simplify, streamline, and enhance operations in order to meet the needs of an increasingly digital economy.

Ready for action

More than three-quarters of business leaders reported deploying AI in pilot programs, in key business areas, or at full scale across the enterprise, in a recent survey of more than 1,000 executives from NTT DATA and Oxford Economics. Cybersecurity remains the top challenge these deployments are intended to address, with supply chain management a close second. The most significant barrier to greater adoption, however, is the sheer complexity of the technology, which must be deployed across a range of processes and throughout disparate infrastructures in order to produce the greatest benefit.

Nevertheless, most organizations view AI as the next step in supply chain management, not simply to recover from the pandemic but to maintain a competitive advantage going forward. Machine learning (ML) and other forms of AI are even finding homes in industries considered technical laggards, like trucking and transportation, Paul Beavers, CTO of AI-driven transportation management platform provider PCS Software, wrote recently for Supply & Demand Chain Executive. With its ability to find hidden patterns in data, AI can lower costs and improve productivity by reducing the number of empty miles incurred during return trips; identifying the optimal mode of transport for selected cargo; and streamlining loading, fueling, and other tasks.

But applying AI to the supply chain is not a simple matter of throwing it at various processes to see what sticks. A more strategic approach that focuses on the value of data as the key driver of productivity is needed, Noodle.ai developer Mike Hulbert wrote on SupplyChainBrain.

Managing risks

One way to do this is to use AI to assess risk. Today’s management stacks tend to flood workers with alerts without assigning any priority. AI has the ability to quantify risk so organizations gain broad visibility into the most crucial detriments to efficient operations. Even if the problem requires time and expertise, that money is well spent, and solutions highlight the ways AI and human intelligence can work together to produce the most desirable outcomes.

Most of the thinking around AI in the supply chain tends to center on how it will enhance today’s processes. But as markets evolve into the new century, AI will also help create and manage entirely new forms of multi-layered, dynamic chains serving highly virtualized and cloud-based business models. Even today’s emerging omnichannel environments require precise coordination between customer-facing infrastructure, warehousing, transportation, fulfillment, and a range of other disparate functions, Global Trading Magazine’s Rumzz Bajwa noted recently. Much of this will have to be automated to accommodate the speed of business, something that can only be done through advanced intelligent systems that talk to each other with perhaps intermittent human oversight.

It should be noted that AI is not like past technology developments that began working their magic as soon as they were deployed. AI must be trained, refined, seasoned — just like any other employee. It’s a fast learner, to be sure, but it makes its share of mistakes. When it comes to the challenges of restarting crucial supply lines in the post-pandemic transition, AI will not provide an instant solution.

But its true benefit to business operations will become clear in the long term.

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AI

Cognite raises $150M to digitize industrial operations

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Cognite, an industrial software-as-a-service (SaaS) company, today announced it has raised $150 million in an equity funding round led by TCV at a $1.6 billion post-money valuation. Cognite says this investment marks one of the largest funding rounds for a SaaS company in Europe and will be used to expand its platform and support hiring efforts.

According to a 2020 PricewaterhouseCoopers survey, companies in manufacturing expect efficiency gains over the next five years attributable to digital transformations. McKinsey’s research with the World Economic Forum puts the value creation potential of manufacturers implementing “Industry 4.0” — the automation of traditional industrial practices — at $3.7 trillion in 2025.

Cognite, which was founded in 2016, supports the data-driven transformation of asset-intensive industries like oil and gas, power and utilities, and manufacturing. Its core product is a DataOps and contextualization platform designed to put raw data into context, enabling the creation of apps and services “at scale.”

Cognite

Cognite’s DataOps platform combines machine learning, rules engines, and subject-matter expertise to convert data into insights. It finds relationships in domain data, representing industrial entities in a connected labeled property graph. Cognite customers can configure pipelines for continuous AI-driven entity matching and diagram parsing, as well as unstructured document rendering that helps identify tags and similar objects and extract text and patterns.

Beyond this, Cognite lets companies bring existing machine learning models onto the platform and provides a dashboard for production optimization. Data scientists can build, train, test, deploy, and manage models and leverage “physics-guided” machine learning and process simulations to synthesize additional data. Cognite also offers an in-field AI assistant for field workers and tools for remote operations asset management. These include tools to extract operational data directly from source systems while integrating it with data from data warehouses and data lakes to calculate statistical features.

Digital transformation

Manufacturing is undergoing a resurgence as business owners look to modernize their factories and speed up operations. According to ABI Research, more than 4 million commercial robots will be installed in over 50,000 warehouses around the world by 2025, up from under 4,000 warehouses as of 2018. Oxford Economics anticipates 12.5 million manufacturing jobs will be automated in China, while McKinsey projects machines will take upwards of 30% of these jobs in the U.S.

Cognite’s customer base includes BP, Saudi Aramco, Alfa Laval, Statnett, and Mitsubishi. Recently, the Lysaker, Norway-based company — which claims it’s among the fastest-growing software companies in the world with over 500 employees — formed a partnership with Microsoft to use Microsoft Azure hosting solutions and a collaboration with Accel to “unlock the potential” of industry data.

“Cognite is on a strong trajectory to help transform industry, and since our founding four years ago, we have managed to attract top global talent and partner with top industrial companies to accelerate modern industrial data management worldwide,” CEO and cofounder John Markus Lervik said in a press release. “The partnership with TCV allows us to amplify our software solutions to empower asset-intensive businesses to improve their sustainability and profitability of operations.”

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Security

Colonial Pipeline says operations back to normal following ransomware attack

Colonial Pipeline said Saturday that all of its systems are back to operating normally, including the pipeline it shut down a week ago amid a ransomware attack. The pipeline is now servicing all its markets including Texas, Louisiana, Mississippi, Alabama, Tennessee, Georgia, South Carolina, North Carolina, Virginia, Maryland, Washington, DC, Delaware, Pennsylvania, and New Jersey, the company tweeted. Colonial carries 45 percent of the fuel supplies for the eastern United States.

The company reportedly paid a $5 million ransom to DarkSide, the group responsible for the incident. DarkSide has since apologized for the “social consequences” of the attack, which included fuel shortages in many of the markets that the 5,500-mile-long pipeline services.

It remains unclear which parts of the Colonial Pipeline were at risk, but a company spokesperson suggested it did not appear that the company’s operational systems were affected.

Colonial said on Twitter that it has invested “meaningfully” in its IT and cybersecurity, and said it would “continue to put safety and system integrity first.”

According to CNBC, there are still fuel shortages in many of the markets the affected pipeline serves; it reported 80 percent of gas stations in Washington, DC were still without fuel as of Saturday morning.



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AI

Datanomix raises $6M to monitor factory operations

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Datanomix, a production intelligence software vendor, today announced it has raised $6 million in series A funding to expand the reach of its software. The round was co-led by Gutbrain Ventures and PBJ Capital, and Datanomix says it will put the funds toward growing its sales, marketing, customer success, and engineering departments.

Manufacturing is undergoing a resurgence as business owners look to modernize their factories and speed up operations. According to ABI Research, more than 4 million commercial robots will be installed in over 50,000 warehouses around the world by 2025, up from under 4,000 warehouses as of 2018. Oxford Economics anticipates 12.5 million manufacturing jobs will be automated in China, while McKinsey projects machines will take upwards of 30% of these jobs in the U.S.

Founded in 2016 and based in Nashua, New Hampshire, Datanomix offers an operations monitoring solution that requires no operator input and automatically benchmarks production using only manufacturing equipment data. Datanomix’s dashboards deliver statuses on factory KPIs and allow users to drill down into specific metrics, jobs, and clients at any time. The platform can connect to existing enterprise resource management systems for maintenance, tuning, and calibration workloads and supports alerts to notify customers when conditions — e.g., temperatures, pressures, and vibrations — require their attention.

“Fundamentally, there were three significant gaps in the real-time factory data market that we sought to address: (1) Is this data meaningful enough right now that it can change my day in progress? (2) Does the system deliver information in such a way that it naturally aligns with the chaotic workflow of manufacturing people? and (3) Is the data contextual enough that it can immediately improve my estimating/costing/profitability metrics?” a spokesperson told VentureBeat via email. “Customers see what benchmarks our software creates, with literally no input required from them at all. They are blown away that we basically know their jobs as well or better than they do.”

Digital transformation

According to a 2020 PricewaterhouseCoopers survey, companies in manufacturing expect efficiency gains over the next five years attributable to digital transformations. McKinsey’s research with the World Economic Forum puts the value creation potential of manufacturers implementing “Industry 4.0” — the automation of traditional industrial practices — in their operations at $3.7 trillion in 2025.

With a customer’s Wi-Fi information, Datanomix draws on devices plugged into a factory’s CNC machines. The platform creates benchmarks for every job run around cycle time, parts per hour, and utilization. Performance is predicted in terms of how many parts a factory should be making when a job is running — Datanomix automatically takes into account scenarios like multiple jobs in a shift, rotating operations, jobs that end partway through a shift, and more.

Datanomix

Above: Datanomix’s monitoring dashboard.

Image Credit: Datanomix

Datanomix has 15 employees and says it has attracted “dozens” of new customers this year and is “consistently” doubling its business every quarter.

“Datanomix is well positioned, given the current landscape of manufacturers who have a clear mandate to digitize the information they use to manage production and growth of their companies, products, and profits,” CEO John Joseph told VentureBeat via email. “We purpose-built our software around the need for real-time intelligence frameworks that drive people to action, higher levels of productivity, and bigger outcomes.”

Beyond Gutbrain and PBJ, CEAA Investments and previous backers participated in Datanomix’s latest funding round — including Argon Ventures, York IE, Wasabi Ventures, Alumni Venture Group, and Millworks Fund. This brings the company’s total raised to date to $9 million.

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AI

Saas provider BoostUp.ai nabs $6M to support revenue operations

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BoostUp.ai, provider of a software-as-a-service (SaaS) platform for managing revenue operations (revenue ops) infused with AI capabilities, today announced it has garnered $6 million in additional series A funding, bringing its total raised to $14 million, after an initial seed round last year.

The company is part of a growing cadre of startups attempting to unify sales, marketing, and customer service processes in a way that enables organizations to boost sales and increase overall profitability. BoostUp claims its revenue increased by more than 1,000% in fiscal 2020, thanks in part to customers such as Udemy, Degreed, Plume, and Windstream.

Dueling platforms

The company’s Connected Revenue Intelligence & Operations platform is usurping customer relationship management (CRM) applications as the single source of truth for organizations that need to tightly integrate sales, marketing, and customer service process, CEO Sharad Verma told VentureBeat. “The CRM is no longer the system of record,” he said.

The Connected Revenue Intelligence & Operations platform differs from rival offerings in that it includes predictive analytics capabilities enabled by machine learning algorithms, Verma added. The platform ingests unstructured data from sources like emails, phone calls, calendars, meetings, and messaging applications that are then matched to accounts and opportunities found in CRM applications. Natural language parsing, sentiment analysis, and proprietary indexing of spoken and written keywords are then applied to better understand patterns of sales trends to forecast more accurately whether deals will close.

BoostUp claims customers have achieved 95% forecast accuracy, reviewed 5 times the number of opportunities per manager, and increased sales manager and sales representative capacity by over 15%.

The latest round of funding was led by Canaan Partners, with participation from Emergent Ventures and BGV Ventures. It will be employed to scale product development and increase customer growth.

It’s not clear to what degree providers of CRM applications are responding to any shift toward a more integrated approach to revenue Ops. Salesforce, for example, offers separate CRM, marketing, and customer service applications that are integrated on the same cloud. But Boostup.ai is making a case for a single platform that aggregates data in a way that makes it simpler to apply AI to identify, for example, when the level of customer engagement is misaligned with the sales forecast.

Revenue Ops

Organizations that are shifting toward a Revenue Ops approach to engaging customers have typically appointed a chief revenue officer (CRO) to assume responsibility for all sales channels. At a time when most sales engagements are occurring via some form of a digital channel, Verma said organizations must measure and monitor the actual level of engagement with customers alongside other historical data that might indicate whether, for example, a customer tends to always wait until the last week of a quarter to sign a purchase order as part of an effort to obtain better pricing.

Conversely, sales representatives may simply not be all that good at forecasting, which Sherma noted would identify an opportunity to improve training.

Regardless of companies’ motivations for embracing Revenue Ops, it’s clear sales, marketing, and customer service processes are becoming more integrated. Many organizations now realize customer service representatives that regularly engage customers are in many cases more adept at identifying additional revenue opportunities they can close on their own. Sales teams in many cases are now focusing the bulk of their time and effort on trying to land new customers versus making sure every product or service has been delivered to the precise specifications on the contract.

Naturally, it may take a while before every organization is able to fully transition to a Revenue Ops model. However, the way organizations engage customers is poised to change fundamentally.

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AI

Cyrebro raises $15M to expand its security operations platform

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Cyrebro, a cloud-based security operations center (SOC), today announced it has raised $15 million in a series B round led by Prytek. CEO Nadav Arbel says the capital, which brings the company’s total raised to $22 million, will be used to support development of the Cyrebro platform and strengthen the startup’s reach in the small and medium-sized business (SMB) market.

Email messages and files have become top attack vectors for hackers looking to steal data, particularly as the pandemic necessitates novel work-from-home arrangements. Cybersecurity Ventures anticipates that the damage related to cybercrime will hit $6 trillion annually this year. Corresponding with this rise, Gartner reports that worldwide spending on cybersecurity is expected to reach $133.7 billion in 2022.

A streamlined approach

Cyrebro aims to address this with a platform that integrates cybersecurity tools in a single dashboard. Cyrebro shows critical incidents across business operations and security solutions, allowing response teams to conduct investigations by type, severity, and status. With Cyrebro, security analysts can drill down into events and see which assets were impacted, as well as recommended actions and real-time status. Beyond this, Cyrebro delivers insights about where most alerts are generated and where attention should be focused for preemptive steps.

“Global businesses of all sizes are facing the new threats of cyberattacks brought on by the pandemic, and companies now need simplified solutions to see, understand, and respond to their cybersecurity needs. With this understanding in mind, we created Cyrebro, a real-time, live security operation platform to enable online operations of the entire security stack,” Arbel told VentureBeat via email. “It is the only platform to address the full scope of cybersecurity needs in the most effective, powerful, and cost-efficient way. More than ever, organizations need holistic detection and response mechanisms while covering the complete suite of security solutions, including monitoring, threat hunting, response, and compliance.”

Aggregating cyber insights

Cyrebro collects and processes data from clouds, networks, and endpoints, including laptops, desktops, and servers. A feature shows the geographic location of hosts with their coverage, connectivity state, and related alerts, and reporting functions enable users to generate audits with visualizations like pie charts.

Cyrebro

Above: Cyrebro’s SOC platform.

Image Credit: Cyrebro

Competition is fierce in a cybersecurity market that’s anticipated to reach $199.98 billion in value by 2025, according to Market Research Future. Securiti.ai, a developer building a platform designed to automate cybersecurity and compliance processes, recently emerged from stealth with $31 million. There’s also Swimland and Tines, a cybersecurity startup that helps enterprise security teams automate repetitive workflows.

But Arbel, which has 80 employees, says it has “hundreds” of paying customers, including casinos, global retailers, banks, insurance companies, and other Fortune 500 companies. “Over the past year, our customer base has grown by 100% and our employees by 20%. Since the start of COVID, SMBs experienced new challenges within their IT and cybersecurity departments. We realized the critical need to develop an accessible platform that simplifies proactive cyber operations for the average small to mid-sized business owner, allowing them to gain the posture of Fortune 500 enterprises,” he added. “Our business has grown exponentially, and we took on this round of funding to meet this new market demand for comprehensive online security solutions.”

InCapital, Mizrahi Bank, and previous investor Mangrove also participated in Tel Aviv, Israel-based Cyrebro’s latest round of funding.

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Tech News

Lawsuit seeks to end Clearview AI’s operations in California

A month after Clearview AI was declared illegal in Canada, activists have filed a lawsuit seeking to stop the company’s operations in California.

The complaint was filed on Tuesday by two immigrants’ rights groups and four individual political activists.

They allege that Clearview has violated the privacy rights of Californians by scraping their photos and extracting their biometric data without gaining consent.

The suit also argues that Clearview’s facial recognition tech facilitates government monitoring of protesters, immigrants, and people of color.

[Read: How do you build a pet-friendly gadget? We asked experts and animal owners]

The software has reportedly been used by more than 2,400 law enforcement agencies. The plaintiffs allege that it’s still used by police in California — even though several cities in the state have banned government use of facial recognition tech.

Sejal Zota, a lead attorney in the case, said there can be no meaningful privacy in a society with Clearview:

Privacy is enshrined in the California constitution, ensuring all Californians can lead their lives without the fear of surveillance and monitoring. Clearview AI upends this dynamic, making it impossible to walk down the street without fear your likeness can be captured, stored indefinitely by the company, and used against you any time in the future.

The suit is seeking an immediate injunction that would prevent Clearview from collecting any biometric data in California. It also seeks the deletion of all the personal data that’s already been collected by the firm.

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Published March 10, 2021 — 17:18 UTC



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Tech News

AI-Powered information operations and new citizenship

Digital information is power, and today citizens have this new power at their fingertips, channeled through reactions, comments, shares, saves and searches on our everyday digital platforms. However, this new power is ubiquitous and its direct effects remain obfuscated by the AI-powered black boxes of tech giants. 

Unfortunately, we’re many times too eager to tell Mark Zuckerberg, Sundar Pichai, or Jack Dorsey how to change their platform policies and algorithms to make the world a better place, but simultaneously failing to answer this: 

What are our responsibilities as citizens in this new reality in which digital and physical, political and commercial, and private and public are seamlessly interwoven?

Today, citizens need new skills for understanding the complex and multidimensional power of digital information and its relationship to democratic society.

AI-powered information ecosystem and new citizenship

Trying to be up-to-date on what’s happening around us is a human condition. Today, everyone is trying to use that condition to catch your attention. And increasingly, AI-powered algorithmic systems decide what kind of information gets through to you

The way information surfaces on your attention has changed. The way you can consume information has changed. The way you can evaluate information has changed. And the way you can react to information has changed. 

Controlling information has always been power—or connected to power. Today’s algorithmically amplified information operations are powered on steroids.

In today’s world election campaigns spend unprecedented amounts of resources on digital platforms, trying to target the right people in the right time in the right place. An information operation in social media can make millions of people take to the streets under the same banner across the globe. Or a social video app of a foreign origin can be used to affect how people participate unpredictably in a local political event.

At the same time, powerful personalized computational propaganda can reach you day and night wherever you are. The people, organizations and machines behind malicious information operations are using, misusing and abusing the current mainstream platforms, such as Facebook, Twitter, Instagram and Youtube, to spread radicalizing material across the globe.

As a result, the way you can manifest your citizenship online and offline has changed. Through your algorithmic information flows and interfaces you have the power to influence—directly and indirectly—on other people’s opinions and choices, on the polls and on the streets.

This fundamental change affects your capacity to use your digital tools and services in an ethical and sustainable way. 

New citizenship skills: data literacy, algorithm literacy and digital media literacy

No single platform or technology can alone solve the socio-technological challenges caused by information operations and computational propaganda. Developing methods against digital propaganda requires international multidisciplinary collaboration among tech companies, academia, societal powers, news media and educational institutions. 

But, to truly get to the bottom of the issue, we need to remember that regardless of the huge power of tech giants or new regulations affecting social media platforms, individuals do have a crucial role in making our digital platforms safer for everyone. 

Importantly, new citizenship skills are required for helping people to act more responsibly on digital platforms.

First, data literacy and algorithm literacy are needed for understanding the basic qualities and effects of data and algorithms that are constantly at work, influencing directly what you see, think and do online and beyond.  

Data literacy lets you assess and observe your data trails and their usage in different systems. Algorithmic literacy gives you a basic idea and awareness on how different AI-powered systems personalize your experience, and how the power of algorithms is used to influence your interpretations, expectations and decision-making. These skills also make you more aware of your own (data) rights in digital platforms. 

Could someone design and develop an engaging tool that would help you in achieving data and algorithm literacy, simultaneously being as frictionless as today’s mainstream social apps?  

Second, up-to-date digital media literacy allows you to make more sense of your feeds that are a continuously changing algorithmic bricolage of serious and entertaining, fact and fiction, news and marketing as well as disinformation and misinformation. 

Digital media literacy enables you to recognize benign and malicious information operations and tell the difference between deliberate spreading of disinformation and unconscious sharing of misinformation. In short, it empowers you to be more thoughtful in reacting to varying information operations that you experience online. 

Importantly, more data-aware, algorithm-informed and digital-media-literate users can demand more sustainable and ethical choices from their digital platforms. At the same time, we need a new practice of citizen experience design that brings citizen-centric thinking and values into the very core of AI design and development. 

It’s time to start talking more seriously and thoughtfully about the responsibilities of individuals, and the new citizenship skills that are required in today’s social media and tech platforms. In the long run, these new emerging citizenship skills will be crucial for the democratic societies across the globe. 

 

Published February 25, 2021 — 22:00 UTC



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Tech News

LoJack Will Wind Down its American Operations in June

LoJack, a pioneer in the field of connected cars, will wind down its American sales operations in 2021. It will stop taking purchase orders in March, but it pledged to continue supporting stolen vehicle services indefinitely.

Founded in 1986, in an era when cars and computers were still largely mutually exclusive, LoJack rose to prominence by offering motorists a stolen vehicle recovery system that law enforcement officials could directly access. This was revolutionary in the 1980s, because even new luxury cars were relatively simple to steal with basic tools. LoJack’s technology was extremely innovative: GPS wasn’t commercially available yet, so its recovery system relied on a small radio transceiver that emits a signal every 15 seconds on a frequency set aside specifically for it.

If your, say, 1990 Ford Thunderbird got stolen, police officers could find it (hopefully in one piece) by tracking its LoJack device. The transceiver helped police officers recover thousands of cars. Commercial GPS systems became increasingly common in the 1990s, however, and trackers encroached on LoJack’s turf. Then, technology like General Motors-developed OnStar gave motorists an alternative to the system that was already built into their car.

LoJack fired back by expanding its roster of features to include boundary alerts and crash detecting, and by branching out into different segments. It notably released a system that tracked stolen laptops. But, much like Nokia, it missed a turn and fell behind. California-based CalAmp purchased the company in 2016 in a bid to turn it around, but the competition (from direct rivals, from start-ups like the freshly launched RecovR, and from carmakers) was already far ahead.

CalAmp explained in a statement that it will continue to support dealership orders for Classic SVR, Connect, and Connect+ products until June 18, 2021, though it’s asking customers to submit all final purchase orders no later than March 15. Suddenly pulling the plug on the project would have a negative effect on the law enforcement officials who use its products, so it will continue to honor its service commitments with police departments indefinitely.

Surprisingly, the announcement only applies to LoJack’s American division. Its international business will continue to operate in locations like Mexico, Italy, and England, among other countries. CalAmp pointed out that its international business operates with a subscription-based business model that’s well-aligned with its strategy.

Editors’ Choice




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