Nvidia’s new liquid-cooled GPUs are heading to data centers

Nvidia is taking some notes from the enthusiast PC building crowd in an effort to reduce the carbon footprint of data centers. The company announced two new liquid-cooled GPUs during its Computex 2022 keynote, but they won’t be making their way into your next gaming PC.

Instead, the H100 (announced at GTC earlier this year) and A100 GPUs will ship as part of HGX server racks toward the end of the year. Liquid cooling isn’t new for the world of supercomputers, but mainstream data center servers haven’t traditionally been able to access this efficient cooling method (not without trying to jerry-rig a gaming GPU into a server, that is).

In addition to HGX server racks, Nvidia will offer the liquid-cooled versions of the H100 and A100 as slot-in PCIe cards. The A100 is coming in the second half of 2022, and the H100 is coming in early 2023. Nvidia says “at least a dozen” system builders will have these GPUs available by the end of the year, including options from Asus, ASRock, and Gigabyte.

Data centers account for around 1% of the world’s total electricity usage, and nearly half of that electricity is spent solely on cooling everything in the data center. As opposed to traditional air cooling, Nvidia says its new liquid-cooled cards can reduce power consumption by around 30% while reducing rack space by 66%.

Instead of an all-in-one system like you’d find on a liquid-cooled gaming GPU, the A100 and H100 use a direct liquid connection to the processing unit itself. Everything but the feed lines is hidden in the GPU enclosure, which itself only takes up one PCIe slot (as opposed to two for the air-cooled versions).

Data centers look at power usage effectiveness (PUE) to gauge energy usage — essentially a ratio between how much power a data center is drawing versus how much power the computing is using. With an air-cooled data center, Equinix had a PUE of about 1.6. Liquid cooling with Nvidia’s new GPUs brought that down to 1.15, which is remarkably close to the 1.0 PUE data centers aim for.

Energy usage for Nvidia liquid-cooled data center GPUs.

In addition to better energy efficiency, Nvidia says liquid cooling provides benefits for preserving water. The company says millions of gallons of water are evaporated in data centers each year to keep air-cooled systems operating. Liquid cooling allows that water to recirculate, turning “a waste into an asset,” according to head of edge infrastructure at Equinix Zac Smith.

Although these cards won’t show up in the massive data centers run by Google, Microsoft, and Amazon — which are likely using liquid cooling already — that doesn’t mean they won’t have an impact. Banks, medical institutions, and data center providers like Equinix compromise a large portion of the data centers around today, and they could all benefit from liquid-cooled GPUs.

Nvidia says this is just the start of a journey to carbon-neutral data centers, as well. In a press release, Nvidia senior product marketing manager Joe Delaere wrote that the company plans “to support liquid cooling in our high-performance data center GPUs and our Nvidia HGX platforms for the foreseeable future.”

Editors’ Choice

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This MacOS Trojan stealthily lifts your data, says Microsoft

You might think that your Mac is invulnerable to viruses and other security threats, but you might want to think again. As part of its commitment to intelligence sharing and collaboration, Microsoft recently exposed the evolution of a MacOS Trojan that can stealthily lift your personal data.

First spotted in September 2020, Microsoft says this piece of malware, known as UpdateAgent,  has increasingly progressed to “sophisticated capabilities.” Though it also indicated that the latest two versions are still more “refined,” Microsoft does warn that the malware is again being developed, and more updates could come soon.

It is so bad, that Microsoft believes this malware can be leveraged to fetch more dangerous payloads beyond just the adware that it is already injecting into victim machines.

But how does it work? Per Microsoft, the UpdateAgent malware can impersonate real software, and then take Mac functionalities under its own control. It is usually first installed to victim Macs by automated downloads without a user’s consent, or advertisement pop-ups, which impersonate video applications and support agents. UpdateAgent can even bypass Gatekeeper, which usually makes sure that only trusted apps can run on Macs. The Malware then takes over a machine and performs malicious acts like injecting adware.

Microsoft worked with Amazon Web Services to pull the URLs used by UpdateAgent to inject adware, but the UpdateAgent campaign has steadily evolved. It went from basic information stealer in December 2020, to the ability to fetch and deliver .DMG files in February 2021, to being able to fetch and deliver .ZIP files in March 2021.

Later in August, the malware expanded its reconnaissance function to scan and collect System_profile and SPHardwaretype information from victim machines. At its worst point in August, the malware even used permissions and wrote its own code to trick Gatekeeper into thinking it’s not even there.

“UpdateAgent is uniquely characterized by its gradual upgrading of persistence techniques, a key feature that indicates this trojan will likely continue to use more sophisticated techniques in future campaigns,” Microsoft said Microsoft.

Microsoft wasn’t clear which versions of MacOS are impacted by UpdateAgent, but it did have some advice that goes beyond using antivirus software. It pointed to using the Microsoft Edge browser, which can block and scan for malicious websites. Other tips include restricting access to privileged resources, installing apps only from the app store, and running the latest versions of MacOS and other applications.

Editors’ Choice

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Lapsus$ gang claims new hack with data from Apple Health partner

After a short “vacation,” the Lapsus$ hacking gang is back. In a post shared through the group’s Telegram channel on Wednesday, Lapsus$ claimed to have stolen 70GB of data from Globant — an international software development firm headquartered in Luxembourg, which boasts some of the world’s largest companies as clients.

Screenshots of the hacked data, originally posted by Lapsus$ and shared on Twitter by security researcher Dominic Alvieri, appeared to show folders bearing the names of a range of global businesses: among them were delivery and logistics company DHL, US cable network C-Span, and French bank BNP Paribas.

Also in the list were tech giants Facebook and Apple, with the latter referred to in a folder titled “apple-health-app.” The data appears to be development material for Globant’s BeHealthy app, described in a prior press release as software developed in partnership with Apple to track employee health behaviors using features of the Apple Watch. Apple did not a request for comment at time of publication.

Globant acknowledged the hack in a press release later the same day. “According to our current analysis, the information that was accessed was limited to certain source code and project-related documentation for a very limited number of clients,” the company said. “To date, we have not found any evidence that other areas of our infrastructure systems or those of our clients were affected.”

On Telegram, Lapsus$ shared a torrent link to the allegedly stolen data with a message announcing, “We are officially back from a vacation.”

If confirmed, the leak would show a swift return to activity after seven suspected members of Lapsus$ were arrested by British police less than a week ago.

The arrests, first reported on March 24th by BBC News, were carried out by City of London Police after a yearlong investigation into the alleged ringleader of the gang, who is believed to be a teenager living with his parents in Oxford. On the other side of the Atlantic, the FBI is also seeking information on Lapsus$ related to the breach of US companies.

The Lapsus$ gang has been remarkably prolific in the range and scale of companies it has breached, having previously extracted data from a number of well-known technology companies, including Nvidia, Samsung, Microsoft, and Vodafone.

Most recently, Lapsus$ was in the spotlight for a hack affecting the authentication platform Okta, which put thousands of businesses on high alert against subsequent breaches. The latter hack has been an embarrassment for a company that provides security services to other businesses and led to criticism of Okta for a slow disclosure.

Correction, 1:38PM ET: A previous version of this post overstated the connection between the breached data and Apple. The data labelled as “apple-health” was not data from Apple itself, but from an app developed in partnership with Apple. The Verge regrets the error.

Update 5:25 PM ET: Added statement from Globant.

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Apple and Meta shared data with hackers pretending to be law enforcement officials

Apple and Meta handed over user data to hackers who faked emergency data request orders typically sent by law enforcement, according to a report by Bloomberg. The slip-up happened in mid-2021, with both companies falling for the phony requests and providing information about users’ IP addresses, phone numbers, and home addresses.

Law enforcement officials often request data from social platforms in connection with criminal investigations, allowing them to obtain information about the owner of a specific online account. While these requests require a subpoena or search warrant signed by a judge, emergency data requests don’t — and are intended for cases that involve life-threatening situations.

Fake emergency data requests are becoming increasingly common, as explained in a recent report from Krebs on Security. During an attack, hackers must first gain access to a police department’s email systems. The hackers can then forge an emergency data request that describes the potential danger of not having the requested data sent over right away, all while assuming the identity of a law enforcement official. According to Krebs, some hackers are selling access to government emails online, specifically with the purpose of targeting social platforms with fake emergency data requests.

As Krebs notes, the majority of bad actors carrying out these fake requests are actually teenagers — and according to Bloomberg, cybersecurity researchers believe the teen mastermind behind the Lapsus$ hacking group could be involved in conducting this type of scam. London police have since arrested seven teens in connection with the group.

But last year’s string of attacks may have been performed by the members of a cybercriminal group called Recursion Team. Although the group has disbanded, some of them have joined Lapsus$ with different names. Officials involved in the investigation told Bloomberg that hackers accessed the accounts of law enforcement agencies in multiple countries and targeted many companies over the course of several months starting in January 2021.

“We review every data request for legal sufficiency and use advanced systems and processes to validate law enforcement requests and detect abuse,” Andy Stone, Meta’s policy and communications director, said in an emailed statement to The Verge. “We block known compromised accounts from making requests and work with law enforcement to respond to incidents involving suspected fraudulent requests, as we have done in this case.”

When asked for comment, Apple directed The Verge to its law enforcement guidelines, which state: “If a government or law enforcement agency seeks customer data in response to an Emergency Government & Law Enforcement Information Request, a supervisor for the government or law enforcement agent who submitted the Emergency Government & Law Enforcement Information Request may be contacted and asked to confirm to Apple that the emergency request was legitimate.”

Meta and Apple aren’t the only known companies affected by fake emergency data requests. Bloomberg says hackers also contacted Snap with a forged request, but it’s not clear if the company followed through. Krebs on Security’s report also includes a confirmation from Discord that the platform gave away information in response to one of these fake requests.

“This tactic poses a significant threat across the tech industry,” Peter Day, Discord’s group manager for corporate communications said in an emailed statement to The Verge. “We are continuously investing in our Trust & Safety capabilities to address emerging issues like this one.”

Snap didn’t immediately respond to a request for comment from The Verge.

Update March 30th 9:24PM ET: Updated to include a statement from a Discord spokesperson.

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For AI model success, utilize MLops and get the data right

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It’s critical to adopt a data-centric mindset and support it with ML operations 

Artificial intelligence (AI) in the lab is one thing; in the real world, it’s another. Many AI models fail to yield reliable results when deployed. Others start well, but then results erode, leaving their owners frustrated. Many businesses do not get the return on AI they expect. Why do AI models fail and what is the remedy? 

As companies have experimented with AI models more, there have been some successes, but numerous disappointments. Dimensional Research reports that 96% of AI projects encounter problems with data quality, data labeling and building model confidence.

AI researchers and developers for business often use the traditional academic method of boosting accuracy. That is, hold the model’s data constant while tinkering with model architectures and fine-tuning algorithms. That’s akin to mending the sails when the boat has a leak — it is an improvement, but the wrong one. Why? Good code cannot overcome bad data.

Instead, they should ensure the datasets are suited to the application. Traditional software is powered by code, whereas AI systems are built using both code (models + algorithms) and data. Take facial recognition, for instance, in which AI-driven apps were trained on mostly Caucasian faces, instead of ethnically diverse faces. Not surprisingly, results were less accurate for non-Caucasian users. 

Good training data is only the starting point. In the real world, AI applications are often initially accurate, but then deteriorate. When accuracy degrades, many teams respond by tuning the software code. That doesn’t work because the underlying problem was changing real-world conditions. The answer: to increase reliability, improve the data rather than the algorithms. 

Since AI failures are usually related to data quality and data drifts, practitioners can use a data-centric approach to keep AI applications healthy. Data is like food for AI. In your application, data should be a first-class citizen. Endorsing this idea isn’t sufficient; organizations need an “infrastructure” to keep the right data coming. 

MLops: The “how” of data-centric AI

Continuous good data requires ongoing processes and practices known as MLops, for machine learning (ML) operations. The key mission of MLops: make high-quality data available because it’s essential to a data-centric AI approach.

MLops works by tackling the specific challenges of data-centric AI, which are complicated enough to ensure steady employment for data scientists. Here is a sampling: 

  • The wrong amount of data: Noisy data can distort smaller datasets, while larger volumes of data can make labeling difficult. Both issues throw models off. The right size of dataset for your AI model depends on the problem you are addressing. 
  • Outliers in the data: A common shortcoming in data used to train AI applications, outliers can skew results. 
  • Insufficient data range: This can cause an inability to properly handle outliers in the real world. 
  • Data drift: Which often degrades model accuracy over time. 

These issues are serious. A Google survey of 53 AI practitioners found that “data cascades—compounding events causing negative, downstream effects from data issues — triggered by conventional AI/ML practices that undervalue data quality… are pervasive (92% prevalence), invisible, delayed, but often avoidable.”

How does MLOps work?

Before deploying an AI model, researchers need to plan to maintain its accuracy with new data. Key steps: 

  • Audit and monitor model predictions to continuously ensure that the outcomes are accurate
  • Monitor the health of data powering the model; make sure there are no surges, missing values, duplicates, or anomalies in distributions.
  • Confirm the system complies with privacy and consent regulations
  • When the model’s accuracy drops, figure out why

To practice good MLops and responsibly develop AI, here are several questions to address: 

  • How do you catch data drifts in your pipeline? Data drift can be more difficult to catch than data quality shortcomings. Data changes that appear subtle may have an outsized impact on particular model predictions and particular customers.
  • Does your system reliably move data from point A to B without jeopardizing data quality? Thankfully, moving data in bulk from one system has become much easier, as tools for ML improve.
  • Can you track and analyze data automatically, with alerts when data quality issues arise? 

MLops: How to start now

You may be thinking, how do we gear up to address these problems? Building an MLops capability can begin modestly, with a data expert and your AI developer. As an early days discipline, MLops is evolving. There is no gold standard or approved framework yet to define a good MLops system or organization, but here are a few fundamentals:

  • In developing models, AI researchers need to consider data at each step, from product development through deployment and post-deployment. The ML community needs mature MLops tools that help make high-quality, reliable and representative datasets to power AI systems.
  • Post-deployment maintenance of the AI application cannot be an afterthought. Production systems should implement ML-equivalents of devops best practices including logging, monitoring and CI/CD pipelines which account for data lineage, data drifts and data quality. 
  • Structure ongoing collaboration across stakeholders, from executive leadership, to subject-matter experts, to ML/Data Scientists, to ML Engineers, and SREs.

Sustained success for AI/ML applications demands a shift from “get the code right and you’re done” to an ongoing focus on data. Systematically improving data quality for a basic model is better than chasing state-of-the-art models with low-quality data.

Not yet a defined science, MLops encompasses practices that make data-centric AI workable. We will learn much in the upcoming years about what works most effectively. Meanwhile, you and your AI team can proactively – and creatively – devise an MLops framework and tune it to your models and applications. 

Alessya Visnijc is the CEO of WhyLabs


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Data leak from Russian delivery app shows dining habits of the secret police

A massive data leak from Russian food delivery service Yandex Food revealed the delivery addresses, phone numbers, names, and delivery instructions belonging to those associated with Russia’s secret police, according to findings from Bellingcat.

Yandex Food, a subsidiary of the larger Russian internet company, Yandex, first reported the data leak on March 1st, blaming it on the “dishonest actions” of one of its employees and noting that the leak doesn’t include users’ login information. Russian communications regulator Roskomnadzor has since threatened to fine the company up to 100,000 rubles (~$1,166 USD) for the leak, which Reuters says exposed the information of about 58,000 users. The Roskomnadzor also blocked access to an online map containing the data — an attempt to conceal the information of ordinary citizens, as well as those with ties to the Russian military and security services.

Researchers at Bellingcat gained access to the trove of information, sifting through it for leads on any people of interest, such as an individual linked to the poisoning of Russian opposition leader Alexey Navalny. By searching the database for phone numbers collected as part of a previous investigation, Bellingcat uncovered the name of the person who was in contact with Russia’s Federal Security Service (FSB) to plan Navalny’s poisoning. Bellingcat says this person also used his work email address to register with Yandex Food, allowing researchers to further ascertain his identity.

Researchers also examined the leaked information for the phone numbers belonging to individuals tied to Russia’s Main Intelligence Directorate (GRU), or the country’s foreign military intelligence agency. They found the name of one of these agents, Yevgeny, and were able to link him to Russia’s Ministry of Foreign Affairs and find his vehicle registration information.

Bellingcat uncovered some valuable information by searching the database for specific addresses as well. When researchers looked for the GRU headquarters in Moscow, they found just four results — a potential sign that workers just don’t use the delivery app, or opt to order from restaurants within walking distance instead. When Bellingcat searched for FSB’s Special Operation Center in a Moscow suburb, however, it yielded 20 results. Several results contained interesting delivery instructions, warning drivers that the delivery location is actually a military base. One user told their driver “Go up to the three boom barriers near the blue booth and call. After the stop for bus 110 up to the end,” while another said “Closed territory. Go up to the checkpoint. Call [number] ten minutes before you arrive!”

In a translated tweet, Russian politician and Navalny supporter, Lyubov Sobol, said the leaked information even led to additional information about Russian President Vladimir Putin’s former mistress and their alleged “secret” daughter. “Thanks to the leaked Yandex database, another apartment of Putin’s ex-mistress Svetlana Krivonogikh was found,” Sobol said. “That’s where their daughter Luiza Rozova ordered her meals. The apartment is 400 m², worth about 170 million rubles [~$1.98 million USD]!”

If researchers were able to uncover this much information based on data from a food delivery app, it’s a bit unnerving to think about the amount of information Uber Eats, DoorDash, Grubhub, and others have on users. In 2019, a DoorDash data breach exposed the names, email addresses, phone numbers, delivery order details, delivery addresses, and the hashed, salted passwords of 4.9 million people — a much larger number than those affected in the Yandex Food leak.

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Report: Data and enterprise automation will drive tech and media spending to $2.5T

Join gaming leaders, alongside GamesBeat and Facebook Gaming, for their 2nd Annual GamesBeat & Facebook Gaming Summit | GamesBeat: Into the Metaverse 2 this upcoming January 25-27, 2022. Learn more about the event. 

According to a new report released by Activate Consulting, the global technology and media spend will balloon to $2.5 trillion by 2025.  This analysis comes as 2021 netted a spend of more than $2 trillion.

The report indicates that one of the major drivers of this tech boom will be data solutions and enterprise automation.  According to the report, “Activate Technology and Media Outlook for 2022,” a set of new companies are paving the way for the future, delivering infrastructure, tools, and applications that will enable all enterprises to operate and innovate as if they were major technology companies.

Businesses and consumers can expect to see accelerated development of customer experiences, better (faster, less bureaucratic) employee experiences, improved intelligence and decision-making, and improved operational and financial efficiency as a result.  Technology like autonomy (self-driving cars, home automation), voice recognition, AR/VR, gaming and more will enable end-user experiences while enterprises will become more productive in their marketing effectiveness, IT service management, cross-planning and forecasting, and more.

New data startups are spurring the next era of innovation.  They’re focusing on leveraging data and information, improving end-user experience, and improving storage and connectivity — all of which will drive the business-to-business and business-to-consumer experiences of the future.


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According to the report, more than 80% of the companies driving this innovation are U.S.-based, half of which are headquartered in the Bay Area.  They’re growing fast thanks to large venture capital infusions – and many of these startup companies have scaled at an unprecedented pace.  Fifteen of them have raised more than $1 billion since their launch.

In order for the next generation of companies to reach their full potential, the report indicates they must zero in on three specific areas of focus: strategy and transformation, go-to-market pricing, as well as their sales and marketing approach.

Read the full report by Activate Consulting.


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Hyperscience buys Boxplot to process and store data in one platform

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Hyperscience, a New York-based machine learning company that enables human-centric enterprise automation, today announced the acquisition of Boxplot, a Berlin, Germany-based startup that provides a graph data modeling tool. Boxplot claims it enables companies to visualize and better understand their customers’ data and explore how it relates to other data across their organization.

A report by Gartner shows the challenge of unstructured data in enterprises, with 80% to 90% of all enterprise data in an unstructured format. Gartner’s Magic Quadrant for Distributed File Systems and Object Storage also estimates large enterprises will triple their unstructured data capacity stored as file or object storage on-premises, at the edge, or in the public cloud by 2026. Gartner also reports that end-users further indicate a 30% year-over-year growth in unstructured data.

With more unstructured data in the world today, enterprises are seeking modern data tools to stay above the dark data problem. Hyperscience says its machine learning platform empowers organizations to read and understand unstructured data at scale — causing a tenfold acceleration of their processing times. The company claims that the combination of both technologies from Hyperscience and Boxplot will drive faster and improved decisions for organizations — such as how to price insurance, who to give a mortgage to, which invoices to pay out, and where fraud exists.

In a press release, Hyperscience reported that the deal marks the company’s first acquisition. In an interview with VentureBeat, Peter Brodsky, CEO, and cofounder of Hyperscience, shared more context on the acquisition and detailed the complex capabilities and intricacies of both companies’ technologies.

Elevating organizational agility and improving customer experience

An article published earlier this year by McKinsey highlights quick answers to strategic questions in the decision-making process as “one of the biggest advantages of an automated, data-driven AI system.” Brodsky said. The goal of Hyperscience’s acquisition of Boxplot is to enable the company to offer a single platform that processes and stores data — a solution that he claims will “elevate organizational agility and improve customer experience across enterprises.”

To foster the company’s vision of human-centered automation, Brodsky noted that Hyperscience wants to help companies turn antiquated business processes into modern, highly automated, flexible digital assembly lines that are deeply human-centric.

“Our human-centered automation means we want people involved every step of the way. We embrace the way people work, so our automation bends to the needs of people rather than the other way around,” Brodsky said. “What Boxplot, the company that we are acquiring, does is that it stores data, not how machines are typically used to store data, but in the way that data actually is in the real world. And so, it’s a much closer representation of our human-centric automation. And that’s really the magic fit between the two companies: We do the processing, while they do the modeling and storage of the data.”

Hyperscience asserts that it will help companies deliver better outcomes to their customers through data organization. “Organizing data into customer-defined graphs (i.e., networks of information, where each piece of data is linked by a relationship to other pieces of data) is important when an organization wants to deploy multiple business processes, as this data will often be shared,” the company’s press release stated. “For example, when processing an insurance claim, it’s important to retrieve the claimant’s policy. That claim needs to be linked to a person, who is in turn linked to a policy.”

Many customers currently perform that lookup manually by diving in and out of various record systems, the company noted, adding that “Boxplot will provide the backplane for business process interoperability, while simultaneously making Hyperscience a system of record, enabling more automation and better machine learning performance.”

A human-centric differentiation for data storage

Brodsky noted that the company’s human-centric focus is precisely what differentiates it from its competitors in the industry. Rather than make people adhere to the way machines work, Hyperscience has gone the other way, programming machines to operate on human-readable data including emails, Microsoft Word documents, PDFs, and more.

“This is how people in businesses communicate with each other and do work. Rather than have people sitting inside a database or a CRM, we let people work the way they work, and the automation we provide works with people’s exact kind of data,” Brodsky said. “The key observation we’ve made is that it’s very hard to change people. It’s much easier to build AI that works the same way or in comparable ways as people do.”

According to Brodsky, Hyperscience’s machine-centric competitors fall into two categories:

  1. Companies that provide retrofit automation, layering automation on top of business processes they take from existing technologies.
  2. Companies that try to deal with human content using legacy technologies like OCR and speech recognition, to get out of the human layer and immediately back into the machine layer.

“Our technology is free from the traditional way of approaching data storage and retrieval, which is typically tables and columns, and very clumsy CRM that’s often not flexible,” he added.

One of Hyperscience’s largest competitors is  DataBank, a Pennsylvania-based technology company that “provides automation business intelligence and enterprise software consulting.” Another major competitor is Decisions, “a company offering a business rule automation and workflow management platform that helps in business optimization.” Some other competitors include Pipefy, Akkio, and Amazon Textract.

The synergy between Hyperscience and Boxplot 

Boxplot is similar to Hyperscience, except instead of processing data, it focuses on data storage, modeling the relationships between the data — according to Brodsky. “Where the synergy lies between what we’ve built and what Boxplot has built is that we’re very good at understanding data, but we don’t record it in its natural form in any way. What Boxplot enables us to do is structure data and preserve it with other pieces of software it can easily operate on.”

“Boxplot fits into our vision for how automation will play out in the future. This acquisition represents an important step forward for us and for the market. The team coming on board will fit well into Hyperscience, and continue to drive us forward to our human-centered automation vision,” Brodsky said.

“Hyperscience and Boxplot complement each other’s technological innovations and like-minded vision for organizations of the future. Their market-leading automation and machine learning capabilities for data structuring, combined with our graph-based enterprise operating system, will be a powerful combination,” Fabian Schmidt-Jakobi, CEO and cofounder of Boxplot,  said in the press release.”


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Instabase adds deep learning to make sense of unstructured data

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Whether they realize it or not, most enterprises are sitting on a mountain of priceless, yet untapped, data. Buried deep within PDFs, customer emails, and scanned documents is a trove of business intelligence and insights that often have the potential to inform critical business decisions – if only it can be extracted and harnessed, that is.

Instabase hopes to help businesses benefit from unstructured data with the help of some good, old-fashioned AI. Today, the business automation platform provider is announcing a set of new deep learning-based tools designed to help enterprises more easily extract and make sense of this unstructured data and build applications that will help them put it all to use.

“Unlocking unstructured data, which is 80% of all enterprise data, is an extremely difficult problem due to the variability of the data,” says Instabase founder and CEO Anant Bhardwaj. “Deep learning algorithms provide greater accuracy as the algorithm learns from the entirety of each training document and identifies many different attributes to make its decision, much like a human does.”

Instabase’s new deep learning features offer low-code and no-code functionality that’s designed to let Instabase customers tap into sophisticated deep learning models and train, run, and make use of these models for their business’s needs. Using drag-and-drop visual development interfaces, Instabase customers can build customized workflows and business applications powered by best-in-class deep learning models.

“These deep learning models have already been trained on very large sets of data and as a result, fewer samples are needed to fine-tune the model for a specific use case,” Bhardwaj explains. “That means enterprises can tackle use cases never before possible, build solutions faster and at unprecedented accuracies for their unstructured data use cases.”

Founded in 2015, Instabase uses technology like optical character recognition and natural language processing to extract and decipher data that is far too often buried in formats that can be difficult for machines to understand. The platform provider, whose customers include companies in the financial services, medical, and insurance industries, hopes that by tapping into this unstructured data, it can help companies automate more of their business processes, inform key decisions, and further their own digital transformation.

With the addition of its new deep learning infrastructure, Instabase hopes to make this unstructured data analysis even faster and more impactful. Using the platform’s Machine Learning Studio, Instabase customers can annotate data points within documents and spin up and train a custom model, which can then be used by others within the organization. The new features also include a Model Catalog, which offers plug-and-play access to a library of deep learning models built by Instabase and other providers.

The platform’s new deep learning features will be publicly available in early 2022.


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How to protect your email from spam and data collectors

Nowadays, it seems like everyone on the web wants your email. When you give your email address to a site in order to log in or just get some info, many companies will take that as a free pass to do anything from relentlessly spamming you with newsletters to selling your email address to advertisers, who will in turn track you relentlessly.

Thankfully, there are also people building tools that can make things harder for the companies trying to get your email address.

There are two ways you can keep your real email address more private in different situations: relay services and temporary inboxes. By the time we’re done, you’ll hopefully be able to fill in fields asking for your email without worrying about who’s getting your “real” digital address.

Email relay services

One way to keep your email address private and keep spam out of your inbox is by using an email relay service. The basic concept behind these services is that they generate a “virtual” email address you can give to apps and websites; any emails sent to that address will automatically get forwarded to your real inbox.

Many of these services will let you create and manage multiple aliases — for example, you could have one alias for all your newsletters, and another that you give out to shopping sites. With services that allow unlimited aliases, there’s nothing to stop you from creating a new one every time you give your email to a company, if you really want to make it difficult to aggregate your email presence.

Another common feature these services provide is letting you turn off email forwarding, so that you won’t get any spam or other email from that site. Using our previous example, you could turn off forwarding when you use your shopping sites alias, keeping any tempting sales out of your inbox.

There’s no shortage of these relay services available, but we’ve rounded up a sampling of them that are either built into tools you may already have available, or that are well-regarded platform agnostics.

Apple’s Hide My Email / Sign In With Apple

If you use Apple devices, the company includes an email relay service called Hide My Email with its paid iCloud Plus plans. If you have iCloud Plus and are using an Apple device, you should see a “Hide My Email” option pop up when you select the email field on a website’s signup page. Selecting the option will auto-generate a private email address for you, which will then be forwarded to the email address you use for your Apple account.

Apple’s iCloud Plus plans include an email forwarding service.

You can also create a private email address outside of the signup process if you want to use it on a signup form for, say, a game console or an app on Windows.

To do so using your iPhone or iPad:

  • Go to Settings and tap your name
  • Go to iCloud > Hide My Email > Create New Address.

On a Mac:

  • Go to System Preferences > Apple ID
  • Select iCloud from the sidebar
  • Click Options next to Hide My Email

You can add a label and notes to help you remember what the address is for, and you can manage your existing email aliases from the Hide My Email screen.

Notes can help you remember what you’re using the alias for, making it easier to manage multiple ones.

While Hide My Email is only available via the paid iCloud Plus plan, you can also use a form of it for free with Apple’s Sign In with Apple service. On supported sites, you’ll see a Sign In with Apple button, which lets you use your Apple ID to set up and log into accounts. If you use Sign In, Apple also lets you hide your email address from the actual site.

To take advantage of this, when you’re setting up your account using Apple’s UI (it will pop up after hitting the “Continue with Apple” button that supported sites use), select the Hide My Email option instead of the Share My Email option. This will more or less work the same way as the paid service — Apple will give the website a randomly generated email address, and forward any email sent to it to the address associated with your Apple ID. Since you’ll be logging in using the Continue With Apple button, you won’t have to keep track of the private email address Apple generates.

Checking the Hide My Email option will have Apple forward you emails.

You can manage the email settings for apps and services you use with Sign In with Apple from either a mobile device or a desktop.

To do so using iOS:

  • Go to Settings and tap your name.
  • Go to Password and Security > Apps Using Apple ID. (If you have iCloud Plus, go instead to iCloud > Hide My Email.)
  • To stop receiving emails from a service, tap its name, then turn off the Forward To toggle.

If you have iCloud Plus, this process will look slightly different. On iOS:

  • Go to Settings and tap your name.
  • Go to iCloud > Hide My Email.
  • To stop receiving emails from a service, tap the service’s name and turn off the Forward To toggle.

Turning off email forwarding can be done on the Mac or iOS devices.

On a Mac, the process is the same whether or not you pay for iCloud Plus.

  • Go to System Preferences > Apple ID.
  • Select Password and Security from the sidebar.
  • Click the Edit button next to Apps Using Apple ID, and select the app you want to change settings on from the sidebar.
  • If you’re using email forwarding, there will be a button labeled “Turn Off” in the Forward To section.

You can also manage your email settings on the web by going to, clicking Sign In with Apple, selecting the app you want to manage, and clicking the Manage Settings button.

Firefox Relay

Firefox Relay may not be the most feature-rich option, but that makes it very easy to use.

Firefox Relay is an alias service made by Mozilla, the same company behind the popular web browser. With the Firefox Relay extension installed in your Firefox browser, you get a button that’ll show up in email fields, letting you automatically generate an alias. The extension also provides a quick link to a settings page you can use to manage your aliases: turning them off or on, renaming them, or deleting them outright. You can also use the settings page to generate a new alias a la carte, and can access the page to view and manage your aliases on other browsers if you want.

One of the obvious advantages (assuming you’re a Firefox user) of Relay is that it’s tied to your Firefox account — that way, you can sign in once when you’re setting up a new device and get all your bookmarks, preferences, VPN service, and email relay controls.

Unfortunately, its free tier is very limited — you can only have up to five aliases, so you’d very likely be using each alias as a category (shopping, social, etc) rather than assigning individual sites their own alias. You also can’t reply to the emails the service forwards to you using the free tier.

Mozilla has recently introduced a 99-cent-per-month premium tier, which lets you create as many aliases as you want, and reply to forwarded emails that are less than three months old.

You can read Firefox’s FAQ about the service here. While Relay is not as feature-rich as the next service we’ll cover, it is by far one of the simplest options (if you’re a Firefox user, of course). Installing the extension is as simple as going to its page on the Firefox add-on store and clicking install.


AnonAddy has a clean, simple dashboard.

AnonAddy is a web-based forwarding service that you can use with any device or browser. The free tier lets you create unlimited aliases and manage them however you need — you can deactivate an alias associated with any sites whose emails’ unsubscribe button doesn’t seem to do its job (or doesn’t exist at all), and enjoy not being bombarded.

A standout feature of AnonAddy is that you can have emails for a specific alias forward to multiple addresses (up to two using the free tier). This could be useful if, for example, you wanted to create a “bills” alias that you use for subscription services, and whose emails get sent to both you and a partner.

AnonAddy puts a useful header above forwarded messages. You even get a link to deactivate the alias if you’re getting too much spam.

The free version of AnonAddy probably isn’t the best option if you expect to be getting a lot of media-heavy emails sent to your aliases — it has a 10MB a month limit, which is generous for text but could be eaten up quickly by pictures.

AnonAddy has browser extensions for Firefox and Chrome-based browsers like Edge, Brave, and obviously Chrome (though sadly, it doesn’t offer one for Safari).

If you’ve got a lot of technical skill, you can also self-host AnonAddy on your own server. If you want to use an email aliasing system, but don’t want your emails being handled by someone else (or don’t want to pay a $1 per month fee for a 50MB bandwidth cap, or a $3 per month fee for unlimited data), the fact that you can roll your own AnonAddy instance makes it worth a look.


You can add a SimpleLogin alias with a single click of a button.

SimpleLogin is another project similar to AnonAddy, though there are different tradeoffs; for example, you get unlimited bandwidth with the free tier, but can only have up to 15 aliases. While it doesn’t have an easy way to replicate the multi-delivery setting of AnonAddy, it’s got tons of settings you can change, so if you’ve got very particular needs it may be the way to go. You can also self-host it, if you’re technically able.

SimpleLogin also has iOS and Android apps for managing your aliases.

DuckDuckGo Email Protection

DuckDuckGo’s email protection service is still in beta.
Image: DuckDuckGo

DuckDuckGo, the privacy-focused search provider, also has its own email relay service that you can set up using its mobile app. In addition to giving you an adorable “” email address to use as an alias, the company also promises that its service will strip out trackers embedded in any emails it’s forwarding you.

We have a separate guide on how to use the service, but before you click over to it there are a few caveats to note. Email Protection is still in beta, so you might not want to use it for absolutely critical emails just yet. It’s also got a waitlist to join (DuckDuckGo says the wait time is “less than a month”), so it may not be the best option if you’re looking to start on this journey right away.

If you subscribe to both 1Password and Fastmail, you can use your password manager to easily create new aliases using Fastmail’s built-in Masked Email feature. To start, go to Fastmail’s website and log in to your account. Then go to Settings and select Masked Email from the Account section in the sidebar. If you don’t use 1Password, you can use this screen to set up an alias (and manage existing ones) by clicking the New Masked Email Address button.

To set up 1Password integration, click the Connect to 1Password button in Fastmail, and log into your account. You’ll then get prompts from both 1Password and Fastmail, asking you to confirm that you want your accounts connected, and you may need to enter your 1Password password one last time. Then your accounts will be linked, and the 1Password browser extension should start letting you create masked emails when you’re on a site’s signup page — unless you use Safari, but we’ll touch on that in a moment.

Clicking Create Masked Email will give you a popup showing the generated address.

Now, clicking the Create Masked Email button in your browser’s iPassword extension will generate an alias for you. You can then click the “Fill Email” button to plug it into the signup form you’re filling out. Of course, 1Password will offer to save the login to your password manager, and generate a random password for your new account as well.

Unfortunately, this experience is a bit murky for Safari users. The 1Password app in the App Store includes a Safari extension, but it’s different from the one you’ll find for other browsers like Firefox and Chrome. Currently, it doesn’t support the Masked Email feature.

The default 1Password Safari extension doesn’t support Masked Email, but the alternate extension has some serious drawbacks.

You can, however, download the separate 1Password for Safari extension from the Mac App Store. After installing it, you’ll have to enable it by going to Safari > Preferences > Extensions and checking the box next to 1Password for Safari (and unchecking the box next to regular 1Password, or else they’ll fight). This method does have some serious drawbacks; 1Password for Safari doesn’t integrate with the current Mac desktop app, so you can’t unlock it with Touch ID. 1Password says this should be changing in the future, but for right now you may just want to go with another solution if you use Safari.

Use a temporary burner email

Temporary email services are good for situations where you only need to receive an email from a sender once.

There are some times when you really only need to receive an email or two from a site — say, it wants to send you a confirmation code, or it stubbornly insists that you enter your email to get results instead of just showing them to you. In those cases, instead of spinning up an entire new alias, you could use a temporary email service like GuerrillaMail or TempMail. These services do what they say on the tin — provide you with a temporary email address that you can use to receive one or two messages, and then never think about again.

In my experience, some sites may try to keep you from using a temporary email address, saying that they’re invalid. Usually that’s not a great sign, but you can sometimes get around it by trying a different service — if the signup form blocks 10MinuteMail, you could try EmailOnDeck, or vice versa.

While these methods are helpful for new services, if you want to keep people who already have your email address from tracking you, you can check out our guide to blocking tracking pixels.

We’ve also written before about Gmail’s built-in alias-like system, where you can give companies your Gmail address with a plus sign and ID tacked onto the end; something like “” The emails will still show up in your MyEmailAddress inbox, and it could make it more difficult for hackers that obtain one of your logins to guess others.

However, we wouldn’t recommend it if you’re concerned about privacy. While it could currently help prevent companies from automatically aggregating your footprint, you are still giving them your base email address — it would be trivial to have tracking systems filter out the plus symbol and extra info after it, if enough people start using this trick. Thankfully, plenty of the options above should help you get some email privacy.

Repost: Original Source and Author Link