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Quantexa raises $64.7 million for AI platform that extracts insights from big data

July 23, 2020 Big Data

Big data analytics startup Quantexa today closed a $ 64.7 million financing round, which a spokesperson told VentureBeat will be put toward accelerating the company’s product roadmap and expansion in Europe, North America, and Asia Pacific regions. It comes after a year in which Quantexa landed customers like SBC, Standard Chartered Bank, and OFX and expanded the availability of its platform to more than 70 countries.

Enterprises have multiple data buckets to wrangle — upwards of 93% say they’re storing data in more than one place — and some of those buckets inevitably become underused, partially used, or forgotten. A Forrester survey found that between 60% and 73% of all data within corporations is never analyzed for insights or larger trends, while a separate Veritas report found that 52% of information stored by organizations is of unknown value. The opportunity cost of this unused data is substantial, with the Veritas report pegging it as a cumulative $ 3.3 trillion by the year 2020 if the current trend holds.

Quantexa uses AI tools to uncover risk and opportunities by providing a view of data in a single place, solving challenges across financial crime, customer intelligence, credit risk, and fraud. The company’s contextual decision intelligence platform connects tens of billions of internal and external data points to create a single overview, enriched with information about relationships between people, organizations, and places.

Quantexa’s entity resolution technology connects internal and external data sources without unique match keys, in real time or batch, and generates a single enterprise-wide portal of profiles even with poor data quality. Network generation links resolve entities into a network of relevant, real-world connections, producing different networks for different use cases that reveal the context of how people, organizations, places, and transactions relate to each other.

On the analytics side, Quantexa leverages resolved entities and network context to build features, scenarios, and models, increasing accuracy. It helpfully manages dependencies between scores, alerts, and data and develops analytics that can be run dynamically, with interfaces supporting a range of languages and libraries. The platform also delivers visualizations that enable thousands of users to search, graph, and explore content while investigating and thematically analyzing it in a single dashboard and reviewing flags within a context, highlighting points of interest.

For one insurance customer, Quantexa says, it helped operationalize data to uncover the connections between events, such as claims and policy renewals, as well as people, places, and organizations. It’s scaling to large data records and using context to provide actionable information for investigators and underwriters, providing teams with a view of all customer data while offering risk assessment services and self-service options.

Quantexa says the funding round announced this week — a series C that brings its total raised to date to $ 90 million — was led by Evolution Equity Partners with “major participation” from investors Dawn Capital, AlbionVC, HSBC, and ABN AMRO Ventures. As a part of it, Evolution Equity Partners founding and manager partner Richard Seewald joined the board of directors.

Founded in 2016, Quantexa now has over 250 employees across offices in New York, Boston, Belgium, Toronto, Singapore, Melbourne, and Sydney. It’s headquartered in London.

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Big Data – VentureBeat

Skydio raises $100 million, announces enterprise-focused drone lineup

July 13, 2020 Big Data

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Redwood City-based drone startup Skydio today revealed it raised $ 100 million, bringing its total raised to $ 170 million. Coinciding with the fundraising, it unveiled a new family of drones — the X2 — and software solutions designed to simplify inspections and enterprise-oriented workflows.

Skydio is the brainchild of Adam Bry, Abe Bachrach, and Matt Donahoe, the last of whom worked as a research assistant at MIT’s Media Lab. Bry and Bachrach — fellow MIT students in the college’s robotics program — spent years researching ways to build aircraft that could fly themselves without GPS, culminating in a fixed-wing autonomous drone. They later contributed to the Google X labs project that became Wing, which designs delivery drones deployed in parts of Australia, Finland, and Virginia. Years afterward, they set out to engineer a system that could power self-navigating drones using commodity hardware.

X2 follows the October 2019 launch of the Skydio 2, and it pairs the company’s autonomous navigation tech with a ruggedized airframe, folding arms for “pack and go” transportation, a thermal camera, and up to 35 minutes of flight time. X2 is equipped with six 4K navigation cameras for obstacle avoidance and a dual-sensor payload that includes 12MP color and LWIR sensors. It’ll come with a new “enterprise-grade” Enterprise Controller with a built-in touch screen, glove-compatible controls, and a wireless system to extend range up to 6.2 kilometers (3.8 miles), and it’ll initially be available starting in Q4 2020 in two configurations:

Skydio X2D, which is built to meet the Short-Range Reconnaissance (SRR) requirements for the U.S. Army. Skydio says it’s tailored to military, defense, reconnaissance, search and rescue, and security patrol missions.
Skydio X2E, which targets enterprises, first responders, and civilian agencies. The company says it’s designed for situational awareness and inspection of infrastructure.

The X2 might be perceived as a doubling down on Skydio’s controversial work with governments. As Wired reports, Skydio recently won a contract with the Drug Enforcement Agency, and the company has agency contracts worth at least $ 7 million including with the U.S. Air Force and Army. Skydio drones are also use by law enforcement in Chula Vista, California, which last week won FAA approval to fly beyond an operator’s line of sight.

“Skydio is firmly committed to developing drone technology, and the rules that govern the use of drones, in a manner that protects privacy and civil liberties,” a spokesperson told VentureBeat last month in a statement. “In our work with government customers, Skydio highlights and promotes the bedrock obligation to uphold civil liberties — including the First Amendment freedom to peaceably assemble and petition for change. [We] stand with those demonstrating for justice and equality, and we condemn anyone — uniformed, elected, or otherwise — who seeks to bring them harm. We are committed to developing technology in a responsible manner that advances both safety and liberty.”

Like Skydio 2, the X2 will benefit from the company’s Autonomy Core, which handles navigation with 360-degree, real-time obstacle avoidance, object detection, and motion prediction. Leveraging advanced computer vision and deep learning, Autonomy Core creates a 3D understanding of the drones’ surroundings in real-time and predicts into the future to make intelligent decisions, dynamically controlling the drone’s flight to avoid obstacles even in GPS-denied environments.

Both the X2 series and Skydio’s existing drones can be enhanced with the forthcoming Autonomy Enterprise Foundation, an aftermarket software upgrade that enhances Autonomy Core with pilot-assist capabilities like 360-degree zoom up to 100 times digital zoom, precision mode for flights within 0.5 meters of obstacles, and a vertical mode that captures images by looking straight up overhead.

Another headliner of Autonomy Enterprise Foundation is 3D Scan, which is designed for inspections of complex industrial structures and locations including bridges, building facades, energy infrastructure, power plants, power stations, railyards, accidents, and crime scenes. Skydio claims that 3D Scan enables a fully automated, structure-agnostic process that doesn’t require any prior knowledge of the inspected structure and can operate without an internet connection. Operators can specify the area or volume to inspect, pick a desired imaging resolution, and the AI-powered Skydio 3D Scan does the rest, autonomously imaging all surfaces with precision by dynamically building a real-time map as the drone flies. The resulting imagery has no gaps in captured data and is optimized to ensure effective photogrammetry.

X2 also boasts House scan, a feature developed in partnership with aerial imagery provider Eagleview. House scan, which will be available in Q4 2020, enables home insurance agents to automatically perform inspection of residential homes.

Skydio — whose customers include Civil Air Patrol, the Ohio Department of Transportation, the North Carolina Department of Transportation, Southern Company, Chula Vista PD, and Japan Infrastructure Waymark — says it plans to use the aforementioned series C funding to accelerate product development and go-to-market expansion in enterprise and public sector markets. Next47 led the round with participation from Levitate Capital, NTT DOCOMO Ventures, and existing investors including Andreessen Horowitz, IVP, and Playground.

The company also today announced three additions to its C-Suite. Former Signalfx COO Mark Cranney will join as chief operating officer alongside Brendan Groves, Skydio’s new head of regulatory and policy affairs who previously served as associate deputy attorney general at the Justice Department. And Alberto Farronato, who formerly held positions at VMware and Mulesoft, has been appointed Skydio’s chief marketing officer.

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Big Data – VentureBeat

Aryballe raises $7.9 million for odor-detecting AI sensors

July 10, 2020 Big Data

Aryballe, a startup developing biochemical sensors, optics, and AI to collect and analyze odor data, today announced it has raised €7 million ($ 7.9 million). The funds will bolster product and machine learning R&D as Aryballe seeks to expand its customer base, a spokesperson said.

Digital olfaction could very well be the next wave in smart sensors. Automotive manufacturers, food and beverage producers, and personal care and cosmetics companies are increasingly piloting aroma sensors to improve their customer experience. Google researchers recently trained an AI model to identify molecules and predict smells. Intel similarly demonstrated a chip that could detect 10 different odors, following IBM’s work on an “artificial tongue” designed to fingerprint various liquids.

Grenoble, France-based Aryballe, which was founded in 2014, released its first product in early 2018. Called the NeOse Pro, it mimics the human brain’s process for identifying aromas and differentiating between them.

Organic and inorganic substances release odor molecules in response to variations in energy and biological processes. When energy increases, evaporation occurs and it becomes possible to absorb odors — otherwise known as volatile organic compounds (VOCs) — through the nasal cavity. This stimulates olfactory neurons and subsequently the olfactory bulb, which in turn prompts the olfactory cortex to identify the smells and their sources.

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Solutions like the NeOse Pro and Aryballe’s silicon photonics platform bind odor molecules to biosensors that stand in for the olfactory bulb and produce signatures in near-real time. Once signatures are detected, software correlates them with over 500 entries in a database of previously collected and analyzed odors. AI and machine learning allow the biosensors to better detect lower concentrations of odors over time.

Aryballe asserts that digital olfaction can inform business decisions — like rejecting or approving raw materials — while reducing the time spent developing products. It could, for example, help determine whether new formulas taste similar to the original. In the automotive domain, products like the NeOse Pro can be used to detect car fluid leaks and control branded “new car smell” odors, or even sniff out signs of overheating by monitoring for burning wire odors.

Investor Hyundai is using NeOse Pro technology to let drivers monitor their car’s interior. International Flavors & Fragrances, another investor, is adapting the startup’s technology to use cases in food, fragrance, cosmetics, and other industries.

Last year, Aryballe launched the Digital Olfaction Automotive Consortium (DOAC), which aims to establish standards for odor measurement in the automotive industry and inform olfaction product development and services. More recently, the company announced a new odor sensor with the same sensitivity as the NeOse Pro that’s smaller than a paperclip, more durable, and designed to be integrated with smart sensor networks. The first samples shipped to customers in the second quarter of this year.

This latest funding round brings Aryballe’s total raised to date to €17 million ($ 19.2 million) and comes after a year in which the company’s headcount grew 30%. Samsung Ventures and Groupe SEB’s Seb Alliance led the round, with contributions from existing investors Innovacom, Cemag Invest, Asahi Kasei, and HCVC.

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Big Data – VentureBeat

MIT takes down 80 Million Tiny Images data set due to racist and offensive content

July 2, 2020 Big Data

Creators of the 80 Million Tiny Images data set from MIT and NYU took the collection offline this week, apologized, and asked other researchers to refrain from using the data set and delete any existing copies. The news was shared Monday in a letter by MIT professors Bill Freeman and Antonio Torralba and NYU professor Rob Fergus published on the MIT CSAIL website.

Introduced in 2006 and containing photos scraped from internet search engines, 80 Million Tiny Images was recently found to contain a range of racist, sexist, and otherwise offensive labels such as nearly 2,000 images labeled with the N-word, and labels like “rape suspect” and “child molester.” The data set also contained pornographic content like non-consensual photos taken up women’s skirts. Creators of the 79.3 million-image data set said it was too large and its 32 x 32 images too small, making visual inspection of the data set’s complete contents difficult. According to Google Scholar, 80 Million Tiny Images has been cited more 1,700 times.

MIT takes down 80 Million Tiny Images data set due to racist and offensive content

Above: Offensive labels found in the 80 Million Tiny Images data set

“Biases, offensive and prejudicial images, and derogatory terminology alienates an important part of our community — precisely those that we are making efforts to include,” the professors wrote in a joint letter. “It also contributes to harmful biases in AI systems trained on such data. Additionally, the presence of such prejudicial images hurts efforts to foster a culture of inclusivity in the computer vision community. This is extremely unfortunate and runs counter to the values that we strive to uphold.”

The trio of professors say the data set’s shortcoming were brought to their attention by an analysis and audit published late last month (PDF) by University of Dublin Ph.D. student Abeba Birhane and Carnegie Mellon University Ph.D. student Vinay Prabhu. The authors say their assessment is the first known critique of 80 Million Tiny Images.

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Both the paper authors and the 80 Million Tiny Images creators say part of the problem comes from automated data collection and nouns from the WordNet data set for semantic hierarchy. Before the data set was taken offline, the coauthors suggested the creators of 80 Million Tiny Images do like ImageNet creators did and assess labels used in the people category of the data set. The paper finds that large-scale image data sets erode privacy and can have a disproportionately negative impact on women, racial and ethnic minorities, and communities at the margin of society.

Birhane and Prabhu assert that the computer vision community must begin having more conversations about the ethical use of large-scale image data sets now in part due to the growing availability of image-scraping tools and reverse image search technology. Citing previous work like the Excavating AI analysis of ImageNet, the analysis of large-scale image data sets shows that it’s not just a matter of data, but a matter of a culture in academia and industry that finds it acceptable to create large-scale data sets without the consent of participants “under the guise of anonymization.”

“We posit that the deeper problems are rooted in the wider structural traditions, incentives, and discourse of a field that treats ethical issues as an afterthought. A field where in the wild is often a euphemism for without consent. We are up against a system that has veritably mastered ethics shopping, ethics bluewashing, ethics lobbying, ethics dumping, and ethics shirking,” the paper states.

To create more ethical large-scale image data sets, Birhane and Prabhu suggest:

Blur the faces of people in data sets
Do not use Creative Commons licensed material
Collect imagery with clear consent from data set participants
Include a data set audit card with large-scale image data sets, akin to the model cards Google AI uses and the datasheets for data sets Microsoft Research proposed

The work incorporates Birhane’s previous work on relational ethics, which suggests that the creators of machine learning systems should begin their work by speaking with the people most affected by machine learning systems, and that concepts of bias, fairness, and justice are moving targets.

ImageNet was introduced at CVPR in 2009 and is widely considered important to the advancement of computer vision and machine learning. Whereas previously some of the largest data sets could be counted in the tens of thousands, ImageNet contains more than 14 million images. The ImageNet Large Scale Visual Recognition Challenge ran from 2010 to 2017 and led to the launch of a variety of startups like Clarifai and MetaMind, a company Salesforce acquired in 2017. According to Google Scholar, ImageNet has been cited nearly 17,000 times.

As part of a series of changes detailed in December 2019, ImageNet creators including lead author Jia Deng and Dr. Fei-Fei Li found that 1,593 of the 2,832 people categories in the data set potentially contain offensive labels, which they said they plan to remove.

“We indeed celebrate ImageNet’s achievement and recognize the creators’ efforts to grapple with some ethical questions. Nonetheless, ImageNet as well as other large image datasets remain troublesome,” the Birhane and Prabhu paper reads.

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Big Data – VentureBeat

Enko Chem raises $45 million to develop crop protection solutions with AI

June 29, 2020 Big Data

Enko Chem, which harnesses machine learning to help farmers sustainably protect their crops from pests and disease, has raised $ 45 million. Founder and CEO Jacqueline Heard said the funds will be used to expand the Boston-based startup’s technology platform and ready its pipeline of crop protection solutions for field testing and regulatory trials. Enko also plans to continue pursuing studies and collaborations with industry partners as part of its broader mission.

Protecting crops from pests is no easy feat. A recent study showed that worldwide, over 600 species of insects and fungi have developed a level of pesticide resistance, threatening losses in yield of between 10.1% and 40.9% for crops like rice, corn, potatoes, and soybeans. In the U.S. alone, insecticides are used to protect crops on approximately 45 million acres, and it’s estimated yields would decrease by 40% to 50% without treatment.

Enko, which was founded in 2017 and incubated by Anterra Capital, a venture fund focused on startups addressing food supply chain challenges, aims to discover safe, economical solutions that promote sustainable staple crop production. The company produces large discovery data sets that are amenable to machine learning and AI models. In tandem with predictive analytics tools, these data sets ostensibly decrease design cycles and bolster success in finding safe, novel, and effective compounds more quickly and cost-effectively than rivals.

Enko uses information about protein targets and pests to inform its product development strategy, sourcing DNA-encoded libraries containing an array of 120 billion compounds. It claims an experimental approach allows it to get an early read on both safety and effectiveness and deliver machine learning- and AI-driven improvements in predictability. Enko says it’s able to cast a broad net and survey hundreds of billions of small molecules while narrowing the scope of the solutions it pursues.

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These predictive analytics tools enable Enko to focus on compounds with the highest likelihood of commercial success, explained a spokesperson. “We use a suite of predictive analytics tools across our pipeline and for every target. We will have billions of data points about target binding, target inhibition, bioactivity, and safety that can be used to train ensembles of machine learning models and heuristics,” the spokesperson told VentureBeat via email. “It would take a very large, concerted, and expensive effort for us to collect a similar training set for a single target using traditional high throughput screening methods. For example, we can use [this] data and models to broadly understand the features that separate compounds that are most likely to be potent, safe, and active in the whole organism versus not. Our database will also grow with time, increasing the power and value of our approach.”

Among other products, Enko is developing insecticides, fungicides, and herbicides for staple crop types in the U.S. and globally. Heard notes that the yields of most fruit and vegetable crops increase 50% to 90% with fungicides and that over 90% of U.S. cropland is now treated with herbicides, an uptick driven by the popularization of no-till farming. No-till farming decreases harmful soil erosion but often isn’t practical without protection against invasive species.

Enko’s goal is to develop alternatives to suites of chemicals like Roundup, which the International Agency for Research on Cancer classified as a “probably carcinogenic” in 2015. Beyond the health risks, overuse of any one chemical eventually allows more resistant pests to flourish, which results in crop damage and leads to food waste. That’s why in addition to its ongoing pilots, Enko hopes to offer some pesticides as a low- or no-cost option to small farms, potentially via a Gates Foundation partnership.

Enko’s recent round was led by the Bill & Melinda Gates Foundation, with participation from existing investors Anterra Capital and Rabo Food & Agri Innovation Fund. It included new investors Finistere Ventures, Novalis LifeSciences, Germin8 Ventures, and TO Ventures Food. The series B follows a $ 7.5 million venture round in July 2018 and brings the company’s total raised to $ 66 million.

As part of the raise, Navalis LifeSciences founder and chair Marijn Dekkers and Finistere Ventures cofounder and partner Spencer Maughan will join Enko’s board of directors. “Small molecule crop protection is a critical lever to sustaining agricultural yields and minimizing land use, but innovation hasn’t kept pace with farmers’ needs and consumers’ safety concerns,” Dekkers told VentureBeat. “By using the unique techniques that led to a paradigm shift in small molecule drug discovery, Enko is now emerging as a new leader in innovation for the next generation of small molecule crop protection products that deliver on the promise of safety and sustainability.”

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Big Data – VentureBeat

JSonar raises $50 million for AI-powered database monitoring products

June 9, 2020 Big Data

Database monitoring platform startup JSonar today announced it raised $ 50 million, the bulk of which it plans to put toward R&D and go-to-market efforts. Notably, it’s the company’s first-ever round of institutional funding as JSonar’s customer base eclipses 5 out of the world’s 10 largest banks.

Database monitoring is a critical part of most apps’ maintenance. Finding issues in time can help apps remain healthy and accessible, not to mention performant. But the adoption of cloud-based and dynamic, modern data systems make this challenging; legacy data activity monitoring and logging solutions sometimes fail to transform data into actionable insights.

JSonar claims its SonarW and SonarG analytics solutions perform better because they’re automated and AI-powered. The models underpinning them transform petabytes of raw database activity data into security recommendations, with capabilities that go beyond detection and deliver preventative controls. They helpfully funnel these controls and recommendations into existing workflows, and into third-party DevOps services and platforms via prebuilt integrations.

Using SonarW and SonarG, the latter of which is designed for IBM Guardium, users can develop custom analytical algorithms atop large-scale databases and automate reporting and governance processes. According to JSonar, this saves companies months they’d otherwise spend developing proprietary platforms.

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Beyond SonarW, JSonar offers Data-Centric Audit and Protection (DCAP) Central, a product that taps the company’s SonarC2 analytics engine to create data lakes capturing, retaining, and facilitating database management. Then there’s DBSec2.0, a fully managed SonarG offering that ingests database audit data and lets customers interact with it directly for security and compliance insights.

The JSonar suite supports virtually any database system on any cloud including infrastructure-as-a-service, platform-as-a-service, and database-as-a-service setups. This includes out-of-the-box support for Amazon Web Services, Google Cloud Platform (GCP), Microsoft Azure, Snowflake, MongoDB, Cassandra, Hadoop, Teradata, and more than 60 others.

The database security and monitoring market is anticipated to be worth $ 7.01 billion by 2022, Markets and Markets reports, and JSonar is far from the only startup attempting to corner it. Among others, there’s SolarWinds, which has been offering management and optimization tools for open systems and databases for years, and Netdata, which makes an open source database monitoring tool.

But JSonar founder and CTO Ron Bennatan, former cofounder of Guardium Database Activity (which IBM acquired in November 2009), believes his company’s reliance on automation sets it apart from the rest. “The rapidly shifting enterprise landscape, including cloud adoption, an explosion of database platforms, the pressing need for data security beyond only compliance, and years of frustration over runaway costs, has created a huge opportunity for us to rapidly expand,” he told VentureBeat via email. “Traditional database security solutions have proven to be too costly to be used broadly and provide little beyond a checkbox; the modern data landscape demands a new approach.”

Goldman Sachs led the investment in JSonar. As a part of the transaction, Goldman Sachs managing director David Campbell will join the startup’s board of directors.

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Big Data – VentureBeat

Locus Robotics raises $40 million to take its warehouse robots global

June 2, 2020 Big Data

Warehouse robotics startup Locus Robotics today announced it has raised $ 40 million, the bulk of which will be put toward accelerating R&D and the company’s expansion into new markets, including in the EU, where it opened a new headquarters. CEO Rich Faulk says Locus also intends to launch strategic reseller partnerships throughout 2020, following a year in which its number of customer deployments passed 50.

Worker shortages attributable to the pandemic have accelerated the adoption of automation. 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. In China, Oxford Economics anticipates 12.5 million manufacturing jobs will become automated, while in the U.S., McKinsey projects machines will take upwards of 30% of such jobs.

Locus’ autonomous robots — LocusBots — can be reconfigured with virtually any tote, box, bin, or container or with peripherals like barcode scanners, label printers, and environmental sensors designed to expedite order processing. They work collaboratively with human associates, minimizing walking with a UI that recognizes workers’ Bluetooth badges and switches to their preferred language. On the backend, Locus’ LocusServer orchestrates multiple robots such that they learn efficient travel routes, sharing the information with other robots and clustering orders to where workers are. As orders come into warehouse management systems, Locus organizes them before transmitting back confirmations — providing managers real-time performance data, including productivity, robot status, and more.

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When new LocusBots are added to the fleet, they share warehouse inventory status and item locations. Through LocusServer, they detect blockages and other traffic issues to improve item pick rates and order throughput. Locus’ directed picking technology actively directs workers to their next pick location, letting them select their own pace while optionally accepting challenges through a gamification feature that supports individual, team, and shift goals plus events and a mechanism managers can use to provide feedback. In addition, Locus’ backend collates various long-tail metrics, including hourly pick data, daily and monthly pick volume, current robot locations, and robot charging levels.

Locus offers a “robot-as-a-service” program through which customers can scale up by adding robots on a limited-time basis. For a monthly subscription fee, the company sends or receives robots to warehouses upon request, and it provides those robots software and hardware updates, in addition to maintenance.

Locus claims that its system — which takes about four weeks to deploy — has delivered a 2 to 3 times increase in productivity and throughput and 15% less overtime spend for brands that include Boots UK, Verst Logistics, Ceva, DHL, Material Bank, Radial, Port Logistics Group, Marleylilly, and Geodis. The company’s robots passed 100 million units picked in February, and in April, UPS announced that it would be piloting Locus machines in its own facilities.

“COVID-19 has dramatically accelerated trends that have been taking shape over several years in the logistics market, including the movement to collaborative robotics to deal with the labor crisis,” Faulk told VentureBeat via email, adding that the company’s annual recurring revenue increased 300% in 2020 year-over-year. “Our pipeline is expanding weekly with major global brands needing to automate prior to peak season to address the labor gap they will face this year.”

Zebra Technologies’ Zebra Ventures led this series D investment in Wilmington, Massachusetts-based Locus, with participation from existing backers, including Scale Venture Partners. This round brings the Quiet Logistics spinout’s total raised to over $ 105 million as it looks to expand its workforce from more than 120 people to 200 by 2021.

Locus competes in the $ 3.1 billion intelligent machines market with Los Angeles-based robotics startup InVia, which leases automated robotics technologies to fulfillment centers. Gideon Brothers, a Croatia-based industrial startup backed by TransferWise cofounder Taavet Hinrikus, is another contender. And then there’s robotics systems company GreyOrange; Otto Motors; and Berkshire Grey, which combines AI and robotics to automate multichannel fulfillment for retailers, ecommerce, and logistics enterprises. Fulfillment alone is a $ 9 billion industry — roughly 60,000 employees handle orders in the U.S., and companies like Apple manufacturing partner Foxconn have deployed tens of thousands of assistive robots in assembly plants overseas.

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Big Data – VentureBeat

Evervault raises $16 million for ‘privacy cages’ that simplify data protection

May 21, 2020 Big Data

Evervault has raised $ 16 million in venture capital to develop technology the Dublin-based company claims will recalibrate the traditional tradeoffs between privacy and regulation. Index Ventures led the round, which includes previous investors Sequoia Capital, Kleiner Perkins, and Frontline Ventures. In addition, the fundraising attracted money from a string of notable angel investors, such as Eventbrite cofounder Kevin Hartz and former Facebook chief security officer Alex Stamos.

The funding is notable because Evervault has not yet developed a product that’s ready for customers. Instead, investors are betting on a concept that promises to effectively eliminate the need for privacy regulations, a shift that would save businesses considerable expenses on compliance while delivering greater data protection for consumers.

“Data privacy is very complicated for people who don’t want to read thousands of pages of regulations,” said Evervault founder and CEO Shane Curran. “I started Evervault with the belief that data privacy is a technology problem rather than a regulatory problem.”

In Europe, the advent of Europe’s General Data Protection Regulation (GDPR) in May 2018 has sparked a wave of startups promising to help businesses comply with the rules. Curran said he first began working on the problem in high school and officially started the company in 2018.

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He began by establishing a privacy manifesto that lays out eight rules any such system should follow. Primary among these is the idea that “data should always be caged.” That means any sensitive data leaving your device should be encrypted so that it can only be treated and processed in a “privacy cage.”

Curran described these cages as containers that allow for fully encrypted data to be processed so that only the owner of the original data can access the resulting information. The company processing the data would be excluded from accessing it directly and would really only have momentary possession of a user’s data to perform a promised service. Once that service is completed, the data would essentially vanish from the company’s system.

“Containers let you run computing over really sensitive data,” Curran said. “Companies can manage without them having to sacrifice privacy or security.”

Evervault plans to offer this service as an API, with the aim of allowing developers to seamlessly build it into the software stack of whatever they are creating. Curran likes to say that developers will be able to do more with one line of code than the entire set of GDPR regulations.

This would theoretically speed product development while also lifting the burden on companies who might otherwise spend huge resources ensuring they are complying with privacy regulations. Likewise, it would help regulators whose enforcement resources are stretched thin, Curran said.

Still, the investors are taking a leap of faith here. But Evervault’s eight employees are hard at work developing the technology and are currently working alongside partners in sectors with sensitive data issues, like finance.

Curran said he doesn’t have a firm timeline for an initial public release yet. “The roadmap is what I think they really bought into,” Curran said. “That, and the team.”

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Big Data – VentureBeat

Dathena raises $12 million for AI that monitors and classifies sensitive enterprise data

May 14, 2020 Big Data

Dathena, a startup employing AI and machine learning to identify and protect sensitive enterprise data, today announced that it secured $ 12 million in equity financing. The fresh capital follows the opening of the company’s U.S. headquarters in New York City, and CEO Christopher Muffat says the funding will support Dathena’s R&D efforts and the hiring of stateside staff across sales, marking, and customer success teams.

Dathena’s platform is designed to help companies protect against breaches while remaining in compliance with GDPR, the California Consumer Privacy Act (CCPA), and other such regulations. About 37% of businesses operating in the EU say they’ve had to hire at least six new employees to achieve GDPR compliance (according to Netsparker), and among those that have experienced a data breach, the average cost amounted to $ 3.6 million or $ 141 for each record lost (according to the Ponemon Institute).

Five products form Darthena’s suite: Discovery, Privacy, Classify, Protect, and Detect.

Discovery and Privacy reveal what kind of files are stored throughout an organization, where they’re located, and who has access to them at any one time. In addition, they identify business-critical data, sort by toxicity and risk, and eliminate duplicates while mapping all personally identifiable data into a comprehensive backend library.

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Classify filters data by business category and level of confidentiality, automatically labeling sensitive information and delivering visualizations of files to managers and administrators. It learns organizations’ documentation patterns for data in motion and at rest, achieving 80% accuracy out of the box and up to 99% after deployment, Darthena claims.

As for Protect and Detect, they ensure classification tags and keywords export to third-party information security systems and shed light on who, how, when, and where employees are creating and accessing data. Detect provides continuous monitoring and spotlighting of data access and storage anomalies, for its part, as well as smart alerts and integrated workflows for IT team remediation.

Dathena competes with well-funded startups like OneTrust and Bitglass in the $ 120 billion data protection market, but it claims to have over 200,000 users and “a multitude” of enterprise clients. Muffat expects to see an uptick in inquiries in the coming months as remote work motivated by the pandemic gives rise to data protection challenges.

Jungle Ventures led this latest funding round (a series A), with participation from Caphorn and Enterprise Singapore’s SEEDS Capital and existing investors Cerracap Ventures and MS&AD Ventures. It follows a previously undisclosed seed round in November 2018.

Muffat, who worked with HSBC as a lead investigator and headed information risk management at Barclays, founded Dathena in 2016. More than 100 employees work out of its headquarters in Singapore and offices in Geneva.

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Big Data – VentureBeat

Intel Capital commits $132 million to 11 AI startups

May 12, 2020 Big Data

Intel today announced that Intel Capital, its global investment organization, committed a total of $ 132 million to 11 startups focused on AI, automation, and chipset design. It follows a year in which the firm invested $ 466 million in 36 new companies (and 35 follow-on investments) and led 72% of its deals through 22 successful exits. In 2020, Intel Capital says it’s on track to invest around the same amount — between $ 300 million and $ 500 million — in startups specializing in AI, with a particular focus on intelligent edge devices and network transformation.

Intel doubling down on AI and machine learning is business as usual. During an earnings call late last year, CEO Bob Swan said the company generated $ 3.8 billion in AI-based revenue in 2019, and that he anticipates the market opportunity will reach $ 25 billion by 2024. To position itself for growth, Intel recently acquired Habana Labs, an Israel-based developer of programmable AI and machine learning accelerators for cloud datacenters, as well as Moovit, a mobility startup that could be central to Intel subsidiary Mobileye’s plans for a robo-taxi service.

Intel Capital’s expanded investment portfolio includes Redwood City, California-based Anodot, which uses machine learning to perform autonomous business monitoring for clients across telco, finance, and digital sectors. The startup’s platform for real-time, contextual alerts helps to spot incidents — e.g., drops in success rate, customer incidents, app performance, and business metrics — that might impact revenue and costs. In fact, Anodot says it cuts incident management by as much as 80%.

Astera Labs, another new Intel Capital investment, is a fabless semiconductor company headquartered in Santa Clara, California that develops purpose-built connectivity solutions for data-centric systems. It aims to remove performance bottlenecks in compute-intensive workloads like AI and machine learning, with products that span system-aware semiconductor integrated circuits, boards and services, and more.

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Intel Capital’s investment in Hypersonix could bolster the San Jose, California-based company’s autonomous analytics platform, which targets consumer industries like retail, restaurants, hospitality, and ecommerce. Through voice and text search and data visualization, Hypersonix provides real-time actionable insights from disparate data sources like regional business performance and website traffic.

Zhejiang, China-based KFBIO, which also received an Intel Capital investment, builds pathology systems such as a scanner that ostensibly improves on traditional microscopes with digital capabilities and connectivity. Using a combination of big data, cloud computing, and AI, the company’s medical image processing tech scans and digitizes images to make them easier to share for remote consultations with experts.

New Intel Capital portfolio company Lilt provides AI-powered language translation software and services. The San Francisco, California-based firm taps machine learning, a translation management system, and professional translators to enable organizations to scale their localization programs and improve their customers’ experiences.

As for Retrace, which recently received a cash infusion from Intel Capital, it applies machine learning to real-time data to improve dental decision-making. The San Francisco startup’s predictive platform aims to reduce the oral disease burden for health plans, providers, and patients by creating a more cost-effective and evidence-based oral healthcare experience.

Other new Intel Capital investments in 2020 include:

Axonne, which is developing next-generation high-speed Ethernet network connectivity solutions for automobiles.
MemVerge, which provides petabyte-size pools of shared persistent memory and data services for AI, machine learning, and high-performance computing workloads.
ProPlus Electronics, an electronic design automation company specializing in device modeling and fast circuit simulation solutions.
Spectrum Materials, a high-purity gas and material supplier for semiconductor fabricators.
Xsight Labs, a developer of chipset designs that promise to enhance scalability, performance, and efficiency.

Intel Capital, which launched in 1991, made a total of 1,588 investments prior to today’s announcement, according to Crunchbase data. Its new commitments soberingly come as an estimated 390 startups, including companies like Uber and Airbnb, have laid off over 44,600 employees as a result of pandemic-related economic headwinds. Crunchbase anticipates a “more pronounced” reset in investments as venture firms reassess their existing funding needs.

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Big Data – VentureBeat

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