Million Archives - Business Intelligence Info

Tag Archives: Million

Data privacy startup Mine raises $9.5 million and expands to the U.S.

October 21, 2020 Big Data

Mine, which is developing a platform that helps discover and manage personal data on the web, today closed a $ 9.5 million funding round led by Gradient Ventures, Google’s AI-focused venture firm. The cash infusion comes as Mine prepares to launch in the U.S. following debuts in Israel and Europe earlier in the year. According to a spokesperson, 100,000 people have used Mine’s app to send over 1.3 million data-reclaim requests to more than 150,000 companies to date.

A majority of people in the U.S. (60%) believe their online and offline activities are being tracked and monitored by companies and the government with some regularity, according to a Pew Research Center survey. It’s a sentiment reflected in reports like PricewaterhouseCoopers’ Consumer Intelligence Series, which found that only 10% of consumers felt they had complete control over their personal information.

Mine’s technology discovers companies that are holding data by analyzing email subject lines, as opposed to the actual content of emails (e.g., “Welcome to Facebook” or “Your Amazon Receipt”). Once connected with an inbox, Mine maps all companies a user has interacted with via email, generating visualizations that show the type of data collected and the corresponding risk level. A dashboard provides a detailed overview of this data, along with shortcuts to remove the data from services no longer in use.

Mine’s algorithms identify companies most likely to have collected personal data based on the sender and the number of times they appear in an inbox. (According to Mine, almost 90% of traces can be found through email subject lines.) The platform independently locates and scans these companies’ privacy policies and data structures to determine what kinds of information they’re collecting from users.

An initial scan takes about 30 seconds, after which the system continues in the background. Mine sends a notification and an email once the audit is completed, and the app continuously provides updates about new traces of companies in a footprint. Mine claims users typically have a 40% cleaner footprint by the end of a session. The company also claims 15,000 Mine users have narrowly avoided data breaches since 2018 by deleting their data right before the breach occurred.

Mine, which has offices in Tel Aviv and New York, is free to use in 10 languages for Gmail, Yahoo, and Microsoft Outlook accounts, though the company plans to eventually charge a subscription fee. For now, it will focus on products that enable businesses to automatically process “right-to-be-forgotten” requests, helping people demand the deletion of certain data pertaining to them. In May 2018, the European Union’s General Data Protection Regulation (GDPR) codified right-to-be-forgotten protections into law. This was followed in January 2020 by the California Consumer Privacy Act (CCPA), which similarly requires that people be able to access and delete certain data.

“Having been part of the tech world from different angles, I noticed how people, myself included, started to lose trust in how companies are collecting and treating personal data. My cofounders and I created Mine to do what we know best — use technology as a solution to shape a new future of equal choice and control to the internet,” CEO Gal Ringel told VenureBeat via email. “Our smart non-intrusive AI makes it easy for people to take back ownership of their online data and minimize digital risks without changing their online behavior.”

Mine’s Gradient Ventures-led round included participation from E.ventures, MassMutual Ventures, and existing investors Battery Ventures and Saban Ventures. It brings the company’s total raised to over $ 12.5 million.

Let’s block ads! (Why?)

Big Data – VentureBeat

Sonrai Security raises $20 million to protect public clouds with automation

October 15, 2020 Big Data

Public cloud security provider Sonrai Security today announced a $ 20 million round that will be used to accelerate R&D and boost global sales and marketing for its identity and data governance products.

Roughly 83% of enterprise workloads have moved to the cloud, according to a 2020 survey from LogicMonitor. But the cloud remains vulnerable to cyberattacks. IBM found last year that the average time to identify a breach was 206 days. Meanwhile, security breaches have increased by 11% since 2018 and 67% since 2014, Accenture reported in a recent study.

Sonrai offers a platform called Sonrai Dig to help companies stay ahead of threats. The platform is built on a graph that identifies and monitors relationships between entities (e.g., admins, roles, compute instances, serverless functions, and containers) and data within public clouds and third-party data stores. An engine automates workflow, remediation, and prevention across cloud and security teams to provide baseline security. At the same time, it provides critical data in object stores like AWS S3 and Azure Blog priority to address suspicious activity and access rights monitoring.

Sonrai’s data governance automation solution helps integrate teams via analyses, alerts, and actions that align with the way organizations use the public cloud. The platform allows customized monitoring and views for development, staging, and production workloads and an API architecture that can be integrated into a continuous integration/continuous development process. Dig also automatically dispatches prevention and remediation bots and provides safeguards in the form of code promotion blocks to provide end-to-end security in public cloud platforms.

Sonrai CEO Brendan Hannigan, formerly a general manager of security at IBM, says improperly configured cloud interdependencies and inheritances can lead to significant security risks. These include excessive access paths to data, over-permissioned identities, and an unwieldy separation of responsibilities. Hannigan estimates that enterprises’ public cloud utilization generates hundreds of cloud accounts, thousands of data stores, and tens of thousands of ephemeral pieces of compute — complexity that legacy cloud security tools have failed to address.

Hannigan won’t disclose Sonrai’s customers, but he says the platform can scale to thousands of roles and compute instances across hundreds of corporate accounts. “The increasing frequency of cloud breaches caused by identity and data access complexity has driven significant traction for our … platform among large enterprises,” Hannigan added. “They see it as the basis of their cloud security model.”

Menlo Ventures led today’s series B round, with participation from Polaris Partners and Ten Eleven Ventures. It brings the New York-based company’s total raised to over $ 38.5 million, following an $ 18.5 million series A round in January 2019.

Let’s block ads! (Why?)

Big Data – VentureBeat

D-Wave’s 5,000-qubit quantum computing platform handles 1 million variables

September 29, 2020 Big Data

D-Wave today launched its next-generation quantum computing platform available via its Leap quantum cloud service. The company calls Advantage “the first quantum computer built for business.” In that vein, D-Wave today also debuted Launch, a jump-start program for businesses that want to begin building hybrid quantum applications.

“The Advantage quantum computer is the first quantum computer designed and developed from the ground up to support business applications,” D-Wave CEO Alan Baratz told VentureBeat. “We engineered it to be able to deal with large, complex commercial applications and to be able to support the running of those applications in production environments. There is no other quantum computer anywhere in the world that can solve problems at the scale and complexity that this quantum computer can solve problems. It really is the only one that you can run real business applications on. The other quantum computers are primarily prototypes. You can do experimentation, run small proofs of concept, but none of them can support applications at the scale that we can.”

Quantum computing leverages qubits (unlike bits that can only be in a state of 0 or 1, qubits can also be in a superposition of the two) to perform computations that would be much more difficult, or simply not feasible, for a classical computer. Based in Burnaby, Canada, D-Wave was the first company to sell commercial quantum computers, which are built to use quantum annealing. But D-Wave doesn’t sell quantum computers anymore. Advantage and its over 5,000 qubits (up from 2,000 in the company’s 2000Q system) are only available via the cloud. (That means through Leap or a partner like Amazon Braket.)

5,000+ qubits, 15-way qubit connectivity

If you’re confused by the “over 5,000 qubits” part, you’re not alone. More qubits typically means more potential for building commercial quantum applications. But D-Wave isn’t giving a specific qubit count for Advantage because the exact number varies between systems.

“Essentially, D-Wave is guaranteeing the availability of 5,000 qubits to Leap users using Advantage,” a D-Wave spokesperson told VentureBeat. “The actual specific number of qubits varies from chip to chip in each Advantage system. Some of the chips have significantly more than 5,000 qubits, and others are a bit closer to 5,000. But bottom line — anyone using Leap will have full access to at least 5,000 qubits.”

Advantage also promises 15-way qubit connectivity, thanks to a new chip topology, Pegasus, which D-Wave detailed back in February 2019. (Pegasus’ predecessor, Chimera, offered six connected qubits.) Having each qubit connected to 15 other qubits instead of six translates to 2.5 times more connectivity, which in turn enables the embedding of larger and more complex problems with fewer physical qubits.

“The combination of the number of qubits and the connectivity between those qubits determines how large a problem you can solve natively on the quantum computer,” Baratz said. “With the 2,000-qubit processor, we could natively solve problems within 100- to 200-variable range. With the Advantage quantum computer, having twice as many qubits and twice as much connectivity, we can solve problems more in the 600- to 800-variable range. As we’ve looked at different types of problems, and done some rough calculations, it comes out to generally we can solve problems about 2.6 times as large on the Advantage system as what we could have solved on the 2000-qubit processor. But that should not be mistaken with the size problem you can solve using the hybrid solver backed up by the Advantage quantum computer.”

1 million variables, same problem types

D-Wave today also announced its expanded hybrid solver service will be able to handle problems with up to 1 million variables (up from 10,000 variables). It will be generally available in Leap on October 8. The discrete quadratic model (DQM) solver is supposed to let businesses and developers apply hybrid quantum computing to new problem classes. Instead of accepting problems with only binary variables (0 or 1), the DQM solver uses other variable sets (integers from 1 to 500, colors, etc.), expanding the types of problems that can run on Advantage. D-Wave asserts that Advantage and DQM together will let businesses “run performant, real-time, hybrid quantum applications for the first time.”

Put another way, 1 million variables means tackling large-scale, business-critical problems. “Now, with the Advantage system and the enhancements to the hybrid solver service, we’ll be able to solve problems with up to 1 million variables,” Baratz said. “That means truly able to solve production-scale commercial applications.”

Depending on the technology they are built on, different quantum computers tend to be better at solving different problems. D-Wave has long said its quantum computers are good at solving optimization problems, “and most business problems are optimization problems,” Baratz argues.

Advantage isn’t going to be able to solve different types of problems, compared to its 2000Q predecessor. But coupled with DQM and the sheer number of variables, it may still be significantly more useful to businesses.

“The architecture is the same,” Baratz confirmed. “Both of these quantum computers are annealing quantum computers. And so the class of problems, the types of problems they can solve, are the same. It’s just at a different scale and complexity. The 2000-qubit processor just couldn’t solve these problems at the scale that our customers need to solve them in order for them to impact their business operations.”

D-Wave Launch

In March, D-Wave made its quantum computers available for free to coronavirus researchers and developers. “Through that process what we learned was that while we have really good software, really good tools, really good training, developers and businesses still need help,” Baratz told VentureBeat. “Help understanding what are the best problems that they can benefit from the quantum computer and how to best formulate those problems to get the most out of the quantum computer.”

D-Wave Launch will thus make the company’s application experts and a set of handpicked partner companies available to its customers. Launch aims to help anyone understand how to best leverage D-Wave’s quantum systems to support their business. Fill out a form on D-Wave’s website and you will be triaged to determine who might be best able to offer guidance.

“In order to actually do anything with the quantum processor, you do need to become a Leap customer,” Baratz said. “But you don’t have to first become a Leap customer. We’re perfectly happy to engage with you to help you understand the benefits of the quantum computer and how to use it.”

D-Wave will make available “about 10” of its own employees as part of Launch, plus partners.

Let’s block ads! (Why?)

Big Data – VentureBeat

Isima emerges from stealth with $10 million to apply intelligence to real-time data

August 4, 2020 Big Data

Isima today emerged from stealth with $ 10 million in funding to launch a data convergence platform called BiOS. The company asserts its solution can reduce or even eliminate disparate databases while improving overall speed and reliability.

IDC expects the global big data analytics market to be worth $ 274.3 billion by 2022, and Forbes found in 2017 that over 50% of companies were adopting big data. This trend is likely attributable to the return on investment (ROI) adopters report. Entrepreneur notes that businesses using big data experience a profit increase of 8-10%, on average. But obstacles remain, with 40.3% of respondents to a Statista survey suggesting big data usage was held up by a lack of organizational agility.

With BiOS, organizations can collate business intelligence and AI-driven applications into a single platform to derive insights from real-time data. The “telco-grade” platform enables developers to build apps by adding data sources and deploying to production in a semi-automated fashion.

BiOS embeds software development kits (SDKs) within microservices, or systems that structure apps as collections of maintainable, testable, loosely coupled, and independently deployable services. It replaces third-party telemetry trackers with Isima’s first-party tracker to assure users that brands aren’t sharing their data without permission, and it offers “replica-free” insights that enable data analysts across a range of departments to collaborate on interdepartmental data without burdening relational database management systems. Off-the-shelf change-data-capture mechanisms wrap SDKs provided by BiOS to ingest changes.

Early adopters of the BiOS platform include PharmEasy, an Indian online pharmacy and medical supply store. Isima also claims to have subscription revenue from several Fortune 500 customers.

Isima was founded in 2016 by Monish Suvarna, who previously headed product development and portfolio licensing at private equity firm Intellectual Ventures. Cofounder Darshan Rawal is the former senior director of products at DataStax, where he led product management for DataStax’s commercial offerings based on open source projects like Apache Cassandra, SOLR, Spark, Titan, and OpsCenter.

Sway Ventures and Engineering Capital led the $ 10 million seed round in Silicon Valley-based Isima.

Isima competes with companies like Dremio, which has raised about $ 115 million for tools that help streamline and curate data, and Incorta, whose hyperconverged analytics platform helps companies garner insights by crunching vast swathes of data from across their cloud-based applications. Isima has another rival in Quantexa, which uses AI and machine learning 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.

Let’s block ads! (Why?)

Big Data – VentureBeat

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.

Let’s block ads! (Why?)

Big Data – VentureBeat

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

July 13, 2020 Big Data

Take the latest VB Survey to share how your company is implementing AI today.

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.

Let’s block ads! (Why?)

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.

VB Transform 2020 Online – July 14-17. Join leading AI executives: Last chance to register!

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.

Let’s block ads! (Why?)

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.

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

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.

Let’s block ads! (Why?)

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.

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

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.”

Let’s block ads! (Why?)

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.

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

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.

Let’s block ads! (Why?)

Big Data – VentureBeat

Tag Archives: Million

Data privacy startup Mine raises $9.5 million and expands to the U.S.

Sonrai Security raises $20 million to protect public clouds with automation

D-Wave’s 5,000-qubit quantum computing platform handles 1 million variables

5,000+ qubits, 15-way qubit connectivity

1 million variables, same problem types

D-Wave Launch

Isima emerges from stealth with $10 million to apply intelligence to real-time data

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

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

Aryballe raises $7.9 million for odor-detecting AI sensors

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

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

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

Recent Posts

Categories

Archives