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TypingDNA raises $7 million for AI that identifies people by how they type

January 5, 2020 Big Data

The risk of online accounts being compromised increases by the day — and it’s costly. A hacker attacks roughly every 39 seconds, and it’s estimated that 65% of companies have over 1,000 stale accounts. Worse still, the average expense of data breaches is expected to hit $ 150 million in 2020.

That’s perhaps why startups like TypingDNA have risen to prominence. TypingDNA — which was founded in Romania in 2016 by Adrian Gheara, Cristian Tamas, and Techstars alum Raul Popa, and which recently moved its headquarters to New York — provides typing biometrics authentication as a service, enabling companies to recognize people by the way they type. After raising $ 1.8 million in capital from the founders of companies such as UiPath and DataDog last year and attracting clients like large banks in Latin America and Romania, it’s gearing up for a major expansion in 2020.

TypingDNA announced this morning that it has raised $ 7 million in series A funding led by Gradient Ventures, Google’s AI-focused venture fund, with participation from Techstars Ventures and EU-based fund GapMinder. According to CEO Popa, the proceeds will fund the build-out of TypingDNA’s developer support network and tools to integrate its services with popular web development tools.

“Advancing the research and distribution of typing biometrics is of global importance. Keyboards are incorporated [into] almost any device today, making typing behavior the most widely available user biometric,” he added. “This round of funding will allow us to further our mission to provide user-friendly, non-intrusive biometrics and increased security to people around the world.”

TypingDNA’s platform records dynamic statistics about pressed keyboard keys and turns them into typing patterns, which its proprietary engine analyzes and verifies against patterns collected from real-world users. As Popa explains, the way a person types on a keyboard is unique and fairly difficult to replicate — in point of fact, it’s behaviorally rich enough to reveal biometric traits like gender and age.

Local clients train models on users’ patterns silently in the background, focusing on 44 commonly used letter and number keys. Stats like individual key presses and flight times are stored for later algorithmic ingestion, but not sequences between two or more keys — that’s to ensure text can’t be recreated.

Popa says most intruders are caught in under 100 typed characters, or on a typical sentence.

TypingDNA offers a number of client-side products, including a PC tool that continuously authenticates users based on their typing patterns. It alerts administrators or automatically locks down the system when it detects anomalous keystrokes, and it optionally signs out accounts, captures screenshots, takes webcam snapshots, and sends alerts (via email or SMS) if it detects anomalous behavior. A complementary Chrome extension serves up two-step verification codes for third-party services like Amazon and Facebook in the browser, and TypingDNA’s API and SDK provide a means of securing logins and enforcing reset passwords on mobile, as well as web apps.

The biometric authentication market is projected to generate $ 65.3 billion by 2024, and TypingDNA is far from the only player in the lucrative space. Pindrop, a firm developing biometric voice authentication solutions, raked in $ 90 million in funding in December 2018. Global identity verification provider Onfido nabbed $ 50 million in April. And Buguroo, which taps deep learning and behavioral biometrics to detect banking fraud, recently secured $ 11 million.

But Gradient Ventures general partner Darian Shirazi isn’t concerned. “We’re excited about TypingDNA’s developer-first approach to [enabling] people to authenticate securely based on how they type,” he said. “With global regulation impacting face-recognition-based authentication and hackers targeting SMS-based two-factor authentication, typing biometrics is the best form of identifying people without compromising privacy or security.”

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Big Data – VentureBeat

Stop Payroll Pains Before They Start

December 12, 2019 NetSuite

Stop Payroll Pains Before They Start

Posted by Marc Holliday, Senior Product Marketing Manager

Most entrepreneurs and startups share a common vision: focus on growth. As they get their businesses off the ground, secure funding and begin to hire their first employees, most turn to simple accounting and payroll solutions like QuickBooks. It provides a low-cost solution that will handle the basics of bookkeeping, pay a small workforce and send summary level payroll information back to the general ledger to account for compensation and benefit expenses.

Rapid growth starts to push entry level payroll software to its limits and could be the first area where businesses feel pain.

To make sure payroll won’t slow growth down, look for these early warning signs:

A lot of hiring occurs rapidly, creating new complexities in terms of payroll size, frequency and accuracy.
Leaders and managers start to request more details about payroll costs from your general ledger for planning purposes.
Timesheets are needed, but your vendor wants an additional fee for that functionality.
You need to move to a different edition of QuickBooks payroll and pay more to get features like user-based security to help protect sensitive payroll information.
The list of payroll related vendors such as insurance carriers and benefit providers begins to grow, and you can’t automate payments through accounts payable.
You need to run an off-cycle payroll batch, and your payroll provider wants to charge you an additional fee.
Troubleshooting and fixing payroll issues takes more than an hour.
Employee information is being entered into more than one software application.
You have a growing need for an HR solution that is integrated with payroll to track time off balances, fixed assets, effective dates on employee records and provide compliance reporting.
You missed a payroll tax filing because you weren’t notified, or it wasn’t accurate.
Employee complaints are increasing because they either can’t find their payroll and HR information themselves or they have to log into multiple applications.

Time for a Change

Growing companies need payroll software that is flexible in its support of organizational structures and therefore helps companies understand payroll expenses in more depth, including payroll by department or by location, which can help improve planning.

Software with built-in role-based security and timesheets included means businesses won’t have to upgrade to another edition to protect and better control labor information. Intuitive, payroll process, including payroll variance views and reports can help prevent payroll pain before it occurs for the ultimate in accuracy control. And when the time comes to address your HR data, growing businesses need a unified system that can put all of their workforce information in a single place, fully unified with payroll and financials.

NetSuite offers unified accounting and payroll that businesses simply won’t outgrow. Natively part of NetSuite, SuitePeople U.S. Payroll allows you to run weekly, bi-weekly, semi-monthly and monthly payrolls all in the same batch and won’t charge an extra fee for an off-cycle payroll.

Learn more about NetSuite SuitePeople Payroll:

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Posted on Wed, December 11, 2019
by NetSuite filed under

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The NetSuite Blog

HOW THE HELL’D THEY GET CLOSE ENOUGH?

December 4, 2019 Humor

Pranksters spray paint polar bear with “T-34.”

Animal activists outraged.

Cruel animal abusers daubed T-34 – the name of an iconic Soviet tank – on a wild polar bear.

The predator, pictured here, was seen in the Russian Arctic after a video was made by workers in a truck who name the wild animal Misha.

The footage was uploaded in the remote Chukotka region but it is not known where the film was shot, or who daubed a large T-34 in black markings on the bear’s right side.

At first the workers struggle to see the the markings on the beast and one says: ‘Why is it so dirty?’

Then they think it is ‘spotty’.

Only when the animal is closer do they see it is sprayed with the name of a tank.

One theory is that soldiers from a remote military base may have painted the dangerous animal, but it is not clear how they subdued or sedated it in order to mark it.

Anatoly Kochnev, senior researcher at Mammal Ecology laboratory at the Institute of Biological Problems of the North, warned that the bear could suffer from the graffiti, reported The Siberian Times.

‘Scientists could not do this,’ he said.

‘It could have been somebody who ‘joked’ like this.’

The black writing will mean problems for the bear when it hunts prey, he said.

It would also be more visible to poachers.

The video has gone viral in Russia and scientists are seeking to find out where it was filmed.

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ANTZ-IN-PANTZ ……

Tech Companies Are Leaving Bay Area In Droves – Where Are They Going?

November 17, 2019 BI News and Info

While Silicon Valley will certainly remain the tech/startup capital of the world for decades to come, it’s lost a bit of its luster and charm in the past couple of years. Many tech companies are on the way out, opting to move east.

Adios, Silicon Valley!

Silicon Valley isn’t what it used to be a decade and a half ago. Companies like Google and Apple, as well as hundreds of smaller tech ventures and startups, are choosing to move portions (or even all) of their operations east. They’re doing so for a variety of reasons, including:

Taxes. The corporate tax rate in California is 8.84% (for a cumulative total of 39.4%). Many other states have corporate tax rates in the four to six percent range. Business owners and executives are realizing they can save millions of dollars per year in taxes simply by moving a few hundred miles to the east.

High cost of living. On top of hefty taxes, California – and Silicon Valley in particular – has some of the highest costs of living in the entire country. Everything from housing to food is very expensive. Moving out of the area is like getting an instant pay raise.

Lack of diversity. Silicon Valley loves to pride itself on diversity, but many say it’s nothing more than a façade. While tech companies in the Valley hire lots of people who look diverse – i.e., different races, ethnicities, and genders – some employees claim they don’t appreciate diversity of thought (unless it aligns with their beliefs). Many business owners say they would prefer to escape to environments where free thought (of all kinds) is embraced.

There are plenty of other reasons for the split from Silicon Valley, but these three factors paint a picture of why entrepreneurs are leaving in droves.

4 popular destinations for tech companies

The question is: Where are all these entrepreneurs and their tech companies going? Some of the destinations are a bit surprising. Let’s take a look:

1. Salt Lake City, Utah

According to data from Cushman & Wakefield, a Chicago-based real estate services company, Salt Lake City has seen the second-most tech-related commercial leases in the country over the past year. It now claims the ninth-largest share of tech tenants in the nation. Tech companies accounted for more than 66% of all major commercial leases signed in 2018 (which is more than San Francisco).

The Salt Lake City area is also renowned for its lower than average cost of living (when compared to surrounding states). This is something local real estate developers regularly hear transplants comment on.

“Just in our master-planned community alone, we’ve already seen a number of families who are moving into the Salt Lake City area to enjoy a lower cost of living,” explains Wildflower, an active family community located south of the city. “When you combine that with strong job growth and beautiful surroundings, it’s a trend we anticipate continuing indefinitely.”

2. Austin, Texas

With companies like Apple, IBM, eBay, and Intel having major presences in the area, Austin has been dubbed “Silicon Hills.” It’s been a major player in the tech startup scene since the 1980s, and the low taxes, affordable cost of living, and pro-business environment have made it even more attractive in recent years.

3. Huntsville, Alabama

Huntsville is one of the best-kept gems of the South. Located in the northern portion of the state – just a short drive south of Nashville – Huntsville is home to a thriving engineering scene. It also has major government support, with Boeing, Northrop Grumman, and NASA all relying heavily on the city. With an extremely low cost of living, it’s the perfect place to start a business and raise a family.

4. Raleigh-Durham, North Carolina

Don’t look now, but the Raleigh-Durham and Charlotte areas are huge landing spots for large tech companies and small ventures alike. With so many medical research universities nearby, it’s a particularly compelling destination for entrepreneurs that are focused on the convergence of technology and healthcare.

The future of technological entrepreneurship

Nobody is suggesting that Silicon Valley will cease to be the tech capital of the world. However, it’s losing some of its shine. As tech companies become less geographically attached to the area, we’ll see more innovation in more places. And, at the very least, this is good for the larger society.

SAP Integration is key to SAP Intelligent Enterprise. Learn about the key ingredient of this concept – SAP APIs – on December 4th.

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Digitalist Magazine

Sharp Shares Why They Chose Act-On as Their Marketing Automation Platform

October 10, 2019 CRM News and Info

Louise also gathered input from the project team and the marketing community as a whole to determine what the ideal marketing automation platform would look like for Sharp’s team. The vast amount of input she received helped her create a thorough RFP document that listed key features and capabilities her team needed to be successful.

“That RFP we created for vendors to respond to listed all our requirements (which were split out by category) and indicated whether these features were considered essential or nice to have. So, we had questions that were specific to functionality such as, What’s the landing page builder like? What are your email marketing capabilities? We also asked questions regarding the vendor and their software like, What’s your customer support like? How reliable is the platform?

Because we have so many business units and offices using different CRMs, there also turned out to be quite a complex need for our new platform to have a parent-child structure and be able to support multiple integrations.”

Louise and her team first evaluated their incumbent vendors and two new ones, asking each team to submit a written response to their questions, present a platform demo, and provide a quote. However, none of the platforms they evaluated quite matched their needs.

“We scored each of these vendors based on how closely they met the different categories and requirements. At that point, there was only one vendor that could genuinely meet the requirements with a high enough score and competitive pricing, but it wasn’t a platform that anyone on the project team really loved.”

Demonstrating that you should never settle when it comes to choosing the right marketing automation platform, Sharp went back to the drawing board in hopes of finding the perfect solution.

Louise and her team reached out to Act-On and another vendor during their second evaluation round. After conducting the same detailed evaluation process, Louise and her team found that Act-On had many of the features Sharp was looking for — including the “parent/child” Marketing Network structure that would enable the company to centralize its marketing efforts.

“We went through a very thorough evaluation process and I would say that, for a number of different factors, Act-On just ended up being the best fit for our organization. A key thing for us was that the platform we picked had to have an interface that was very easy to use and intuitive.

Unlike other companies that might have a centralized team of specialists who are always in the system, we weren’t going to have a big team of people to manage our marketing automation platform. Therefore, the solution we picked had to be intuitive enough so that a marketing generalist could use it easily without knowing things like how to code HTML.

We also have a very complex organization structure where we need to do certain things on the central level and other things on the local level. So, Act-On’s marketing network feature ended up being a really important factor in our selection process because it allows us to set up an account hierarchy and easily share assets between business units. Equally as important was that this platform had to connect to multiple CRMs, such as Salesforce, used by our various business units, which Act-On did.”

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Act-On Blog

AI classifies people’s emotions from the way they walk

July 1, 2019 Big Data

The way you walk says a lot about how you’re feeling at any given moment. When you’re downtrodden or depressed, for example, you’re more likely to slump your shoulders than when you’re contented or upset. Leveraging this somatic lexicon, researchers at the University of Chapel Hill and the University of Maryland recently investigated a machine learning method that can identify a person’s perceived emotion, valence (e.g., negative or positive), and arousal (calm or energetic) from their gait alone. The researchers claim this approach — which they believe is the first of its kind — achieved 80.07% percent accuracy in preliminary experiments.

“Emotions play a large role in our lives, defining our experiences and shaping how we view the world and interact with other humans,” wrote the coauthors. “Because of the importance of perceived emotion in everyday life, automatic emotion recognition is a critical problem in many fields, such as games and entertainment, security and law enforcement, shopping, human-computer interaction, and human-robot interaction.”

The researchers selected four emotions — happy, sad, angry, and neutral — for their tendency to “last an extended period” and their “abundance” in walking activity. Then they extracted gaits from multiple walking video corpora to identify affective features and extracted poses using a 3D pose estimation technique. Finally, they tapped a long short-term memory (LSTM) model — capable of learning long-term dependencies — to obtain features from pose sequences, which they combined with a random forest classifier (which outputs the mean prediction of several individual decision trees) to classify examples into the aforementioned four emotion categories.

AI classifies people’s emotions from the way they walk

Above: This AI system classifies people’s emotions from the way they walk.

The features included things like shoulder posture, the distance between consecutive steps, and the area between the hands and neck. Head tilt angle was used to distinguish between happy and sad emotions, while more compact postures and “body expansion” identified positive and negative emotions, respectively. As for arousal, which the scientists note tends to correspond to increased movements, the model considered the magnitude of velocity, acceleration, and “movement jerks” of hands, feet, and head joints.

The AI system processed samples from Emotion Walk, or EWalk, a novel data set containing 1,384 gaits extracted from videos of 24 subjects walking around a university campus, both indoors and outdoors. Roughly 700 participants from Amazon Mechanical Turk labeled emotions, and the researchers used these labels to determine valence and arousal level.

In tests, the team reports that their emotion detection approach offered a 13.85% improvement over state-of-the-art algorithms and a 24.60% improvement over “vanilla” LSTMs that don’t consider affective features. That isn’t to say it’s foolproof — its accuracy is largely dependent on the precision of the 3D human pose estimation and gait extraction. But despite these limitations, the team believes their method will provide a strong foundation for studies involving additional activities and other emotion identification algorithms.

“Our approach is also the first approach to provide a real-time pipeline for emotion identification from walking videos by leveraging state-of-the-art 3D human pose estimation,” wrote the coauthors. “As part of future work, we would like to collect more data sets and address [limitations].”

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Big Data – VentureBeat

Cyber Scores: What Do They Mean?

June 28, 2019 FICO

It’s a great question, and needs to be asked.

Cyber scores and ratings have been around for some time now, gaining steady momentum over the last five years. That said, the market for security risk assessment scores and ratings remains nascent, with a double-digit CAGR that will likely continue into the foreseeable future.

With new data protection and privacy regulations coming online — such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA) –– interest in understanding and managing cyber risk is at an all-time high. A drumbeat of high-profile breaches underscores the risk, and the new regulations demand more diligence in managing first-party (your company) and third-party (supply chain) risk.

Third-Party Risk Is Top of Mind

The latter topic (third-party risk) is increasingly important. While organizations can readily gain some insight into their own security posture (and a second opinion from one of the commercial cyber rating firms, if they want it), the appeal of an independent, non-intrusive assessment of supply chain partners’ cyber risk is clear and compelling.

The same is true for cyber insurance brokers, underwriters and reinsurers. As more carriers enter the market (intensifying competition) and coverage moves down-market (requiring carriers to underwrite policies with lower premiums and less information), there is an acute need for an efficient, accurate way to assess cyber risk.

What Do Cyber Scores Mean?

As organizations that have done proof-of-concept pilots with cyber scores or ratings consider exactly how to leverage them in supply chain decision workflows, the question naturally comes up: Just what does this score actually mean? Insurance carriers using these scores and ratings to underwrite and price cyber risk policies are asking the same thing.

At FICO, we encourage you to ask. If you’re using one of these scores/ratings, or are considering doing so, you deserve an up-front answer.

The reality is that some of the providers in this space can’t answer the question. The scores or ratings they produce are generated by judgmental scorecards that apply “informed but arbitrary” weighting to myriad risk signals they collect. Certainly there are experts in these companies who can render a directionally correct opinion on any given input –– but the weights assigned to these signals have no statistical basis or mathematical foundation. Their relationship to actual security outcomes was never established.

And for that matter, what specific security outcome are they attempting to measure? When you compile a score based on multiple signals that are evaluated in this way, without a well-defined objective outcome, you really don’t know what you are measuring.

A Score Built on Real Data and Sound Methods

At FICO, we take a different approach. And we have the experience, tools, methods and data to back it up. FICO’s Cyber Risk Score is empirically derived, with a transparent and documented objective outcome. Our model is built to forecast the likelihood of a material breach event in the next 12 months. It’s not an opinion, a current-state assessment, or an arbitrary grade attached to a long list of potential security vulnerabilities.

The FICO Cyber Risk Score translates directly to the “event odds” of a material breach occurring in a specified time period (12 months from the score date). It is built using the measured correlations between signals and the objective outcome. Subscribers are provided with a detailed model report that describes the objective outcome, outlines the score-to-odds relationship, and exposes the population distribution across the score range.

FICO’s users know exactly what the score means.

The veracity of our approach and the transparency behind the meaning of FICO’s Cyber Risk Score are key reasons why Chartis Research recently named FICO a category leader in Cyber Risk Quantification solutions. You can read their analysis of FICO here.

We’re proud of the recognition, but even more proud that we’re able to answer the question, “What does the FICO Cyber Risk Score mean?” If you’re using a competing score, we encourage you to ask that question of your provider. If you don’t like the answer, give us a call or visit https://cyberscore.fico.com.

Follow me on Twitter @dougoclare.

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FICO

7 Key Areas to Cover in Your RFP Template for BI (& Why They Matter!)

May 28, 2019 Sisense

The trouble with investing in a new, unknown technology is that it’s sometimes difficult to know what questions to ask a potential vendor.

Sure, you know broadly what it is you want to achieve – and any vendor will be adamant that they’re the right guys to do it for you. But how do you cut through the marketing spiel to figure out if they really do have the tools and capabilities you need to get the job done in the best possible way? What specifically do you need to know?

That’s where a Request for Proposal (RFP) document comes in super-handy.

What is an RFP Template?

An RFP template is essentially a framework of questions that covers all the key bases for the type of solution under assessment, which you can send to a vendor. You gain peace of mind that you haven’t overlooked something majorly important in the procurement stage – plus, it gives you a clear and easy way to evaluate vendors side-by-side.

If you don’t know what to include in an RFP template for a BI solution or think the one you have needs a refresher, not to worry. We’ve created a template you can (and should!) use freely when evaluating BI.

But, even before then, here are seven key business and technical areas you should be sure to cover:

Complexity of Data
Why it Matters: It’s highly likely that you need to bring multiple, complex data sources into the mix to get a complete picture of your business. These questions are designed to ensure that your solution is flexible enough to handle anything you throw at it and to treat it like a single source of truth. For example, you’ll need to know how each vendor connects to different data sources, what connectors they offer, and if there are any external extract, transform, and load (ETL) requirements.
Implementation Time
Why it Matters: The longer it takes to fully implement your BI solution, the longer you’ll wait until you start to see a return on investment. These questions are designed to establish how long you can expect to wait until your technical solution is rolled out, your team’s on board, and you’re ready to start getting results. You should clarify how many weeks or months you should factor into your timeline for implementation as well as how long it typically takes for training and onboarding. Adoption can often be overlooked but has a huge impact on when you’ll start to see ROI.
Total Cost of Ownership
Why it Matters: Some BI vendors calculate TCO purely in terms of your initial outlay, but this can be misleading. Instead, these questions will gather the information you need to figure out how much the solution will really cost you in long term staffing costs, data storage, and the real TCO per report or analysis. In order to make sure your bases are covered your BI tool evaluation template should include questions about data warehousing, internal resource commitment, and specifics on how pricing is calculated.
Data Warehousing/Storage
Why it Matters: If the solution you’re looking at does not offer a streamlined way to store, handle, and access data, you may be looking at huge data warehousing costs. These questions will help you clarify what options this vendor offers for data storage and unfettered access. Check if the vendor allows users to access your entire pool of data in real-time and what kind of tech they use to facilitate this without impacting performance.
Agility/Ease of Use
Why it Matters: It’s crucial that everyone who needs to can use your BI solution when they want, how they want, without IT intervening each time. Use these questions to establish whether your solution is genuinely self-service and directly accessible to non-technical business users. We recommend you check, among other things, if users can run queries without advanced skills like SQL and if they can build and manipulate their own dashboards and reports.
Future-Proofing
Why it Matters: Your BI vendor might do everything you need them to now, but are they innovating enough to cope with your needs in the future? These questions look to figure out how much on-going R&D they’re doing – to make sure they won’t get complacent later on. Questions about innovation strategy, how upgrades are managed, and how often the platform is updated should give you a pretty good idea of how committed the vendor is to consistent improvement and innovation.
Customer Service/Customer Experience
Why it Matters: No matter how powerful your BI solution, without the right guidance, support, and training, you won’t get the full value out of it. These questions aim to clarify what you can expect after you sign on the dotted line. Not all customer success is the same. To make sure you’re getting the best, be sure your RFP template includes questions about post-sales support including if you’ll receive a dedicated customer success manager and what kind of tutorials and community exists.

Don’t stop here! Now that you’ve seen seven of the most important key areas to outline, we’ve got four more for you to consider, plus a free and complete RFP template to make gathering information from vendors even easier.

RFP Template 7 Key Areas to Cover in Your RFP Template for BI (& Why They Matter!)

Tags: how to’s | RFP template

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Blog – Sisense

AI teaches 4-legged robots how to recover when they fall

January 23, 2019 Big Data

Teaching robots to walk is one thing, but what about helping them to learn how to recover from a stumble? That’s the not-so-easy feat researchers at ETH Zurich set out to accomplish with the help of artificial intelligence (AI). In a newly published paper (“Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning“) submitted to the preprint server Arxiv.org, they describe an AI system that can aid robots in getting up from the ground after they trip and fall.

“In case of a fall, animals show the remarkable ability to recover from any posture by pushing against their surroundings and swinging limbs to gain momentum,” the researchers wrote. “Having similar abilities in legged robots would significantly improve their robustness against failure and extend their applicability in harsh environments. We address this topic in the present work by developing a control strategy for the robust recovery maneuver of quadrupedal robots.”

Their model taps an AI training technique known as deep reinforcement learning, which uses a system of rewards to drive agents toward certain goals and to control the robots’ recovery maneuvers. Four neural network policies (i.e., strategies) were trained individually in a simulated environment and then deployed on ANYmal, a four-legged, dog-sized robot with 12 degrees of freedom.

A controller component selected one of three behaviors — locomotion, standing up, or self-righting — for the robot’s given situation, depending on factors like recent observations, commands, previously chosen behavior types, and previous actions. An additional module — a height estimator — gauged its base height to prevent the robot from drifting off course.

The AI-driven policies obviated the need to handcraft recovery rules and sequences, the researchers said, providing an advantage over a number of popular control schemes. Additionally, they proved to be remarkably robust, because they were able to handle unpredictable scenarios, such as when the robot’s legs become stuck below its base.

The preliminary results are encouraging. Over the course of 50 tests in which the robot started lying down, the self-righting policy managed to recover within 5 seconds. Moreover, it found success even when the robot’s base was almost upside-down and when its legs were stuck beneath it, sometimes by instructing the robot to flip to its side before having it try an upright post. In a second experiment in which the researchers kicked the ANYmal while it was walking or standing, the policy managed to recover 47 times, failing only when the mechanical quadruped’s joint position was unusually high.

The researchers concede that the system was only trained and tested on flat ground, meaning it would likely fail when faced with steep inclines or rough terrain. But in future work they plan to address that limitation by randomizing the simulation environment.

“The proposed controller exhibits dynamic recovery maneuvers involving multiple ground contacts, and the resulting motions are consistent with the simulated ones,” they wrote.

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Big Data – VentureBeat

Data Integrity vs. Data Quality: How Are They Different?

January 19, 2019 Big Data

Syncsort Editors

January 16, 2019

Data is incredibly valuable, but that doesn’t mean it’s always an asset. When companies work with data that is substandard for any reason, it delivers incorrect insights, skewed analysis, and reckless recommendations.

Two terms describe the condition of data: Data integrity and data quality. These two terms are often used interchangeably, but there are important distinctions. Any company working to maximize the utility and value of data needs to understand the difference:

Data Quality

This is the broader of the two terms. Data can have integrity but not have quality. To understand why, consider what defines data quality:

Completeness – The data present is a large percentage of the total amount of data needed.
Uniqueness – Unique data sets are free of redundant or extraneous entries.
Validity – Data conforms to the syntax and structure defined by the business requirements.
Timeliness – Data is up to date or relevant to the timelines referenced by the business requirements.
Accuracy – Data accurately describes the real-world context it refers to.
Consistency – Data is consistent throughout the dataset.

Quality data must meet all these criteria. If it is lacking in just one way, it could compromise any data-driven initiative.

Data Integrity

Data quality, overall, really refers to whether data is useful. Data integrity, by contrast, refers to whether data is trustworthy. Obviously, it must be trustworthy to be useful, but just because it is trustworthy does not mean it’s also useful. Data integrity is judged based on these variables:

Physical Integrity – Utilizing data is difficult when there are problems either storing or retrieving it. It raises questions about the data’s completeness, accuracy, and validity. The physical integrity of data hardware can become compromised due to age, malfunction, maintenance problems, natural disasters, or power outages.
Logical Integrity – Data becomes illogical when it is incorrect or irrational in some way. Data has to “make sense” for its context. Otherwise, it distorts the perspective of anyone basing decisions off the data. Logical problems can happen because of design flaws, human errors, or software bugs.

bigstock mail 728854 600x Data Integrity vs. Data Quality: How Are They Different?

A Simple Example About Quality and Integrity

In 1993, the United States Postal Service discovered that 23% of all mail is incorrectly addressed. That figure is surprising considering that addresses are fairly simple pieces of data that are easy to store, reference, or remember. Widespread address mistakes are a good example of how easily data can suffer from quality or integrity problems.

If the street number in an address if off by just one digit, the mail would not arrive at the right house. This is an example of poor data integrity because the data itself is invalid and untrustworthy. Now think about a person who has moved but not updated their address. The mail would arrive at the right house but not to the right recipient. This is an example of poor data quality because the data lacks timeliness, accuracy, and consistency.

Data integrity and quality issues are inevitable, but they are not unresolvable. Companies that make a proactive effort to fix existing data issues and prevent future ones see better outcomes from all their data-driven initiatives. To explore this issue further, read our eBook: 4 Ways to Measure Data Quality.

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Tag Archives: they

TypingDNA raises $7 million for AI that identifies people by how they type

Stop Payroll Pains Before They Start

Stop Payroll Pains Before They Start

HOW THE HELL’D THEY GET CLOSE ENOUGH?

Tech Companies Are Leaving Bay Area In Droves – Where Are They Going?

Adios, Silicon Valley!

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The future of technological entrepreneurship

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