AI/Machine Learning/NLP/AR/VR 2018-03-13T16:28:30+00:00
Quantilus AI NLP ML

Artificial Intelligence, Machine Learning, Natural Language Processing, Augmented and Virtual Reality

Intelligent Automation for Work and Life

At Quantilus, we have been working with AI before it became cool (and scary). Our first foray was in the field of Natural Language Processing – which we used for automated grammar and style checks of written content. Subsequently, we built tools to classify untagged content in intelligent, usable ways, and to present it for consumption with a high degree of personalization. More recently, we have been working on personality assessment of individuals based on 1) the words they speak (a relatively simple task), and 2) changes in their facial patterns based on verbal and visual cues (a much more complex task).

Want to build Virtual Reality or Augmented Reality apps for your business? We built some of the first business-focused AR apps for mobile and wearable platforms through our SAP partnership. Our apps help technical support personnel visualize product models, and also let customers visualize retail products in empty space. With the added complexity of tight integration with backend ERP systems.

FEATURED WORK

Appliqant - Automated Video Interviews
APPLIQANT – the Automated Interview Robot. Disrupt recruitment through the automated screening of job candidates.
Appliqant - Automated Video Interviews
Wearable Apps for Technical Support

Discover Simple Assist – Wearable Apps for Technical Support

Wearable Apps for Technical Support
Visual Showroom - Augmented Reality

Visual Showroom – Augmented Reality App for Product Display

Visual Showroom - Augmented Reality
Deloitte - Automated Document Editing, Data Extraction

Deloitte – Automated Document Editing, Validation and Data Extraction

Deloitte - Automated Document Editing, Data Extraction
Intel - Machine Learning

Intel – Machine Learning for Customer Classification and Segmentation

Intel - Machine Learning
Intelligent Lease - Data Extraction using NLP

Intelligent Lease – Automated Data Extraction from Unstructured Lease Documents

Intelligent Lease - Data Extraction using NLP
BluePencil - Grammar and Writing Style Checks using NLP
BluePencil – Automated Grammar and Writing Style Checking using NLP
BluePencil - Grammar and Writing Style Checks using NLP

TECHNOLOGY STACK

Some of the frameworks and tools that our development teams have used recently. A list that grows by the day.

RELATED RESEARCH

Relevant, interesting and current curated research content in the field.

The Best of CES 2021: 5 Product Innovations to Watch in 2021

January 14th, 2021|Categories: AI, NLP, Machine Learning, Emerging Tech - AR, Cloud, Blockchain|Tags: |

As the first-ever virtual CES 2021 comes to a close, let’s look back at, or forward to, some of the highlights of the unique version of consumer technology’s yearly showcase. From health and safety to the usual innovations in consumer tech, this all-digital CES still left viewers and attendees with tons of new products and concepts to talk about. Here are a few innovations geared towards navigating the changes occurring all over the world.

Best of CES 2021 in Health & Safety: Razer’s Project Hazel

A mask became a necessity in 2020 as COVID-19 shifted the way people needed to navigate their daily lives. Razer announced a new project at CES 2021 showcasing what they call the world’s smartest mask that boasts safety and social features, as well as being a non-disposable mask.

The mask features a surgical N95 respirator and has a high fluid resistance. The primary safety feature and differentiator for Project Hazel is the auto-sterilization function. The mask comes with a chargeable case that has a UV light interior that kills bacteria and potential viruses while the mask is charging.

From a social standpoint, the new mask features a transparent design, a low light mode that helps mask wearers still show their expressions in dark areas or environments, and has a built-in mic and amplifier. The most common complaints from mask wearers try to get addressed by Project Hazel. When people feel they’re not their usual selves when needing to wear a mask or don’t feel appropriately understood when speaking, Project Hazel is looking to up the comfort.

Speaking of comfort, the mask’s ventilation brings in cool air and doesn’t trap heat or CO2, and no part of the adjustable mask rests on the wearer’s mouth.

The expected release of the mask to consumers is by the end of 2021.

Best of CES 2021 in Productivity: Kensington’s StudioDock

Kensington’s StudioDock is exclusive for Apple users; however, it’s the future answer to the problem many Apple users have, especially those working from home, of trying to find the best set-up to utilize Apple products for work and actively charge them.

The StudioDock provides a compact place to charge your iPhone and AirPods, as well as a dock for most iPads currently on the market. The port can allow the iPad to act as a mini iMac, and the dock can wirelessly connect to an iMac so your iPad can become an extra screen for your home office. There is also optional charging support for an Apple Watch.

Kensington’s StudioDock ultimately offers flexibility to the user’s workspace better than some other competitors currently on the market. The dock also has a USB-C port, three USB-A ports, an audio jack, an HDMI port, an SD card reader, and an Ethernet jack. So for those not looking to invest in a new computer, the dock provides an option to turn your tablet into a mini-desktop computer.

There’s currently no release date set yet for the StudioDock.

Best of CES 2021 in Accessibility: CareClever’s Cutii

Senior care is one area that 2020, unfortunately, brought much change to and broke down support networks for many elders. CareClever debuted a robotic answer to help those feeling isolated with Cutii. Cutii is a companion robot and one of the first that is ready to be released in 2021.

Cutii’s differentiator is that it is entirely mobile, so no voice-activation is needed to use the companion. If, for example, an elder falls and needs assistance, Cutii will answer your call and try to get in touch with an emergency contact on its own. Like many other smart applications and devices, Cutii also can remind users of appointments.

Voice and video calls can also run through Cutii, and the companion can also act as a platform to host teleconsultations with doctors.

Cutii commercially launched in France, and CES 2021 kicked off the start-up’s marketing efforts in the United States.

Best of CES 2021 in Cleanliness: Roborock S7

With more time spent in the home, chances are the messes become more frequent as well. The Roborock S7 is a smart vacuum and mop that scrubs the floor up to 3,000 times per minute to clean the more challenging to get out messes it identifies. The mopping feature also identifies surfaces like rugs or carpets to ensure those surfaces don’t get wet.

The cleaning device’s corresponding app also gives users the ability to schedule when each room should be cleaned or if a particular part of the living space is a No-go Zone. Users can also adjust suction power at any time.

The Roborock S7 releases to consumers at the end of March and sells for $649.

Best of CES 2021 in Software: Owl Labs’ Whiteboard Enhance

The classroom or office definition has varied in the past year with changing circumstances due to the pandemic; however, Owl Labs’ Whiteboard Enhance provides a solution to help participants feel comfortable as hybrid meetings or classrooms continue to be a reality.

The Whiteboard Enhance software works in businesses and classrooms that already utilize Meeting Owl Pro and gives any user the ability to zoom in or out of any images on the whiteboard. For example, a student using the software can zoom in on the teacher’s board and not obstruct the view that other students have on their screens.

Meeting Owl Pro provides teachers or businesses to present in a 360-degree smart video conferencing environment.

CES 2021 saw innovations in areas that aren’t always a focal point at the yearly tech show; however, the best of CES 2021 still could bring excitement and maybe even some reassurance for those looking to make their lives a little more comfortable in 2021.

Contact us at info@quantilus.com for a consultation and learn more about what Quantilus has to offer here.

Gartner’s Top Nine Tech Trends of 2021

December 18th, 2020|Categories: AI, NLP, Machine Learning, Emerging Tech - AR, Cloud, Blockchain, News and Announcements|

A new year means new technology and advancements. With a seemingly endless amount of technology and innovations being created daily, it can be challenging for businesses to pinpoint which technologies are worth the investment. In 2020, the impact of the COVID-19 pandemic resulted in immense disruption for organizations, influencing technology adoption and requiring agile strategies. Global research and advisory firm, Gartner, recently released their predictions for the upcoming year’s top tech trends that CIOs and executives need to consider. Many of these trends highlight how organizations can maintain continuity amid changing or unforeseen conditions moving forward. Gartner’s tech trends for 2021 are as follows… 

  1. Internet of Behaviors (IoB)
  2. Total Experience
  3. Privacy-Enhancing computation
  4. Distributed cloud
  5. Anywhere Operations
  6. Cybersecurity mesh
  7. Intelligent composable business
  8. AI engineering
  9. Hyperautomation

Continue reading to explore each trend and find out their benefits. 

1) Internet of Behaviors:

The Internet of Behaviors, also referred to as IoB, utilizes collected user data to influence behaviors. This data is mined from various sources, including actions on a company’s website, social media profiles, computer vision, sensors or RFID tags, and more.  

An example of this is setting up computer vision on work devices. Computer vision can analyze employees’ faces and detect if they are wearing a face mask. If they are not wearing a face mask, it can remind the employee and alert this information to other stakeholders in the company.

Accordingly, there are ethical implications with the IoB as it collects data on users and attempts to change their behaviors. The data collected can also have varying levels of intrusiveness due to the technology being able to capture a wide variety of personal information. Laws on the IoB vary widely, which will play a critical role in adopting and advancing this tech trend.

      

2) Total Experience:

Total experience links the customer, user, and employee experiences together using multi-experience. Multi-experience utilizes various technologies to engage and interact with consumers. Therefore, total experience focuses on leveraging different technologies to create a more seamless process for customers, users, and employees. 

Gartner’s tech trends report provided an example of a telecommunications company improving its total experience by interlinking both customer and employee in-store experiences to enforce social distancing guidelines. Specifically, the telecommunications company leveraged its consumer-facing app for scheduling appointments, geo-tracking, and push notifications to manage the customer’s in-store experience in a safe, orderly way. Simultaneously, employees in the store leveraged digital kiosks to service the customer as well as personal tablets to co-browse customers’ devices, thus avoiding the need to touch consumers’ devices.

So instead of perfecting the customer, user, and employee experiences independently, they should be thought of as one experience and flow harmoniously into each other. Meanwhile, integrating various technology solutions into these experiences will help automate tasks, improve efficiencies, guide customers and staff, maintain social distancing guidelines, engage customers, and more. 

      

3) Privacy-enhancing computation

Privacy-enhancing computation aims to protect personal information in the following ways:

  • Provide a safe environment for sensitive data to be processed and analyzed. 
  • Perform processing and analytics in a decentralized manner. 
  • Transform data and algorithms before processing or analytics.

Privacy enhancing computation is becoming increasingly important because as teams become more geographically widespread and more people require access to sensitive information, the demand for digital and online protection also grows. Privacy enhancing computation also allows for better collaboration while not compromising security. 

 

4) Distributed cloud

The purpose of distributed cloud is to have cloud providers implement their services near their customers. Perks of having cloud services physically closer to customers include reducing low-latency, decreasing data costs, and complying with laws that require data to remain in a specified geographical location. Additionally, businesses are not accountable for managing their cloud when using distributed cloud, which can be an expensive and elaborate responsibility.

 

5) Anywhere Operations

Anywhere operations is an IT model that allows businesses to operate…from anywhere. This means employees can work remotely and companies can operate from nearly anywhere in the world, and still complete their tasks. However, this does not mean that employees and businesses must always be remote. 

 Anywhere operations require businesses to become digital-centric. This means digital processes and operations should be the default for organizations. Therefore, business is less likely to be interrupted due to things such as social distancing guidelines and inclement weather. Lastly, physical stores still have a place in business, but the stores should adopt digital processes like contactless payment. Anywhere operations minimizes downturns in business due to foreseen and unforeseen circumstances (such as COVID-19).

 

6) Cybersecurity mesh

An increase in remote workers equates to more opportunities for online security to be compromised. Cybersecurity mesh allows users to safely access digital information, files, and assets securely from anywhere geographically.  

To create a cybersecurity mesh, companies need to take security measures outside of the ‘normal’, physical location where information was originally intended to be accessed…which in most cases is the company office(s). To do so, businesses should implement scalable cloud models that set user identity as the security parameter. This way, employees can securely access company information and assets anywhere around the globe without worrying about data leaks. 

 

7) Intelligent Composable Business

Successful businesses can quickly adapt their processes, operations, and model based on data. Therefore, intelligent composable business means as technology continues to transform, it is vital that companies:

  • Have greater access to all pertinent information.
  • Gather more insightful findings from the information.
  • Adapt their model based on their findings.

Furthermore, autonomy will be essential in helping companies quickly adapt so outdated processes do not delay businesses. Find out more information on automation by reading trend nine below. 

 

8) AI Engineering

Gartner’s findings reveal that only 53% of artificial intelligence projects make it from prototype to production. Therefore, many AI projects appear to lack resources, scalability, and internal company support. 

That’s where AI engineering comes into the equation. The foundation of AI engineering is made of three pillars – DataOps, ModelOps, and DevOps. Thus, AI engineering is essentially lifecycle management for artificial intelligence projects. More planning, strategy, team-members, financial backing, etc., increases the likelihood of AI projects making it to production. Therefore, giving the AI projects a chance to make a profit. Also, it’s important to note that once an AI project does come to production, it is easier for the product to evolve as new technology and findings are revealed. 

 

9) Hyperautomation

As Gartner’s top tech trend of 2020, hyperautomation landed another spot on this year’s list. Hyperautomation is automating anything in a business that can be automated. If processes are left un-automated, companies are likely operating less efficiently than they could be and are therefore missing out on profit. Working at less than optimal efficiency also makes organizations more susceptible to falling behind competitors. 

 

Conclusion

Businesses in all industries can implement these tech trends, not just companies in the technology sector. Furthermore, companies can cherry-pick which trends they wish to implement in their business. However, it is essential to note that technology tends to be more efficient and successful when combined with other technologies. 

Which tech trends do you think will be the most vital during 2021? Let us know in the comments below. Interested in learning more about how technology can help your business? Contact us at info@quantilus.com for a consultation to learn how we can help.

Behavioral Biometrics’ Continuing Growth into Business Applications

October 27th, 2020|Categories: AI, NLP, Machine Learning, Mobile Development|Tags: |

With the current work climate accelerating towards remote offices and customer services, cybersecurity and, to be more specific, behavioral biometrics, is quickly becoming a focal point for any potential or recent innovation.

In fact, a recent report from Grand View Research concluded that the market size for behavioral biometrics is expected to grow at a compound annual growth rate of 24.5% from 2020 to 2027. The pandemic has accelerated interest in an already growing space, but should you start to consider adding behavioral biometrics to your service or business?

What is Behavioral Biometrics?

Behavioral biometrics is a form of cybersecurity that relies on the behavior of the user rather than static information or physical characteristics. Traditional security or static information for applications or consumer websites would include a password, phone number, or social security number. Physical characteristics would include security functions like fingerprint or face ID.

More recently, dual-step authentication is also a form of static information. For example, this would be when you’re asked to enter a password and also a numerical code sent to another device, like your phone, to confirm you’re signing in.

And while most of these types of security are relatively new, they are also still vulnerable. You’ve probably heard or read countless stories of data breaches even within companies keeping user information protected behind two-step authentication or face ID.

Behavioral biometrics is powered by artificial intelligence (AI) and works in the background of a user session and tries to identify if the person using the device or application is actually the normal user of that device. As an example, this version of biometrics can identify a user by the way the device is being held, the way a user normally types, or the way they navigate with the app’s interface. Behavioral biometrics can detect abnormalities between user interaction and automated/fraudster attacks.

How is Behavioral Biometrics Currently Being Applied?

In the past few years, the financial sector has seen the greatest investment in behavioral biometrics followed by insurance and eCommerce.

A top bank in the UK, for example, utilizes behavioral biometrics through industry leader Biocatch for its online banking app. Behavioral biometrics was able to alert the business in real-time of attempted fraud of almost 1.6 million pounds. The bank also utilized anti-malware and device recognition, but behavioral biometrics was the only security technology to alert the company of this attack.

Mastercard ran a report earlier in 2020 and revealed 7 out of 10 consumers believe the shift to digital payments is permanent. With this train of thought from the public, the growing need for investing in security measures to build trust with users is even more important now. Mastercard also began issuing contactless biometric bank cards back in 2019 and started trials for both debit and credit cards.

Why Should My Business Consider Frictionless Cybersecurity?

One of the more frustrating aspects of the common e-commerce website or mobile app is having to reset passwords or go through two-step authentication. These potential pitfalls of losing users to these frustrating and sometimes time-consuming tasks can ultimately lose business for good.

Behavioral biometrics removes any of those burdens and frictions on the user with a passive cybersecurity measure that also keeps the user safe. In addition, related traditional security costs tied to IT or help desks can also potentially be reduced when investing in behavioral biometrics.

More and more security companies are beginning to add behavioral biometrics solutions to their offerings, so now is the time to do research in this space. Just recently, private security company, Incognia, also added behavioral biometrics as an offering to their authentication services.

Is Behavioral Biometrics a Viable Option in 2020?

American consumers lost almost $17 billion last year because of identity fraud according to lead fraud and security analyst John Buzzard at Javelin Strategy & Research.

With the increased demand by consumers in wanting a contactless option for payments or e-commerce that also offers trusted security and frictionless user experience, behavioral biometrics is a potential, long-term solution for any company. And, with the growing competition in the market, there are more options than ever for a business of any size to do their due diligence in looking into cybersecurity that can ultimately save IT expenses and security costs.

Contact us at info@quantilus.com for a consultation and learn more about what Quantilus has to offer here.

Machine Learning: The Brain Behind Self-Driving Cars

October 26th, 2020|Categories: AI, NLP, Machine Learning, Emerging Tech - AR, Cloud, Blockchain|

Previously, we have explored machine learning and its impact on small businesses. However, machine learning is integrated in many aspects of life. Let’s switch gears and explore the applications of machine learning in the context of our lives as consumers. The automotive industry commonly uses artificial intelligence to optimize the design and manufacturing of vehicles released to the market. One area where machine learning plays a critical role is in the advent of self-driving cars. 

A handful of tech and car companies are currently racing to have their self-driving cars on the road first. The self-driving cars being developed rely on advanced technologies, including machine learning, to power these cutting-edge vehicles. So, before the day comes that you look in the rearview mirror and notice you are surrounded by self-driving cars, let’s go over the technology that propels these innovative cars.

 

Machine Learning to Drive Decisions

Machine learning is at the center of self-driving cars. For a self-driving car to drive on the road, it utilizes specially designed hardware and software. The hardware continuously collects data from the surrounding environment, while the software takes the collected data and sorts it. The software then relinquishes the data to machine learning algorithms to process the data and make decisions.

With machine learning algorithms, the more data the algorithm is exposed to, the more intelligent the algorithm becomes. This is because the algorithms take what they learn from prior experiences, draw conclusions based on the data, and then perform the action that will result in the best possible outcome.

 

What Data Does Machine Learning Look At?

Machine learning algorithms in self-driving cars are fed data and process it in real-time. The machine learning algorithms analyze data, including everything from recognizing upcoming stop signs to identifying deer on the side of the road and cars braking ahead. The vehicle then interprets this data and decides the best course of action. 

Also, it is important to note that machine learning is already utilized by certain cars on the market. For example, lane detection uses machine learning to monitor the lanes on the road. When machine learning detects the car is drifting out of its lane, it will either notify the driver or actually steer the car so it remains in the correct lane. 

 

Breaking It Down Further

We can further break down the use of machine learning in self-driving cars into smaller segments. These segments are object detection, object recognition, and movement prediction. When machine learning algorithms in self-driving cars analyze data, the first step is to recognize an object. Once an object is detected, the algorithms then need to determine what exactly the object is. Lastly, the algorithm needs to determine if the object will potentially get in the car’s path.

For example, if the car recognizes an object and then determines it is a gas station on the side of the road, the car knows it can continue driving since the store will not move into its path. However, if the vehicle recognizes an object and then determines it is a kid riding a scooter, the car decides that the kid may ride out in front of the vehicle, and the car will slow down.

 

Machine Learning for Prescriptive Alerts

Machine learning in cars is also used for the next level of predictive maintenance. Currently, most drivers experience notifications when their vehicle requires maintenance, such as the check engine light illuminating on the dashboard. However, machine learning takes it a step further by discovering budding issues before they happen. Additionally, machine learning can also provide prescriptive alerts. The purpose of prescriptive alerts is “to produce outcome-focused recommendations for operations and maintenance.” 

Essentially, prescriptive alerts determine when something is going to be an issue and then provides the solution, all before the issue actually arises. Examples of prescriptive alerts include notifying drivers when to change tires, replace brakes, and fix cooling systems. Prescriptive alerts and maintenance are extremely beneficial to the shipping industry and truck drivers. This is because drivers will not have to stop mid-trip to fix these issues which would delay the shipment, but instead can have their truck maintained ahead of time to avoid delays. Prescriptive alerts and maintenance are still new but are thought to be the next level of predictive maintenance.

 

The Dilemma with Self-Driving Cars

One of the most significant issues with self-driving cars is making sure they collect enough real-world experience and data on public roads. It is a catch-22 because on the one hand, self-driving cars need to be on public roads to collect data and be exposed to real-world situations to improve accuracy and safety. However, allowing self-driving cars on public roads also puts the public’s safety at risk by exposing them to the emerging technology.

 

Conclusion

Self-driving cars are not expected to be widely available and legal to the public for years to come. The leading companies currently building and testing these vehicles have predominantly already passed their initial predictions of when the cars will be ready. Therefore, they are still perfecting the technology behind the wheel (if there even is a wheel). However, without machine learning, self-driving cars probably would not have made it this far in their development. 

Machine learning is vital to the success and advancement of self-driving cars and is central to the vehicles’ operation. As advancements in machine learning are made, not only will self-driving cars improve, but so will countless other aspects of the publics’ everyday lives. From eCommerce to marketing and healthcare, machine learning will be innovating life as we know it. 

Interested in developing more about machine learning and how it can apply to your business? Contact us at info@quantilus.com for a consultation and learn more about what Quantilus has to offer here.

Bitnami