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.


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


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


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

AI Explained: Understanding the Basics of Artificial Intelligence

July 31st, 2020|Categories: AI, NLP, Machine Learning|Tags: , |

Many people have seen The Terminator and know what happens if you’re busy playing video games instead of preparing for SkyNet. That world’s artificial intelligence isn’t too far from what’s available today—but how, exactly, can AI be explained for everyone to understand?

Let’s start with its history. In 1956, Marvin Minsky and John McCarthy, who coined the term AI, described it as a task performed by a machine or program that—were a human to do the same task—would require at least some intelligence to complete. The definition has evolved somewhat in the past 68 years, but generally, all AI systems include the following behaviors which we associate with human intelligence:

  • Planning
  • Learning
  • Reasoning
  • Knowledge representation
  • Perception
  • Problem-solving
  • Manipulation
  • Social intelligence
  • Creativity

Two types of artificial intelligence

There are two types of AI: narrow AI and general AI. It is the narrow AI that permeates our world today, in all fields from medical to mechanic, financial to engineering, and everything in between. General AI is still a ‘pipe dream’ that computer engineers are working to creating. Most experts say we’re still a decade or two away from achieving true general AI because of its complexity.

Narrow Artificial Intelligence

Most computers use narrow AI. They’re intelligent systems that know how to conduct specific tasks without having been explicitly programmed to do so. Apple’s Siri is a perfect example. So is Amazon’s Alexis, Google’s new virtual assistant, and IBM’s Watson supercomputer.

These systems simulate a human being’s knowledge and cognitive ability within specific parameters. The systems can include self-driving cars and spam filters. Why? Because the systems use pattern recognition, natural language processing, machine learning, and data recognition to make decisions.

Narrow AI, in addition to telling you a joke or the weather, has a host of applications. These systems can identify inappropriate content online or in emails, respond to customer service requests, read video feeds from drones, organize and coordinate business/personal calendars, analyze data to make predictions, and more.

General Artificial Intelligence

This AI—also referred to a human-level, strong, and superintelligence AI—can understand and reason within its environment, just like a human. Think Data from Star Trek: The Next Generation, or Hal from 2001 A Space Odyssey.

It’s “strong” because this AI will be stronger than us humans and “general” because we’ll be able to apply it to all problems. However, it’s nearly impossible to create a computer that can think abstractly, innovate, or plan. Experts agree that it’s really difficult—at this point still impossible—to teach a computer how to invent something that doesn’t exist.

AI is gaining strength—it can produce more accurate predictions about the data it’s fed. That DeepMind algorithms can win more games and transfer learning from one game to another is another indication that AI is growing stronger.

Whole Brain Architecture Approach

Dr. Hiroshi Yamakawa, Director of Dwango AI Laboratory, is one of the world’s foremost authorities on AI. He says that currently, AI can solve particular issues or address specific problems. His organization is using the Whole Brain Architecture Approach which is an engineering-based approach to “create a human-like artificial general intelligence (AGI) by learning from the architecture of the entire brain.” This AGI uses the human brain’s hard wiring as a model to integrate machine learning modules and artificial neural networks. He theorizes that the WBAI will be achieved by 2030 and will help to find solutions for global problems that include environmental, food, and space issues.

Still a journey to achieve artificial general intelligence

Computer scientists continue to work to develop an actual AI that can think like a human. We’ve seen the “results” of such successes in Terminator, I, Robot, A.I. Artificial Intelligence, Ex Machina, Blade Runner, and many other sci-fi books movies and books.

But the reality is that even the world’s best machine learning engineers, with access to millions of dollars, are struggling to build a general AI product. Nearly $15.2B of the capital venture was given to AI startups in 2017 and over 45,000 research papers on AI have been published since 2012. Read this article to learn more about what’s propelling the recent surge in general AI development.

What’s next?

The closest thing we’ve got today to general AI is machine learning (ML). This term describes feeding vast amounts of data into a computer system which then extrapolates it to carry out a specific task—like Facebook’s algorithms that can recognize faces from your contacts list or Waze and Google Maps, that can analyze traffic speeds and plot alternate routes. And there are many other examples of machine learning, a growing field designed to create machines that are faster and more accurate.

How does machine learning work?

In a nutshell, this subset of AI uses statistical techniques to enable a computer to learn without explicit programming. According to Dr. Yoshua Bengio, from the University of Montreal, machine learning uses data, observations, and world interactions to provide computers with acquired knowledge which then facilitates the computers’ ability to accurately generalize to new settings.

ML groups a variety of algorithms by learning style or similarity in form or function. These algorithms include representation, evaluation, and optimization—their goal is to provide computers with the “skill” to interpret never-before-seen data and apply it to new situations.

The field of ML and data science continues to grow, but while these mathematical concepts can be implemented into real-world applications, this so-called deep learning isn’t real intelligence… yet. It’s a type of mathematical optimization that does have limits. The “thinking” is limited to specific domains and the intelligence depends on the training dataset (so humans are still in control). It’s difficult to use it within constantly-evolving, dynamic environments and can’t be used for control problems—only classification and regression. And to ensure the greatest accuracy, it requires huge datasets.

Will we ever achieve true artificial intelligence?

It’s hard to say. Sixty-two years after its inception, we’re still working to achieve true AI. Weak AI systems make more and more decisions as scientists and engineers develop ways to gather, quantify, and feed more data into more algorithms.

And we must, caution Phil Torres, an Affiliate Scholar at the Institute for Ethics and Emerging Technologies, consider the human element—as AI develops, it’s incumbent upon those in the field to program human values into algorithms. After all, he says, “If we suddenly decided, as a society, that we had to solve the problem of morality—determine what’s right and what’s wrong, and feed it into a machine—in the next 20 years… would we even be able to do it?”

Interested in implementing AI in your business process? Contact us at for a consultation and learn more about what Quantilus has to offer here.

The AI Interview Evolution: What are the Benefits?

July 31st, 2020|Categories: AI, NLP, Machine Learning, Emerging Tech - AR, Cloud, Blockchain|Tags: , , , , |

An emerging trend in recruiting is the rise of automated interviewing, including the integration of artificial intelligence technology. This transition is a boon for the largely outdated recruiting industry. AI components allow companies to easily sift through dozens (or hundreds) of interviews quickly with objective metrics and consistent results. But as the rise in AI automation becomes more widespread, many readers may ask themselves, “how did artificial intelligence make its way into the hiring process in the first place? Isn’t hiring a fundamentally human process?”

Benefits of Artificial Intelligence in Recruiting

The emergence of new technologies in the recruiting space has been met with plenty of skepticism over the years. However, the advantages of this new technology far outweigh the fear of AI’s ability to understand and analyze human behavior. Utilizing AI in recruiting brings many benefits for organizations in every industry. These include:

  1. Saving time by automating tasks
  2. Standardizing assessment across the board
  3. Improving the candidate experience
  4. Decreasing turnover and hiring costs

For a nonprofit organization, small business, or startup, the cost of a bad hire could be the make or break point for their company. With the average cost of a bad hire ranging from $25,000-$50,000 (and beyond), the stakes are high to recruit the best talent as quickly as possible.

Current State of the Industry

The hiring and recruitment process has remained largely stagnant for decades. In fact, a recent study by Fast Company showed that the processes from application to acceptance are becoming even less efficient, increasing from 13 days in 2011 to nearly a month today. This equates to lost productivity and a greater strain on teams who are missing staff while searching for quality talent.

In the past two years, artificial intelligence has become a trending topic in the recruiting space. AI has been utilized in the hiring process by applying techniques like natural language processing and facial expression recognition. Natural language processing analyzes the word choice and patterns an individual uses to create a comprehensive personality profile. This profile will then be checked against the job requirements to understand the best personality fit for the role and rank candidates accordingly. Through Appliqant’s platform, candidates are profiled on the Big 5 OCEAN characteristics – openness, conscientiousness, extraversion, agreeableness, and neuroticism.

Algorithms are able to detect facial expressions in recorded interviews, including when someone is smiling or frowning. Appliqant’s technology is always evolving, as we are currently working to measure these facial expressions against pre-set questions and phrases. This will allow you to see how a candidate reacts to each question and create a more robust understanding of their experience and working style.

What’s Next

The ultimate goal of integrating AI is to have a robot interviewer lead a natural-feeling, AI interview for every candidate. The machine will be able to speak to a person and evaluate their skills and personality accurately every time.

Our AI technology is currently learning how to evaluate changes in a person’s expressions based on verbal and visual clues. This mimics the way humans form opinions about people, looking at their facial cues and body signals to understand their mood and disposition.

By utilizing AI to analyze automated video interviews, we are revolutionizing the recruitment industry to reduce waste in time and resources and bring you the best quality candidates for every position.

Appliqant is an AI-infused, blockchain-driven, automated video interview platform developed by the team at Quantilus. Interested in implementing AI in your business process? Contact us at for a consultation.

Why is Machine Learning Important and How will it Impact Business?

July 31st, 2020|Categories: AI, NLP, Machine Learning|Tags: , |

It’s a bit difficult to narrow down one specific definition of machine learning (ML) because you’ll get a different explanation depending on whom you ask.

Nvidia defines machine learning as “the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” McKinsey&Company agree with Nvidia, saying that machine learning is “based on algorithms that can learn from data without relying on rules-based programming.” Stanford suggests that machine learning is “the science of getting computers to act without being explicitly programmed.”

And Carnegie Mellon’s definition—also a favorite of many other experts in the machine learning industry—states “the field of Machine Learning seeks to answer the question ‘How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?’”

Regardless of the definition you choose, at its most basic level, the goal of machine learning is to adapt to new data independently and make decisions and recommendations based on thousands of calculations and analyses. It’s done by infusing artificial intelligence machines or deep learning business applications from the data they’re fed. Machine learning models learn, identify patterns, and make decisions with minimal intervention from humans. Ideally, machines increase accuracy and efficiency and remove (or greatly reduce) the possibility of human error.

The importance of machine learning

The nearly limitless quantity of available data, affordable data storage, and the growth of less expensive and more powerful processing has propelled the growth of machine learning. Now many industries are developing more robust machine learning models capable of analyzing bigger and more complex data while delivering faster, more accurate results on vast scales. Machine learning tools enable organizations to more quickly identify profitable opportunities and potential risks.

The practical applications of machine learning drive business results which can dramatically affect a company’s bottom line. New techniques in the field are evolving rapidly and expanded the application of machine learning to nearly limitless possibilities. Industries that depend on vast quantities of data—and need a system to analyze it efficiently and accurately, have embraced machine learning as the best way to build models, strategize, and plan.

Industries that use machine learning

Healthcare. The proliferation of wearable sensors and devices that monitor everything from pulse rates and steps walked to oxygen and sugar levels and even sleeping patterns have generated a significant volume of data that enables doctors to assess their patients’ health in real-time. One new machine learning algorithm detects cancerous tumors on mammograms; another identifies skin cancer; a third can analyze retinal images to diagnose diabetic retinopathy.

Government. Systems that use machine learning enable government officials to use data to predict potential future scenarios and adapt to rapidly changing situations. Machine learning can help to improve cybersecurity and cyber intelligence, support counterterrorism efforts, optimize operational preparedness, logistics management, and predictive maintenance, and reduce failure rates. This recent article highlights 10 more applications for machine learning within the healthcare industry.

Marketing and sales. Machine learning is even revolutionizing the marketing sector as many companies have successfully implemented artificial intelligence (AI) and machine learning to increase and enhance customer satisfaction by over 10%. In fact, according to Forbes, “57% of enterprise executives believe that the most important growth benefit of AI and machine learning will be improving customer experiences and support.

E-commerce and social media sites use machine learning to analyze your buying and search history—and make recommendations on other items to purchase, based on your past habits. Many experts theorize that the future of retail will be driven by AI and machine learning as deep learning business applications become even more adept at capturing, analyzing, and using data to personalize individuals’ shopping experiences and develop customized targeted marketing campaigns.

Transportation. Efficiency and accuracy are key to profitability within this sector; so is the ability to predict and mitigate potential problems. Machine learning’s data analysis and modeling functions dovetail perfectly with businesses within the delivery, public transportation, and freight transport sectors. Machine learning uses algorithms to find factors that positively and negatively impact a supply chain’s success, making machine learning a critical component within supply chain management.

Within logistics, machine learning facilitates the ability of schedulers to optimize carrier selection, rating, routing, and QC processes, which saves money and improves efficiency. Machine learning’s ability to analyze thousands of data points simultaneously and apply algorithms more quickly than any human enables machine learning to solve problems that people haven’t yet identified.

Financial services. The insights provided by machine learning in this industry allow investors to identify new opportunities or know when to trade. Data mining pinpoints high-risk clients and informs cyber surveillance to find and mitigate signs of fraud. Machine learning can help calibrate financial portfolios or assess risk for loans and insurance underwriting.

The future of AI and machine learning in this industry include an ability to evaluate hedge funds and analyze stock market movement to make financial recommendations. Machine learning may render usernames, passwords, and security questions obsolete by taking anomaly -detection to the next level: facial or voice recognition, or other biometric data.

Oil and gas. Machine learning and AI are already working to find new energy sources and analyze mineral deposits in the ground, predict refinery sensor failure, and streamline oil distribution to increase efficiency and shrink costs. Machine learning is revolutionizing the industry with its case-based reasoning, reservoir modeling, and drill floor automation, too. And above all, machine learning is helping to make this dangerous industry safer.

Manufacturing. Machine learning is no stranger to the vast manufacturing industry, either. Machine learning applications in manufacturing are about accomplishing the goal of improving operations from conceptualization to final delivery, significantly reducing error rates, improving predictive maintenance, and increasing inventory turn.

Not unlike the transportation industry, machine learning has helped companies improve logistical solutions that include assets, supply chain, and inventory management. Machine learning also plays a key role in enhancing overall equipment effectiveness (OEE) by measuring the availability, performance, and quality of assembly equipment.

Machine learning & artificial intelligence: here to stay

Is all the hype surrounding machine learning really worth it? Most experts say “yes” – with this caveat: The key is understanding how to use it to meet each individual business’s challenges and goals. It’s clear, based on a significant volume of data and evidence, that machine learning and artificial intelligence are here to stay. The trick, however, is recognizing that machine learning and AI aren’t a magic spell that works for every situation.

Experts agree that it’s important to clearly understand the value that incorporating machine learning will bring to your business. If it’s negligible, the expense may not bring a significant enough return on investment (ROI). This article from Business highlights four questions to ask before you consider beginning a machine learning project.

Why is machine learning important for your business in particular? Contact us at for a consultation and learn more about what Quantilus has to offer here.

5 Ways Your Company Can Benefit From Robotic Process Automation

July 30th, 2020|Categories: AI, NLP, Machine Learning|Tags: |

5 Ways Your Company Can Benefit From Robotic Process Automation 

In almost every business, mundane tasks exist. Tasks that employees wish someone else or something else would complete for them so they can focus on more pressing assignments. This is where robotic process automation enters the equation.

What is Robotic Process Automation?

What exactly is robotic process automation (RPA)? Contrary to popular belief, there is no physical, moving robot in robotic process automation. Instead, the robot or ‘bot’ is software implemented into a machine, which in most cases is a computer. This bot can then be trained by a human to complete a variety of repetitive computer tasks with little to zero errors. 

As a side note, RPA should not be confused with artificial intelligence. To differentiate the two, robotic process automation can be associated with “doing”, while artificial intelligence aligns with “learning” and “thinking”.  

Certain qualifications should be met to ensure a task is worth incorporating robotic process automation. David Landreman, the CPO at Olive which is an artificial intelligence company, lists them as

  1. The process must be rule-based.
  2. The process must be repeated at regular intervals, or have a pre-defined trigger.
  3. The process must have defined inputs and outputs.
  4. The task should have sufficient volume.

Assuming the task aligns with these four qualifications, businesses should reap the many benefits of integrating robotic process automation.


Use Cases for Robotic Process Automation

Before diving into five use cases of robotic process automation, let’s go over a real-world example of how RPA is used. There are boundless use cases for robotic process automation, including implementing RPA for collecting data, handling transactions, and capturing information. One of the most commonly used applications of RPA is data migration and form processing. For example, if a business is transitioning from paper forms to a digital database, RPA can analyze the paper forms, extract the required data, and then enter the collected data into the system. Therefore, RPA almost entirely removes the human from this tedious and time-consuming process.


The Benefits

Now that we covered what robotic process automation is and some examples of how it can be used, let’s reveal some of the numerous benefits RPA offers.

  • Greater Efficiency
    • RPA is efficient. Once the robot is trained with a specific set of instructions, it can complete a task much quicker than its human counterpart. The robot does not require breaks, does not experience distractions, and can work around the clock.
  • Increased Productivity
    • Implementing RPA allows human employees to focus their attention on more pressing tasks. No longer do employees need to waste time on repetitive, manual tasks that are better suited for robotic process automation. Instead they can use their time tackling complex assignments that a robot is not able to solve.
  • Cost Savings
    • RPA is a great way to help a company’s bottom line. Less full-time workers are needed because employees are not spending time completing the mundane tasks which robotic process automation can handle. David Schatsky, a managing director at Deloitte LP, reveals the savings one bank experienced while utilizing RPA, “Deploying 85 bots to run 13 processes, handling 1.5 million requests per year. The bank added capacity equivalent to more than 200 full-time employees at approximately 30 percent of the cost of recruiting more staff.”
  • Unmatched Accuracy
    • Humans are not perfect, and consequently, make mistakes. Whether it is attaching the wrong file to an email or spelling the address wrong on a tax form, errors occur. However, RPA rarely, if ever, makes mistakes since it is specially trained software.
  • Unlimited Scalability
    • There are three main ways to RPA automation to fit the growing needs of a business:
      1. Increasing the bots workload. This simply means providing the bots with a greater share of assignments.
      2. Diversifying the bots’ responsibilities. Bots can understand and complete different processes. Thus, when a bot is caught up on work in one process, it can switch gears to another different process.
      3. Expanding RPA access. With new technologies continually being created, robotic process automation is regularly updated with new capabilities. By integrating new software to a company’s robotic process automation solutions, RPAs can be scaled by increasing their workloads and diversifying their responsibilities.


Implementing Robotic Process Automation 

If utilized correctly, RPA saves businesses and employees time, money, and accuracy. Three things everyone likes to hear. Whether it’s a bank processing credit and background checks, or a health insurance company processing claims, robotic process automation’s versatility can be deployed. For more information on robotic process automation, please reach out to us at