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.
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.
Natural Language Processing Explained: How Can it Impact My Business?
Computers were invented to interact with humans. These machines have always functioned by receiving commands from a user and performing a task accordingly. In the past, those tasks were relayed through code, or in the case of the earliest computers back in the 1970s, a punch card.
But times change, and as technology advances, so too does the means by which we communicate with it. That’s where Natural Language Processing comes into play.
Natural Language Processing, or NLP, allows computer programs to understand spoken or written language. It is an advancement in the field of artificial intelligence.
The main issue surrounding the inability of humans and computers to interact seamlessly was a language barrier. Machines do not speak the same language as us. They understand binary code, which is a series of millions of ones and zeroes that instruct a computer in completing their tasks. Systems have been set up where, with the press of a button or the click of a mouse, that computing language is relayed at the speed of light, allowing machines to understand our wishes.
That has all changed with the birth of NLP. We’ve all experienced advancements in this field. From Amazon’s Alexa, to Google Assistant, to Apple’s Siri, we now communicate with technology directly on an everyday basis.
“Alexa, order a new set of lightbulbs.”
“Ok Google, where is the best Chinese food near me?”
“Hey Siri, what song is this?”
Through the advancement of NLP, we’re always just a sentence away from the information and actions that we want.
But how does NLP work? What other uses does it have? What advantages can we expect to see from this technology in years to come?
How Does NLP Work?
NLP currently works through a process called deep learning.
Deep learning has the artificial intelligence look at data patterns to deepen its understanding of language. Huge amounts of labeled data are inputted to help the system identify relevant correlations.
Language is broken down into shorter elemental pieces in order to teach the machine to understand their relationships and how they work together. By doing this, the computer can ascertain the meaning behind a sentence.
Some of these data pieces include:
Categorization: This is a document summary based on linguistics. It includes indexing and searching, detection of duplicates, and content alerts.
Modeling and Topics: This helps machines understand the themes and meanings within a collection of text. They then take that meaning and approach it from an advanced analytical standpoint.
Context: Computers gain the ability to pull specific contextual information from the text.
Sentiment: Systems can understand the mood behind text. It analyzes the opinions expressed.
Speech-To-Text: This is a back and forth system that allows a machine to take a voice command and transform it into text. It also allows the computer to take written text and relay it vocally.
Summarization: Allows the computer to create a synopsis of a large text body.
Machine Translation: The computer can automatically translate text or speech from one language into another.
Deep learning represents a more fluid and intuitive approach to learning. By understanding the intention of the users, computers are able to learn language in the same way a child would. A human toddler listens to language, ascertains its meaning, and relays it back through a series of trial and error until fluency is achieved.
NLP works the same way, only in a much faster way.
What are the Uses of NLP?
Does NLP have a higher purpose beyond just telling you the movie times or reading off Yelp reviews?
Of course it does.
Search is the main function of NLP right now. We use many of the services mentioned above to find the information that we need. Whether that’s some arbitrary fact you and a friend are arguing over or a piece of information you need for a research project, searching via voice is far easier and more effective than manually inputting information.
When you ask your phone a question the machine is able to isolate the most important elements of your query. That’s why voice searches are usually so fast.
NLP can also be used to digitize hard copy information, making it easy to analyze and search for. Whereas once, a human operator would have to manually input all of the information, telling the computer what it all means and how to file it, NLP allows the system to understand the text on its own and file it accordingly.
Using the sentiment analysis that we discussed in the last section, businesses could have a better understanding of how their customers feel about their product or service. Computer systems can analyze large amounts of online comments and reviews to determine whether a business is succeeding or failing in the public eye.
Natural Language Processing is the future of technology. We’ve already come so far since the creation of the first virtual assistant programs. Many of us entrust artificial intelligence with vital tasks like keeping track of our schedules. As we continue to deepen and enrich artificial intelligence programs, we can be sure that NLP will have a strong place in our daily lives for years to come.
Interested in implementing ML or AI in your business process? Contact us at email@example.com for a consultation and learn more about what Quantilus has to offer here.
3 Ways to Implement Machine Learning and AI into Small Businesses
Earlier this month, John Giannandrea, Apple’s head of artificial intelligence (AI), gave insight into how Apple is leveraging machine learning (ML) within their iOS and the future of machine learning at Apple. Anything from language translation to only sorting photos on your phone into pre-made galleries is made possible with machine learning. These various types of machine learning applications are becoming more and more common and essential.
However, a common misperception of AI and machine learning is that these advanced and sophisticated technologies are only for big brands with budgets that allow for experimentation and implementation.
A small business owner reading a report like Gartner’s 2019 survey of CIO’s would find that although 37% of organizations have installed some form of AI or machine learning, most of the CIO’s interviewed were from large brands. This type of survey may further intimidate small and medium-sized business owners into thinking the growing age of AI and machine learning isn’t ready for their company yet. But, you’d be surprised by how easily small companies can adopt cutting-edge technology without having to rely on an extensive budget.
For small businesses, ML software as a service can be a great tool to utilize, especially in consumer and B2B marketing spaces. In fact, 40% of marketers prioritize AI and machine learning more than any other department and consider them critical to their success.
One familiar and accessible ML tool marketers at small businesses can leverage is a chatbot tool for their website. Chatbots now utilize natural-language processing (NLP) that can interact conversationally with website visitors and collect information like preferences visitors have as they browse a website.
Chatbot tools like Botsify also allow for integration with several services and offer an easy interface to help customize your company’s brand into their templates.
In terms of ML digital marketing tools, a chatbot is just one of the many corners that can be explored. Also, consider implementing machine learning marketing applications for email marketing, ad targeting, voice search, or predictive analysis to help create your campaigns reach new, multiple touchpoints.
Like marketing, cybersecurity is witnessing a fast-growing trend with investments into machine learning tools to help protect their own company and customers. ABI Research estimates ML, AI, and big data spending will increase to $96 billion by 2021.
Machine learning technology can track users’ patterns and make assessments of these patterns, such as the iPhone creating galleries from related pictures, as previously mentioned. This technology can be applied for security responsibilities by implementing security algorithms into something like your mobile application.
If your business relies on the consumer making financial transactions, it’s the company’s responsibility to keep any entered information secure.
Biocatch, for example, utilizes behavioral biometrics, a machine learning technology that tracks user behavior within an application as another form of security. Behavioral biometrics can identify when a different person uses an application by the way they move around the app or the way they type.
Sometimes even just adding a single line of code can improve your mobile application’s security and add another form of authentication working in the background.
Another aspect of any business that requires managing a ton of data, repetitiveness, and predictably is the accounting department, which means machine learning for accounting tasks is a relationship that makes too much sense to not explore.
The future of accounting is heavily entangled with AI and ML, and back in 2018, a report suggested tasks like taxes and payroll would eventually become fully automated. In 2020 machine learning is now applied to generating and processing invoices, even including specific requirements with each task.
Xero, a New Zealand-based accounting software company, provides a cloud accounting software that creates and processes invoices for small businesses. The machine learning aspect in the software enables the creation of invoices based on past behaviors for customers.
Whether you leverage machine learning through marketing, security, or accounting, the decision ultimately depends on where you possibly see the value and ROI of any machine learning tool. For a small-to-medium-sized business, where that value is found can vary.
More and more companies are tapping into or at least exploring how machine learning can impact their business as the machine learning market is expected to grow at 42% CAGR by 2024. While adoption of AI and ML may be daunting, machine learning models are proving to show increasing value to any business size.
Interested in implementing ML or AI in your business process? Contact us at firstname.lastname@example.org for a consultation and learn more about what Quantilus has to offer here.
AI Explained: Understanding the Basics of Artificial Intelligence
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:
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.
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 email@example.com for a consultation and learn more about what Quantilus has to offer here.
The AI Interview Evolution: What are the Benefits?
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:
Saving time by automating tasks
Standardizing assessment across the board
Improving the candidate experience
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.
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 firstname.lastname@example.org for a consultation.
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