Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG) are closely related topics but are distinct from one another. These are related because they deal with the human language. These are all natural language topics because they are addressing how humans naturally speak with one another rather than how a computer may speak to another computer. All three components play a part just as they do in the human experience of conversation. In our normal conversation, we not only speak words in a certain way, but we also try to understand other persons’ words as we listen to them. Further, when we answer, we are generating speech for our response.

What Is NLP?

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI). Besides AI, it is a product of disciplines such as computer science, linguistics, and data science. These are used to enable computers to understand human language as written or spoken. NLP especially uses the technologies of Machine Learning (ML) and deep learning to be enabled to accomplish such things as language translations or answering questions.

NLP processes the unstructured data of human language, spoken or written, by restructuring it into digital data more easily understandable to intelligent computer programs. This is done by detecting named entities and word patterns and looking at the root forms of words.

What Is NLU?

Natural Language Understanding (NLU) is a subset of NLP. It is employed by NLP to perform the part of understanding the language that NLP is processing. For example, our general thinking is like the NLP part, and our understanding of what we hear or read is like the NLU part of the process.

Without getting too deep, this NLU part does an analysis of semantics and syntaxes of text and speech. It tries to discern the meaning of what is received. Analyzing the syntax of a sentence helps us understand the grammatical structure: what is being said. Think of the phrase, “Are you asking me or telling me?” Analyzing the semantics of a sentence helps us understand the intended sense: what is being implied. Think of the phrase, “What are you implying by that question?”

What Is NLG?

Natural Language Generation (NLG) is another subset of NLP. It does the function of providing output language in response to the input language. It does the answering in written language understandable to humans. This response can also be converted to spoken words by using text-to-speech services. Originally, the NLG component simply performed a method of filling in blanks according to a template in response to key phrases or questions. But NLG has evolved into a more dynamic process by using such services as recurrent neural networks, transformers, and hidden Markov chains. A hidden Markov model is a statistical method to guess what will be said based on some other known parameters.


NLP and the companion components of NLU and NLG are a group of significant Artificial Intelligence (AI) software technologies. They have revolutionized the way digital technology serves human intelligence. These technologies enable computers to interact with people in a much more human-like way. They make the services that computers provide much more accessible for the greatest number of average users possible.

Benefits Of Automation in IT

Benefits Of Automation in IT

Automation in Information Technology (IT) means the automating of data calculations, algorithms, and processes required for digital technology needs, which in turn serves other needs required in various industries. In a sense, to speak about automation in IT is to speak redundantly because the very principle of information technology is automation itself. With the clever manipulation of a binary system, calculations and processes are automated with inhuman efficiency and speed. This is because the underlying actions of calculations, arrangements, discoveries, and other processes are themselves first being done with amazing ease, speed, and reliability. These first actions are the results deriving from the technologies of Natural Language Processing (NLP) and Artificial Intelligence (AI). Common business tasks can be accomplished with the largest parts being automatically performed.

Automated Reasoning Technology

Every high-tech industry has products or services that are suited for human consumption. These commodities are sensible for our existence. When qualified people provide these products or services in some small measure, without the advantage of IT assisting the many intermediate processes, then the overall output of the accomplished work may be of good quality but will inevitably be very limited. But the abilities of IT use such basic tools as calculation, computation, arranging, and sorting. These IT tools increase output exponentially. All these abilities are done by underlying human reasoning design. Natural Language Processing (NLP) and Artificial Intelligence (AI) are two other technological advantages that help with data management needs by the associative reasoning. These learn, in a sense, of what people are saying online, and then leveraging that data for business and other purposes.

Automated Document Data Management

Important business documents that need to be perused or analyzed, such as proposals, contracts, and resumes, require much time and care when being done by individuals themselves. This method will be slow and possibly inaccurate. The larger the documents, such as request for proposals (RFPs) and business proposals, legal contracts and government bills, the more cumbersome to be parsed. They demand many personnel and manhours of labor to accurately absorb and analyze the data content for successful response or manipulation. But imagine what you could do if you had a single application that could comprehensively, accurately, and speedily analyze content data automatically with a few keystrokes? Such a software tool would effectively demonstrate the key benefit of automation in IT.


There are many benefits of automation within Information Technology (IT) itself. With IT, we have the assistance of efficient robots in the workforce, which in turn provides the many benefits of accurate products, economic use of resources, tireless output, a safer workplace for personnel, lower overhead costs, and affordable products and services for the consumers. The remarkable benefit of automation provided by, and due to, IT itself is what the Content Analytics Platform (CAP), developed by Scion Analytics, is about. The application uses such key technologies as Natural Language Processing (NLP) and Artificial Intelligence (AI) to quickly analyze document data, and structures that data so that it can be leveraged for successful business accomplishments

Benefits Of Sentiment Analysis

Benefits Of Sentiment Analysis

What Is Sentiment Analysis

Sentiment analysis, also known as opinion mining or emotional AI, is a way of knowing the thoughts or feelings of people regarding any topic expressed on the Internet. It means analyzing people’s online textual comments regarding a certain matter, service, or product to gather the essential mood they feel. Sentiment analysis uses the digital technologies of Natural Language Processing (NLP) or Artificial Intelligence (AI). This process is beneficial especially for various business products and services but can serve any area of life. In business, it is valuable for marketing, e-commerce, advertising. It can also be useful in politics or any global research need.

Benefit For Business Information

Perhaps the primary use for sentiment analysis is providing businesses the ability to be aware of how their products or services are appreciated by the consumers. This lets companies know how to improve or limit their offerings. People naturally want to see that their efforts at helping in some way is useful. Sentiment analysis is a digital way for a company to assess this.

Source For Customer Feedback

Every high-quality business cares what consumers think about their purchased products or services. Sentiment analysis can gather these publicly expressed attitudes through the canvassing of social media, blogs, articles, reviews, and discussion forums. Using the technologies of NLP and AI, general customer feedback can be known.

Monitor For Market Buzz

Vast amounts of online textual data is readily available for sentiment analysis to help see market interests or trends regarding products or services. These can be new or old, niche or ordinary. In a sense, online textual data is like scattered commodities that just need to be picked up by the savvy marketer. When the public express what they like or dislike, then a company can know what will be successful or not.

Means For Crisis Prevention

Most companies care about their brand’s quality. When consumers are dissatisfied with a product or service, they feel compelled to mention these opinions through social media, websites, or product reviews. These sentiments can be detected in real-time to prevent large-scale damaging impressions. If monitored well, it also helps with real-time customer service in the effort to prevent widespread public disapproval. Not only in the marketplace but also in national or global political affairs can sentiment analysis be useful for preventing bad public persona or threats of violence.


Sentiment analysis is becoming important in the business of marketing, e-commerce, advertising, and even politics, because it is a process of analyzing the opinions and attitudes of people anywhere technology is available. Things which would not be possible without the digital technologies of NLP and AI, which uses algorithms and rules to learn how people speak and express their opinions about anything posted through any digital medium, such as social media, websites, blogs, and discussion forums. The Content Analytics Platform (CAP), developed by Scion Analytics, is a robust software application that uses these technological advances for companies to leverage invaluable structured data.

How Sentiment Analysis Can Analyze Mood

How Sentiment Analysis Can Analyze Mood

COVID-19 And Sentiment Analysis 

2020 was a year that changed history with the spread of the COVID-19 pandemic. As the world shut down and people congregated online, social media channels lit up with collective moods and feelings about the pandemic. Historically, such data would have been subjective however with technological advances in text mining, it was not. Sentiment analysis has evolved in recent years with more uses in the business and public sector.

For the past 50 years, researchers have been working on technology that would enable computers to analyze the emotional tone of words. Such findings have had many real-world applications. For example, a large collection of tweets can be analyzed for collective mood something that would be impossible given human limitations. Now, music researchers can quantify how much a minor chord is sadder than a major chord. And businesses are tapping into websites like Yelp to understand customer behavior better. By assessing a vast number of reviews businesses can align brands with what customers feel and want. Sentiment analysis has medical applications as well with popular platforms like Facebook being able to recognize if users are depressed or suicidal.

When algorithms can gauge mood from what people write online it opens a new world of data and opportunity for society.

How Sentiment Analysis Works

How does sentiment analysis work? Advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP) have mastered analyzing the context of language. However, AI has yet to understand the nuances of language. It was a significant win for science that artificial cognition can decipher the emotions behind words without understanding them.

In the beginning, sentiment analysis used word-counting for analysis. It added up the number of positive words and subtracted the negative words. Some content analytics platforms use weighted analysis to determine the mood of the content. For example, the word “atrocious” is given a higher weight than the word “bad”. Humans assign weights to words by creating

customized dictionaries within the software. In the sphere of sentiment analysis, these dictionaries are called lexicons.

However, human work is prone to error. Word-counting is fundamentally effective but ultimately unreliable. There are too many variables in language for word-counting to be precise. A more sophisticated approach to sentiment analysis uses Machine Learning (ML) algorithms to teach computers to recognize patterns and relationships between words. For example, a computer can recognize that “computer” and “mouse” are related and that it is a different type of “mouse” than the rodent variety.

Sentiment Analysis And Mental Health

Computer scientists are working on further advancements in sentiment analysis which has a foundation in psychology. As early as the 1960s, psychologists in leading universities were designing programs that were giving insights into individual’s psychological worlds. For example, computers were able to pick up on specific patterns for patients diagnosed with depression. Depressed patients were apt to use pronouns more often such as “I” and “me” as well as negative affect words or words related to death. These insights have empowered social media platforms to be proactive about user’s mental health. For example, Facebook has an algorithm that can identify suicidal users and if necessary, send the user helpline numbers.

With roots in psychology, sentiment analysis can give tremendous insight into the collective mood on social media. This has implications in the lives of the consumer and the individual as businesses become better at understanding consumer behavior. Also, individuals become engaged in social media platforms that can accurately decipher their mental health. It represents a shift in how much influence collective mood has on society.

Text Mining vs. Natural Language Processing

Text Mining vs. Natural Language Processing

In the 21st century data has become as much of a commodity as oil was in the 20th century. As businesses become more reliant on data-driven decisions, the importance of utilizing the right type of data methods to process the right information for businesses becomes paramount. Access to unstructured data has emphasized the need to leverage large data sets. Businesses are no longer able to process large sets of textual data manually and have come to rely on automation. To learn how to best extract value from data, it is important to understand the difference in benefits of Natural Language Processing (NLP) versus text mining.

Natural Language Processing (NLP) is a subset of AI in which computers can analyze and interpret human language in an efficient and useful way. It is a way to get a human-level understanding of the language for machines. Using Natural Language Generation (NLG) and Natural Language Understanding (NLU), NLP can process different types of speech including misspellings. It does that by utilizing Machine Learning (ML), a system that relies on special types of databases to extract language.

Text mining is a subtype of data mining. It focuses on data mining and ML methods as it relates to textual information. More specifically, it extracts the information from text files. A wide array of text files can be used in text mining including structured and unstructured data in emails, social media posts, and web content. Text mining works for qualitative data analysis and it helps distinguish between different types of data.

Differences Between Text Mining v. Natural Language Processing

1. Purpose

NLP and text mining differ in the goal for which they are used. 

NLP is used to understand human language by analyzing text, speech, or grammatical syntax. 

Text mining is used to extract information from unstructured and structured content. It focuses on structure rather than the meaning of content.

2. Tools

Businesses use different tools when working with NLP and text mining. 

NLP uses businesses leverage advanced ML models, artificial neural networks (ANN), and tools like NLTK in Python. 

Text mining also uses ML models along with statistical models and text processing languages like Perl. A business must invest in the tools necessary for specific data processing to be successful.

3. Capability

NLP is geared towards mimicking natural human communication. It uses text and speech as input to extract grammatical structure and syntax meaning. 

Text Mining is geared towards analyzing qualitative data.

4. Results

 The outcomes for using different data methods differ. 

NLP can be used to extract grammatical structure as well as the sentiment from language. 

Text mining uses statistical indicators like frequency of words, patterns of words, and correlation within words to explain the text.

Differences Between Text Mining v. Natural Language Examples

The major difference between NLP and text mining is in potential application.

Natural Language Processing

In everyday use, NLP can be found in search engines that provide correct answers when users enter queries. It can also be seen in intelligent chatbots that are integrated into communication channels and websites to provide customer service. Another application of NLP is spellchecking apps, tools like Grammarly, have huge databases of words, grammatical rules, and combinations that are powered by NLP.

Text Mining

Text mining is used for SEO and website marketing purposes. It can guide contextual advertising and target promotions. Another application of text mining is analyzing data from website content and social media platforms.


While NLP and text mining have core differences in application, they both provide advantages when used to analyze data for a business. These data methods save time and resources, are more efficient than human intelligence, and track information flow. Gathering insights from structured and unstructured data, NLP and text mining provide valuable insights that move businesses into the future.

Natural Language Processing (NLP) Consulting

Natural Language Processing (NLP) Consulting

What Is Natural Language Processing

Natural Language Processing (NLP) is a subset of AI in which computers can analyze and interpret human language in an efficient and useful way. It is a way to get a human-level understanding of the language for machines. Considering the nuances of language, NLP is an interesting development in the field of AI. While tools such as content analytics platforms can understand the context of language, a computer is yet to “read between the lines” or intuit language the way a human does.

NLP In Business

As technology advances and humans leverage data for everyday life, the field of NLP becomes more sophisticated. NLP is used to improve the quality of life for humans. It is being integrated into daily lives with intelligent virtual assistants such as Siri and Alexa. These virtual assistants are responsive to human needs and become more intelligent with every interaction with a human. Soon, intelligent automation will enhance human intelligence enabling humans to pursue higher value-added projects requiring ingenuity and creativity.

Businesses can tap into data they did not even know they had to make actionable data-driven decisions. Enterprises benefit from insights, analytics, and decision-making from analyzing 80-90% of hidden data. Unstructured data has many formats from text, voice, audio, images, and videos that can be processed to yield new marketing, sales, and operations initiatives for businesses. By extracting value from unstructured data, a business learns more about its customers and their preferences. For example, a lot of social media channels are full of unstructured data where NLP can be applied to do sentiment analysis. Conducting sentiment analysis on social media, a business gains insights into customer mood and feelings over a product or service. Now, the decisions the business makes surrounding that product or service are not based on guesswork but data.

NLP Consulting

At Scion Analytics, we focus on delivering AI & NLP consulting services with the Content Analytics Platform (CAP). The CAP has NLP capabilities to liberate value from unstructured data that automate processes, scale business, and leverage data to transform the enterprise. By using the CAP, the enterprise can automatically extract value out of content to reduce the sales cycle. It can also use NLP technology to understand the contextual relationship between large data sets which enable the enterprise to execute on opportunities. By leveraging real-time analytics, the enterprise can capitalize on data-driven decisions much faster and gain a competitive advantage. The CAP uses AI & NLP technologies to analyze and process unstructured data by configuring customized microservices for clients. Scion Analytics use cases have found that each time a client uses 1 microservice for every use it saves the client 8 hours of time. For example, a microservice designed for business requirements could parse business documents for predefined keywords and create a dictionary. Therefore, a process that would take hours manually for an employee to go through business documents is automated in minutes.

For the enterprise, the benefits of using a content analytics platform are saving time, increasing revenues, and efficiency. It makes sense that a business that is shaping the future would embrace NLP technology and leverage NLP consulting services to succeed.

Text Summarization In NLP

Text Summarization In NLP

What Is Text Summarization

The advancements in NLP have led to the development of machine learning algorithms such as text summarization that can automatically shorten longer texts and extract summaries of sections of text without losing the message. In a society constrained by time and obsessed with efficiency, text summarization is a leading-edge way to process information. It reduces reading time and makes researching, processing, and digesting the right information easier.

In the modern business landscape, data is a commodity. A comparison can be made that data in the 21st century is akin in value to oil in the 20th century. The analysis and use of vast amounts of data is applied to decision-making for many businesses across industries. Research predicts that by 2025 the total amount of data globally will exceed 180 zettabytes. A meteoric rise from the projected 4.4 zettabytes of data circulating the world in 2013.

The vast amount of data available to the world represents tremendous opportunity especially with tapping into unstructured data. Natural Language Processing (NLP) is a subset of Artificial Intelligence (AI) in which computers can interpret and analyze human language in efficient and useful ways. It is a way to get a human-level understanding of the language for machines. While tools such as content analytics platforms can understand the context of language, a computer is yet to “read between the lines” or intuit language the way a human does.

Text Summarization Examples

NLP extracts the context from a human language using machine learning. In NLP, text summarization shortens a set of data computationally to create a subset of the most meaningful information. It works in two different ways:

Extraction-Based Summarization

 This summarization technique operates by extracting keywords from the document and combining

them into a summary. The text remains unchanged as the extraction of keywords is made according to predefined metrics.

Extraction-Based Examples

Source text

Jill and Jack went to a farm to pick roses in bloom in Vermont. Jill picked a yellow rose to give to Jane.

Extractive Summary

Jill and Jack pick roses in Vermont. Jill gives it to Jane.

The words in bold have been used to create a summary. Because keywords are extracted, the summary may not always be grammatically accurate.

Abstraction-Based Summarization

 This summarization technique works by paraphrasing and shortening parts of the source document. The benefit of abstraction being used in text summarization is that it does not struggle with the same grammatical inconsistencies. Also, abstractive text summarization is more natural at summarizing language like humans. It creates new phrases and sentences with only the most relevant information from the text.

Abstraction-Based Summarization Examples

Abstractive Summary

 Jack and Jill went to the farm to pick roses with Jane.

Executive Summary

 Jack, Jill, and Jane went to the farm.


Abstraction-based summarization is more accurate in capturing the grammatical syntax and context of the text. However, abstraction-based summarization algorithms are harder to develop therefore extraction-based summarization remains the most popular used method. As NLP technology progresses to understand the nuances of language so will the text summarization algorithms to make information more accessible and meaningful to businesses.

What Is Natural Language Processing

What Is Natural Language Processing

Natural Language Processing (NLP) is a subset of AI in which computers can analyze and interpret human language in an efficient and useful way. It is a way to get a human-level understanding of language for machines. Considering the nuances of language, NLP is an interesting development in the field of AI. While tools such as content analytics platforms can understand the context of language, a computer is yet to “read between the lines” or intuit language the way a human does.

Technological advancements have changed the paradigm for doing business. As part of a digital transformation, businesses are exploring means to innovate and scale past the competition. The field of Artificial Intelligence (AI) is one arena that has seen a rapid acceleration in practical application. Nowadays, AI is no longer an abstract theory or a figment of science fiction but a tangible tool for businesses to gain a competitive advantage.

On a high level, NLP leverages computer programs to interpret unstructured content using AI and machine learning. This mechanism allows computers to understand the context of language much as human brains do. Advancements in AI neural networks have taught computers how to mimic the neural networks of human beings. In much the same way that a human being learns by continually being exposed to stimuli, a computer gets more efficient in interpreting language through exposure.

By structuring and interpreting unstructured information, computers generate insights from untapped data for businesses. This enables businesses across industries to get a deeper understanding of the public perception around consumer behavior, products, and brand equity. Decisions that were previously made on guesswork are now guided by data-driven insights. This is because machines are better, more accurate, and more consistent in interpreting language than humans. NLP processes data in real-time, 24/7, for consistent and accurate results. As a competitive advantage, NLP can be used to analyze large data sets such as social media monitoring, customer support, and online reviews.

Natural Language Processing Examples 

AI tools are most effective when they find an application in daily life. Here are some examples of the way NLP has improved daily life across industries:

NLP and Healthcare

 In healthcare, NLP tools can predict diseases based on electronic health records and the patterns of a patient’s speech. This innovation has been used to diagnose health conditions in areas such as mental health and cardiovascular health. As healthcare moves to telehealth, NLP can be used to help in service delivery.

NLP and Sentiment Analysis

 Businesses can listen on social media to what customers are saying by using NLP. Using this technique provides businesses with a lot of insights into customer’s choices and decision-making processes. Access to previously unmanageable large sets of unstructured data gives businesses access to data they did not even know they had on social media.

NLP and Email

Tech giants such as Google and Yahoo use NLP to filter and classify emails by instantly analyzing incoming text. This enables tech companies to filter and stop spam before it enters a user’s inbox.

NLP and Home Assistants

Apple’s Siri and Amazon’s Alexa are great examples of voice driven interfaces that use NLP to make everyday life better for humans. From turning off the lights at ome to playing favorite songs, these home assistants use voice recognition to help humans.

A document-heavy industry, legal is getting help from NLP to automate routine litigation tasks. It can help legal teams be more efficient and save money by having a better strategic focus.

What Industries Use NLP

Most industries will be disrupted by the availability of NLP technology including legal. While lawyers are not going to be replaced by technology, the way lawyers do their job will be changed. NLP automates the time-consuming, redundant tasks that slow down lawyers. This technology will save time and reduce errors by extracting meaning from content and

collecting data and documents instantaneously. Lawyers work on billable hours and NLP technology can save hours of labor allowing legal professionals to focus on cases and clients that matter the most. In addition, NLP may make legal support more affordable to a larger population of people.

Financial Services

More than other industries, the financial services industry is time sensitive. News around the community spreads quickly and can influence an investor’s decision-making. For an investor, filtering relevant information from the 24/7 news cycle is laborious and time-consuming. NLP technology can help make smart financial decisions by pre-filtering steps automatically. Also, financial services can use NLP technology to increase compliance in large amounts of paperwork eliminating mundane and repetitive steps in the process.


Since the COVID-19 pandemic, the healthcare industry has had a significant paradigm shift. With the prevalence of telehealth, efficient coordination of medical data is a top priority. NLP technology can be used to analyze doctor’s records and find hidden correlations between diagnoses, symptoms, medications, and treatment. These new insights give healthcare professionals the ability to manage quality care initiatives and better predict treatment outcomes for patients. /

The Two Subfields Of Natural Language Processing (Understanding & Generation)

Given the high-level definition of NLP and the extent of its capability, it is no wonder that NLP has many subfields. More specifically, there is Natural Language Understanding (NLU) and Natural Language Generation (NLG).

So, what is the difference? NLU is more focused on the process of reading and interpreting language such as giving a command to Apple’s Siri. NLG is geared towards the process of generating a natural language. For example, after asking Siri a question about the weather in Portland, OR the computer would use NLG to respond with “rainy with a chance of thunderstorms”.

To further understand the subsets of NLP, let’s put them in the context of the eCommerce industry. The ecommerce industry is focused on the virtual customer experience and interactions. NLU would drive site search in eCommerce. It is activated when users have a search query, they expect to be understood and to get an appropriate search result based on the query. In eCommerce, NLG would be used to power chatbots that generate language. Chatbots speak back to the users and facilitate an interactive and informative user experience for the customer. Together, NLG and NLU make eCommerce more powerful and responsive to user’s needs and preferences. This in turn drives customer loyalty and increases revenue.

NLP, what is the future?

Given the capabilities of NLP in select industries and the increased reliance on technological advances across the business sector, the future for NLP looks bright. Perhaps, what is most surprising to the average person is how NLP technology has seeped into and improved everyday aspects of life. By touching all areas of life and improving daily interactions, NLP can have a positive effect on the individual’s quality of life as well as society.

Foremost, NLP technology makes communication easier and more efficient. This ensures that every interaction we have is in alignment with our goals and has a positive outcome. NLP also influences mental health and helps individuals overcome personal obstacles and psychological barriers. Because NLP mimics the human brain, it augments tools and techniques inherent to us without us being aware of its psychological impact.

The human brain is prone to error and inconsistency. By relying on machine cognition, everything becomes a lot faster and more automated. The adoption of NLP technology has allowed major organizations to become more agile and flexible with decision-making and strategic initiatives. For businesses, access to unstructured data, data they did not even know they had provided new insights. The intelligence gathered from the data by NLP-powered machines changed the way businesses make decisions.

The prediction is that in the future NLP will become more mainstream. Such NLP practices as text summarization, NLP consulting, and text mining will become more sophisticated with new developments.NLP technology will not only influence select industries but empower decision-making across global markets.


How will humans respond to a major integration of NLP?

Much like with the adoption of AI, the proliferation of NLP in society has been met with enthusiasm rather than disdain. Humans see the potential of technological advancements to make work and life easier. Humans becoming more comfortable with advanced technology can be attributed to the widespread adoption of smartphones and the increased demand for elevated customer service. Nowadays, humans see the power of automating the mundane and repetitive tasks in life so they can focus on higher value-added activities requiring human intelligence, ingenuity, and creativity.

In the future, NLP technology will become an integral part of life. As AI continues to become more advanced, the next level of progress for these technologies to be able to “read between the lines” for human language. To become as sensitive and nuanced to the context of human language as the human brain. This will cause the AI and NLP robots to become more sophisticated and useful in the workplace. As these technological capabilities are becoming more mainstream, they are ushering in a new era of work.