Computers are amazing when it comes to compiling and analyzing structured data, such as math equations or financial records. Your humble home PC can process structured data more quickly than even the sharpest mathematicians. Yet when it comes to processing unstructured information, such as everyday conversations, computers fall far short of us humans.
That’s slowly starting to change as key technologies, including Artificial Intelligence (AI) and Natural Language Processing (NLP), evolve and advance. The development of these technologies will have a big impact of the use of voice in technology the Voice-over-the-Internet-Protocol (VoIP), which itself has already disrupted a variety of industries and services.
VoIP allows you to conduct voice and multimedia transmissions over the Internet. This means customers can easily get in touch with companies, customer service representatives, and other relevant parties. Companies, meanwhile, can reduce infrastructure costs and coordinate activities across the world.
As the Internet-of-Things (IoT) grows and more people connect to the World Wide Web, Voice Technology will only become more prominent. This is especially true given the advances in natural language processing that are allowing computers to increasingly process audible conversations and commands.
Let’s take a look at how natural language processing and Voice Technologies (including VoIP) will impact society.
Natural Language Processing, or NLP for short, is a subfield of artificial intelligence and computer science. With NLP, researchers are working to develop software and hardware that can process vast amounts of natural language data.
As powerful as computers are, they are not conscious and lack the intuitive understanding of language that humans possess. Since natural language data is unstructured and heavily reliant on real-world context, it’s especially difficult for computers to analyze.
Computers operate by strict rules, answering straight forward questions and carrying out simple commands. Let’s take a look at a classic example of python code:
Print ‘hello, world’
‘Print’ tells the computer what to do: print something. The apostrophes [ ‘ ’ ] inform the computer what is to be printed and “hello world” is what’s actually printed. For a computer, this line of code is very easy to understand. The vast majority of computer programs are programmed using straightforward, simple commands.
Of course, a software program could contain millions of simple commands that interact with one another. Writing so many commands is difficult for us humans. Understanding how they all relate and fit together is likewise challenging for us but is relatively straightforward for computers.
When it comes to natural languages, however, computers struggle while humans excel. Natural languages involve a myriad of complex rules. Yet, these rules are often flexible and heavily dependent on context. Further, languages can quickly evolve and meanings can change.
Consider this statement:
“I was on fire at work today.”
For a human, the above statement is pretty easy to understand. When the speaker says “on fire”, she means that she was very productive and got a lot of work done. We know this because of the context of the statement.
Computers analyze things much more literally. A simple software program would not understand the context. It would analyze the above statement and conclude that the person was literally on fire. So how do you teach computers to understand context?
This is where Natural Language Processing comes in. The ultimate goal of NLP is to enable computers to decipher and understand human language. When appropriate, the software will also respond and execute actions based on natural language.
Let’s take a deeper look at how NLP actually works.
How Does Natural Language Processing Work?
With NLP, a human can speak to a machine which will then capture the audio. From there, the audio is converted into text and then processed. At this stage, the NLP software will try to parse the text words and decipher the meaning. Afterward, the software may respond to the questions and/or commands that it understands.
Let’s say you want to know what the weather will be like on Friday, so you ask your NLP-enabled smartphone “what will the weather be like this Friday in San Francisco.” The NLP program will use semantic analysis to determine what the text actually means. The NLP software can identify “what” to recognize that you are asking a question. “Weather” and “this Friday,” tells the software what the question is about, while “San Francisco” provides the location.
From there, the NLP software can check with a data source to find the weather for the upcoming Friday in San Francisco. Once the software locates this information, it can pass it back to you, probably by converting the data into audio. And just like that you know have the answer to your question.
The above scenario may appear to be simple, on paper. For humans, the above process would indeed be very simple. However, writing the algorithms that allow a computer to actually carry out the above is exceptionally difficult. In fact, NLP is one of the most difficult fields of artificial intelligence.
The science behind NLP is rather complex. Generally, NLP programs will use tokenization to break up text into smaller chunks. Then, normalization is used to strip away unnecessary punctuation, expand contractions, and the like. The language might also be stemmed, or reduced to stem words (walking becomes walk). These steps make semantic analysis easier, although NLP remains one of the most advanced and challenging fields of AI.
Of course, the most challenge work often produces the most rewarding outcomes. NLP could revolutionize human-computer interactions and VoIP would be a big part of that.
Natural Language Processing will have a dramatic impact on voice in tech. As computers learn to understand human language, they will be able to interact more directly with us. As a result, they will become more deeply enmeshed in our daily lives. AI and ML in voice technology will likewise enable more human-computer interactions.
VoIP enables voice and multimedia transmission over the Internet. The days of relying on phone lines have long since passed and many businesses have been shifting to VoIP. At first glance, VoIP may simply seem like an alternative to simple data transmission but the impact of this technology goes far deeper. VoIP has already allowed companies to cut costs, coordinate activities across the world, and increase touches with customers.
With NLP, Voice ML and AI Technology, and VoIP could become even more disruptive. In order to better understand the impact of natural language processing and VoIP, let’s consider a specific use case. These technologies have been especially useful for customer service, allowing customers to quickly and easily get in touch with customer service departments.
In the past, calling up customer service often meant navigating through complicated phone menus and the like. Now, however, customers can contact customer service right through an app or website using VoIP. Given that 60 percent of Americans will have contacted a customer service department in the past month, VoIP could benefit a lot of people.
However, maintaining customer service departments can be expensive. The customer service outsourcing industry alone is projected to be worth $84.7 billion by 2020. Companies are spending many billions more to maintain their own internal customer service departments. Ultimately, these can be solved by employing ML and AI Voice Technology tools to solve simple problems, help companies boost their Customer Service Rating (CSAT) and improve customer experience.
Traditionally, customer service has been a human-specific job role. You can’t automate an organic, natural conversation. Or can you? Natural language processing, artificial intelligence-enabled software and machine learning will be able to answer questions from people.
NLP-enabled bots have already been a boon for customer service. Now, they allow customers to ask simple questions, like “what are your business hours”, “Can I adjust a reservation”, “Maybe I request a refund” etc . Many call centers are likewise using NLP to allow people to ask simple questions over the phone. This reduces the need for human service agents to spend their time on answering repetitive, monotonous questions, and reduce the customer effort.
As NLP evolves, people will be able to ask increasingly nuanced questions. For customers, this means they will be able to find out the answers to their questions more quickly. Companies, meanwhile, can enjoy cost savings as they can maintain smaller customer service departments. Companies can then pass cost savings onto customers in the form of lower prices.
So is this ML voice technology and VoIP limited strictly to customer support and the like? Absolutely not. It is increasingly being used to facilitate other activities, such as e-commerce. This, in turn, will provide more opportunities for natural language processing to make an impact.
For example, some devices now allow you to put in e-commerce orders through voice commands. With these devices, you no longer have to log onto your computer or smartphone to place an order. Instead, you can simply speak to the device, which will then send your spoken commands off into the cloud where they are processed via NLP software.
When you ask a question or put in an order, the software will try to figure out what you’re saying. When you ask the device to put in an order for paper towels, for example, it will use NLP to both figure out what you want and what the correct response is. In this case, the device will send you more paper towel.
Natural language processing requires computers that are far more powerful than those found on most local devices. Instead, audible conversations are processed in the cloud. VoIP offers one method for facilitating the transmission of audio data from a local device to the cloud where it can be processed.
This means this technology could be vital for enabling human-computer interactions. Likewise, NLP will allow these interactions to be more nuanced and organic. As a result, human-computer interactions will become more common and useful.
Cisco estimates that roughly 50 billion devices will be connected to the Internet by 2020. Smartphones and tablets are probably the most familiar devices for most consumers. Increasingly, however, refrigerators, ovens, water heaters, and other devices are being connected to the web.
With NLP and ML, we will be able to interact more directly with all of these devices. That could mean turning off the oven or ordering more milk. Or if you’re having a problem with one of your appliances, say the compressor motor is making a weird sound, you could use VoIP to call customer service.
Instead of waiting on the line to talk to a representative, an ML-based technology bot could be used. The software can determine that you have a serious problem that should be addressed by a technician, which would allow you to then schedule an appointment. All of this could be facilitated with VoIP, AI and ML. At no point would you have to interact with another person. This means your problem can be addressed quickly and efficiently.
This is a benefit for both the customer and the company. As a customer, your problem is solved more quickly. For the company, this means a happy customer and reduced customer service costs. As a result, AI, ML and VoIP software programs will be increasingly integrated into our daily lives.
In fact, Replicant AI is developing AI solutions for call centers that will be equipped with advanced NLP capabilities. With our AI and ML solution, call centers will be able to automate many of the most tedious tasks, allowing customer service representatives to focus on more challenging issues.
The above use cases and interactions could have a dramatic impact on our daily lives. However, we’re just starting to understand the full potential of Natural Language Processing and Voice ML. Thirty years ago, few people understood how much the Internet would revolutionize society.
Likewise, the full scope of the impact of NLP and Voice ML remains unknown. However, given the potential to lower costs, enrich experiences, and solve problems, these two technologies together will likely have a dramatic and positive effect on our lives.