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Voice user interfaces are filled to the brim with human language. Human languages are beautiful; they are geographically diverse and filled to the brim with uncertainties. Some languages have words that derive their semantic meaning from their context; for others, meaning derives from the tone in which the speaker says it or the syntactical placement of the word in the sentence. In linguistics, there are five main categories to human language that help linguists understand how each language harmonizes into a unified whole: syntax, semantics, pragmatics, morphology and phonology. Because of how ambiguous human languages are and how uniquely innate they are, it may be evident that they would not translate smoothly into a computer. While partly true, it is an inevitably complex subject. Computational linguistics is the academic study of computing natural language. On the theoretical and artificially intelligent level, this is referred to as machine language learning. All of the above is used in voice user interfaces.

Machine language learning is a component of deep learning. Deep learning is a core part of artificial intelligence and is critical to an artificially intelligent system’s performance – whether text-based, voice-based, or neither. Machine language learning possesses many layers, with its main one being natural language processing – also commonly referred to as NLP. Like deep learning at the core of artificial intelligence, natural language processing is at the core of machine language learning. It is an essential component of the fundamentals of any computational linguistic algorithm. Natural language processing often goes together with natural language understanding (NLU), and together, they make up the most successful chatbots and other text and voice-based artificial intelligence systems.

The use of computation to store text-based data is not a new concept. Any simple computer programming language will have a text-based database. It thus makes sense to want to apply language analysis, language translation, conversational design, and language databasing to computation. From a computational perspective, computational linguists can draw from their interdisciplinary background and apply it to artificial intelligence in the form of conversational artificial intelligence within chatbots, virtual assistants, and even virtual reality.

Voice User Interfaces

Voice user interfaces such as those seen from Google (Google Home) or those as technologically iconic as Amazon’s Alexa have become commonplace within many people’s day-to-day lives. For example, FitBit Sense now allows one to talk to Amazon’s Alexa right from the watch itself with the press of a button. As visual interfaces begin to intertwine themselves with voice user interfaces, it is critical that we not only understand why they work but how this will impact our lives as humans who already use language to connect on many levels with other people. How this phenomenon will translate into chatbots is an entirely different narrative.

person reaching out to a robot
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As mentioned previously, machine language learning is at the heart of computational linguistics. Within artificial intelligence, cognitive scientists aim to create artificial intelligence that is ‘strong’ – ‘strong’ in that it does not rely on a user for its input. ‘Strong’ in that it can use past inputs and outputs to create new algorithms. Essentially, computational linguists and cognitive scientists want artificially intelligent systems and voice user interfaces that can learn and speak without the help of a human. They want a piece of artificial intelligence that can evolve with time, independent of any individual behind the scenes working on simulating the learning experience of the chatbot’s experiences with the user. We want the artificial intelligence system to be phenomenally conscious, not just simulate it.

Strong artificial intelligence systems such as those mentioned previously are indeed the future. With self-driving cars on the way and mobile GPS apps with voice user interfaces that are almost entirely voice-based, the push for complex voice user interfaces and conversational artificial intelligence will continue to grow. With the success of social media and social communication apps, it only makes sense that cognitive scientists and computational linguists would want to have chatbots and voice user interfaces that mirror human-to-human communication and interaction. Humans are social creatures, and we crave social interaction. Whether or not an artificially intelligent robot can satisfy that craving is up to our ability to make conversational AI and voice user interface systems naturalized.

READ MORE: Machine Learning 101: The Revolutionary Side of Artificial Intelligence

Unfortunately, an area with such high potential results in high expectations. Much like the expectation of strong artificial intelligence to understand us and evolve and learn from their experiences with the user, this would ultimately translate into a piece of conversational artificial intelligence or voice user interface that is more human-like than its predecessor. The study of cognitive science is, after all, to naturalize and formalize the human mind into a machine.

Why Personalization Requires Strong Conversational Artificial Intelligence

With the expectations of users and those creating the robot to be more human-like and therefore more personalized, it becomes apparent that conversational artificial intelligence branches out into a much more complex area of technology and technological development. To make voice user interfaces that are as engaging and realistic as a human at the other end of a telephone would be, we would need it to function as a human. It would need to be personalized to each and every user, not your stereotypical cookie-cutter chatbot. A significant problem with the rise of conversational AI is its lack of personalization and user-friendliness. At this point in time, it is still too robotic, and therefore, unfriendly.

Presently, chatbots and other voice user interfaces are believed by the average user to not handle complex inquiries and tasks. Synthesizing language and natural language processing to allow artificial intelligence systems to process and understand natural language is one thing. Providing personalized implementations of conversational AI to the user that tailors to them and their experiences in a human-like manner is an entirely different story – one that must technologically evolve as the digital era continues.

While hard to imagine, this is crucial to keep in mind whilst thinking about the future of artificial intelligence, chatbots, virtual assistants, mobile apps, and other viable pieces of technology and their future in marketing. Going beyond artificial intelligence that manually simulates learning from a human and actually learns from the user, their prompts and experiences are the next steps in creating artificial intelligence eco-systems that would have consumers be open to the idea that chatbots can, indeed, handle complex tasks in an empathetic manner. Communication automation is the goal, yes, but we must consider that having communication automation be personalized to the individual is the ultimate goal regarding a voice user interface’s strength as an artificially intelligent system.

Personalization
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This is not to say that we have not already reached artificial intelligence that is strong and, thus, formalized and personalized. We are far from completing such a task with all the natural human languages that exist – especially for voice user interfaces. Unfortunately for machines, human language is not as innate to machines as they are to humans.

The Applications of Conversational AI

Fortunately, natural language processing and conversational artificial intelligence as a scientific discipline of computer science have uses that go beyond user experiences on mobile apps or other hyper-personalized experiences with technology. Because machine intelligence weaves itself into computational linguistics and all its subfields, it is important to note its uses beyond futuristic speech-based interaction with humans. 

Statistical natural language processing is a massive component of this aspect of conversational artificial intelligence. It involves complex computer algorithms combined with machine language learning, deep learning, and neural networks to analyze and categorize voice and/or text data. There are many aspects of technology used in – presumably, types of technology you are familiar with and have encountered. It is an important aspect of voice user interfaces, specifically.

For example, many websites want to check that you are not a robot. What is so ironic about this is that it is a robot making sure you are not a robot. They do this by asking you questions that are typically beyond most standardly automated robot capabilities. To put it more simply, they ask you questions that are more ‘human’ – questions that often require critical thinking, common sense, and a lack of repetition, all of which are core components to the ‘humanity’ of humans. Said subjects are also very complex and have yet to be fully formalized within pieces of artificial intelligence machinery.

Therefore, it is easy to see why conversational artificial intelligence combines with natural language processing and understanding to form a subcategory of artificial intelligence that does not revolve around a user. Instead, it relies upon its own unique machine language learning algorithms to help with spam detection (especially within the financial sector), thorough machine translation, social media rule-analysis and even summarizations of voices or texts.

One of the greatest things to hit the customer service world recently is the development of chatbots. The days of waiting (not so patiently) on hold to find out the answer to your most burning questions are becoming a thing of the past. 

The chatbot market size is expected to grow from $2.6 billion in 2019 to $9.4 billion by 2024 at a compound annual growth rate (CAGR) of 29.7%.

While chatbots are simple for customers to use, they do take thorough and thoughtful planning while in the development stage. With upfront planning, working with all stakeholders synergistically and the guidance of a proven chatbot consultant/project manager, you have the best chances for successful implementation, adoption and ROI.

Chatbots are useful in customer service, including providing product recommendations and engaging with customers through a variety of campaigns.

For example, chatbots have been used to:

– Answer consumers questions quickly, at any time of the day or night
– Provide recommendations based on the customer’s past purchases
– Let customers know about sales or promotions
– Help customers find information or products on a company website

Business Benefits of Chatbot Development

Keep Things Running 24/7: Typical office productivity is usually 6-8 hours/day, but chatbots work around the clock allowing your business to never truly sleep. In a recent study, it was determined that 71% of consumers want the ability to solve customer service issues on their own, which also means on their own time.

Deliver Personalized Responses: Because of the built-in AI, bots can read human intent and deliver personalized responses. In turn, customer satisfaction increases, which drives customer acquisition. 

Increase Retention Rates: Customer acquisition, however, is expensive. As more customers are retained, your ROI increases. The inclusion of a chatbot will provide insight into how your customers truly feel towards your business, allowing you to make data-driven decisions to increase customer lifetime value. 

person choosing document in folder
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4 Steps to Start the Chatbot Development Process on the Right Foot

Here’s what you need to know before you kickoff the chatbot development process:

1. Determine your main goal for the chatbot: Brainstorm what problems you are hoping your bot will solve for you and your customers, and then pick one very specific use case.

2. Identify the target audience for the bot: Determine whether there is a specific persona that will have their needs met by your chatbot. 

3. Create a chatbot project plan: Document what your project requires, including experts you need to lean on, timing expectations, and forward-thinking implementation strategies.

4. Develop a minimum viable product (MVP): Finally, you need to develop the most simple version of the bot with the bare minimum functionality. This is the first baby step within an agile chatbot development process.

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The Agile Chatbot Development Process

As developers often quip: Failure is a feature. Failing fast during the chatbot development process means fixing errors quickly and more affordably throughout the design cycles. Agile consultants and software teams use a distinct process to step through the chatbot development process in order to not only make the most of the budget but to also ensure that the product developed meets all stakeholders’ expectations. 

Chatbot Development Process

To illustrate the agile chatbot development process in action, ChatC’s proven process is summarized below. You can get a feel for what’s involved at each step of the project and for more details on timing or your commitments at each stage, check out our process here

1. Discover automation opportunities: Automation is the delegation of the human control function to a machine. So, how can you let a chatbot take over human functions? Ultimately, if you have staff entering data or responding to repetitive questions, there are opportunities for automation. The first step includes:

– Exploring the business value of chatbots
– Understanding and determining the feasibility of applying conversational AI

2. Define use case: A use case is a list of actions that define the interactions between a system and a role to achieve a goal. Include the following in your documentation:

– Decide on the exact business application 
– Document KPIs to ensure project success
– Learn about the vendor landscape

3. Determine vendor and finalize budget: The agile approach to vendor selection makes use of a streamlined process to pick the right supplier and finalize the budget, including:

– Leverage ChatC’s extensive pre-vetted vendor relationships
– Facilitate RFI and/or RFP if required
– Determine the budget
– Shortlist vendors and evaluate proposals

4. Document system architecture: System architecture is the behaviour or structure of the software that provides services and automation. More specifically, you’ll want to:

– Decide on tech stack integrations
– Strategize how and where the automated chatbot will be deployed

5. Develop proof of concept: A proof of concept shows that the goal of a project is viable and will be a success. Include the following:

– Reimagine the customer/employee journey and come up with a simple strategy to test out your solution
– Kick-off the development team
– Conduct small user group testing and feedback

6. Deploy minimal viable product (MVP): An MVP is a product developed with the most minimal functionalities, to see how the target audience will respond. Include the following in your plans and execution:

– Set Minimum Viable Product (MVP) requirements
– Mobilize the right team to build a minimal viable product (MVP)
– Begin learning together with all stakeholders (customers, operations, IT, etc.)

7. Determine scalability: Scalability is when a software solution is able to handle an increased amount of work. This is where the user group can increase and your team can begin to look at expanding the bot’s reach. Include the following:

– Review performance and KPIs of the MVP
– Determine how quickly the chatbot can be scaled 

8. Data analytics and support: By paying close attention to the data collected by the chatbot, your business decisions can be confidently driven using customer preferences moving forward. Be sure to:

– Put regular data monitoring practices in place (chatbots can be configured to make this even easier)
– Explore custom analytics to discover key insights
– Determine level of support required

Chatbots are the Present and Future – Explore Your Use Case Now

Many brands have already jumped on the chatbot train to reap the benefits of the next-generation technology. Delaying this decision could cause you to lag behind your competition. 

However, developing a chatbot is a substantial and ongoing investment for your team, and should be handled with care up-front. Consider the benefits of working with industry veterans, who have managed chatbot development projects for major Fortune 500 companies. 

Are you wondering how a Chatbot Consultant can help you to develop your company’s chatbot? Get in touch with one of our team members today.