EducationTech

What is Conversational AI?

By February 21, 2020May 28th, 2020No Comments

The development of conversational AI is a huge step forward for how people interact with computers. The menu, touchscreen, and mouse are all still useful, but it is only a matter of time before the voice-operated interface becomes indispensable to our daily lives.

Conversational AI is arguably the most natural way we can engage with computers because that is how we engage with one another, with regular speech. Moreover, it is equipped to take on increasingly complex tasks. Now, let’s breakdown the technology that makes applications even easier to use and more accessible to more people.

Table of Contents

 

 Defining Conversational AI

Conversational Artificial Intelligence or conversational AI is a set of technologies that produce natural and seamless conversations between humans and computers. It simulates human-like interactions, using speech and text recognition, and by mimicking human conversational behavior. It understands the meaning or intent behind sentences and produces responses as if it was a real person.

Conversational interfaces and chatbots have a long history, and chatbots, in particular, have been making headlines. However, conversational AI systems offer an even more diversified usage, as they can employ both text and voice modalities. Therefore, it can be integrated into a user interface (UI) or voice user interface (VUI) through various channels – from web chats to smart homes.

AI-driven solutions need to incorporate intelligence, sustained contextual understanding, personalization, and the ability to detect user intent clearly. However, it takes a lot of work and dedication to develop an AI-driven interface properly. Conversational design, which identifies the rules that govern natural conversation flow, is key for creating and maintaining such applications.

Users are presented with an experience that is indistinguishable from human interaction. It also allows them to skip multiple steps when completing certain tasks, like ordering a service through an app. If a task can be completed with less effort, it’s a bonus for both businesses and consumers.

 

How Does Conversational AI Work?

Conversational AI utilizes a combination of multiple disciplines and technologies, such as natural language processing (NLP), machine learning (ML), natural language understanding (NLU), and others. By working together, these technologies enable applications to interpret human speech and generate appropriate responses and actions.

Natural Language Processing

Conversational AI breaks down words, phrases, and sentences to their root form because people don’t always speak in a straightforward manner. Then, it can recognize the information or requested action behind these statements.

The underlying process behind the way computer systems and humans can interact is called natural language processing (NLP). It draws intents and entities by evaluating statistically important patterns and taking into account speech peculiarities (common mistakes, synonyms slang, etc.). Before being employed, it is trained to identify said patterns using machine learning algorithms.

User intent refers to what a user is trying to accomplish. It can be expressed by typing out a request or articulating it through speech. In terms of complexity, it can take any form – a single word or something more complicated. The system’s goal then is to match what the user is saying to a specific intent. The challenge is to identify it from a large number of possibilities.

Intent contains entities referring to elements, which describe what needs to be done. For example, conversational AI can recognize entities like locations, numbers, names, dates, etc. The task can be fulfilled as long as the system accurately recognizes these entities from user input.

Training Models

Machine learning and other forms of training models make it possible for a computer to acknowledge and fulfill user intent. Not only does the system identify specific word combinations, but it is continuously learning and improving from experience.

Such methods imply that a computer can perform actions that were not explicitly programmed by a human. In terms of how exactly ML can be trained, there are two major recognized categories:

  • Supervised ML: In the beginning, the system receives input data as well as output data. Based on a training dataset and labeled sample data, it learns how to create rules that map the input to the output. Over time, it becomes capable of performing the tasks on examples it did not encounter during training.
  • Unsupervised ML: There are no outcome variables to predict. Instead, the system receives a lot of data and tools to understand its properties. It can be done to expand a voice assistant or bot’s language model with new utterances.

The key objective is to feed and teach the conversational AI solution different semantic rules, word match position, context-specific questions, and their alternatives and other language elements.

 

What Constitutes Conversational Intelligence

People interact with conceptual and emotional complexity. The exact words are not the only part of conversations that convey meaning – it is also about how we say these words. Normally, computers are unable to grasp these nuances. A well-designed conversational AI, on the other hand, takes it to the next level.

Here are four key elements that ensure conversational intelligence and that voice-operated solutions should include.

Context

A response can be obtained solely based on the input query. However, conversational intelligence takes into account that a typical conversation lasts for multiple turns, which creates context. Previous utterances usually affect how an interaction unfolds.

So, a realistic and engaging natural language interface does not only recognize the user input but also uses contextual understanding. Before queries can be turned into actionable information, conversational AI needs to match it with other data – why, when, and where.

Memory

Conversational systems based on machine learning, by their nature, learn from patterns that occurred in the past. It is a huge improvement from task-oriented interfaces. Now, users can accomplish tasks in a more concise and simple way.

When appropriate, voice-first experiences should utilize predictive intelligence. Whether it’s something the user said 10 minutes ago or a week ago, the system can refer back to it and change the course of the conversation.

 Tone

Depending on what you’re trying to achieve with your conversational AI solution, you can make your bot’s “persona” formal and precise, informal and peppy, or something in between. You can achieve this by tweaking the tone and incorporating some quirks to mimic a real conversation. Make sure it’s consistent and complements your brand’s message.

Engagement

This requirement for conversational intelligence is a natural progression from the previous points. By using context, memory, and appropriate tone, our AI-driven tool should create a feeling of genuine two-way dialogue.

Conversations are dynamic. Naturally, you want to generate coherent and engaging responses unless you want users to feel they are talking to a rigid, predefined script.

 

How Businesses Can Use Conversational AI

Artificial intelligence and automation can make a practical impact on different business functions and industries. We look at industries that can benefit from this technology and how exactly this transformation takes shape for the better.

Online Customer Support

The automation of the customer service process helps you deliver results in real-time. When users have to search for answers themselves or call customer service agents, it increases the waiting time. If you want to reduce user frustration and delegate some tasks to an automated system, you can configure the bot to provide:

  • Product information and recommendations
  • Orders and Shipping
  • Technical Support
  • FAQ-style Queries

Banking

A large portion of requests that banks receive does not require humans. Users can just say what they need, and a bot will be capable of collecting the necessary data to deliver it. Here are a few examples of what conversational AI can easily handle in this sector:

  • Bill payments
  • Money Transfers
  • Credit Applications
  • Security notifications

Healthcare

Conversational AI can make a big difference in an industry where that relies on fast response times. You can customize and train language models specifically for healthcare and medical terms. While technology will never replace real doctors and other medical professionals, it ensures easy access to better care for some specific areas of healthcare:

  • Patient registration
  • Appointment scheduling
  • Post-op instructions
  • Feedback collection
  • Contract management

Retail & e-commerce

Even with the digitization of shopping, customers enjoy the social aspects of retail. Implementation of conversational commerce into your website or application opens up more possibilities. Engage customers with interactive content and offer conversational control of:

  • Product search
  • Checkout
  • Promotions
  • Set price alerts
  • Reservations

Travel

Voice assistants can do everything from booking flights to hotel selection. Travel can be frustrating, however, bots can make it a more pleasant experience. It can be used for:

  • Vacation planning
  • Reservations/cancellations
  • Queries and complaints

Media

Algorithms are able to provide news fast and on a large scale, while also providing convenient access for users. Conversational AI can create an engaging news experience for busy individuals. Applications include:

  • News Delivery
  • Opinion Polling

Real Estate

B2C businesses like real estate rely on personal contact. Conversational AI algorithms can greet potential clients, gauge their level of interest, and qualify them as potential leads. As a result, human agents can address customers, depending on their priority.

 

Benefits of Conversational AI

While innovations like conversational AI are new and exciting, they should not be disregarded as something trivial or inconsequential for business. This technology has actual revenue-driving benefits and the ability to enhance a variety of operations.

Provides Efficiency

Conversational AI delivers responses in seconds and eliminates wait times. It also operates with unmatched accuracy. So, whether your employees use it to complete workflows or customers to track their purchases or order statuses, it will be done quickly and error-free.

Increases Revenue

It is not a surprise that optimized workflows are good for business. The advantages of conversational AI solutions are consistently effective, which translates to better revenue. Plus, when you create better experiences for customers, they will be more likely to stay loyal and purchase from you.

Reduces Cost

This benefit is the logical aftermath of enhancing productivity within your company. The technology leads to better task management and quickly reduces customer support costs. Also, implementing conversational AI requires minimal upfront investment and deploys rapidly.

Generates Insights

AI is a great way to collect data on your users. It helps you track customer behavior, communication styles, and engagement. Overall, when you introduce new ways of interacting with your existing application or website, you can use it to learn more about your users.

Makes Businesses Accessible and Inclusive

If there are no tools to ensure a seamless user experience for everyone, businesses are essentially alienating some of their users. Conversational AI keeps those with impaired hearing and other disabilities, in mind. Accessibility to all is something employers in the modern workplace need to adhere to.

Scales Infinitely

As companies evolve, so do their needs, and it might get too overwhelming for humans and traditional technologies to handle. Conversational AI scales up in response to high demand without losing efficiency. Alternatively, when usage rates are reduced, there are no financial ramifications (unlike maintaining a call center, for example).

 

Key Considerations about Conversational AI

Like any other technology, AI is not without its flaws. At this point, conversational AI faces certain challenges that specific solutions need to overcome. Even though we’ve come a long way since less-advanced applications, let’s look at several areas, which have room for improvement.

Security and Privacy

New technology deals with an immediate need for cybersecurity defense. Since your business and associated data could be at risk, your solution needs to be designed with robust security policies. Users often share sensitive personal information with conversational AI applications. If someone gained authorized access to this data, it could potentially lead to devastating consequences.

Changing, Evolving and Developing Communications

Considering the number of languages, dialects, and accents, it is already a complex task to include them into conversational AI. There are many other factors that further complicate this process. Developers also have to account for slang and any other developments. Thus, language models have to be massive in scope, complexity and backed by substantial computing power.

Discovery and Adoption

Conversational AI applications do not always catch on with the general consumer. Although technology is becoming easier to use, it can take some time for users to get accustomed to new forms of interaction. It’s important to evaluate the technological literacy of your users and come up with ways to make AI-powered advancements create better experiences so they are better received.

Technologies mature once weaknesses have been identified then resolved. We are working to address challenges caused by changes in language and cyber-security threats. It’s not an easy task or a fast one, but it’s essential in order to make sure AI-powered interactions run smoothly.

 

What to Expect from Conversational AI in the Future

Our smartphones already allow us to do things hands and vision-free. But as more and more companies use conversational technology, it gets us thinking about how it can be improved. Here are some trends aimed at creating seamless conversations with technologies.

The Elimination of Bias

As the application of AI expands, regulatory institutions will be making factual findings of how this technology impacts society and whether it holds ramifications affecting individual wellbeing.

The European Union has introduced guidelines on the ethics of AI. Along with covering human oversight, technical robustness, and transparency, they touched on discriminatory cognitive biases. We might expect more regulatory requirements with legal repercussions. If the technology is found to have negative implications, it will not be deemed trustworthy.

The key is to use fair training data. Since the machine learning algorithms are impartial by themselves, the focus will be shifted toward eliminating prejudice and discrimination from the initial data.

Collaboration of Conversational Bots with Different Tools

As new devices emerge, e.g., drones, robots, and self-driving cars, we are facing challenges of how we can simplify interactions with them. Conversing with these new technologies requires collaboration and input from across different platforms.

Disparate bots will need to learn how to collaborate through an intelligent layer. Thus, solutions will be able to bridge the gap between collaborative conversational design and the implementation. For example, if there are multiple stand-alone conversational bots within the organization, it will be easier to combine them for a more consistent experience.

New Skill Sets

Building conversational AI systems isn’t exclusive to developers and researchers. It also involves scriptwriters that map conversation workflows, draw up follow-up questions, and match them to brand values. Team leaders will shed more light on internal business processes. Understanding visual perception will enable designers to create more effective user interfaces. Together, this team effort will enable new skills that conversational AI will put to use.

The New Norm for Personalized Conversations

Many industries are using big data and advanced analytics to personalize their offerings. This unspoken requirement has become so ubiquitous that no service industry can afford to ignore it. Conversational AI can be a driving force for crafting a relationship-based approach and personalizing your application or website.

 

Conversational AI, a Guide - 8 - Employing Conversational AI with Alan

Employing Conversational AI with Alan

Now that you understand the potential of conversational AI, you need to be thinking about how you can properly implement it within your organization. However, designing synchronous conversations across different channels requires a systematic approach. Here are some principles that help us meet this objective:

1. Determine areas with the greatest conversational impact. 

Not all business processes will benefit from the conversational interface. Consider high-friction interactions that can be enhanced with context-aware dialogues. Then, assign a relative value to each opportunity to prioritize them.

2. Understand your audience.

Use the knowledge gleaned from your current audience to reach a bigger audience. Do you want to transform the way your employees accomplish tasks or are looking into expanding your international customer base? You can also target your audience by demographics, product engagement levels, platforms they already use, etc.

3. Build the right connections for an end-to-end conversation. 

Identify all service integrations required for future conversations and make sure the bot has access to the full range of services that it needs. For example, if you need a sales chatbot, it should not only provide information about services and products but also locate them on your website and guide users there.

4. Make sure all your content is ready

If you want the conversational AI system to respond appropriately, refine, and expand the data it receives. It should include call transcripts, interactions via web chats and emails, social media posts, etc. After you provide existing conversational content, the mechanism will learn how to build on it without your involvement.

 5. Generate truly dynamic responses.

Your goal is to transition from using structured menu-like interactions to natural language dialogues. In order to do that, you need to generate responses based on applied linguistics and human communication.

6. Create a persona for your business.

Identify what characteristics and values you want to enhance with conversational AI. The “personality” of your bot should adopt key traits that support your brand strategy. Make it recognizable and unique so that your users can form a real, human-like connection with it.

7. Prioritize Privacy.

Comprehensive privacy policies are imperative for handling any data. Since users can share personally identifiable information, you need to create a product they can trust. In some cases, users even provide more information than necessary. Overall, you need to implement security control proactively and prevent data leaks as much as possible.

As you can see, there are no shortcuts for creating a good conversational system. The checklist above requires iteration and analysis along the way. However, you can still easily embed a conversational voice AI platform, into your existing application, with Alan. We do all the heavy lifting, breaking down the process into logical steps and making bespoke AI strategies for you. Our solutions are quick, hassle-free and so both you and your customers will see the results in no time at all.

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