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Have you interacted with a chatbot? Does this seem familiar to you?

In the example, you’re interested in visiting an attraction site and want to find out how much the entrance tickets are, so you ask,

User: “How much are tickets for 2 adults and 1 child to the cloud forest?”

Surprisingly, the chatbot didn’t know the answer, despite having the relevant API integrations.

Bot: “Sorry, I’m still learning.”

With a bit of guidance, the chatbot redirects you to a guided (rule-based) conversation flow. It suggests that you should say “Buy tickets” first, followed by “Ticket prices”, and finally “Cloud Forest” to get to the answer.

Bot: “Tickets are available on the website.”

Not quite close yet.

Why couldn’t the bot answer the question directly?

The vast majority of virtual agents use a natural language understanding (NLU) model, but users are still stunted with the unnatural dialogues.

One cannot simply explain the intelligence of a chatbot by saying that one NLP platform is better or worse than the other. It is a convenient reason, but it is not in this case. Why? The purpose of a well-trained NLU model is to help map an input (user utterance) to an output (user intent). For example, both “Send curry chicken pizza to 20 Sunshine Avenue” and “I want fish and chips” refer to the same “Food Order” intent.

However, that is where the intent detection ends. As a conversation designer or developer, you need to consider what happens after intent detection. It’s called context to give a direct response as much as possible.

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What is context?

In real life, if you and your friend finally meet up after months of lockdown, all the moments in the last trip that both of you remember shapes the context. It has specific parameters such as the city names and the people you meet along the way. Context is also perishable, which means that the pre-COVID holiday moments aren’t the first thing in mind if you and your friend have met up multiple times talking about other things.

When you’re programming chatbots, you may want to do something with the specific information uttered by the user. For example, a good idea for your virtual agent is to proactively extract the food name and delivery address during the conversation session and commit to a memory state (the context). The bot should not ask for the same information when the user has already said them down the path.

Unfortunately, some chatbots today can’t remember essential parameters to hold a helpful dialogue with the user, who will eventually have to repeat critical details to the chatbot to help it along.

How did context-less chatbots happen?

These are some possibilities:

1. Designing happy paths only under tree-like conversation design tools in some low-code software
2. Treating intents as turns or checkpoints in the flow, rather than goals the customer have in mind
3. Presenting conversation mind maps or flowcharts to software engineers with no specifications about user error corrections and chat detours
4. Having difficulty accounting for large permutations in a non-linear application, unlike a web or mobile app with finite flows to success/failure states

How should context work in chatbots?

User: “what are the ticket prices for 2 adults and 1 child to the cloud forest again?”

This time, the chatbot extracts the entities it looks for in a ticket price inquiry intent. Those are the participants and the attraction site. As there are sufficient data to look up ticket prices, the chatbot presents a couple of relevant rich cards.

Supposedly you made a mistake. You correct the error by saying

User: “what about 1 adult, 1 child and 1 senior instead?”

Instead of a fallback (“Sorry, I didn’t understand”), the message leads to a parameter-based intent. The chatbot has already remembered your preferred attraction site and now only accounts for the new participant information. It also knows that you’re in the state of ticket price inquiry, so without requiring you to repeat, it tells you the new total price.

Bot: “The standard rates are $20 per adult, $12 per child and $10 per senior. The total is $42.”

You continue to mention that you’re a local citizen.

User: “i’m a local”

Again, without having you repeat the attraction site and the number of people and changing the current conversation topic, the chatbot looks up ticket prices based on all the updated information gathered. Success!

Bot: “Local rates are $12 per adult, $8 per child and $8 per senior. The total is $28.”

In the following article, I’ll run through serverless code examples to build context in conversations.

Opinions expressed are solely my own and do not express the views or opinions of my employer. If you enjoyed this, subscribe to my updates or connect with me over LinkedIn.

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Why good chatbots need context, not tree-based flows was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

The use of voice assistants has been on a rise over the last few years. According to a recent eMarketer report, 38.5% of the US population used a voice assistant in 2020 — that is almost 120+ million users in a year and an increase of 6 million from the previous year.

The report also predicted stable growth for 2021 at a growth rate of 3.2% — bringing the total users to 132 million or 39.4% of the US population.

The rapid adoption of conversational AI and voice assistant technology leads us to an important question: why should businesses incorporate voice technology in their service offering and how can they achieve a successful implementation? This article explores the answer to that question and more.

Exploring the Potential of Voice Technology for Enterprise-Level Usage

Potentially, there are 4 major ways enterprises can leverage voice assistants to add more value to the customer experience:

Voice Assistants Can Lead to Lack of Socializing

According to Juniper Research’s prediction, users of digital voice assistants may reach 8 billion in 2023.

After all, integrating these voice assistants for customer service could be helpful in 3 different ways:

• Provide customer support 24/7 with AI-based voice assistants.
• Bypass the language barrier using automatic translation mechanisms.
• Automate repetitive steps to save customers time.

Voice Shopping at Home

According to Invesp — sales through voice shopping may reach $40 Billion in the US by 2022.

People prefer voice shopping because:

• Voice shopping collects data from your device and personalizes your shopping experience
• The use of such technology is as easy as speaking
• Quicker than the normal process of surfing through websites for buying a product

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

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4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Reducing Human Workload & Making All Processes Faster

In any enterprise, customer service is often a hectic and exhausting job. Customers can’t always be happy and thus customer-facing jobs are among some of the jobs with the highest turnover rate. To fix this, companies are actively replacing employees with AI-powered conversational bots, both text-based and voice assistants.

The reduced workforce requirements for customer service also result in significant cost savings for the business.

Using Voice Assistants Within the Enterprise

Deploying voice assistants within the enterprise as a means to improve productivity and as a stepping stone towards advanced automation is becoming increasingly popular. These enterprise-ready voice assistants use machine learning for custom scenarios and a wide range of jobs. In addition, Machine Learning allows these voice agents to analyze usage patterns and personal preferences for a more human-like experience.

In essence, the more employees use an AI voice assistant, the better it gets. Some of the popular use cases for voice assistants within an enterprise include:

• Scheduling meetings
• Tracking projects
• Accessing databases and online information
• Facilitating communication through interconnected voice assistants

Key Challenges of Implementing Voice Assistants For Enterprises

Deploying any form of technology comes with challenges and pitfalls. Thankfully, in the case of voice assistants, the majority of pitfalls can be avoided by simply understanding them and planning in advance (and in some cases, by managing expectations).

Following are four such challenges that companies should account for to ensure a successful deployment of voice assistants:

• Difficulty in Understanding Certain Voice Types

According to Techcrunch, the understanding and reliability of voice assistants toward children is quite erratic. A squeaky voice, smaller vocal folds, and mispronunciation of words due to an underdeveloped larynx all play a role in reducing the effectiveness of voice assistants.

Unfortunately, children aren’t the only demographic that voice assistants are less effective with. A recent report suggested that Google Assistant isn’t equally effective with all races and genders. This is a result of a lack of racial and gender diversity in Google’s machine learning (ML) voice models. In essence, because Google mostly uses the voice of a white male to train Google Assistant’s ML engine, it faces trouble understanding different accents and languages.

Amazon, Apple, IBM, and Microsoft also face similar challenges but thankfully, this issue has been acknowledged by all tech giants leading the AI voice development and the accuracy rate for speech recognition is increasing every day.

• Background Noises & Voice Quality

The voice interface devices (VID) used in personal settings such as one’s home are different from ones used in an enterprise. The background noise at an average home is in the range of 40 to 60 db, which means voice assistants require powerful noise-canceling algorithms to provide accurate responses.

A poor voice assistant without proper noise cancellation and voice enhancement can’t suppress so much disturbance and will most likely not be able to process everyday requests.

Thankfully, this issue, should it arise, can be resolved with a software patch from your AI voice assistant vendor remotely.

• A Threat to Privacy & Security Concerns

Recently, McDonalds was sued for collecting their customer voiceprints with its new AI-powered automated drive-thru windows. As per reports, the AI-powered voice assistant did not take any permission to collect customer voiceprints. This led to a violation of the Illinois Biometric Information Privacy Act (BIPA).

And with governments imposing stricter regulations and compliance frameworks, privacy and security are becoming a greater concern with new technologies such as AI voice assistants. However, vendors have been anticipating such regulations and greater security and privacy has been a top priority for every company.

• Voice Assistants Can Lead to Lack of Socializing

When a groundbreaking technology starts evolving and going viral, it’s a human tendency to use it. Such is the case with voice assistants. The problem here is that voice assistant may, in certain cases, begin to cannibalize other marketing and sales channels such as in-store purchases. For many companies, in-store shopping is crucial for maintaining customer loyalty. To avoid this, voice assistants are beginning to offer a greater level of personalization in order to improve customer loyalty.

Similarly, a lack of socializing within an enterprise is a concern for many C-suite executives. However, voice assistants will not be completely replacing in-person communication any time soon.

• Deploying within a legacy architecture

Legacy architectures are still very common in enterprise-level organizations. Due to the size of their IT systems, quickly migrating to the cloud to deploy voice assistants isn’t an option. Instead, legacy businesses must either develop integration modules that make cloud-based assistants compatible with their on-prem hardware, software, and processes.

Businesses can either develop these modules in-house or outsource development to external partners — the latter being the more popular option. more challenge.

How Has COVID-19 Impacted the Use of Voice Technology at Enterprise-Level?

The COVID-19 pandemic was a truly black-swan event that had an impact on nearly every industry, the AI voice assistants industry being no exception.

According to Voicebot.ai, 90 million people in the US used smart speakers in 2020. These 90 million people account for 35% of the total population of the US.

The study compares 2020 growth figures with previous years and finds that the number of smart speaker users has been increasing every year since 2017 (and very likely even before that). However, what stands out is the rate of growth per year.

While smart speaker usage in the US increased by 42% between 2017–2018 and another 32% between 2018–2019, it only grew by 1.8% in 2020. The growth rate did not drop so drastically because it reached a saturation point — we’re far from that. One of the biggest reasons for this drop was COVID-19. Everything from global quarantines to limited shipping/supply chain operations played a role in handicapping the growth of smart speakers.

On the other hand, the COVID-19 pandemic has been a catalyst for boosting the use of conversational AI in certain industries including the health sector. Since 2020, the telehealth (remote health care) sector saw a growth of 5% as AI voice assistants played a key role in providing essential healthcare to a world that had no medical workers to spare.

The Future of Voice Assistants in the Enterprises

Despite a long history and a user base in the millions, voice assistant technology is just getting started. In fact, we’ve only scratched the surface of what’s possible and with continued R&D and investment, voice assistants are offering a more personalized and human-like experience every day.

For instance, at Master of Code, we’ve embraced human-centric conversation design to build more effective conversational agents (both text-based and voice-based). The compound learning ability of these voice assistants, made possible by the latest in natural-language-understanding (NLU), makes them extremely versatile and nearly indistinguishable from their human counterparts in numerous business applications.

But the future of voice assistants will not be limited within an enterprise. As we’ve already seen, voice assistants are playing a key role in managing this global health crisis — helping screen and triage patients, walking patients through CDC COVID-19 assessments and recommending next steps through telehealth apps.

Similarly, the automobile industry, especially the likes of Tesla, Ford, and Mercedes Benz, has been showing a keen interest in AI voice assistants in recent years. Capgemini Research Institute says 95% of the people who own cars worldwide will be using voice assistants in the next 3 to 4 years. In essence, touch screens replaced buttons in cars and as AI voice technology became a bigger part of our daily lives, voice assistants will replace those touch screens in the coming decade.

Wrapping up…Where Are We Heading?

According to Markets and Markets, the conventional AI industry is expected to grow from $4.8 billion in 2020 to $13.9 billion by 2025.

As a result, the pressure to integrate speech recognition systems into their IT systems and consumer-facing applications quickly is growing. However, these systems lack the human-centric behavior that customers have come to expect from modern voice assistants.

On the other hand, the Ai-based voice assistants offer open architecture platforms, a myriad of integration solutions, and advanced machine learning capabilities that make them far more capable of delivering that important human touch to the customer experience.

But developing and deploying your own conversational AI solutions comes with its own challenges. Fortunately, most of them can be overcome with the right conversational AI partner.

Looking to Add Some Voice to your Experience?

Let’s connect

Don’t forget to give us your 👏 !

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How AI Voice Assistants Are Transforming The Enterprise was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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​ Important lessons from a chatbot developer People’s interactions with websites and digital devices are changing as a result of chatbots. People can use these at any time of day or night to get answers to their questions. In fact, we come across them almost every day as we use the internet in a variety of settings. Because of the widespread use of chatbots in online services, chatbot developer are in high demand. As a result, young people can now receive chatbot training and begin working in this field. So, who better to learn from than an expert if you want to build your chatbot while avoiding costly mistakes? Take a look at the following key takeaways from a chatbot developer: What exactly is a chatbot? A chatbot is a computer programme that can converse with humans via speech or text. Its primary goal is to facilitate human-to-digital-device communication. Chatbots can be single-line responsive programmes or can provide personalised assistance to users by processing relevant data. What you can learn from a chatbot designer For newcomers to the field, here are some tips from chatbot developers: Lesson 1: Create a chatbot capable of performing a specific function Many inexperienced developers make the mistake of falling victim to marketing hype. A golden rule for chatbot development is to work on a programme that serves a purpose rather than something trendy. When you first introduce your chatbot to a new group of people, make sure they understand its purpose and capabilities. This keeps your audience’s expectations of the chatbot from becoming unrealistic. Lesson 2: Don’t cram too many features into your chatbot In an attempt to create a one-size-fits-all solution, never overburden your chatbot with features. As a result, your chatbot might not work properly. Furthermore, training a chatbot to perform each task is difficult and may cause the bot’s functions to fail. According to a chatbot developer, it is preferable to design a chatbot with complete expertise in one or two tasks rather than allowing it to fail in multiple tasks. When delivering to your audience, remember to prioritise quality over quantity. Lesson 3: Keep your discussion brief and to the point Chatbots are conversational artificial intelligence (AI) systems that assist users in navigating a website or online service. It is, however, critical to maintain the audience’s attention. As a result, keep the following points in mind when designing your chatbot: Maintain as much clarity and conciseness in its interactions as possible. When communicating with the audience, avoid using jargon words. Check that the user understands the purpose of your chatbot. Make Lesson 4 more interesting Although the concept of a chatbot is appealing, users can quickly become bored with inanimate text conversations. So don’t be afraid to experiment with its layout. You should experiment with emojis, animated GIFs, images, and other media. Your primary goal should be to make the conversation between your chatbot and the users engaging and entertaining. Furthermore, the chatbot developer recommends that you give your bot a distinct personality. As a result, it has the potential to differentiate itself in the eyes of customers. Lesson 5: Redirect it to human help After countless hours of using and testing your chatbot, you will become acquainted with navigating your website using the chatbot. However, once your audience begins to use your bots, you may receive a never-ending stream of “I don’t know” responses. And it is almost certain that they will have a bad time. As a result, instead of trapping your audience in an endless loop, provide them with the option of speaking with a human assistant. Configure your chatbot to redirect the user to an option to “connect to a human” after three “I don’t Know” responses. You must ensure that when your website visitors leave, they are satisfied. You should first go through chatbot training and become familiar with key concepts to ensure that you follow all of the Dos and Don’ts of creating a chatbot smoothly. Conclusion Chatbots are the way users and brands will interact in the future. In fact, most digital users prefer to interact with a chatbot to solve their problems rather than contact a member of the human support team. As a result, it is the responsibility of a chatbot developer to provide customers with an easy-to-use and satisfying chatbot experience. To gain in-depth knowledge and best practises for creating a chatbot, we recommend enrolling in achatbot certification programme. It will also assist you in developing a high-quality chatbot. If you’re a tech nerd or simply interested in new developments in this field, the GLOBAL TECH COUNCIL is a great place to keep up with the latest technological developments. submitted by /u/techcouncilglobal [link] [comments]