The media is abuzz with news about large language models (LLM) doing things that were virtually impossible for computers before. From generating text to summarizing articles and answering questions, LLMs are enhancing existing applications and unlocking new ones.

However, when it comes to enterprise applications, LLMs can’t be used as is. In their plain form, LLMs are not very robust and can make errors that will degrade the user experience or possibly cause irreversible mistakes. 

To solve these problems, enterprises need to adjust the LLMs to remain constrained to their business rules and knowledge base. One way to do this is through fine-tuning language models with proprietary data. Here is what you need to know.

The hallucination problem

LLMs are trained for “next token prediction.” Basically, it means that during training, they take a chunk from an existing document (e.g., Wikipedia, news website, code repositories), and try to predict the next word. Then they compare their prediction with what actually exists in the document and adjust their internal parameters to improve their prediction. By repeating this process over a very large corpus of curated text, the LLM develops a “model” of the language and model contained in the documents. It can then produce long stretches of high-quality text.

However, LLMs don’t have working models of the real world or the context of the conversation. They are missing many of the things that humans possess, such as multi-modal perception, common sense, intuitive physics, and more. This is why they can get into all kinds of trouble, including hallucinating facts, which means they can generate text that is plausible but factually incorrect. And given that they have been trained on a very wide corpus of data, they can start making up very wild facts with high confidence. 

Hallucination can be fun and entertaining when you’re using an LLM chatbot casually or to post memes on the internet. But when used in an enterprise application, hallucination can have very adverse effects. In healthcare, finance, commerce, sales, customer service, and many other areas, there is very little room for making factual mistakes.

Scientists and researchers have made solid progress in addressing the hallucination problem. But it is not gone yet. This is why it is important that app developers take measures to make sure that the LLMs that power their AI Assistants are robust and remain true to the knowledge and rules that they set for them.

Fine-tuning large language models

One of the solutions to the hallucination problem is to fine-tune LLMs on application-specific data. The developer must curate a dataset that contains text that is relevant to their application. Then they take a pretrained model and give it a few extra rounds of training on the proprietary data. Fine-tuning improves the model’s performance by limiting its output within the constraints of the knowledge contained in the application-specific documents. This is a very effective method for use cases where the LLM is applied to a very specific application, such as enterprise settings. 

A more advanced fine-tuning technique is “reinforcement learning from human feedback” (RLHF). In RLHF, a group of human annotators provide the LLM with a prompt and let it generate several outputs. They then rank each output and repeat the process with other prompts. The prompts, outputs, and rankings are then used to train a separate “reward model” which is used to rank the LLM’s output. This reward model is then used in a reinforcement learning process to align the model with the user’s intent. RLHF is the training process used in ChatGPT.

Another approach is to use ensembles of LLMs and other types of machine learning models. In this case, several models (hence the name ensemble) process the user input and generate the output. Then the ML system uses a voting mechanism to choose the best decision (e.g., the output that has received the most votes).

While mixing and fine-tuning language models is very effective, it is not trivial. Based on the type of model or service used, developers must overcome technical barriers. For example, if the company wants to self-host its own model, it must set up servers and GPU clusters, create an entire MLOps pipeline, curate the data from across its entire knowledge base, and format it in a way that can be read by the programming tools that will be retraining the model. The high costs and shortage of machine learning and data engineering talent often make it prohibitive for companies to fine-tune and use LLMs.

API services reduce some of the complexities but still require large efforts and manual labor on the part of the app developers.

Fine-tuning language models with Alan AI Platform

Alan AI is committed to providing high-quality and easy-to-use actionable AI platform for enterprise applications. From the start, our vision has been to create AI Platform that makes it easy for app developers to deploy AI solutions to create the next-generation user experience. 

Our approach ensures that the underlying AI system has the right context and knowledge to avoid the kind of mistakes that current LLMs make. The architecture of the Alan AI Platform is designed to combine the power of LLMs with your existing knowledge base, APIs, databases, or even raw web data. 

To further improve the performance of the language model that powers the Alan AI Platform, we have added fine-tuning tools that are versatile and easy to use. Our general approach to fine-tuning models for the enterprise is to provide “grounding” and “affordance.” Grounding means making sure the model’s responses are based on real facts, not hallucinations. This is done by keeping the model limited within the boundaries of the enterprises knowledge base and training data as well as the context provided by the user. Affordance means knowing the limits of the model and making sure that it only responds to the prompts and requests that fall within its capabilities.

You can see this in the Q&A Service by Alan AI, which allows you to add an Actionable AI assistant on top of the existing content.

The Q&A service is a useful tool that can provide your website with 24/7 support for your visitors. However, it is important that the AI assistant is truthful to the content and knowledge of your business. Naturally, the solution is to fine-tune the underlying language model with the content of your website.

To simplify the fine-tuning process, we have provided a simple function called corpus, which developers can use to provide the content on which they want to fine-tune their AI model. You can provide the function with a list of plain-text strings that represent your fine-tuning dataset. To further simplify the process, we also support URL-based data. Instead of providing raw text, you can provide the function with a list of URLs that point to the pages where the relevant information is located. These could be links to documentation pages, FAQs, knowledge bases, or any other content that is relevant to your application. Alan AI automatically scrapes the content of those pages and uses them to fine-tune the model, saving you the manual labor to extract the data. This can be very convenient when you already have a large corpus of documentation and want to use it to train your model.

During inference, Alan AI uses the fine-tuned model with the other proprietary features of its Actionable AI platform, which takes into account visuals, user interactions, and other data that provide further context for the assistant.

Building robust language models will be key to success in the coming wave of Actionable AI innovation. Fine-tuning is the first step we are taking to make sure all enterprises have access to the best-in-class AI technologies for their applications.

Conversational AI has become an increasingly popular technology in recent years. This technology uses machine learning to enable computers to communicate with humans in a natural language. One of the key components of conversational AI is language models, which are used to understand and generate natural language. Among the various types of language models, the large language model (LLM) has become more significant in the development of conversational AI.

In this article, we will explore the role of LLMs in conversational AI and how they are being used to improve the performance of these systems.

What are LLMs?

In recent years, large language models have gained significant traction. These models are designed to understand and generate natural language by processing large amounts of text data. LLMs are based on deep learning techniques, which involve training neural networks on large datasets to learn the statistical patterns of natural language. The goal of LLMs is to be able to generate natural language text that is indistinguishable from that produced by a human.

One of the most well-known LLMs is OpenAI’s GPT-3. This model has 175 billion parameters, making it one of the largest LLMs ever developed. GPT-3 has been used in a variety of applications, including language translation, chatbots, and text generation. The success of GPT-3 has sparked a renewed interest in LLMs, and researchers are now exploring how these models can be used to improve conversational AI.

Role of LLMs in Conversational AI

LLMs are essential for creating conversational systems that can interact with humans in a natural and intuitive way. There are several ways in which LLMs are being used to improve the performance of conversational AI systems.

1. Understanding Natural Language

One of the key challenges in developing conversational AI is understanding natural language. Humans use language in a complex and nuanced way, and it can be difficult for machines to understand the meaning behind what is being said. LLMs are being used to address this challenge by providing a way to model the statistical patterns of natural language.

In particular, LLMs can be used to train natural language understanding (NLU) models that identify the intent behind user input, enabling conversational AI systems to understand what the user is saying and respond appropriately. LLMs are particularly helpful for training NLU models because they can learn from large amounts of text data, which allows them to capture the subtle nuances of natural language.

2. Generating Natural Language

Another key challenge in developing conversational AI is natural language generation (NLG). Machines need to be able to generate responses that are not only grammatically correct but also sound natural and intuitive to the user.

LLMs can be used to train natural language generation (NLG) models that can generate responses to the user’s input. NLG models are essential for creating conversational AI systems that can engage in natural and intuitive conversations with users. LLMs are particularly useful for training NLG models because they can generate high-quality text that is indistinguishable from that produced by a human.

3. Improving Conversational Flow

To create truly natural and intuitive conversations, conversational AI systems need to be able to manage dialogue and maintain context across multiple exchanges with users.
LLMs can also be used to improve the conversational flow of – these systems. Conversational flow refers to the way in which a dialog progresses between a user and a machine. LLMs help model the statistical patterns of natural language and predict the next likely response in a conversation. This lets conversational AI systems respond more quickly and accurately to user input, leading to a more natural and intuitive conversation.

Conclusion

Integration of LLMs into conversational AI platforms like Alan AI has revolutionized the field of natural language processing, enabling machines to understand and generate human language more accurately and effectively. 

As a multimodal AI platform, Alan AI leverages a combination of natural language processing, speech recognition, and non-verbal context to provide a seamless and intuitive conversational experience for users.

By including LLMs in its technology stack, Alan AI can provide a more robust and reliable natural language understanding and generation, resulting in more engaging and personalized conversations. The use of LLMs in conversational AI represents a significant step towards creating more intelligent and responsive machines that can interact with humans more naturally and intuitively.

In the past few months, advances in large language models (LLM) have shown what could be the next big computing paradigm. ChatGPT, the latest LLM from OpenAI, has taken the world by storm, reaching 100 million users in a record time.

Developers, web designers, writers, and people of all kinds of professions are using ChatGPT to generate human-readable text that previously required intense human labor. And now, Microsoft, OpenAI’s main backer, is trialing a version of its Bing search engine that is enhanced by ChatGPT, posing the first real threat to Google’s $283-billion monopoly in the online search market.

Other tech giants are not far behind. Google is taking hasty measures to release Bard, its rival to ChatGPT. Amazon and Meta are running their own experiments with LLMs. And a host of tech startups are using new business models with LLM-powered products.

We’re at a critical juncture in the history of computing, which some experts compare to the huge shifts caused by the internet and mobile. Soon, conversational interfaces will become the norm in every application, and users will become comfortable with—and in fact, expect—conversational agents in websites, mobile apps, kiosks, wearables, etc.

The limits of current AI systems

As much as conversational UX is attractive, it is not as simple as adding an LLM API on top of your application. We’ve seen this in the limited success of the first generation of voice assistants such as Siri and Alexa, which tried to build one solution for all needs.

Just like human-human conversations, the space of possible actions in conversational interfaces is unlimited, which opens room for mistakes. Application developers and product managers need to build trust with their users by making sure that they minimize room for mistakes and exert control over the responses the AI gives to users. 

We’re also seeing how uncontrolled use of conversational AI can damage the user’s experience and the developer’s reputation as LLM products are going through their growing pains. In Google’s Bard demo, the AI produced untruthful facts about the James Webb telescope. Microsoft’s ChatGPT-powered Bing has been caught making egregious mistakes. A reputable news website had to retract and correct several articles that were written by an LLM after they were found to be factually wrong. And numerous similar cases are being discussed on social media and tech blogs every day.

The limits of current LLMs can be boiled down to the following:

• They “hallucinate” and can state wrongful facts with high confidence 
• They become inconsistent in long conversations
• They are hard to integrate with existing applications and only take a textual input prompt as context
• Their knowledge is limited to their training data and updating them is slow and expensive
• They can’t interact with external data sources
• They don’t have analytics tools to measure and enhance user experience

Multimodal conversational UX

We believe that multimodal conversational AI is the way to overcome these limits and bring trust and control to everyday applications. As the name implies, multi-modal conversational AI brings together voice, text, and touch-type interactions with several sources of information, including knowledge bases, GUI interactions, user context, and company business rules and workflows. 

This multi-modal approach makes sure the AI system has a more complete user context and can make more precise and explainable decisions.

Users can trust the AI because they can see exactly how and why the AI decided and what data points were involved in the decision-making. For example, in a healthcare application, users can make sure the AI is making inferences based on their health data and not just on its own training corpus. In aviation maintenance and repair, technicians using multi-modal conversational AI can trace back suggestions and results to specific parts, workflows, and maintenance rules. 

Developers can control the AI and make sure the underlying LLM (or other machine learning models) remains reliable and factful by integrating the enterprise knowledge corpus and data records into the training and inference processes. The AI can be integrated into the broader business rules to make sure it remains within the boundaries of decision constraints.

Multi-modality means that the AI will surface information to the user not only through text and voice but also through other means such as visual cues.

The most advanced multimodal conversational AI platform

Alan AI was developed from the ground up with the vision of serving the enterprise sector. We have designed our platform to use LLMs as well as other necessary components to serve applications in all kinds of domains, including industrial, healthcare, transportation, and more. Today, thousands of developers are using the Alan AI Platform to create conversational user experiences ranging from customer support to smart assistants on field operations in oil & gas, aviation maintenance, etc.

Alan AI is platform agnostic and supports deep integration with your application on different operating systems. It can be incorporated into your application’s interface and tie in your business logic and workflows.

Alan AI Platform provides rich analytics tools that can help you better understand the user experience and discover new ways to improve your application and create value for your users. Along with the easy-to-integrate SDK, Alan AI Platform makes sure that you can iterate much faster than the traditional application lifecycle.

As an added advantage, the Alan AI Platform has been designed with enterprise technical and security needs in mind. You have full control of your hosting environment and generated responses to build trust with your users.

Multimodal conversational UX will break the limits of existing paradigms and is the future of mobile, web, kiosks, etc. We want to make sure developers have a robust AI platform to provide this experience to their users with accuracy, trust, and control of the UX. 

Looking for implementing a virtual assistant and considering alternatives for Nuance Mix? Find out how your business can benefit from the capabilities of Alan AI.

Choosing a conversational AI platform for your business is a big decision. With many factors in different categories to evaluate – efficiency, flexibility, ease-of-use, the pricing model – you need to keep the big picture in view.

With so many competitors out there, some companies still clearly aim only for big players like Nuance Mix. Nuance Mix is indeed a comprehensive platform to design chatbots and IVR agents – but before making a final purchasing decision, it makes sense to ensure the platform is tailored to your business, customers and specific demands. 

The list of reasons to look at conversational AI competitors may be endless:

• Ease of customization 
• Integration and deployment options
• Niche-specific features or missing product capabilities  
• More flexible and affordable pricing models and so on

User Experience

Customer experience is undoubtedly at the top of any business’s priority list. Most conversational AI platforms, including Nuance Mix, offer virtual assistants with an interface that is detached from the application’s UI. But Alan AI takes a fundamentally different approach.

By default, human interactions are multimodal: in daily life, 80% of the time, we communicate through visuals, and the rest is verbal. Alan AI empowers this kind of interaction for application users. It enables in-app assistants to deliver a more intuitive and natural multimodal user experience. Multimodal experiences blend voice and graphical interfaces, so whenever users interact with the application through the voice channel, the in-app assistant’s responses are synchronized with the visuals your app has to offer.

Designed with a focus on the application, its structure and workflows, in-app assistants are more powerful than standalone chatbots. They are nested within and created for the specific aim, so they can easily lead users through their journeys, provide shortcuts to success and answer any questions.

Language Understanding

Technology is the cornerstone of conversational AI, so let’s look at what is going on under the hood.

In the conversational AI world, there are different assistant types. First are template-driven assistants that use a rigid tree-like conversational flow to resolve users’ queries – the type of assistants offered by Nuance Mix. Although they can be a great fit for straightforward tasks and simple queries, there are a number of drawbacks to be weighted. Template-driven assistants disregard the application context, the conversational style may sound robotic and the user experience may lack personalization.

Alan AI enables contextual conversations with assistants of a different type – AI-powered ones.
The Alan AI Platform provides developers with complete flexibility in building conversational flows with JavaScript programming and machine learning. 

To gain unparalleled accuracy in the users’ speech recognition and language understanding, Alan AI leverages its patented contextual Spoken Language Understanding (SLU) technology that relies on the data model and application’s non-verbal context. Owing to use of the non-verbal context, Alan AI in-app assistants are provided with awareness of what is going on in any situation and on any screen and can make dialogs dynamic, personalized and human-like.

Deployment Experience

In the deployment experience area, Alan AI is in the lead with over 45K developer signups and a total of 8.5K GitHub stars. The very first version of an in-app assistant can be designed and launched in a few days. 

The scope of supported platforms, if compared to the Nuance conversational platform,  is remarkable. Alan AI provides support for web frameworks (React, Angular, Vue, JS, Ember and Electron), iOS apps built with Swift and Obj-C, Android apps built with Kotlin and Java, and cross-platform solutions: Flutter, Ionic, React Native and Apache Cordova.

Understanding the challenges of the in-app assistant development process, Alan AI lightens the burden of releasing the brand-new voice functionality with:

• Conversational dialog script versioning
• Ability to publish dialog versions to different environments
• Integration with GitHub
• Support for gradual in-app assistant rollout with Alan’s cohorts
  

Pricing

While a balance between benefit and cost is what most businesses are looking for, the price also needs to be considered. Here, Alan AI has an advantage over Nuance Mix, offering multiple pricing options, with free plans for developers and flexible schemes for the enterprise.

Discover the conversational AI platform for your business at alan.app.

The world economy is clearly headed for “stormy waters”, and companies are bracing for a recession. Downturns always bring change and a great deal of uncertainty. How serious will the pending recession be – mild and short-lived or severe and prolonged? How can the business prepare and adapt?

When getting through hard times, some market players choose to be more cash-conservative and halt all new investment decisions. Others, on the contrary, believe the crisis is the best time to turn to new technology and opportunities.

What’s the right move?

A recession can be tough for a lot of things, but not for the customer experience (CX). Whether the moment is good or bad, CX teams have to keep the focus on internal and external SLAs, satisfaction scores, and churn reduction. In an economic slowdown, delighting customers and delivering an exceptional experience is even more crucial.

When in cost-cutting mode, CX departments find themselves under increasing pressure to do more with less. As before, existing systems and products require high-level support and training, new solutions brought in-house add to the complexity – but scaling the team and hiring new resources is out of the question.

And this is where technology comes to the fore. To maintain flexibility and remain recession-proof, businesses have started looking towards AI-powered conversational assistants being able to digitize and modernize the CX service.

Re-assessing investments in Al and ML

Over the last few years, investments in business automation, AI, and ML have been at the top of priority lists. Successful AI adoption brought significant benefits, high returns, and increased customer satisfaction. This worked during financially sound times – but now investments in AI/ML projects need to be reassessed.

There are several important things to consider:

• Speed of adoption: for many companies, the main AI adoption challenge rests in significant timelines involved in the project development and launch, which affects ROI. The longer the life cycle is, the more time it will take to start reaping the benefits from AI solutions – if they ever come through.
  
• Ease of integration: an AI solution needs to be easily laid on top of existing IT systems so that the business can move forward, without suffering operational disruptions.
  
• High accuracy level: in mission-critical industries where knowledge and data are highly nuanced, the terminology is complex and requirements to the dialog are stringent, accuracy is paramount. AI-powered assistants must be able to support contextual conversations and learn fast.
  
• Personalized CX: to exceed customer expectations, the virtual assistant should provide human-like personalized conversations based on the user’s data.

Increasing productivity with voice and text in-app assistants

Alan AI enables enterprises to easily address business bottlenecks in productivity and knowledge share. In-app (IA) assistants built with the Alan AI Platform can be designed and implemented fast – in a matter of days – with no disruption to existing business systems and infrastructure.

Alan’s IA assistants are built on top of the existing applications, empowering customers to interact through voice, text, or both. IA assistants continuously learn from the organization’s data and its domain to become extremely accurate over time and leverage the application context to provide highly contextual, personalized conversations.

With both web and mobile deployment options, Alan AI assistants help businesses and customers with:

•  Always-on customer service: provide automated, first-class support with virtual agents available 24/7/365 and a self-help knowledge base; empower users to find answers to questions and learn from IA.
  
• Resolving common issues without escalation: let IA resolve common issues immediately, without involving live agents from CX or support teams.
  
• Onboarding and training: show the users how to complete tasks and find answers, guiding them through the application and updating visuals as the dialog is being held.
  
•  Personalized customer experience: build engaging customer experiences in a friendly conversational tone becoming an integral part of the company’s brand.
  

Although it may seem the opposite, a recession can be a good time to increase customer satisfaction, reduce overhead and have a robust ROI. So, consider investing in true AI and intelligence with voice and text IA assistants by Alan AI.

At Alan AI, we care about making all technology easily accessible to everyone and aim to bridge this gap using voice AI. Accessibility is a value we hold in high regard and it starts within our company. We believe building a diverse team is critical in crafting our platform and covering AI ethics blindspots.

Internally, we want to do our part in changing the statistic that today, women hold 25% of jobs in the tech industry while making up half of the entire workforce. Externally, we want to praise those that have been the first to break barriers and encourage future generations that anything is possible.

Here are some of the many incredible women who have or currently are shaping technology.

Ada Lovelace (1815 – 1852)

Being the world’s first computer programmer, Ada Lovelace was a key contributor to the technological revolution. In 1870, Lovelace joined Charles Babbage’s work on the Analytical Engine by translating the lecture notes of an Italian engineer. During these nine months of intense analysis, she found many errors in the notes and expanded on them, leading to what is now considered the first ever algorithms that could be used in a computing machine. 

Lovelace didn’t receive the recognition she deserved until a century later when her notes were republished in the 1950s. Following this, the U.S. Department of Defense named a programming language “Ada” in her honor.

Image: Wikipedia

Grace Hopper (1906 – 1992)

Grace Hopper was an American mathematician, teacher, U.S. Navy rear admiral and pioneer in developing computer technology. Her significant work consists of helping in the WWII efforts with the Harvard Mark I computer, inventing the first compiler to translate a programmer’s instructions into computer codes and paving the way for one of the first high-level programming languages, COBOL.

When she was awarded the National Medal of Technology in 1991, she said “If you ask me what accomplishment I’m most proud of, the answer would be all the young people I’ve trained over the years; that’s more important than writing the first compiler.” Hopper is remembered at the annual Grace Hopper Celebration, the world’s largest gathering of women technologists.

Image: Computer History Museum

Mary G. Ross (1908 – 2008)

Mary Golda Ross was the first known Native American female engineer. In 1942, she joined the Lockheed Corporation, an American aerospace company, as the first female engineer. She is one of the 40 founding members of the renowned and highly secretive Skunk Works project. Much of her research and writing remains classified, even today.

Mary G. Ross is featured on the US 2019 one dollar coin.

Image: transportationhistory.org

Evelyn Boyd Granville (1924 – )

Evelyn Boyd Granville is one of first African American women to earn a Ph.D. in mathematics. After graduating from Yale and struggling to find a job due to race discrimination, she accepted a teaching position at Fisk University in Nashville, Tennessee where she taught two African American women who would go on to earn doctorates in mathematics. In 1956, she started working at IBM’s Aviation Space and Information Systems division on various projects for NASA’s Apollo space program, studying rocket trajectories and orbit computations. 

After her years in government work, Granville returned to teaching mathematics of all levels. Today she is retired but is continuously advocating for women’s education in technology. 

Image: undark.org

Annie Easley (1933 – 2011)

Annie J. Easley was an American computer scientist, mathematician, and rocket scientist. After reading an article about twin sisters working as “human computers” at the National Advisory Committee for Aeronautics (NACA), she applied for a job the next day. In 1955, she started her 34-year career at NASA (previously known as NACA), doing computations for researchers by hand and then computer programming for important projects like the Centaur high-energy booster rocket and alternative systems to solve energy problems.

Easley also served as an Equal Employment Opportunity officer and was the founder and first President of the NASA Ski Club. 

Image: Salon.com

Radia Perlman (1951 – )

The title of “Mother of the Internet” has been rightfully given to Radia Perlman, a MIT math graduate, computer programmer and network engineer. Her invention of Spanning Tree Protocol (STP) was a major contributor to making today’s internet possible. Her most recent work has been on the TRILL protocol to correct some of the shortcomings of spanning-trees.

She has done keynotes speeches across the world and is currently employed at Dell EMC. When asked about diversity in STEM, Perlman replied, “The kind of diversity that I think really matters isn’t skin shade and body shape, but different ways of thinking.”

Image: eniac.hu

Marissa Mayer (1975 – )

Former Yahoo! CEO and early Google employee, Marissa Mayer is now a co-founder of Sunshine, focusing on artificial intelligence and consumer media.

After completing her studies at Stanford University, Mayer joined Google as the first female engineer at 24 years old. Her contributions during her time there include the design of the Google homepage, Gmail, Chrome, Google Earth, and being one of the three members to develop Google Adwords. From 2012 to 2017, she held the role of president and CEO of Yahoo!. Today, Mayer is working as the co-founder of Sunshine.

Image: Martin Klimek — ZUMA Press/Alamy

Building the future

The future of technology is at the fingertips of today’s students, but the road to their success isn’t always an easy one. Here are three women-founded organizations set up to lift developers of all backgrounds, demographic and skill levels.

Girls Who Code

Working to close the gender gap in technology, Girls Who Code “envisions a world where women are proportionally represented as technical leaders, executives, founders, VCs, board members, and software engineers.” This non-profit organization was founded in 2012 by Reshma Saujani, an American lawyer and politician, who during her run for the US Congress noticed a lack of girls in computer science classrooms while campaigning. With several bestsellers like “Girls Who Code: Learn to Code and Change the World” and Ted Talk “Teach girls, bravery not perfection” viewed by thousands and sparking a worldwide conversation, Girls Who Code has today reached 500 million people and 300,000 girls in USA, Canada, India, and the United Kingdom.

Image: Carey Wagner

Black Girls Code

“The great economic equalizer of our generation, the great revolution of this generation, is indeed technology. And by embedding these skills and abilities in our youth today, we can change the nation — one girl, one woman and one generation at a time.”

~ Kimberly Bryant, founder of Black Girls Code

After her daughter’s disappointing experience at male-dominated computer camp, Kimberly Bryant decided to build an environment that encourages girls, especially from underrepresented communities, to pursue careers in STEM. This led to the creation of Black Girls Code, a non-profit organization that provides African American youth with programming skills through community outreach programs such as workshops and after school programs. Since 2011, BGC has served over 200,000 students and has the ultimate goal of teaching 1 million girls how to code by 2040.

Image: Black Girls Code

CodeNewbie

Starting as a weekly Twitter Chat to connect people that are learning to code by fellow coder Saron Yitbarek, CodeNewbie has since grown into a supportive, international online community of people learning and supporting one another’s coding journey with weekly Twitter Chats every Wednesday at 9PM EST. 

Saron Yitbarek also hosts several podcasts like CodeNewbie involving stories and interviews about new developers transitioning into tech careers and joining developer communities.

Image: freecodecamp.org

Happy International Women’s Day!

Inspired by the women you see here? Get started with Alan AI and build your own AI powered voice interface today.

Technologies make our society resilient in the face of a Black Swan event like the Covid-19 pandemic. Some of these technologies might even have a long-lasting impact beyond Covid-19. A technology that is of huge benefit during this time of chaos and uncertainty is that of voice enabled apps. According to the Adobe Voice Survey, a majority of the users found that voice interfaces made their lives faster, easier, and more convenient. 77% of the respondents in the survey said that they were planning to increase their usage of voice technology in the next 12 months. 

Thanks to voice AI’s ability to understand natural language, its adoption will keep increasing and there will be more use cases added to its repertoire. Voice user interfaces are great for companies in certain industries: finance, education, healthcare, technology and more. 

Voice Technology Creates Safer Alternatives in Times of Covid

The Covid-19 pandemic has forced us to be wary of touching anything in a public place for the fear of contracting (and spreading) the virus, as new variants like Omicron emerge. AI-powered voice technology has enabled a contactless ecosystem by providing safer alternatives to get things done. Booking a medical appointment or checking one’s bank balance might not have been activities that consumers would have used voice technology for earlier, but that is fast changing. 

INtelligent voice interfaces provide frictionless, contactless, responsive and predictive interactions. It changes the way we access information and how we navigate between the physical and digital worlds. According to a study by Juniper Research, 52% of voice interface users said that they used them a number of times a day or nearly every day, in 2021. You can imagine that the numbers will only keep increasing. 

In mobile applications, voice AI reduces the complexity of navigation, increases conversion, offers greater convenience, and boosts engagement. 

Voice AI has Enabled a Contactless Paradigm

Voice enabled apps have emerged to the forefront during the pandemic in many instances, for example,

•  A significant drop in cash payments. Voice and touch and type apps are leading the way for payment for goods and services.
• In Quick Service Restaurants (QSR), voice-enabled kiosks offer hands-free ordering. 
• The hospitality industry uses voice-enabled kiosks for not only checking-in and checking-out customers, but also to control in-room amenities for the guests.
• Voice shopping makes advanced filtering easy as customers don’t have to navigate through complicated menus. It is expected to reach $40 billion this year. 
• From virtual health guides to real-time medication reminders, voice AI has multiple use cases in healthcare.
• In the education sector, voice AI can be used in conducting online viva exams, authenticate access to learning materials, act as smart campus assistants, and so on.   

The pandemic has influenced a shift in the mindset and habits of consumers. They have been forced to adopt technology that promotes contactless experiences — to mitigate chances of getting infected from touching surfaces. To survive and thrive beyond the pandemic, brands should enable voice-based technologies to latch on to new consumer behavior and provide a superior user experience.

Wrapping Up

Consumers are using voice technology more than ever in the pandemic,  and expect it to be a standard for all digital experiences. Voice is infinitely easier to use and advancements in voice technology gives quick, accurate results. With the massive societal and economic shifts that have occurred because of the pandemic, our lives, both personal and professional, will continue on the path of tectonic changes – and voice AI is going to be a huge part of it. 

If you are looking to include AI-powered voice apps to your business, get in touch with the team at Alan AI. The Alan Platform can help you create voice enabled apps in a matter of days. 

Write to them at sales@alan.app. 

Jane Dove: “Hey Alan,  transfer $250 to Mark Smith?”

With this simple voice command, Mark’s bank account gets credited with money. 

If you are surprised, here’s a warm welcome to the world of voice technology in banking. Artificial Intelligence Voice Interfaces provide a much faster way to complete tasks in financial institutions and is a boon for customer support, account management, user authentication, and more.

The simple reason why voice tech is changing the business world of finance is that it is simple to use, and highly efficient. The number of digital voice interfaces in the world will be 8.4 billions- that’s more than the world’s population!  

Neobanking or pure-play digital banks is an emerging trend and the struggle is to bring  the friendly, personal  service of physical retail bank offices to digital fronts – websites and mobile apps. Traditional banks  have to reduce their operating costs to compete in the neobanking sectors. Intelligent Voice Interfaces will be a tremendous asset here.

 Voice Technology in the Banking industry:

Many mobile banking apps have 100s of features exposed to their customers via app screens. One voice button will give them access to all the features and set the stage for infinite functionality with voice. That’s reason enough for them to bank on voice interfaces.

 Wealth Management

A voice interface can interact with the portfolio customers in a humanized fashion about the latest options available to them, offer personalized research on markets, and even perform tasks like booking a meeting with their portfolio manager. Banks can easily pair voice interfaces with its investment portfolio management software so that users can ask questions and get informed, intelligent  responses. 

 Account Servicing

Customers can use voice commands to check their account balance, transaction history, account activity, card services, request for cheque book, etc. The voice technology provides a seamless onboarding experience to a new customer with electronic Know Your Customer (eKYC). The context-based query resolution capability of the voice technology provides a superior customer experience. 

Loan Disbursal

Voice tech can even help determine the customer’s eligibility for a loan and enable a smooth disbursal- this fast tracks the otherwise slow process that can be frustrating for the customers. 

It can also educate the customer on the different loan schemes available and provide real-time updates on any changes. 

Collections

One of the most interesting use cases of voice tech in the financial industry is in the collections department. The collections department has a loathsome reputation, but the voice interface does a great job as it has the ability to handle such interactions with the right mix of empathy and assertiveness.

In conclusion, every bank has to improve it’s Customer Experience on the digital fronts as their world is embracing self-service, customer centric banking.

If you are looking for a voice-based solution for your fintech application that will work with your app’s existing UI and can be deployed in a matter of days, the Alan Platform is the right solution for you. 

The team at Alan AI will be more than happy to assist you. Just email us at sales@alan.app

Imagine being able to order your favorite dish from your favorite restaurant with the help of voice commands when you are taking a drive in your sedan. How wonderful would that be! The entire experience would be hands-free, hassle-free, and it will get completed in a jiffy.

Today, there are a number of applications which leverage voice technology. According to Capgemini Research Institute, the use of voice assistants will grow to 31 percent of US adults by 2022.

In this article, we are going to discuss the usage of voice technology in food ordering and delivery.

Will customers be eager to order from restaurants using an intelligent voice interface?

A heartening statistic that shows how customers are open to using voice for ordering food- according to a research by Progressive Business Insights, 64% of Americans are interested in ordering food with the help of voice assistants.

With the help of intelligent voice interfaces, what was once a four to five minute exercise (often with some fumbling back and forth between screens and menus), gets completed in a few moments. The demand for a contact-less, fast and accessible food ordering option is gaining more momentum, thanks to the pandemic. COVID has ushered in a wave of digital tools, including voice technology, which makes efficient, touchless, and accurate food ordering a possibility.

The restaurant industry is quite adept at understanding what customers’ want. We will soon see most of them leveraging the full spectrum of voice technology in food ordering and delivery.

The User Experience:

Personalized, humanized voice interactions

An intelligent voice interface for food ordering will be a joy to the user. Just by uttering a few words, the in-app voice assistant is capable of ordering the right menu items including special requests, for example,

“ Can I get the regular veggie sandwich”?

“ And can you please omit the onions”

It can also pull up past favorites and allow the user to quickly reorder dishes, suggest similar dishes based on customer’s preferences or dietary restrictions, communicate ‘Specials of the Day’, and gather feedback from the user on any menu improvements- all in a smooth, interactive manner.

The Restaurateur Experience:

Works with the existing user interface

The voice technology is not a separate app where your customers will be redirected to place the orders. It works seamlessly with the existing app interface of the restaurant and the voice command will be reflected in the app visually, so that the customer knows exactly what’s happening. Moreover, multimodal assistants allow voice in combination with touch and type, giving the customer ample freedom of choice.

Accurate ordering

Incorrectly inputting information or hearing the wrong words can result in errors that will botch up the food orders. It will also result in customer complaints, and such a hit on one’s reputation is very bad news for restaurants. Voice tech that is accurate strives to eliminate manual tasks and reduce errors in ordering food.

Reduction in operational costs

One of the biggest contributors to the expenses of a restaurant business are its overheads. From paying the staff to managing inventories, an issue here or there could lead to a lot of resources wasted. COVID has hit the restaurant industry hard as is highlighted in the article Forbes: Restaurant industry is fighting to stay alive and avenues to reduce costs will be welcomed by restaurant owners.

When voice enabled apps handle the job of taking orders, restaurants can cut costs and only hire the services of experienced staff who will take care of preparing the food. Also, restaurants won’t have to train employees to take orders nor have to invest in systems which do that.

Easy Upsell

As per an article in Forbes, The average ticket size increased by 20–40% when voice enabled apps were used to place a food order. This increase in the size of the order can be attributed to upselling, since the voice interface technology recommends more products based on the past history and customer’s preferences.

Coherent Brand Experience

Using the brand elements at all places consistently is something that every marketer believes in, and for the right reasons. Voice technology is capable of adding a restaurant’s brand elements into the ordering system into every interaction. By doing so, the customer will get the same consistent experience while ordering food from the restaurant’s app. The voice of the voice tech can also be tailored to reflect the personality of your restaurant.

In summary, the restaurant industry has jumped into the voice technology bandwagon as it comes with a host of conveniences for both consumers and restaurateurs. By combining traditional delivery systems with modern voice assistant technology, superior service delivery becomes a cakewalk. It is very likely that voice command-driven food ordering and delivery will become the norm, thanks to its ease and speed.

If you are looking for a voice-based solution for food ordering and delivery that will work with your app’s existing UI and can be deployed in a matter of days, the Alan Platform is the right solution for you.

The team at Alan AI will be more than happy to assist you. Just email us at sales@alan.app

Is Voice technology a boon to the healthcare patient community? Their growing adoption attests to their value and destiny to become an essential and reliable piece of the healthcare ecosystem. The global market for healthcare virtual assistant is expected to grow from $1.1 billion in 2021 to $6.0 billion by 2026 (Source: Global Voice Assistant Market by Market and Research, 2019 ). Microsoft’s $19.7 billion deal announcement in April 2021 to acquire speech-to-text software company Nuance Communications, proves that this is a red-hot technology sector.

The US population is aging, and long-term and assisted living is on an upward spiral. While aging is not by itself a disease, the elderly often need special care and assistance to maintain optimal health. Many of them living at home are expected to use technology aids, such as health apps on their phone, in addition to receiving assistance from caregivers, either family, friends, or professionals. Imagine how difficult or impossible it is for an aged person to work with complex screens in apps and access the information they are looking for. 

Chronic disease needs constant vigilance by the healthcare provider. Stats in US healthcare spending reveal that 80% is spent on chronic disease management like cancer, alzheimers, dementia, diabetes, and osteoporosis, versus 20% for other care. These patients have to take daily medication, check their disease state at defined intervals during the day, perform recommended exercises, set up regular doctor appointments, and more. Remote monitoring of chronic disease patients is now a reality as technology can transmit patient data wirelessly from the patient’s home into the offices of their physician. But, these remote systems are often connected to a home device with a companion app that monitors and collects the patient’s health data. These patient-facing apps often have multiple screens and features that require time and effort to onboard, use, and keep up with. It’s not surprising that patients easily get frustrated and abandon use of these applications or call the doctor’s office frequently with questions.

Adding to the above scenario, US physicians and healthcare workers are strained and often pushed to the limit in caring for the patient population. With the current ratio of 2.34 doctors per 1,000 people, it is often impossible for a doctor or assistant to respond to general patient questions in a timely fashion.

Enter the empathetic voice interface. With voice interfaces, the elderly and patients can now speak to the device for tasks such as- booking medical appointments, searching for any data on their condition, relaying information to their doctor- and more. And the app can converse with them in a natural way on topics such as “How are you feeling today?’ or ‘Did you take your medication at 2 PM?” and record the responses. Voice assistants empower a patient as he or she can progress in their self-care and management of their health. Additionally, the healthcare provider’s time is freed up as the voice assistant can provide quick, accurate responses to general patient queries.

What about the caregiver? Caregivers can also benefit from an empathetic voice assistant as they are always seeking ways to better care for their sick, aging, or chronic disease patients. In the digital age, caregivers are using apps such as AARP Caregiving that allows patient symptom monitoring, tracking medication intake and appointments, coordinating care with others, and a help center for questions. Wouldn’t it help to have a voice attached to these caregiving apps, an intelligent one that can provide a hands free, contactless experience? It will surely make life a bit easier for the strained caregiver.    

Voiceinterfaces come in many guises, but they all provide the patient with a conversational experience. The Alan AI platform is an advanced,  complete in-app voice assistant platform that works with the existing UI of any healthcare app and adds a visual, contextual experience. Moreover, it can be deployed in simply a matter of days. 

For further information, contact sales@alan.app.

To be entered into the competition, click on the link here to register. In the meantime, here are some video we’ve created for you to check out:

• Alan AI Overview
• How to Build a Voice Assistant

Hope you enter the competition. Best of luck!

In the meantime, please check out this course we designed for you. If you send in a submission of your project, let us know and we’ll provide a free code for the course.

If you would like to learn more about Alan AI in general or have any questions, please feel free to book a meeting with one of our Customer Success team members here.

Chatbots and voice assistants weren’t created with marketers first in mind. Both are task-oriented products at their cores, serving users when actual human beings can’t, which is quite often. So it should come as no surprise that both support an estimated 5 billion global users combined.

And now marketers are showing up in droves. 

Rise of Chatbot Marketing

Online stores turned to chatbots to fight cart abandonment. Their automated service around-the-clock provided a safety net late into user journeys. Unlike traditional channels broadcasting one-way messages, chatbots (like Drift and Qualified) fostered two-way interactions between websites and users. Granting consumers a voice boosted engagement. As it turned out, messaging a robot became much more popular than calling customer service.

Successful marketing chatbots rely on three things: 1) Scope, 2) Alignment, and 3) KPIs. 

Conversational A.I. needs to get specific. Defining a chatbot’s scope — or how well it solves a finite number of problems — makes or breaks its marketing potential. A chief marketing officer (CMO) at a B2C retail brand probably would not benefit from a B2B chatbot that displays SaaS terminology in its UX. Spotting mutual areas of expertise is quite simple. The hard part requires evaluating how well the chatbot aligns with the strategies you and your team deploy. If there is synergy between conversational A.I. and a marketer, then they must choose KPIs that best measure the successes and failures yet to come. Some of the most common include the amounts of active users, sessions per user, and bot sessions initiated.

Chatbots vs. Voice Assistants

Chatbots and voice assistants have a few things in common. Both are constantly learning more about their respective users, relying on newer data to improve the quality of interactions. And both use automation to communicate instantly and make the user experience (UX) convenient.

Chatbots carry out fewer tasks repetitively. Despite their extensive experience, they require a bit more supervision. Voice assistants, meanwhile, oversee entire user journeys. If needed, they can solve problems independently with a more versatile skill set. 

Voice assistants are just getting started when it comes to marketing. Upon taking the global market by storm for the last decade, there is still room for voice products to reach new customers. But the B2B space presents a larger addressable market. The same way voice assistants gained traction rather quickly with millions of users is how they may pop up across organizations. 

Rise of Voice Assistant Marketing

Many marketers considered voice a “low priority” back in 2018. Lately, the tides have changed: 28 percent of marketers in a Voicebot.ai survey find voice assistants “extremely important.” Why? Because they enjoy immediate access to a mass audience. Once voice products earn more public trust, they can leverage bubbling user relationships to introduce products and services. 

Voice assistants are stepping beyond their usual duties and tapping into their marketing powers. Amazon Alexa enables brands to develop product skills that alleviate user and customer pains. Retail rival Wal-Mart launched Walmart Stories, an Alexa skill showcasing customer and employee satisfaction initiatives. 

Amazon created a dashboard to gauge individual Alexa skill performance in 2017. For example, marketers can see how many unique customers, plays, sessions, and utterances a skill has. Multiple metrics can be further broken down by type of user action, thus indicating which moments are best suited for engagement.

Google Assistant also amplifies brands through an “Actions” feature similar to Alexa’s skills. TD Ameritrade launched an action letting users view their financial portfolios via voice command. 

The Bottom Line

Chatbots aren’t going anywhere. According to GlobeNewswire, they form a $2.9B market predicted to be worth $10.5B in 2026. Automation’s strong tailwinds almost guarantee chatbots won’t face extinction anytime soon. They are likely staying in their lane, building upon their current capabilities instead of adding drastically different ones. 
Meanwhile, voice e-commerce will become a $40B business by 2022. Over 4 billion voice assistants are in use worldwide. By 2024, that number will surpass 8 billion. It’s hard to bet against voice assistants dominating this decade’s MarTech landscape. Their future ubiquity, easy access to hands-free searches, and the increased likelihood A.I. improves its effectiveness will leave plenty of room for growth. If future voice products address recurring user pains, they will innovate with improved personalization and privacy features. 

If you’re looking for a voice platform to bring your application, get started with the Alan AI platform today.

References

1. The Future is Now – 37 Fascinating Chatbot Statistics (smallbizgenius)
2. 2020’s Voice Search Statistics – Is Voice Search Growing? (Review 42)
3. 10 Ways to Measure Chatbot Program Success (CMSWire)
4. Virtual assistants vs Chatbots: What’s the Difference & How to Choose the Right One? (FreshDesk) 
5. Digiday Research: Voice is a low priority for marketers (Digiday)
6. Marketers Assign Higher Importance to Voice Assistants as a Marketing Channel in 2021 – New Report (Voicebot.ai)
7. Use Alexa Skill Metrics Dashboard to Improve Smart Home and Flash Briefing Skill Engagement (Amazon.com)
8. The global Chatbot market size to grow from USD 2.9 billion in 2020 to USD 10.5 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 23.5% (GlobeNewsWire)
9. Chatbot Market by Component, Type, Application, Channel Integration, Business Function, Vertical And Region – Global Forecast to 2026 (Report Linker)
10. Voice Shopping Set to Jump to $40 Billion By 2022, Rising From $2 Billion Today (Compare Hare)
11. Number of digital voice assistants in use worldwide 2019-2024 (Statista)
12. The Future of Voice Technology (OTO Systems, Inc.)

After a decade of taking the commercial market by storm, it’s official: voice technology is no longer the loudest secret in Silicon Valley. The speech and voice recognition market is currently worth over $10 billion. By 2025, it is projected to surpass $30 billion. No longer is it monumental or unconventional to simply speak to a robot not named WALL-E and hold basic conversations. Voice products already oversee our personal tech ecosystems at home while organizing our daily lives. And they bring similar skill sets to enterprises in hopes of optimizing project management efforts. 

The future arrived a long time ago. Problems at work and in life are now more complex. Settling for linear solutions will not suffice. So how do we know what separates a high-quality modern voice user experience (UX) from the rest? 

Navigating an ever-changing voice technology landscape does not require a fancy manual or a DeLorean (although we at Alan proudly believe DeLoreans are pretty cool). A basic understanding of which qualities make the biggest difference on a modern voice product’s UX can catch us up to warp speed and help anyone better understand this market. Here are four features each user experience must have to create win-win scenarios for users and developers. 

• Multiple Contexts
• Workflow Capabilities
• Seamless Integration
• Privacy

MULTIPLE CONTEXTS

Pitfalls in communication between people and voice technology exist because limited insights were available until a decade ago. For instance, voice assistants were forced to play a guessing game upon finally rolling out to market and predict user behavior in their first-ever interactions. They lacked experience engaging back and forth with human beings. Even when we communicate verbally, we still rely on subtle cues to shape how we say what we mean. Without any prior context, voice products struggled to grasp the world around us. 

By gathering Visual, Dialog, and Workflow contexts, it becomes easier to understand user intent, respond to inquiries, and engage in multi-stage conversations. Visual contexts are developed to spark nonverbal communication through physical tools like screens. This does not include scenarios where a voice product collects data from a disengaged user. Dialog contexts process long conversations requiring a more advanced understanding. And Workflow contexts improve accuracy for predictions made by data models. Overall, user dialogue can be understood by voice products more often. 

When two or more contexts work together, they are more likely to help support multiple user interfaces. Multimodal UX unites two or more interfaces into a voice product’s UX. Rather than take a one-track-minded approach and place all bets on a single UX that may fail alone, this strategy aims to maximize user engagement. Together, different interfaces — such as a visual and voice — can flex their best qualities while covering each of their weaknesses. In turn, more human senses are interacted with. Product accessibility and functionality improve vastly. And higher-quality product-user relationships are produced. 

WORKFLOW CAPABILITIES

Developers want to design a convenient and accurate voice UX. This is why using multiple workflows matters — it empowers voice technology to keep up with faster conversations. In turn, optimizing personalization feels less like a chore. The more user scenarios a voice product is prepared to resolve quickly, the better chance it has to cater to a diverse set of user needs across large markets. 

There is no single workflow matrix that works best for every UX. Typically, voice assistants combine two types: task-oriented and knowledge-oriented. Task-oriented workflows complete almost anything a user asks their device to do, such as setting alarms. Knowledge-oriented workflows lean on secondary sources like the internet to complete a task, such as searching for a question about Mt. Everest’s height.

SEAMLESS INTEGRATION

Hard work that goes into product development can be wasted if the experience curated cannot be shared with the world. This mantra applies to the notion of developing realistic contexts and refining workflows without ensuring the voice product will seamlessly integrate. While app integrations can result in system dependencies, having an API connect the dots between a wide variety of systems saves stress, time, and money during development and on future projects. Doing so allows for speedier and more interactive builds to bring cutting-edge voice UX to life. 

PRIVACY 

Voice tech has notoriously failed to respect user privacy — especially when products have collected too much data at unnecessary times. One Adobe survey reported 81% of users were concerned about their privacy when relying on voice recognition tools. Since there is little to no trust, an underlying paranoia defines these negative user experiences far too often.

Enterprises often believe their platforms are designed well-enough to side-step past these user sentiments. Forward-thinking approaches to user privacy must promote transparency on matters regarding who owns user data, where that data is accessible, whether it is encrypted, and for how long. A good UX platform will take care of computer infrastructure and provide each customer with separate containers and their own customized AI model.

If you’re looking for a voice platform to bring your application, get started with the Alan AI platform today.

It’s no secret voice tech performs everyday magic for users. By now, basic voice product capabilities and features are well-known to the public. Common knowledge is enough to tell us what this technology does. Yet we fail to consider what factors and mechanisms behind the scenes enable these products to work. Multiple frameworks oversee different methods that people and products communicate with. But as a concept, the frequent lifeblood of the user experience — Spoken Language Understanding (SLU) — is quite concrete.  

As the name hints, SLU takes what someone tells a voice product and tries to understand it. Doing so involves detecting signs in speech, coding inferences correctly, and navigating complexities as an intermediary between human voices and scripts featuring calligraphy. Since typed and spoken language form sentences differently, self-corrections and hesitations recur. SLUs leverage different tools to navigate user messages through traffic. 

The most established is Automatic Speech Recognition (ASR), a technology that transcribes user speech at the system’s front end. By tracking audio signals, spoken words convert to text. Similar to the first listener in an elementary school game of telephone, ASR is most likely to precisely understand what the original caller whispered. Conversely, Natural Language Understanding (NLU) determines user intent at the back end. Both ASR and NLU are used in tandem since they typically complement each other well. Meanwhile, an End-to-End SLU cuts corners by deciphering utterances without transcripts. 

This Law & Order: SLU series gets juicier once you know all that Spoken Language Understanding is up against. One big challenge SLU systems face is the fact ASR has a complicated past. The number of transcription errors ASRs have committed is borderline criminal — not in a court of law — but the product repairs and false starts that resulted left a polarizing effect on users. ASRs usually operate at than the speed of sound or slower. And the icing on the cake? The limited scope of domain knowledge across early SLU systems hampered their appeal across targeted audiences. Relating to different niches was difficult as jargon was scarce. 

Let’s say you are planning a vacation. After deciding your destination will be New York, you are ready to book a flight. You tell your voice assistant, “I want to fly from San Francisco to New York.” 

That request is sliced and diced into three pieces: Domain, Intent, and Slot Labels. 

1. DOMAIN (“Flight”)

Before accurately determining what a user said, SLU systems figure out what subject they talked about. A domain is the predetermined area of expertise that a program specializes in. Since many voice products are designed to appeal broadly, learning algorithms can classify various query subjects by categorizing incoming user data. In the example above, the domain is just “Flight.” No hotels were mentioned. Nor did the user ask to book a cruise. They simply preferred to fly to the Big Apple.    

Domain classification is a double-edged sword SLU must use wisely. Without it, these systems can miss the mark, steering someone in need of one application into another. 

The SLU has to guess if the user referred to flights or not. Otherwise, the system could produce the wrong list of travel options. Nobody should prompt the user to accidentally book a rental car for a cross-country road trip they never wanted. 

2. INTENT (“Departure”)

Tracking down the subject a speaker talks about matters. However, if a voice product cannot pin down why that person spoke, how could it solve their problem? Carrying out a task would then become unnecessary. 

Once the domain is selected, the SLU identifies user intent. Doing so goes one step further and traces why that person communicated with the system. In the example above, “Departure” is the intent. When someone asks about flying, the SLU has enough information to believe the user is likely interested in leaving town. 

3. SLOT LABELS (Departure: “San Francisco”, Arrival: “New York”)

Enabling an SLU system to set domains and grasp intent is often not enough. Sure, we already know the user is vouching to leave on a flight. But the system is yet to officially document where they want to go.   

Slots capture the specifics of a query once the subject matter and end goal are both determined. Unlike their high-stakes Vegas counterparts, these slots do not rack up casino winnings. Instead, they take the domain and intent and apply labels to them. Within the same example, departure and arrival locations must be accounted for. The original query includes both: “San Francisco” (departure) and “New York” (arrival). 

IMPACT

Spoken Language Understanding (SLU) provides a structure that establishes, stores, and processes nomenclature that allows voice products to find their identity. When taking the needed leap to classify and categorize queries, SLU systems collect better data and personalize voice experiences. Products then become smarter and channel more empathy. And they are empowered to anticipate user needs and solve problems quickly. Therefore, SLU facilitates efficient workflow design and raises the ceiling on how well people can share and receive information to accomplish more.

If you’re looking for a voice platform to bring your application, get started with the Alan AI platform today.

Co-founder and CEO of Alan Ramu Sunkara, sat down to talk about how Alan and Incture are bringing the world’s first conversational voice experience to field asset management.

Together, Incture and Alan have deployed touchless mobile field asset management in the Energy industry with the Murphy Oil IOP application for Oil Well operations. This solution is now a finalist for the 2020 SAP Innovation Award.

Touchless, Conversational Voice in mobile applications make it easy and safe for employees to enter operations data while on the go. The solution uses Machine Learning and Artificial Intelligence to recognize unique terms
and phrases specific to Oil Well operations with Alan’s proprietary Language Understanding Models.

After the introduction of the Conversational Voice User Experience, employees adoption and engagement with their mobile applications increased. This led to an increase in revenue, productivity, and operational effectiveness. As a result of this deployment, Murphy Oil was able to get complete real time visibility into their production operations and make informed business decisions quickly.

Field Asset Management Operations can benefit with hands-free:

• Check-ins when employees start work
• Task management
• Communication to other employees in the field using voice comments
• Onboarding for new employees on proper procedures and protocol
• Training for existing employees on new processes

Learn more about the Incture and Alan Touchless Mobile solution for Murphy Oil here.

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
• How Does Conversational AI Work?
• What Constitutes Conversational Intelligence
• How Businesses Can Use Conversational AI
• Benefits of Conversational AI
• Key Considerations about Conversational AI

   

  

   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.

   

  

  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.

  ]]> https://alan.app/blog/what-is-conversational-ai/feed/ 0 2952 https://alan.app/blog/what-is-conversational-user-interface-cui/ https://alan.app/blog/what-is-conversational-user-interface-cui/#respond Tue, 18 Feb 2020 06:01:56 +0000 https://alan.app/blog/?p=2814 The Evolution of the CUI In many industries, customers and employees need access to relevant, contextual information that is quick and convenient. Conversational User Interfaces (CUIs) enable direct, human-like engagement with computers. It completely transforms the way we interact with systems and applications. CUIs are becoming an increasingly popular tool,...]]>

  The Evolution of the CUI

  In many industries, customers and employees need access to relevant, contextual information that is quick and convenient. Conversational User Interfaces (CUIs) enable direct, human-like engagement with computers. It completely transforms the way we interact with systems and applications.

  CUIs are becoming an increasingly popular tool, which the likes of Amazon, Google Facebook, and Apple, have incorporated into their platforms. With the right approach, you can do the same.

  Table of contents

  • What is a Conversational User Interface?
  • How Do CUIs Work?
  • Types of Conversational User Interfaces
  • Business Use Cases
  • Benefits of Conversational UI
  • Conversational UI Challenges
  • The Future of Conversational UI
  • Checklist for Making a Great Conversational UI for your Applications 
  • Conclusion

  

  What is Conversational User Interface?

  A Conversational User Interface (CUI) is an interface that enables computers to interact with people using voice or text, and mimics real-life human communication. With the help of Natural-Language Understanding (NLU), the technology can  recognize and analyze conversational patterns to interpret human speech. The most widely known examples are voice assistants  like Siri and Alexa. 

  Voice interactions can take place via the web, mobile, desktop applications,  depending on the device. A unifying factor between the different mediums used to facilitate voice interactions is that they should be easy to use and understand, without a learning curve for the user. It should be as easy as making a call to customer service or asking your colleague to do a task for you. CUIs are essentially a built-in personal assistant within existing digital products and services.

  In the past, users didn’t have the option to simply tell a bot what to do. Instead, they had to search for information in the graphical user interface (GUI) – writing specific commands or clicking icons. Past versions of CUI consisted of messenger-like conversations, for example, where bots responded to customers in real-time with rigidly spelled-out scripts.

  But now it has evolved into a more versatile, adaptive product that is getting hard to distinguish from actual human interaction.

  The technology behind the conversational interface can both learn and self-teach, which makes it a continually evolving, intelligent mechanism.

  

  How Do CUIs Work?

  CUI is capable of generating complex, insightful responses. It has long outgrown the binary nature of previous platforms and can articulate messages, ask questions, and even demonstrate curiosity. 

  Previously, command line interfaces required users to input precise commands using exact syntax, which was then improved with graphical interfaces. Instead of having people learn how to communicate with UI, Conversational UI has been taught how to understand people. 

  The core technology is based on:

  • Natural Language Processing – NLP combines linguistics, computer science, information engineering, and artificial intelligence to create meaning from user input. It can process the structure of natural human language and handle complex requests.
  • Natural Language Understanding – NLU is considered a subtopic of natural language processing and is narrower in purpose. But the line between them is not distinct, and they are mutually beneficial. By combining their efforts, they reinterpret user intent or continue a line of questioning to gather more context.

  For example, let’s take a simple request, such as:

  “I need to book a hotel room in New York from January 10th to the 15th.”

  In order to act on this request, the machine needs to dissect the phrase into smaller subsets of information: book a hotel room (intent) – New York (city) – January 10 (date) – January 15 (date) – overall neutral sentiment.

  Conversational UI has to remember and apply previously given context to the subsequent requests. For example, a person may ask about the population of France. CUI provides an answer to that question. Then, if the next phrase is “Who is the president?”, the bot should not require more clarification since it assigns the context from the new request. 

  In modern software and web development, interactive conversational user interface applications typically consist of the following components:

  • Voice recognition (also referred to as speech-to-text) – A computer or mobile device captures what a person says with a microphone and transcribes it into text. Then the mechanism combines knowledge of grammar, language structure, and the composition of audio signals to extract information for further processing. To achieve the best level of accuracy possible, it should be continuously updated and refined.
  • NLU – The complexity of human speech makes it harder for the computer to decipher the request. NLU handles unstructured data and converts it into a structured format so that the input can be understood and acted upon. It connects various requests to specific intent and translates them into a clear set of steps.  
  • Dictionary/samples – People are not as straightforward as computers and often use a variety of ways to communicate the same message. For this reason, CUI needs to have a comprehensive set of examples for each intent. For example, the request “Book Flight”, the dictionary should contain “I need a flight”, “I want to book my travel”, along with all other variants.
  • Context – An example with the French president above showed that in a series of questions and answers, CUI needs to make a connection between them. These days, UIs tend to implement an event-driven contextual approach, which accommodates an unstructured conversational flow.
  • Business logic – Lastly, the CUI business logic connects to specific use cases to define the rules and limitations of a particular tool.

  

  Types of Conversational User Interfaces

  We can distinguish two distinct types of Conversational UI designs. There are bots that you interact with in the text form, and there are voice assistants that you talk to. Bear in mind that there are so-called “chatbots” that merely use this term as a buzzword. These fake chatbots are a regular point-and-click graphical user interface disguising and advertising itself as a CUI. What we’ll be looking at are two categories of conversational interfaces that don’t rely on syntax specific commands.

  Chatbots

  Chatbots have been in existence for a long time. For example, there was a computer program ELIZA that dates back to the 1960s. But only with recent advancements in machine learning, artificial intelligence and NLP, have chatbots started to make a real contribution in solving user problems.

  Since most people are already used to messaging, it takes little effort to send a message to a bot. A chatbot usually takes the form of a messenger inside an app or a specialized window on a web browser. The user describes whatever problem they have or asks questions in written form. The chatbots ask follow-up questions or meaningful answers even without exact commands.

  Voice recognition systems

  Voice User Interfaces (VUI) operate similarly to chatbots but communicate with users through audio. They are hitting the mainstream at a similar pace as chatbots and are becoming a staple in how people use smartphones, TVs, smart homes, and a range of other products.

  Users can ask a voice assistant for any information that can be found on their smartphones, the internet, or in compatible apps. Depending on the type of voice system and how advanced it is, it may require specific actions, prompts or keywords to activate. The more products and services are connected to the system, the more complex and versatile the assistant becomes. 

  

  Business Use Cases

  Chatbots and Voice UIs are gaining a foothold in many important industries. These industries are finding new ways to include conversational UI solutions. Its abilities extend far beyond what now dated, in-dialog systems, could do. Here are several areas where these solutions can make an impressive impact.

  Retail and e-commerce

  A CUI can provide updates on purchases, billing, shipping, address customer questions, navigate through the websites or apps, offer products or service information, along with many other use cases. This is an automated way of personalizing communication with your customers without involving your employees.

  Construction sector

  Architects, engineers, and construction workers often need to review manuals and other text chunks, which can be assisted by CUI. Applications are diverse:  contractor, warehouse, and material details, team performance, machinery management, among others.

  First Responders

  Increasing response speed is essential for first responders. CUI can never replace live operators, but it can help improve outcomes in crises by assessing incidents by location, urgency level, and other parameters.

  Healthcare

  Medical professionals have a limited amount of time and a lot of patients. Chatbots and voice assistants can facilitate the health monitoring of patients, management of medical institutes and outpatient centers, self-service scheduling, and public awareness announcements.

  Banking, financial services, and insurance

  Conversational interfaces can assist users in account management, reporting lost cards, and other simple tasks and financial operations. It can also help with customer support queries in real-time; plus, it facilitates back-office operations.

  Smart homes and IoT

  People are starting to increasingly use smart-home connected devices more often. The easiest way to operate them is through vocal commands. Additionally, you can simplify user access to smart vehicles (open the car, plan routes, adjust the temperature).

  

  Benefits of Conversational UI

  The primary advantage of Conversational UI is that it helps fully leverage the inherent efficiency of spoken language. In other words, it facilitates communication requiring less effort from users. Below are some of the benefits that attract so many companies to CUI implementations.

  Convenience

  Communicating with technology using human language is easier than learning and recalling other methods of interaction. Users can accomplish a task through the channel that’s most convenient to them at the time, which often happens to be through voice. CUI is a perfect option when users are driving or operating equipment.

  Productivity

  Voice is an incredibly efficient tool – in almost every case, it is easier and faster to speak rather than to use touch or type. Voice is designed to streamline certain operations and make them less time consuming. For example, CUI can increase productivity by taking over the following tasks:

  • Create, assign, and update tasks
  • Facilitate communication between enterprises and customers, enterprises and employees, users and devices
  • Make appointments, schedule events, manage bookings
  • Deliver search results
  • Retrieve reports

  Intuitiveness

  Many existing applications are already designed to have an intuitive interface. However, conversational interfaces require even less effort to get familiar with because speaking is something everyone does naturally. Voice-operated technologies become a seamless part of a users’ daily life and work.

  Since these tools have multiple variations of voice requests, users can communicate with their device as they would with a person. Obviously, it’s something everyone is accustomed to. As a result, it improves human-computer interactivity.

  Personalization

  When integrating CUI into your existing product, service, or application, you can decide how to present information to users. You can create unique experiences with questions or statements, use input and context in different ways to fit your objectives.

  Additionally, people are hard-wired to equate the sound of human speech with personality. Businesses get the opportunity to demonstrate the human side of their brand. They can tweak the pace, tone, and other voice attributes, which affect how consumers perceive the brand.

  Better use of resources

  You can maximize your staff skills by directing some tasks to CUI. Since employees are no longer needed for some routine tasks (e.g., customer support or lead qualification), they can focus on higher-value customer engagements.

  As for end-users, this technology allows them to make the most out of their time. When used correctly, CUI allows users to invoke a shortcut with their voice instead of typing it out or engaging in a lengthy conversation with a human operator.

  Available 24/7

  There are restrictions no when you can use CUI. Whether it’s first responders looking for the highest priority incidents or customers experiencing common issues, their inquiry can be quickly resolved.

  No matter what industry the bot or voice assistant is implemented in, most likely, businesses would rather avoid delayed responses from sales or customer service. It also eliminates the need to have around-the-clock operators for certain tasks.

  

  Conversational UI Challenges

  Designing a coherent conversational experience between humans and computers is complex. There are inherent drawbacks in how well a machine can maintain a conversation. Moreover, the lack of awareness of computer behavior by some users might make conversational interactions harder.

  Here are some major challenges that need to be solved in design, as well as less evident considerations:

  • Accuracy level – The CUI translates sentences into virtual actions. In order to accurately understand a single request and a single intent, it has to recognize multiple variations of it. With more complex requests, there are many parameters involved, and it becomes a very time-consuming part of building the tool.
  • Implicit requests – If users don’t say their request explicitly, they might not get the expected results. For example, you could say, “Do the math” to a travel agent, but a conversational UI will not be able to unpack the phrase. It is not necessarily a major flaw, but it is one of the unavoidable obstacles.
  • Specific use cases – There are many use cases you need to predefine. Even if you break them down into subcategories, the interface will be somewhat limited to a particular context. It works perfectly well for some applications, whereas in other cases, it will pose a challenge.
  • Cognitive load – Users may find it difficult to receive and remember long pieces of information if a voice is all they have as an output. At the very least, it will require a decent degree of concentration to comprehend a lot of new information by ear.
  • Discomfort of talking in public – Some people prefer not to share information when everyone within earshot can hear them. So, there should be other options for user input in case they don’t want to do it through voice.
  • Language restrictions – If you want the solution to support international users, you will need a CUI capable of conversing in different languages. Some assets may not be suitable for reuse, so it might require complete rebuilds to make sure different versions coexist seamlessly.
  • Regulations protecting data – Making sure interactions are personalized, you may need to retrieve and store data about your users. There are concerns about how organizations can make it comply with regulation and legislation. It is not impossible but demands attention. 

  These challenges are important to understand when developing a specific conversational UI design. A lot can be learned from past experiences, which makes it possible to prevent these gaps from reaching their full potential.

  

  The Future of Conversational UI

  The chatbot and voice assistant market is expected to grow, both in the frequency of use and complexity of the technology. Some predictions for the coming years show that more and more users and enterprises are going to adopt them, which will unravel opportunities for even more advanced voice technology.

  Going into more specific forecasts, the chatbots market is estimated to display a high growth continuing its trajectory since 2016. This expected growth is attributed to the increased use of mobile devices and the adoption of cloud infrastructure and related technologies.

  As for the future of voice assistants, the global interest is also expected to rise. The rise of voice control in the internet of things, adoption of smart home technologies, voice search mobile queries, and demand for self-service applications might become key drivers for this development. Plus, the awareness of voice technologies is growing, as is the number of people who would choose a voice over the old ways of communicating.

  Naturally, increased consumption goes hand-in-hand with the need for more advanced technologies. Currently, users should be relatively precise when interacting with CUI and keep their requests unambiguous. However, future UIs might head toward the principle of teaching the technology to conform to user requirements rather than the other way around. It would mean that users will be able to operate applications in ways that suits them most with no learning curve.

  If the CUI platform finds the user’s request vague and can’t convert it into an actionable parameter, it will ask follow-up questions. It will drastically widen the scope of conversational technologies, making it more adaptable to different channels and enterprises. Less effort required for CUI will result in better convenience for users, which is perhaps the ultimate goal.

  The reuse of conversational data will also help to get inside the minds of customers and users. That information can be used to further improve the conversational system as part of the closed-loop machine learning environment.

  

  Checklist for Making a Great Conversational UI for your Applications

  There are plenty of reasons to add conversational interfaces to websites, applications, and marketing strategies. Voice AI platforms like Alan, makes adding a CUI to your existing application or service simple. However, even if you are certain that installing CUI will improve the way your service works, you need to plan ahead and follow a few guidelines.

  Here are steps to adopt our conversational interface with proper configuration:

  1. Define your goals for CUI – The key factor in building a useful tool is to decide which user problem it is going to address.
  2. Design the flow of a conversation – Think of how communication with the bot should go – greeting, how it’s going to determine user needs, what options it’s going to suggest, and possible conversation outcomes.
  3. Provide alternative statements – As you know, users frame their requests in different ways, sometimes using slang, so you need to include different types of words that the bot will use to recognize the intent.
  4. Set statements to trigger some sort of action so that it can make a corresponding API call. What does a user have to say to make the bot respond appropriately for the situation? It is useful to use word tokenization to assign meaning at this point.
  5. Add visual and textual clues and hints – If a user feels lost, guide them through other mediums (other than voice). This way, you will improve the discoverability of your service.
  6. Make sure there are no conversational dead ends – Bot misunderstandings should trigger a fallback message like “Sorry, I didn’t get that”. Essentially, don’t leave the user waiting without providing any feedback.

  Overall, you should research how CUI can support users, which will help you decide on a type of CUI and map their journey through the application. It will help you efficiently fulfill the user’s needs,

  keep them loyal to the product or service, and simplify their daily tasks.

  Additionally, create a personality for your bot or assistant to make it natural and authentic. It can be a fictional character or even something that is now trying to mimic a human – let it be the personality that will make the right impression for your specific users.

  

  Conclusion

  A good, adaptable conversational bot or voice assistant should have a sound, well-thought-out personality, which can significantly improve the user experience. The quality of UX affects how efficiently users can carry out routine operations within the website, service, or application.

  In fact, any bot can make a vital contribution to different areas of business. For many tasks, just the availability of a voice-operated interface can increase productivity and drive more users to your product. Many people can’t stand interacting over the phone – whether it’s to report a technical issue, make a doctor’s appointment, or call a taxi.

  A significant portion of everyday responsibilities, such as call center operations, are inevitably going to be taken over by technology – partially or fully. The question is not if but when your business will adopt Conversational User Interfaces.

   

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