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 first it was the deadly buzz of the pandemic, and now it’s the thunder of markets crashing and lightening flashing an impending recession. Will restaurant owners have no relief? Restaurants were just opening up and attending to the happy crowds as COVID ebbed, when out-of-the-blue the stock market nosedived. Complicating the state of affairs is a labor shortage in restaurants and signs for “Help Wanted’ are way to common. Given the precarious climate, owners cannot pay their employees high wages as profit margins have been squeezed for many restaurants. What do they do?

Voice Technology Helps Ease the Shortage

Voice automation is a cost-effective solution with long-term benefits. It creates back-end operational efficiency and lends a hand in front-end ordering, easing any lack of available labor. You can easily give back an hour to each employee with an intelligent voice assistant.

Backend Operational Efficiencies

A. Tasks: The voice assistant is a hands-free buddy who reminds an employee to login, enter work schedules, complete tasks, adhere to special instructions etc. When the employee finishes a task, he can just inform the voice assistant and the app will automatically check off the task. No need to take off gloves to touch and type task fulfillment in apps. And nothing beats a friendly voice prompt to remind employees to complete tasks in a timely manner. Operational efficiencies = cost savings. It is estimated that approximately 5 seconds are shaved off per task by using an intelligent voice assistant.

B. Order Fulfillment

Modern kitchens have Kitchen-Display-Systems with screens that can bring up orders according to priority, highlight special dietary requests, flag ad hoc changes, and showcase item inventory. With voice technology, the employee no longer needs to take out time to read the screen or re-engage with it while he steps away to get something- as it audibly prompts the employee for order fulfillment. The employee can ask questions and get intelligent and accurate responses if he did not understand the prompt. The hands-free voice assistant enables every restaurant employee to save approximately 10 seconds per order fulfillment.

Front-end Operational Efficiencies

Adding voice tech to restaurant food ordering mobile or web apps save precious employee time in taking the order, ensuring accuracy of order, getting payment etc. The customer orders on an app and can either get the food delivered at home or the office or can pick-up from the restaurant premises. Lengthy menus and frequent changes to the restaurant food items are common, making voice user interfaces faster and more desirable than using a touch screen. Instead of touch and type, swipes, and going through menu items, users can simply ask the app for their menu choice, exactly the way they want it, and get their items ordered in a few seconds, thus increasing their satisfaction.

A touchless restaurant kiosk facilitate a self-service, unhurried experience, and reduce the potential health risks of touch screens. Industry calculations indicate that each drive thru order is $1.56 vs one penny for a voice activated order. Kiosks have the potential to significantly increase each ticket size by prompting upsells and cross-sells, and making personalized menu recommendations based on historical buyer behavior.

Additional Benefits

Besides resolving the labor shortage and operational costs, voice technology can reduce food wastage and makes for quicker, superior customer service.

If you are looking for a voice-based solution for your restaurant app, the team at Alan AI will be able to deliver exactly that. Email us at sales@alan.app

Alan AI has patent protections for its unique contextual Spoken Language Understanding (SLU) technology to accurately recognize and understand human voice, within a given context. Alan’s SLU transcoder leverages the context to convert voice directly to meaning by using raw input from speech recognition services, imparting the accuracy required for mission-critical enterprise deployments and enabling human-like conversations, rather than robotic ones. Voice based interactions, coupled with the ability to allow users to verify the entered details without having the system to reiterate inputs, provides an unmatched end-user experience.

An intelligent voice assistant can be a boon for increasing restaurant employee productivity and scaling operational efficiency, ensuring food safety compliance, and increasing order ticket sizes.

A heartening statistic mentions how customers are open to using voice for ordering food- 64% of Americans are interested in ordering food with the help of voice user interfaces and more than one quarter of all USA consumers who own voice activated devices have used them to order food service. Recently, Opus Research published a research report ‘The Business Value of Customized Voice Assistants’ based on a global survey of 320 business leaders in 8 industries to obtain the state of voice assistant implementation and global trendsthat recognizes that restaurateurs are rapidly realizing the benefits of voice assistants for accurate, efficient food ordering.

Voice assistants have come a long way from consumer voice experiences with products like Alexa smart speakers, Google Assistant, and Siri. Interactive, AI powered voice for business apps are inside your app and drive context-aware conversations. Using natural language, the user can navigate through the application to quickly get exactly what he wants.

Natural Language Processing (NLP), Speech apis, text to speech, speech to text, are common technology terms tossed around as the world grapples with the rapid change from touch and type to humanlike voice interfaces for apps.

Let’s now delve into how voice assistants can be valuable to restaurant management software vendors and franchise owners.

Voice Assistants for Restaurant Management ISVs

Efficient Operational Tasks/Employee Training

A voice assistant increases efficiency and accuracy in restaurant maintenance tasks by easing the process of Real-Time Operational Reports & Notifications  The voice assistant is like a buddy who reminds an employee to login, enter work schedules, complete tasks, adhere to special instructions etc. When the employee finishes a task, he can just inform the voice assistant and the app screen will instantly check off the task as complete. Alan’s hands-free voice assistant enables every restaurant employee to shave off 5 seconds for any task that previously involved taking off gloves such as manual entries for work logs, task completion, etc. Each employee can get an hour back with the help of the voice driven automation and use the extra hours in their work shifts to perform higher level tasks to drive customer satisfaction and loyalty.

Productivity gains = 5 secs per task x number of employees x daily tasks

For employee training, any new employee would be delighted to have an onboarding and self-service training help on-demand. With interactive instructions and reminders, training is easy and effective. Employees can now get trained in the shortest time possible and become productive on the job much faster.

Faster Food Order Tracking

Modern kitchens have display screens that can bring up orders according to priority, highlight special dietary requests, flag ad hoc changes, and showcase item inventory. 

With voice technology, the employee no longer needs to take out time to read the screen, as it audibly prompts the employee accurately for order fulfillment and the employee can ask questions and get intelligent and accurate responses if he did not understand the prompt. The hands-free voice assistant enables every restaurant employee to save approximately 10 seconds per order fulfillment and make more efficient use of their time working on food order fulfilment.

                       Productivity gains = 10 secs per order x number of orders daily

Decrease in Liability for Food Safety and Hygiene Compliance

The restaurant industry has strict safety and hygiene regulations mandated by the state and federal law agencies. Restaurants have to comply with these rules to keep their doors open. Additionally, each restaurant may have their own roster of do’s and don’ts. A voice assistant can go a long way in prompting, reminding, and quickly upgrading safety and hygiene protocols for restaurant employees, and thus reducing liability.

Voice assistants increase food safety and hygiene compliance by 3X

Voice Assistants for Restaurant Franchise Owners

A Voice enabled restaurant ordering mobile device or web app and touchless restaurant kiosk facilitate a self-service, unhurried experience, and reduce the potential health risks of touch screens. Ordering food can actually be a pleasure with a friendly voice. Moreover, industry calculations indicate that each drive thru order is $1.56 vs one penny for a voice activated order. Restaurant employees also benefit from a voice assistant that can help with some of the mundane tasks while they focus on food preparation, food presentation,  and customer service.

Lengthy menus and frequent changes to the restaurant food items are common, making voice user interfaces faster and more desirable than using a touch screen. Instead of touch and type, swipes, and going through menu items, users can simply ask the voice assistant for their menu choice, the exact way they want it, and get their items ordered in a few seconds, thus decreasing customer frustration while increasing their satisfaction.

Upselling to the customer is also easier with a voice interface.  Consider this scenario: A customer orders a burger at a kiosk. Alan’s voice assistant asks “would you like to add a side of fries for $2.00?”. A personalized, humanlike voice influences the customer to reach a faster decision and more likely a “Yes”- they may have an interest in the item but did not happen to notice it in the menu or did not have the time to look through the entire menu.

A Forbes article mentions that average ticket size increased by 20–40%  when voice assistants were used to place a food order. Therefore, an order for $10 can very easily be converted to $12 or $14 with interactive voice apps.

Wrapping it up, Voice Interfaces which interact with customers like normal conversations for food ordering, operations, and delivery are fast becoming a norm in the restaurant industry. The need for a hands-free, touchless application has gained popularity with the onset of the COVID pandemic.

If you are looking for a voice-based solution for your restaurant app,, the team at Alan AI will be able to deliver exactly that. Write to us at sales@alan.app

Alan has patent protections for its unique contextual Spoken Language Understanding (SLU) technology to accurately recognize and understand human voice, within a given context. Alan’s SLU transcoder leverages the context to convert voice directly to meaning by using raw input from speech recognition services, imparting the accuracy required for mission-critical enterprise deployments and enabling human-like conversations, rather than robotic ones. Voice based interactions, coupled with the ability to allow users to verify the entered details without having the system to reiterate inputs, provides an unmatched end-user experience.

Decades ago, talking to a computer was only possible in advanced scientific labs or in science fiction stories. Today, voice assistants have become a reality of everyday life. People talk to their phone, smart speaker, doorbell, and even microwave oven. Voice is gradually becoming one of the main ways to interact with consumer applications and devices and the use of our natural language as a mode of interaction is extremely appealing. Software like text to speech (TTS), automatic speech recognition (ASR), and Spoken Language Understanding (SLU) are used to recognize and process human language.

But while we’ve seen a lot of progress in the application of voice interfaces like google assistant or Siri in consumer applications, the business sector still lags behind, even though enterprises can be the main beneficiaries of advances in speech recognition and overall interactive voice technology. Where workers are engaged in hands-on activities and can’t interact with graphical user interfaces, voice user interfaces can make a huge difference in user engagement, productivity, and safety. However, the enterprise voice sector must overcome several challenges, one of them being privacy and security concerns. Today’s consumer voice assistants are not renowned for being very privacy friendly. There have been several documented incidents of smart speakers and voice assistants mistakenly recording conversations and replaying them elsewhere. And the massive user data that these assistants collect gets sucked into the black hole of the data-hungry tech giants that run them.

The expansion of the voice interface to your living room, car, office, pocket, and wrist has created fierce competition between tech giants. Manufacturers of smartphones, smart speakers, wearable devices and other mobile devices aim to create the ultimate voice experience that can respond to every possible query, whether it’s asking the weather, turning on the lights, responding to emails, or setting timers. Currently, the only way speech api vendors can get ahead of competitors is to improve their AI models by expanding their repertoire of actionable voice commands. This puts them in a position to have a vested interest to collect more user data and assemble larger training datasets for their AI models.

What’s also worth noting is that all major consumer voice assistants are owned by companies that  have built their business on collecting user information and creating digital profiles to serve ads, provide content and product recommendations, and keep users locked in their apps. In this regard, voice interfaces become another window for these companies to collect more data and know more about their users.

This brings us to an important takeaway: Tech giants will do anything they can to own your data because that is their key differentiating factor.

From a security and privacy standpoint, this causes several key concerns:

– These intermediaries will get to hear private conversations of enterprises’ users. For instance, if you allow a consumer voice assistant to check on your bank balance, you’re giving them access to this sensitive information.

– You don’t know what kind of data is being collected and where it is stored.

– Data is stored centrally in the servers of the voice AI provider. And as numerous security incidents have shown, centralized stores of data are attractive targets for malicious actors.

– As an enterprise, you have no ownership or control of your data and can’t use it to improve your products or gain insights about how users interact with your applications.

– In case you’re handling sensitive health, financial, or business data, you’re at the mercy of the Voice AI vendor to keep your data safe and not share it with third parties.

On the other hand, the Alan Platform is designed to ensure security and privacy for users of the enterprises and organizations. The key privacy tenet of the Alan platform is that each enterprise is the sole owner of their user conversations data. They decide where it is stored and who has access to it. And regardless of a customer’s choice for where to store their data, Alan AI secures this data, making sure it’s encrypted in transit and at rest. Not only does this model create more value for businesses in comparison to the classic voice AI platform, but it also addresses the key privacy and security pain points that organizations face when considering voice interfaces for their applications.

The Alan platform is based on solving specific problems for each enterprise, not answering every possible query in the world. Each deployment of our AI system will be tuned for one or more applications of a single enterprise.

The value of the Alan Platform does not come from creating digital profiles and selling ads and products to users, therefore there’s no incentive to collect, hoard, and monetize user data. Instead, Alan AI seeks success by creating value and helping businesses reduce costs, improve operational efficiencies and safety with employee facing deployments, and increase revenue acceleration for the customer facing deployments.

The goal is to increase ROI for businesses by deploying voice interfaces for apps being used by their customers and employees. This is why Alan AI believes every company should have full control and ownership of their data and AI models to provide the required privacy for their users. An added benefit is that the AI of each customer will improve as it continues to interact with the users of its application, and the business will have a chance to glean actionable insights from its data and develop new features and products.

Having access to the right quality and amount of data can give an enterprise the edge in providing a higher quality voice interface. Therefore, every enterprise should put data ownership and security at the center of its product innovation strategy. Will you prefer to use the technology of a company that works behind a black box, taking control and ownership of your data and not providing clear safeguards, or do you prefer to be in control of your data and work in a secure environment where you can continuously innovate and improve the voice interface of your products? If you’re in the latter camp,  the Alan Platform is for you. At Alan, we believe the future is a human voice interface to app. 

Reach out to sales@alan.app to set up a free private demo of the platform or answer any questions that you may have about the technology.

Intelligent voice interfaces for apps have become ubiquitous. From making calls to purchases, the use cases are explosive. Its use in educational institutions as a learning aid is yet to reach its full potential, but we are certain it is on the right track and can revolutionize the way students are educated. Given the hybrid learning trend, remote learning intertwined with the physical classroom, the challenge is to keep the student engaged and learning, regardless of the environment they are in.

To achieve critical education outcomes, it is important to focus on both technology and 21st century skills. Voice-based learning is an aid that includes a broad range of tools and enables a blended learning model- augmenting the learner’s experience and help educators with their teaching methodologies.

Voice tech applications are getting smarter over time with advances in artificial intelligence and their application in voice technology. Instructors  can simply ask their app “How many students submitted their homework assignment?”, “Who was absent in class today?”, “What are the instructions for today’s science fair?”, “ Summarize the progress of student Andy Jacobs” etc. Voice interfaces enable applications to give the right answer, faster.

Let us look at what makes voice technology a terrific aid in classrooms:

1. Collecting data in real-time:

Voice enabled devices are great at collecting data in real-time. Using them, teachers can record students’ engagement in class, monitor participation, attendance, etc. This is a productivity tool for teachers as they can just speak to the app instead of tedious touch and type. Moreover, built in artificial intelligence and analytics capabilities empower the instructor with data analysis and real-time insights. This empowers the instructor to quickly tackle situations that need a bias for action. 

2. Academic tracking and feedback:

The academic progress of students can be tracked over a period of time using voice interfaces. It can be done at scale for every student in the institution. It can also remind students about what is expected of them regarding assignments, deadlines, subjects they have enrolled in, etc. The institution can also use voice interfaces to deliver personalized feedback and constant updates on what is happening in the classrooms. Timely feedback on student performance is an important mechanism for management of academic outcomes- voice tech can definitely be used to accomplish this.

3. Communal learning:

When using voice interfaces, everyone in the classroom hears the same information uniformly without any bias. The students can maintain eye contact without looking down at the computer screen. The teachers also don’t have to break eye contact with the students, thereby helping build a closer rapport with the students.  

4. Accessing records:

Accessing student record details quickly is often difficult, even if you use a student information system. Voice interfaces make this an easy process as they are adept at large data sets. For example, if a teacher wants targeted information about a student, all they need to do is ask the app. It will share the information as soon as the question is asked. It is not just limited to academic records as educational institutions can get their entire data that was input into the system.

5. Personalizing learning:

Thanks to the human-like features of a voice interface, it makes learning more personal and humane for the students. Students will find it easy to follow instructions from voice interfaces because of their ability to express a wide range of emotions and voice modulations. Also, personalization means accommodating different learning styles of a student body and making some adjustments to fit their unique needs.

6. Introducing routine learning exercises:

Institutions can use voice assistants to introduce routine learning exercises such as learning words, memorizing spellings and facts, learning common phrases from foreign languages, etc. The use of voice as an aid for this is definitely a time saver for the teacher. Such methods can also positively drive engagement of students, as the voice can be blended with music to make it more appealing.

7. Customizing tests:

Voice technology can be customized according to the needs of the student. It can come with a bunch of ready-made templates that the teacher can choose from, on a whim. Moreover, a teacher can use different templates to match the diverse student personas in a classroom.

8. Storytelling:

We all love stories. Voice interfaces can be used to read stories to students. It could be specific to the subjects that are being taught or for the pure joy of listening to a story. It keeps the students engaged, but it also gives an opportunity for the teachers to rest. Interactive stories with engaging plot lines as a mode of instruction can help the student perform much better in classrooms. 

9. Controlling the environment:

Teachers can use voice interfaces to control the classroom by setting the tone with instructions. These can be repeated and also modified rapidly to inform students in the classroom on protocols for learning sessions, tests, and any special announcements.

In summary, Voice interfaces can be used to deliver an immersive learning experience and make learning more attractive to both student and instructor. They are a useful aid to the institution to deliver the right education at the right time to the right student.

The team at Alan AI will be more than happy to assist you with any questions or provide a personalized demo of our intelligent voice interface  platform. Just email us at sales@alan.app

About Alan AI:

Alan’s voice interface leverages the user context and existing UI of applications, a key to understanding responses for next-gen human voice conversations. Alan AI has patent protections for its unique contextual Spoken Language Understanding (SLU) technology to accurately recognize and understand human voice, within a given context. Alan’s SLU transcoder leverages the context to convert voice directly to meaning by using raw input from speech recognition services, imparting the accuracy required for mission-critical enterprise deployments and enabling human-like conversations, rather than robotic ones. Voice based interactions, coupled with the ability to allow users to verify the entered details without having the system to reiterate inputs, provides an unmatched end-user experience.

The evolution of Web 3.0 is fundamentally to create a more transparent, intelligent, and open internet for creators and users to share value, bringing back control of the internet from big technology players into the palm of the users.

Gavin Wood coined the term “Web 3.0” in 2014, laying out his vision of the future of the internet. Web 3.0 is underpinned by blockchain technology– to decentralize data and distribute it across devices- while reducing risks of massive data leaks by eliminating a central point of failure. 

By implementing artificial intelligence (AI) coupled with blockchain technology, Web 3.0 aims to redefine the web experience with structural changes for decentralization, democratization, and transparency in all facets of the internet.

Features of Web 3.0 include: 

The Semantic Web: A web of linked data, combining semantic capabilities with NLP to bring “smartness” to the web for computers to understand information much like humans, interpreting data by identifying it, linking it to other data, and relating it to ideas. The user can leverage all kinds of available data that allow them to experience a new level of connectivity.

Customization: Web personalization refers to creating a dynamic, relevant website experience for users based on behavior, location, profile, and other attributes.Web 3.0 is all about providing users with a more personalized experience within a secure and transparent environment.

Trust: Web 3.0’s decentralization promotes more transactions and engagement between peers. Users can trust the technology(blockchain) to perform many tasks in lieu of trusting humans for services such as contracts and transfer of ownership. Trust is implicit and automatic — leading to the inevitable demise of the middleman.

Ubiquity: IoT is adding billions of devices to the Web. That means billions of smart, sensor driven devices, being used by billions of users, by billions of app instances. These devices and apps consistently talk to each other, exchanging valuable data.

Voice Interface: A voice interface is expected to be a key element of Web 3.0, driving interactions between humans to devices to apps. One of the pivotal changes underway in technology today is the shift from user-generated text inputs to voice recognition and voice-activated functions. 

Some of the technologies used in creating voice interfaces include:

Automatic Speech Recognition (ASR) technology transcribes user speech at the system’s front end. By tracking audio signals, spoken words convert to text.  

Text to speech (TTS). A voice-enabled device will translate a spoken command into text, execute the command, and prepare a text reply. A TTS engine translates the text into synthetic speech to complete the interaction loop with the user.

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 for all text chat bots but not for voice interfaces. Voice has a lot of noice, accents and highly contextual on what we see at the moment and here Alan AI has developed a Global Spoken Language Understanding Model for Apps for Spoken Language Understanding (SLU)

Spoken Language Understanding (SLU) technology understands and learns the nuances of spoken language in context, to deliver superior responses to questions, commands, and requests. It is also a discovery tool that can help and guide users with human-like conversational voice through any workflow process. 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, empowered to anticipate user needs and solve problems quickly. Exactly in tune with the intent of Web 3.0.

The Alan AI platform is a SLU-based B2B Voice AI platform for developers to deploy and manage Voice Interfaces for Enterprise Apps- deployment is a matter of days, for any application. 

Alan’s voice interface leverage the user context and existing UI of applications, a key to understanding responses for next-gen human voice conversations. 

Alan has patent protections for its unique contextual Spoken Language Understanding (SLU) technology to accurately recognize and understand human voice, within a given context. Alan’s SLU transcoder leverages the context to convert voice directly to meaning by using raw input from speech recognition services, imparting the accuracy required for mission-critical enterprise deployments and enabling human-like conversations, rather than robotic ones. Voice based interactions, coupled with the ability to allow users to verify the entered details without having the system to reiterate inputs, provides an unmatched end-user experience.

Voice is no longer just for consumers. Alan’s Voice Assistants deployed in Ramco’s key enterprise business applications scale user productivity and deliver ROI. 

Alan AI and global enterprise software provider Ramco Systems have announced a key partnership to deploy in-app voice assistants for key applications. In its initial stages of partnership, the organizations will primarily focus on building business use cases for Ramco’s Aviation, and Aerospace & Defense sector, followed by those for other industry verticals including Global Payroll and HR, ERP, and Logistics.

Alan’s voice assistant technology works seamlessly with Ramco’s applications, as a simple overlay over the existing UI.  Alan provides enterprise grade accuracy of understanding spoken language for daily operations, synchronization of voice with existing graphical interfaces, and a hands-free app experience which will truly delight the user, from the very first interaction. Alan’s Voice UX also enables rapid and continuous iterations based on real-time user feedback via the analytics feature- a huge improvement over the painstakingly slow process of software development and release cycles for graphical user interfaces. Alan’s AI rapidly learns the nuances of the app’s domain language and can be deployed in a matter of days. 

 Commenting on the Alan AI-Ramco partnership, Ramesh Sivasubramanian, Vice-President – Technology & Innovation, Ramco Systems, said, “Voice recognition is a maturing technology and has been witnessing huge adoption socially, in our day-to-day personal lives. However, its importance in enterprise software has been a real breakthrough and a result of multitudinous innovations. We are excited to enable clients with this voice user interface along with Alan AI, thereby ensuring a futuristic digital enterprise”.

Alan’s voice interface leverage the user context and existing UI of applications, a key to understanding responses for next-gen human voice conversations. Alan has patent protections for its unique contextual Spoken Language Understanding (SLU) technology to accurately recognize and understand human voice, within a given context. Alan’s SLU transcoder leverages the context to convert voice directly to meaning by using raw input from speech recognition services, imparting the accuracy required for mission-critical enterprise deployments and enabling human-like conversations, rather than robotic ones. Voice based interactions, coupled with the ability to allow users to verify the entered details without having the system to reiterate inputs, provides an unmatched convenient end-user experience.

Maintenance, Repair, and Operation (MRO) employees in aviation and other industries increasingly use mobile and other device-based apps to plan projects, write reports based on their observations, research repair issues, and write logs to databases etc. This is exactly where Alan’s voice interface can help- with a hands-free option to increase productivity and support safety, thereby eliminating the distraction of touch and type while working on a task.

For example, Alan’s intelligent voice interface responds to spoken human language commands such as:

User: “Hey Alan, can you help me record a discrepancy?”

Alan: “Hi Richard, sure! Navigating to the ‘Discrepancy Screen’.”

User: “Enter description- ‘Motor damage’.”

Alan: “Updated ‘Motor damaged’ in the description field”

User: “Enter corrective action- ‘Motor replaced’.”

Alan: “Updated ‘Motor replaced’ in corrective action field.”

User: “Set action as closed.”

Alan: “Updated ‘Closed’ in quick action field.”

User: “Go ahead and record the discrepancy.”

Alan: “Sure, Richard. Creating the discrepancy… You’re done. Discrepancy has been registered against the task. Please review this at the bottom of the screen.”

Alan enables friendly conversations between humans and software. It helps to create outstanding outcomes by allowing users to go hands-free as well as  error-free with the ability to instantly review generated actions.

 Alan plans to continuously augment the voice experience to improve employee productivity with voice in their daily operations. Voice can now support a vision of a hands free, productive, and safe environment for humans.

Please view and share the Alan-Ramco partnership announcement on LinkedIn and Twitter

Smart technology is changing the way work gets done, regardless of the industry. It enables simple requests and provides efficient services for various industries, including the maintenance, repair, and operations (MRO) industry. 

The equipment in the MRO industry needs regular servicing. Industries such as aviation have particularly complex maintenance procedures.  Servicing them requires organized knowledge of the user guides and manuals. The procedures might be different for each unit, and it asks for patience, thoroughness, and the right set of skills– all in plenty. 

For example, finding the correct manual or the right procedure might not be easily possible, especially when you are strapped for time. These processes require the complete attention and focus of the technician or engineer. 

Now, how does having an intelligent vouce interface sound? What if it is your voice that can be used to request for information? Voice interfaces are ripe to go mainstream with advances in technology. The technician can say “Walk me through the inspection of X machine” to the voice assistant and get a guided workflow. They can get the work done in peace without wondering if they are following the right steps. 

Industry stats indicate that deploying voice interfaces in MRO apps result in a 2X increase in productivity, 50% reduction in unplanned downtime, and a significant 20% increase in revenue stream.

How Voice AI helps the maintenance, repair and operations industry: 

1. Increases productivity:

When maintenance workers engage with handsfree apps, they are capable of accomplishing tasks faster and are presented with an opportunity to multitask. The overall business productivity will increase in leaps and bounds. Moreover, voice enables smoother and faster onboarding and gets the new employee to be productive in a shorter span of time.

2. Allows a wide range of MRO activities:

Voice interfaces have a device-based implementation- it allows workers to be distant and still be able to collect data or listen to guided workflows. It includes laptops, smartphones, tablets, and other smart devices that can install and run a mobile application. 

The ability to have a voice interface on these devices, regardless of the connectivity, allows voice enabled applications to fit a wide range of MRO deployments in the field. 

3. Provides detailed troubleshooting:

One more critical advantage of using voice interfaces in the MRO industry is how speech recognition provides detailed error messages. The voice assistant warns when the data being input falls out of ranges that are not acceptable. It can even pre-load information collected in the previous screens and provides detailed instructions for new screens. 

4. Allows for smoother operations:

Voice assistants seamlessly integrate responses within a maintenance or inspection procedure. It is capable of doing this while following the updated guidelines. The technical operator gets additional information during the complicated repair process. Since voice assistants can provide the information in the form of audio, there is no interruption. 

5. Eradicate language barriers:

Some technicians might not be fully versed with the language that the maintenance procedure handbook is written in. It can be a barrier in getting the work done properly. Doing maintenance work in a faulty manner without following the procedures exactly as it is can result in problems. Listening to the instructions via voice can ease the stress of trying to read and make sense and allow for better comprehension.

6. Immediate solutions:

When the operator uses an intelligent voice interface, they can simply ask for any of the information that is already fed in the voice assistant, and the corresponding content will be provided by it. You will get exactly what you asked for. It eliminates the need for manual search, thereby even reducing the time taken for the procedure. 

7. Better training opportunities: 

Apart from providing assistance to service personnel, the voice assistants can also act as a great training tool for new operators. The newly hired operators can learn to operate the machine while listening to audio that can be synchronized with  visual instructions from the voice assistants. 

Wrapping up:

The advantages of using voice assistants in the MRO industry are multiple. The flexibility and capability that voice assistants offer enables greater attention to work, helps focus on the job, and reduces the time that is usually wasted by moving between applications and errors. Give your workers an error free, productive and safer environment with intelligent voice assistants. 

If industrial enterprises are looking for a voice-based solution that will make operations safer and more effective, the Alan Platform is the right solution for you. Check out the Ramco Systems testimonial on their partnership with Alan AI for enterprise MRO software apps.

The team at Alan AI will be more than happy to assist you with any questions or provide a personalized demo of our intelligent voice assistant platform. Just email us at sales@alan.app

Bolstering its enterprise applications with intelligent voice interfaces SUNNYVALE, CA 94582, USA, March 29, 2022 /EINPresswire.com/ — 

Alan AI, a Silicon Valley company enabling the next generation of voice interfaces for Enterprise Apps, has announced a strategic partnership with Ramco Systems¹, a leading global cloud enterprise software provider, to embed intelligent voice interfaces for its enterprise offerings. In its initial stages of partnership, the organizations will primarily focus on building business use cases for the Aviation, Aerospace & Defense sector, followed by use cases for other industry verticals.

Ramco Systems offers an integrated and smart platform engineered to develop robust and scalable solutions, thereby offering a competitive edge to its end users. By embedding Alan AI’s voice interface, Ramco’s customers will be able to interact with their applications with natural human language and receive intelligent responses for daily workflows. Features such as accuracy of understanding spoken language, synchronization of voice with existing graphical interfaces, and a hands-free app experience will truly delight the user- from the very first interaction. Voice Assistant will drive smoother app onboarding, higher user engagement and scale adoption and loyalty.

Commenting on the partnership, Ramesh Sivasubramanian, Vice-President – Technology & Innovation, Ramco Systems, said, “Voice recognition is a maturing technology and has been witnessing huge adoption socially, in our day-to-day personal lives. However, its importance in enterprise software has been a real breakthrough and a result of multitudinous innovations. We are excited to enable clients with this voice user interface along with Alan AI, thereby ensuring a futuristic digital enterprise”.

“We are so excited to be able to help support Ramco’s applications and empower their customers with intelligent voice interfaces. Our advanced Voice AI Platform enables enterprises to deploy and manage intelligent and contextual voice interfaces for their Applications in days, not months/ years” said Blake Wheale, Chief Revenue Officer, Alan AI. “This partnership is a great testament of how voice can support a vision of a hands free, productive and safe environment for humans”.
Learn more about Alan AI

#VoiceAssistant #VoiceAI #RamcoSystems #AlanAI

We are transitioning into a world where our digital experiences will be shaped with the help of voice. Marketers should find ways to come up with voice interfaces at various touchpoints in a mobile application or website. 35% of US adults own a smart speaker, up from zero at the beginning of 2015. 

The fast-paced voice adoption is a huge opportunity for marketers. Making users choose voice isn’t a big deal as most of them carry smart phones equipped with a microphone. All that they need to do is extend the current features to a voice app. 

How Can Voice Apps Scale App Adoption and Customer Loyalty?

1. Understands their Intent

43% users aged between 16 and 64 use voice search and voice commands extensively. Voice apps have already processed millions of real-world conversations, and have a deep understanding of natural language. Customers can speak naturally and freely. Advanced Voice interfaces will be able to understand a wide range of natural speech nuances, understand their internet and save time. Triggering the wrong intent will give out wrong information to customers. Internet trends report says that internet voice searches were mostly done using natural conversational language. Voice AI should be designed in such a way that it will understand the natural conversational flow. 

2. Creates Greater Empathy:

When customers connect with a brand on a deeper level, they feel understood, and that helps with building a connection. Through voice, brands can come across as relatable, empathetic, and honest. The pitch and tonality combine to become the brand’s voice. A recent study by Apple says that voice assistants which mimic the conversational style of humans are considered more likable and trustworthy. 

Brands need to invest in creating voice interfaces which reflect the unique aspects of their customers. Communication these days has become even more personalized through Account-Based Marketing (ABM). Here, deep knowledge of the prospective customer is formed through data and analytics, your voice AI can be designed to reflect the data. 

3. Creates Authentic Experiences:

Voice interfaces should be designed to reflect the values and mission of the company. It will make the users feel as if you are being authentic with them. Provide truly conversational voice experiences to your users, we are not referring to bot interactions. 

Your users should forget that they are talking to an AI machine, and believe that they are interacting with the most helpful sales rep in your team. It will help foster a relationship for which the customers will keep coming back. 

4. Proactively Provides Customer-Centric Updates

Communicating with customers at the right time is important. For example, the addition of a new favorite item on a restaurant menu or the new dress line available in the style preferred by the customer. Also, calls to confirm deliveries and order status updates translates into happy customers who will want to stay with you. 

5. Simplifies Onboarding Process

Onboarding a new customer is the first opportunity for a brand to delight them. It is imperative that you provide a smooth onboarding process. The more difficult it is to use an app, the higher are the chances that they will abandon ship.

One of the most effective ways to simplify user onboarding is to reduce the innumerable steps that are necessary to create user accounts. Voice interfaces can solve this by making the customer register with their voice. Every time they try to log in, all they need to do is use their voice. 

The Wrap

Personalized Voice Interfaces which interact with your customers like normal conversations will be able to increase customer adoption and loyalty. To increase customer and scale app adoption with the help of voice requires work. Your voice engine’s user interface should be conversational, responsive, and offer frictionless experiences. It should also be capable of providing fast and accurate responses. 

If you are looking for a voice-based solution to increase customer loyalty and app adoption, the team at Alan AI will be able to deliver exactly that. 

Write to us at sales@alan.app

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. 

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.

As product manager, your job is to constantly look for ways to improve your application, delight your customers, and resolve pain points. And in this regard, voice interfaces provide a unique opportunity to secure and expand your app’s position in the market where you compete.

Voice assistants are not new. Siri is now ten years old. But the voice interface market is nearing a turning point, where advances in artificial intelligence and mobile computing are making them a ubiquitous part of every user’s computing experience.

By giving multimodal interfaces to applications, voice assistants bring the user experience closer to human interactions. They also provide app developers with the opportunity to provide infinite functionality, an especially important factor for small-screen mobile devices and wearables. And from a product management perspective, voice interfaces enable product teams to iterate fast and add new features at a very fast pace.

However, not all voice interfaces are made equal. The first generation of voice interfaces, which made their debut on mobile operating systems and smart speakers, are limited in the scope of benefits they can bring to applications. Absence of cross-platform support, privacy concerns, and lack of contextual awareness make it very difficult to integrate these voice platforms into applications. Otherwise put, they have been created to serve the needs of their vendors, not app developers. 

Meeting these challenges is the vision behind the Alan Platform, an intelligent voice interface that has been created from ground up with product integration in mind. The Alan Platform provides superior natural language processing capabilities thanks to deep integration with your application, which enables it to draw contextual insights from various sources, including voice, interactions with application interface, and business workflows. 

Alan Platform works across all web and mobile operating systems and is easy to integrate with your application, requiring minimal changes to the backend and frontend. Your team doesn’t need to have any experience in machine learning or technical knowledge of AI to integrate the Alan Platform and use it in your application.

The Alan Platform is also a privacy- and security-friendly voice assistant. Every Alan customer gets an independent instance of the Alan Server, where they have exclusive ownership of their data. There is no third-party access to the data, and the server instance lives in a secure cloud that complies with all major enterprise-grade data protection standards.

Finally, Alan AI has been designed for super-fast iteration and infinite functionality support. The Alan Platform comes with a rich analytics tool that enables you to have fine-grained, real-time visibility into how users interact with your voice interface and graphical elements, and how they respond to changes in your application. Alan’s voice is a great source for finding current pain-points, testing hypotheses, and drawing inspiration for new ideas to improve your application.

Please send a note to sales@alan.app to get access to the white paper on “why voice should be part of your 2022 digital roadmap” and find out what the Alan Platform can do for you and how our customers are using it to transform the user experience of their applications.

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.

Have you ever wanted to develop voice-enabled mobile or web applications?
Well, You’re in the right place!

Alan AI is hosting its first virtual hackathon about Voice AI and inviting developers worldwide to take part! With the Voice AI Hackathon, we are challenging developers (as individuals or teams of 3) to integrate a voice assistant to their new or existing applications or other open-source apps through our conversational voice platform. All you need is basic JavaScript knowledge and the determination to win the TOP PRIZE of $500!

Participants can choose to voice-embed any application — from gaming, social networking, food delivery, or any other industry — the opportunities are endless.

Project submissions are due on July 15, 2020 at 11:59 PM (PST) which can be submitted with a website link, App Store/Play Store link or photo proof of app in the process of being published to App Store/Play Store. Participants will have Alan developers and mentors ready to assist with any questions or concerns throughout your progress. The top three submissions will win a cash prizes and be featured on our Alan platforms.

Ready to sign up? Fill out our sign up form 

Learn more about the hackathon on our official Hackathon Website 

We are excited to see what you build with the Alan Platform!

Alan AI is a Startup Sponsor for VOICE Global 2020 and will be giving a presentation on Multimodal Voice Assistants presented by James Shelburne, Senior Product Manager at Alan.

Learn how Multimodal Voice Assistants are transforming industries with an integrated visual and voice experience — an experience where users can switch between touch and voice. You’ll see how these revolutionary experiences are providing enterprise and consumer value, strategic differentiation, and gain insights on the ROI of voice from our partner use cases.​

Register for free at voicesummit.ai/global and join us on June 17 at 11:00 AM PST on the VG6 channel.

Search for “Multimodal Voice Assistants: a revolutionary experience transforming industries” in the agenda search bar or click here after registering​.

Forward-looking businesses are starting to explore the possibilities of introducing voice control into their applications. Therefore, we are seeing a noticeable increase in Android apps with voice-operated software that provide a hands-free experience.

We’ve gathered some of the most popular and useful hands-free apps for Android to see what they can offer and why other businesses should be heading in that direction as well.

The term “hands-free” refers to equipment or software that requires limited or no use of hands. One of the most popular ways to access controls for hands-free apps is through voice. The main goal is to make sure all users can use features within the app – regardless of their ability to physically operate the device.

Voice is being integrated into all kinds of devices, and it’s reshaping the usual state of things. Here are a few reasons why making your application hands-free is a good idea, business-wise and in general:

• Convenience – Hands-free apps can be used anywhere: while driving, doing chores around the house, carrying things, or when you’re simply far away from the device.
• Accessibility – These apps can be operated by people with limited hand mobility, those who are visually impaired, and other groups in need of assistive technology.
• Time efficiency – In many situations, making a quick call takes less time than typing a lengthy message and waiting for a response. The same principle applies to voice control; it requires no clicks, no typing, or any other time-consuming actions. 
• Simplicity – Users don’t have to be familiar with the interface to handle it. Unlike traditional apps, you hardly need any computer literacy or technical skills.
• Multi-use – Voice control isn’t strictly tied to one function. This kind of software is incredibly versatile in terms of potential applications.

Hands-free technology is particularly useful in countries where it’s illegal to use a handheld mobile phone when you drive. These laws have been adopted in many jurisdictions around the world, which gave developers another incentive to develop the technology.

The market of hands-free applications is an interesting space right now. Let’s look at the best offerings available in the Play Store for Android users.

1. Google Assistant

Google Assistant is considered an undisputed champion of personal assistant apps developed for Android. Although it may not work on every device, the coverage is extensive. In addition to running the app on your phone, you can also integrate with smart devices such as Philips Hue lights.

The assistant can run basic functions like making calls, sending texts, emails, setting alarms and reminders, etc. On top of that, you can look up weather reports and news updates, send web searches, and play music. The range of features is constantly getting updated and expanded.

The company states the app was originally designed for people with disabilities and conditions like Parkinson’s and multiple sclerosis. However, it should come in useful for anyone who’s multitasking or has their hands full. To activate Google Assistant, users need to say “OK Google,” and it will be all ears.

2. Amazon Alexa

Amazon Alexa has pushed the trend of endless integration with many emerging smart home devices to the forefront. Contrary to popular belief, this service runs not only on Amazon Echo but also on mobile devices. 

Alexa for Android is mostly used to control integrated devices. But the functionality also supports web searches, playing music, and even ordering deliveries. If you want to launch the hands-free app, say “Alexa” and it will be ready to hear commands whether the screen is on or off. 

The device restrictions are by far the biggest downside of Amazon Alexa. So far, there is a limited number of mobile phones supporting this system. However, in terms of its abilities and intelligence, it rightly occupies the top of the list.

3. Bixby

Bixby is a relatively new addition, but it is already among the best. It’s important to mention that it’s only compatible with Samsung devices. The company may be looking into other platforms, but at this point, it only runs on devices and appliances connected to Samsung’s proprietary hub.

The app can accomplish a variety of tasks – from sending text messages and responding to basic questions to activating other applications in the device (dialer, settings menus, camera app, contacts list, and gallery). 

One of the greatest benefits of Bixby is that it adapts to the user’s voice and manner of speaking. From the get-go, it can understand different request variations like “Show me today’s weather,” “What’s the weather like?” or “What’s the forecast for today?” and it only gets smarter with time.

4. Dragon

Powered by Nuance, which is the technology behind Siri, Dragon Mobile has been in operation for many years. Essential functionality includes dictating emails, checking traffic and weather, sharing your location, and a lot more. 

There are also many customizable features aimed at simplifying how you live, work, and spend leisure time – all while minimizing touch-based interactions. Users can add their unique and personalized Nuance Voiceprint. Then, voice biometrics will only let a designated user talk and ask questions.

You can also set your own wake-up word. Unlike other services, this one gives you options to launch it with “Hi, Dragon”, “What’s up,” or anything else you like. The company is working on adding languages other than English, as well as support for the international market. 

5. Hound

While the apps described above cover the most widely used basic functionalities, Hound takes a step further. Along with doing simple searches, it can accomplish advanced tasks such as hotel booking, a sing/hum music search, looking up stocks, or even calculating a mortgage. On a lighter side, you can play interactive games like Hangman. 

The company launched partnerships with Yelp and Uber to make features like getting restaurant information and hailing a ride more precise. Another interesting feature is that it can translate whole sentences practically in real-time. 

This speech-based app is only available for United States residents. However, the process of getting the app out of beta and ready for public consumption was pretty quick, so we may see some international development. Also, there are still occasional bugs within the app. 

6. Robin

Robin has been around for a while as one of the original “Siri alternatives”. Like its counterparts, the app supports calling, sending messages, and providing the latest information on the weather, news, and more. However, the functionality still needs some work.

Intentionally or not, a lot of features available on Robin are related to car use. For example, it offers GPS navigation, gives live traffic updates, and shows the prices for gas directly on the map. You can even specify what kind of gas you need, and it will guide towards the closest station.  

To call the app into action, you can tap on the microphone button, say “Robin,” or just wave hello twice in front of your phone (which is quite a unique innovation).

7. AIVC

AIVC stands for Artificial Intelligent Voice Control. It comes in two versions: free, which contains a number of ads, and Pro. The former option covers basic functionality, whereas the Pro one provides some appealing features like TV-Receiver control, wake up mode, and others. You can control devices that are accessible over a web interface with your own preset commands.

As far as voice commands go, the app gives you the option to define specific phrases to invoke a certain action. This is done to minimize the risk of the app not understanding what you want.

AIVC performs actions on other websites and services so you can compose emails, make Facebook posts, or move over to a navigation app.

8. DataBot

DataBot is one of the simpler Android Personal assistants. You can play around with it, ask for jokes and riddles, or do other goofy stuff, but it can actually be pretty useful for various tasks. You can ask the bot to make searches online, schedule events, and make calls by just using your voice.  

It is a cross-platform application so you can sync it across all your devices: smartphones, tablets, and laptops. That way, you get a coherent, all-around hands-free experience. Also, DataBot gains experience while you’re using it. 

A slight inconvenience that DataBot has is that it comes with ads and in-app purchases. If you aren’t bothered by that, it should be a good addition to your daily routine.

9. Car Dashdroid

Car Dashdroid includes everything you could possibly need while driving – navigation, music, contacts, messages, voice commands, and more. It is also integrated with popular messaging apps like WhatsApp, Telegram, and Facebook Messenger.

What makes this app stand out as a specifically car-oriented solution is that it comes with a compass, speedometer, and plenty of other features. 

There are also customization blocks that help you arrange all tasks based on their priority. For example, if you mostly use the app for navigation, you can put it at the top. Then, you can place music control below navigation, and the list of frequently contacted people at the bottom. 

10. Drivemode

Drivemode is a simple app meant to assist users while they’re driving. Users can select from their preferred navigation app (for example, Google Maps, Waze, and HERE Maps). You can also input favorite destinations (such as home, work, and so on), play music from multiple supported apps, and access messages in a low-distraction “driving mode” overlay with audio prompts. 

Even though it’s not entirely hands-free, there is a function that presents shortcuts that you can access through tapping or swiping. Drivemode can also be integrated with Google Assistant, so the functionality can potentially be extended way beyond driving assistance.

Integrating a Hands-Free Experience with Alan

Voice AI offers immense benefits for businesses – from completing tasks more quickly to offering better user experience with verbal communication. You can add unique voice conversations, no matter the industry you’re in. The Alan platform allows you to implement hands-free, interactive functionality in your existing application with ease. 

Part 1

This is the first in a three part series how to get started with the Alan Platform.

If you would like to follow along with this tutorial yourself, all the files necessary will be available on our GitHub, and you can also follow along using this video tutorial!

To begin, visit https://studio.alan.app/register to create your Alan Studio account. Once you create your account and verify your email, it will direct you to the main project page, so let’s take a look!

Project Page

Once you login, Alan will direct you to: https://studio.alan.app/projects

From here, there are many important things to note.

• Tutorial: In our Menu Bar up top, you will see a button labeled “Tutorial” This will take you to https://alan.app/blog/docs/intro.html Where you can start with our documentation as well as how to integrate your script on any platform.
• Create New Project: Click this button to start a new project quickly and easily.
• Billing: On the top right of our menu bar, you will also see your monthly charge as well as how many free interactions you have left.
• Menu Dropdown: This dropdown has quick shortcuts to our documentation, billing, and settings page.
• Current Projects: The majority of this page will be taken up with cards that display your current project as well as quick analytics.

Creating our first project

Now that we are familiar with our project page, let’s get create our first sample project!

Go ahead and Click “Create New Project”, for this tutorial we are going to name our project, “Food Ordering”.

Scripting UI

Our Scripting page is the main page where you will do all of your scripting and project work that divides into five main sections:

• The menu bar at the top
• Our Scripts Navigation pane on the left
• Our Script Development Window in the middle
• The Debugging Pane on the right
• The Logs bar featuring all input/output phrases and unrecognized phrases.

Script Basics

For this tutorial, we are going to be focusing on creating a fully voice enabled Food Ordering application. You will notice that the Script Development window is prompting us to create a new script, so let’s go ahead and add one now.

Click the “Create New Script” button and we will add a predefined script template called “Food_Ordering”.

Quick Tip: Go through our predefined scripts to learn more about the features of Alan and generate new script ideas!

Once you add your new script, you will see it open in our main window. The Source Code for this application is also available in our GitHub so you can download and follow along.

Let’s try out this script by clicking on the Alan Button and saying, “Order two pepperoni pizzas”. 

From here, we can see how Alan associates our keywords with:

An intent on line 296:

intent(`(add|I want|order|get|and|) $(NUMBER) $(ITEM ${ITEMS_INTENT})`,

And a response on line 351:

p.play(answer);

A sample with more details on the Answer function is found on line 320.

If you look in the debugging chat, you can see the actual instructions that are being sent to the application in order to achieve commands.

Now that we have created our first project and understand the basics of Voice Scripts, we’ll give you some time to play around with your project and adjust the scripts as you wish. We’ll see you in the next blog post where we will discuss more about customizing scripts, version control, development stages, and logs.

A voice assistant is a digital assistant that uses voice recognition, language processing algorithms, and voice synthesis to listen to specific voice commands and return relevant information or perform specific functions as requested by the user.

Based on specific commands, sometimes called intents, spoken by the user, voice assistants can return relevant information by listening for specific keywords and filtering out the ambient noise.

While voice assistants can be completely software based and able to integrate into most devices, some assistants are designed specifically for single device applications, such as the Amazon Alexa Wall Clock. 

Today, voice assistants are integrated into many of the devices we use on a daily basis, such as cell phones, computers, and smart speakers. Because of their wide array of integrations, There are several voice assistants who offer a very specific feature set, while some choose to be open ended to help with almost any situation at hand.

History of voice assistants

Voice assistants have a very long history that actually goes back over 100 years, which might seem surprising as apps such as Siri have only been released within the past ten years.

The very first voice activated product was released in 1922 as Radio Rex. This toy was very simple, wherein a toy dog would stay inside a dog house until the user exclaimed its name, “Rex” at which point it would jump out of the house. This was all done by an electromagnet tuned to the frequency similar to the vowel found in the word Rex, and predated modern computers by over 20 years.

At the 1952 World’s fair, Audrey was announced by Bell Labs. The Automatic Digit Recognizer was not a small simple device however, its casing stood six feet tall just to house all the materials required to recognize ten numbers!

IBM began their long history of voice assistants in 1962 at the World’s Fair in Seattle when IBM Shoebox was announced. This device was able to recognize digits 0-9 and six simple commands such as, “plus, minus” so the device could be used as a simple calculator. Its name referred to its size, similar to the average shoebox, and contained a microphone connected to three audio filters to match the electric frequencies of what was being said and matched it with already assigned values for each digit.

Darpa then funded five years of speech recognition R&D in 1971, known as the Speech Understanding Research (SUR) Program. One of the biggest innovations to come out if this was Carnegie Mellon’s Harpy, which was capable of understanding over 1,000 words.

The next decade led to amazing progress and research in the speech recognition field, leading most voice recognition devices from understanding a few hundred words to understanding thousands, and slowly making their way into consumers homes.

Then, in 1990, Dragon Dictate was introduced to consumers homes for the shocking price of $9,000! This was the first consumer oriented speech recognition program designed for home PC’s. The user could dictate to the computer one word at a time, pausing in between each word waiting for the computer to process before they could move on. Seven years later, Dragon NaturallySpeaking was released and it brought more natural conversation, able to understand continuous speech at a maximum of 100 words per minute and a much lower price tag of $695.

In 1994, Simon by IBM was the first smart voice assistant. Simon was a PDA, and really, the first smartphone in history, considering it predates HTC’s Droid by practically 25 years!

In 2008, when Android was first released, Google had slowly started rolling out voice search for its Google mobile apps on various platforms, with a dedicated Google Voice Search Application being released in 2011. This led to more and more advanced features, eventually leading to Google now and Google Voice Assistant.

Then, this was followed by Siri in 2010. Developed by SRI International with speech recognition provided by Nuance Communications, the original app was released in 2010 on the iOS App Store and was acquired two months later by Apple. Then, with the release of the iPhone 4s, Siri was officially released as an integrated voice assistant within iOS. Since then, Siri has made its way to every Apple device available and has linked all the devices together in a  single ecosystem.  

Shortly after Siri was first developed, IBM Watson is announced publicly in 2011. Watson was named after the founder of IBM, and was originally conceived in 2006 to beat humans at a game of Jeopardy. Now, Watson is one of the most intelligent, naturally speaking computer systems available.

Amazon Alexa is then announced in 2015. It’s name being inspired by the Library of Alexandria and also the hard consonant “X” in the name, helping with more accurate voice recognition. With Alexa, the Echo line of smart devices are announced to bring smart integration to consumers homes for an inexpensive route.

Alan is finally publicly announced in 2017 to take the Enterprise Application world by storm. Being first born as “Synqq”, Alan is created by the minds behind “Qik”, the very first video messaging and conferencing mobile app. Alan is the first voice AI platform aimed at enterprise applications, so while it can be found in many consumer applications, it is designed for enterprises to be able to develop and integrate quickly and efficiently!

At the bottom of the post we’ve included a Timeline to summarize the history of voice assistants!

Technology behind Voice Assistants

Voice assistants use Artificial Intelligence and Voice recognition to accurately and efficiently deliver the result that the user is looking for. While it may seem simple to ask a computer to set a timer, the technology behind it is fascinating.

Voice Recognition

Voice recognition works by taking an analog signal from a users voice and turning it into a digital signal. After doing this, the computer takes the digital signal and attempts to match it up to words and phrases to recognize the users intent. To do this, the computer requires a database of pre-existing words and syllables in a given language to be able to closely match the digital signal with. Checking the input signal with this database is known as pattern recognition, and is the primary force behind voice recognition.

Artificial Intelligence

Artificial intelligence is using machines to simulate and replicate human intelligence.

In 1950, Alan Turing (The namesake of our company) published his paper “Computing Machinery and Intelligence” that first asked the question, can machines think? Alan Turing then went on to develop the Turing Test, a method of evaluating a computer to test its capability of thinking like a human. There were four approaches later developed that defined AI, Thinking humanly/rationally, and acting humanly/rationally. While the first two deal with reasoning, the second two deal with actual behavior. Modern AI is typically seen as a computer system designed to accomplish tasks that typically require human interaction. These systems can improve upon themselves using a process known as machine learning.

Machine Learning

Machine learning refers to the subset of Artificial Intelligence where programs are created without the use of human coders manually creating the program. Instead of writing out the complete program on their own, programmers gives the AI “patterns” to recognize and learn from and then gives the AI large amounts of data to sift through and study. So instead of having specific rules to abide by, the AI searches for patterns within this data and uses it to improve its already existing functions. One way machine learning can be helpful for Voice AI, is by feeding the algorithm hours of speech from various accents and dialects.

While traditional programs requires an input and rules to develop an output, machine learning tools are given an input and an output and use that to create the program itself. There are two approaches to machine learning, supervised learning and unsupervised learning. In supervised learning, the model is given data that is already partly labeled, this means some of the data given will be already tagged with the correct answer. This helps guide the model into categorizing the rest of the data and developing a correct algorithm. In unsupervised learning, none of the data is labeled, so it is up to the model to find the pattern correctly. One of the reasons this is very useful is because it allows the model to find patterns that the creators might have never found on their own, but the data is much more unpredictable.

Different Voice Assistant approaches

Many conversational assistants today combine both a task-oriented and knowledge-oriented workflow to carry out almost any task that a user can throw at it. A task-oriented workflow might include filling out a form, while a knowledge-oriented workflow includes answering what the capital of a state might be or specifying the technical specifications of a product.

Task-oriented approach

A task-oriented approach is using goals to tasks to achieve what the user needs. This approach often integrates itself with other apps to help complete tasks. For example, if you were to ask a voice assistant to set an alarm for 3PM, it would understand this to be a task request and communicate with your default Clock application to open and set an alarm for 3PM. It would then communicate with the app to see if anything else was necessary, such as a name for the alarm, then it would communicate this need back to you. This approach does not require an extensive online database, as it is mainly using the knowledge and already existing skills of other installed applications.

Knowledge-oriented approach

A knowledge-oriented approach is the use of analytical data to help users with their tasks. This approach focuses on using online databases and already recorded knowledge to help complete tasks. An example of this approach is anytime a user asks for an internet search, it will use the online databases available to return relevant results and recommend the highest search result. If someone is searching up a trivia question, this would use a knowledge-oriented approach as it is searching for data instead of working with other apps to complete tasks.

Benefits of Voice Assistants

Some examples of what a Voice Assistant can do include:

• Check the weather
• Turn on/off connected smart devices
• Search databases

One of the main reasons of the growing popularity of Voice User Interfaces (VUI) is due to the growing complexity within mobile software without an increase in screen size, leading to a huge disadvantage by using a GUI (Graphical User Interface). As more iterations of phones come out, the screen sizes stay relatively the same, leading for very cramped interfaces and creating frustrating user experiences, which is why more and more developers are switching to Voice User Interfaces.

Efficiency and Safety

While typing has become much faster as people have gotten used to using standard keyboards, using your voice will always be quicker, much more natural, and lead to less spelling errors. This leads to a much more efficient and natural intelligent workflow.

Quick learning curve

One of the greatest benefits of voice assistants is a quick learning curve. Instead of having to learn how to use devices like mice and touch screens and get used to using specific physical devices, you can just use your natural conversation tendencies and use your voice.

Wider Device Integration

Since a screen or keyboard isn’t necessary, it’s easy to place voice integration into a much wider array of devices. In the future, smart glasses, furniture, appliances, will all come with voice assistants already integrated into the device.

Why and When to use Voice Assistants

There are many use cases for using a voice assistant in todays’ world. For example, when your hands are full and you are unable to use a touch screen or keyboard, or when you are driving Let’s say you are driving and you need to change your music, you could just ask a voice assistant, “play my driving playlist”. This leads to a safer driving experience, and helps avoid the risk of distracted driving.

User Interfaces

To further understand voice assistants, it is important to take a look at the overall user Experience and what a User Interface is and how a VUI differs from a more traditional graphical user Interface that modern apps currently use. 

Graphical User Interface (GUI)

A Graphical User Interface is what is most commonly used today. For example, the internet browser you’re using to read this article is a graphical user interface. Using graphical icons and visual indicators, the user is able to interact with machines quicker and easier than before.

A Graphical User Interface can be used in something like a chatbot, where the user communicates with the device over text, and the machine responds with natural conversation text. The big downside to this is since it is done all in text, it can seem cumbersome and inefficient, and can take longer than voice in certain situations.

Voice User Interface (VUI)

An example of a VUI is something like Siri, where there is an audio cue that the device is listening, followed by a verbal response.

Most apps today combine a sense of both Graphical and Voice User Interfaces. For example, when using a maps application, you can use voice to search for destinations and the application will show you the most relevant results, placing the most important information at the top of the screen.

Some examples of popular smart assistants today are Alan, Amazon Alexa, Siri by Apple, and Google Voice Assistant.

Popular Voice Assistants

Siri

Siri is the most popular voice assistant today. Created in 2010 by SRI Inc, and purchased in 2011 by Apple, Siri has quickly become an integral part of the Apple ecosystem in bringing all the Apple devices and applications together to use in tandem with one another.

Alexa

Created by Amazon in 2014, Alexa was named due to its similarity to the Library of Alexandria. Alexa was originally inspired by the conversational voice system found on board the U.S.S. Enterprise in Star Trek. Alexa was released alongside The Amazon Echo, a smart speaker intended for consumers to dive into the world of home automation, uses the Alexa platform to allow users to interact with the Amazon ecosystem and allow for a plethora of smart devices to be connected.

Google Assistant

Originally unveiled in 2016, Google Assistant was the spiritual successor of Google Now, with the main improvement being the addition of two-way conversations. Where Google now would return answers in the form of a search results page on Google, Google Assistant gives answers in the form of natural sentences and returns recommendations in the form of Feature cards.

Cortana

Beginning in 2009, Cortana by Microsoft has had one of the longest visions of giving people access to voice assistants in their daily lives. Microsoft began shipping Cortana with all Windows 10 and Xbox devices, leading to a huge increase in the amount of registered Cortana users. In 2018 it was reported that Cortana had over 800 Million users.

Alan

In 2017 Alan set out to take voice assistants to the next level, by enabling voice AI for all applications. Using domain specific language models and contextual understanding, Alan is focused on creating a new generation of Enterprise Voice AI applications. By using the Alan Platform, developers are able to take control of voice, and create an effective workflow that best fits their users with the help of vocal commands.

Future of Voice Assistants

As AI becomes more advanced and voice technology becomes more accepted, not only will voice controlled digital assistants become more natural, they will also become more integrated into more daily devices. Also, conversations will become much more natural, emulating human conversations, which will begin to introduce more complex task flows. More and more people are using voice assistants too, as it was estimated in early 2019 that 111.8 million people in the US will use a voice assistant at least monthly, up 9.5% from last year. 

Further Integration

In the future, devices will be more integrated with voice, and it will become easier and easier to search using voice. For example, Amazon has already released a wall clock that comes enabled with Amazon Alexa, so you can ask it to set a timer or tell you the time. While these devices aren’t full blown voice activated personal assistants, they still show a lot of promise in the coming years. Using vocal commands, we will be able to work with our devices just by talking.

Natural Conversations

Currently, as users are getting more used to using voice to communicate with their digital devices, conversations can seem very broken and awkward. But in the future, as digital processing becomes quicker and people become more accustomed to using voice assistants in their everyday devices, we will see a shift where users won’t have to pause and wait for the voice assistant to catch up, and instead we will be able to have natural conversations with our voice assistants, creating a more soothing and natural experience.

More complex task flows

As conversations with voice assistants become more natural and voice recognition and digital processing becomes quicker, it won’t be uncommon to see users begin to adopt more advanced tasks in their daily routines with voice assistants. For example, instead of asking a voice assistant how long a commute is, and then asking about different options, you might be more inclined to say, “If Uber is quicker than taking the bus to work, can you reserve an Uber ride from home to work, and how long will it take?”

How to make your own voice assistant

As the amount of voice assistants available publicly begin to grow, tools are beginning to appear to create your own to make it as easy as possible to find a voice assistant that fits your needs!

For example, if you just wanted to create a specific skill, or command for a voice assistant, it might be more efficient to look into integrating a skill into an already existing voice assistant, such as Alexa. 

Amazon has actually made it incredibly simple to add your own command to the vastly growing set of publicly available Alexa Skills. You can login to AWS with the same account you have an Echo linked to, and use the tools to create a free Alexa Skill! 

Using Alan Studio, the completely browser based Voice AI IDE, you can develop, test, and push voice integration straight from your browser.

Why Alan?

Alan is a highly customizable Voice AI platform designed to work with any pre-existing application. Built with enterprise use in mind, security and business functionality are a top priority. You can leverage visual and voice context to support any workflow and improve efficiency today, and since Alan is a completely browser based IDE, you can edit your scripts on the go whenever the need arises. Long gone are the days of creating multiple versions of scripts to run on each platform, with Alan, you can use a single script version and embed into any app, iOS, Android, or Web. You can sign up today for Alan Studio and see how you can create an AI voice assistant solution to improve your quality of life!

Voice Assistant Timeline

• 1922 – First Voice activated consumer product hits store shelves as “Radio Rex”
• 1952 – Audrey, or the Automatic Digit Recognition Machine, is announced
• 1962 – IBM Shoebox is shown for the first time at the State Fair
• 1971 – Darpa funds five years of speech recognition research and development
• 1976 – Harpy is shown at Carnegie Mellon
• 1984 – IBM releases “Tangora” the first voice activated typewriter
• 1990 – Dragon Dictate is released
• 1994 – Simon by IBM is the first modern voice assistant released
• 2010 – Siri is released as an app on the iOS app store
• 2011 – IBM Watson is released
• 2012 – Google Now is released
• 2014 – Amazon Alexa and Echo are released
• 2015 – Microsoft Cortana is released
• 2017 – Alan is developed and released with the Alan Platform

Resources

https://whatis.techtarget.com/definition/voice-assistant

https://www.smartsheet.com/voice-assistants-artificial-intelligence

https://www.ibm.com/ibm/history/ibm100/us/en/icons/speechreco

http://www.bbc.com/future/story/20170214-the-machines-that-learned-to-listen

https://towardsdatascience.com/build-your-first-voice-assistant-85a5a49f6cc1

This article was reposted at dev.to here:
https://dev.to/alanvoiceai/what-is-a-voice-assistant-492p

A Voice User Interface(VUI) enables users to interact with a device or application using spoken voice commands. VUIs give users complete control of technology hands free, often times without even having to look at the device. A combination of Artificial Intelligence(AI) technologies are used to build VUIs, including Automatic Speech Recognition, Name Entity Recognition, and Speech Synthesis among others. VUIs can also be contained either in devices or inside of applications. The backend infrastructure, including AI technologies used to create the VUI’s speech components, are often stored in a public or private cloud where the user’s speech is processed. In the cloud, AI components determine the intent of the user and return a given response back to the device or application where the user is interacting with the VUI.

Well known VUIs include Amazon Alexa, Apple Siri, Google Assistant, Samsung Bixby, Yandex Alisa, and Microsoft Cortana. For the best user experience, VUIs have visuals created by a Graphical User Interface and additional sound effects to accompany them. Each VUI today has its own way of handling sound effects are used so that users know when the VUI is active, listening, processing speech, or responding back to the user. The benefits of VUIs include hands-free accessibility, productivity, and better customer experience that will change how the world interacts with artificial intelligence. 

The Creation of VUI 

Audrey

The first traces of VUI started as the first speech recognition system in 1952 with a device called Audrey. Audrey was invented by K.H. Davis, R. Biddulph and S. Balashek, it was known as the “automatic digit recognizer” due to its ability to recognize numbers 0 through 9. Although Audrey’s skill was limited to numbers, it was seen as a technological breakthrough. Audrey was also not a small device like usually seen today, Audrey stood 6 feet tall with a large and rather complicated analog circuit system.

During the creation of Audrey there was an input and output procedure like used today in modern VUI devices. First, a speaker recited a digit or digits into a telephone and made sure to make a 350 milliseconds pause between each word. Next, Audrey listened to the speaker’s input and with speech processes it sorted the speech sounds and patterns to understand the input. Audrey would then visibly respond by flashing a light like modern VUI devices. 

Although Audrey could distinguish the numbers, Audrey could not universally understand everyone’s voice or language style and could only respond to a familiar speaker. Unfortunately this was not a feature like modern day VUI in devices, Audrey was simply not advanced enough and needed a familiar speaker to maintain a 97 percent digit recognition accuracy. With a few other designated speakers, Audrey’s accuracy was 70-80 percent, but far less with other speakers it was unfamiliar with. Why was Audrey created in the first place if manual push-button dialling was cheaper and easier to work with? Recognized speech requires less bandwidth (less frequencies for transmitting a signal) than the original sound waves in a telephone. It would also be more practical for reducing data traveling through wires and future technology. 

Tangora

Shortly after the creation of Audrey, the most significant voice technology advancement was in 1971 when the U.S Department of Defense’s research team funded five years of a Speech Understanding Research program. Their goal was to reach a minimum of 1,000 vocabulary words with the help of companies such as IBM. In the 1980s, IBM built a voice activated typewriter called Tangora. Tangora was capable of understanding and handling a 20,000-word vocabulary. Today voice activated typing systems have evolved to be used in smartphones to send a text or write a research paper in a matter of moments. 

Overtime, computer technology advanced VUI, Graphical User Interface (GUI), and User Experience (UX) design is placed into a small device that fits in the palm of a hand. Even GUI and UX is becoming old news due to the quick adoption of voice-only devices that no longer use these features. Speech recognition technology went from understanding 9 numbers to millions of phrases and words from any voice. This advancement was made possible with new speech recognition software processes such as Automatic Speech Recognition, Name Entity Recognition, and Speech Synthesis. 

Technology used to create a VUI

A range of Artificial Intelligence technologies are used to create VUIs, including Automatic Speech Recognition, Name Entity Recognition, and Speech Synthesis. 

Automatic Speech Recognition

Automatic Speech Recognition(ASR) is a technology used to analyze and process human speech into text. For a given audio input, ASR is required to filter out any distracting acoustic noises and identify human speech instead. Distortions in the audio and streaming connectivity can make this a challenge. Several underlying technologies have been tested and used to build ASR technology, including Gaussian mixture models (a probabilistic model) and deep learning with neural networks that process and distribute information to collect data. Often times, the words recognized by ASR are not an exact match to entities within a user intent. In these cases, augmented entity matching is used, which will take similar words or similar sounding words and match them to a predefined entity in the VUI.  

Name Entity Recognition

Name Entity Recognition(NER) is used to classify words as their underlying entity. For example, in the command “Get directions to New York City”, ‘New York City’ is recognized as a location. In addition to locations, NER locates entities or semi-structured text that can be a person, a subject, or something as specific as a scientific term. NER often takes surrounding text or words to determine the value of the entity. In the “Get directions to New York City” example, pre-trained probabilistic models assume that whatever word(s) come after “Get directions to” can be safely classified as a location. Examples like “Get directions to the nearest gas station” can also work for the same reasons, with ‘the nearest’ being a defined qualifier that precedes location.

NER assists ASR in resolving words as their entities. On the basis of voice input alone, “New York City” is recognized as “new” “York” “city”. NER then identifies this as a unique location and adjusts to “New York City”. NER is highly contextual and needs additional input to confidently determine entities. Sometimes, NER is reliant on previous training and will not be able to confidently determine an input’s entity. 

Speech Synthesis

Speech Synthesis produces artificial human voice and speech using input text. VUI does the job in three stages. The stages are input, processing, and output. Speech Synthesis is simply a text-to-speech (TTS) output where a device reads out loud what was input with a simulated voice through a loudspeaker.

 These AI technologies analyze, learn, and mimic human speech patterns and can also adjust the speech intonation, pitch, and cadence. Intonation is the way a person’s voice rises or falls as they speak. Factors that affect intonation is emotion, accent, and diction. Pitch is the tone of voice, but it is not affected by emotion. Pitch is high or low and can be best described as a squeaky or deep voice. Cadence is the flow of voice that fluctuates in pitch as someone is speaking or reading. For example, a public speaker will change their cadence by descending their voice during a declarative sentence to make an impact on their audience.

Once all of this information is stored and analyzed, these technologies will use it to improve itself and the VUI through what is called machine learning. The clouds and technologies will determine the intent of the user and return a response through the application or device.

Intents & Entities

Voice commands consist of intents and entities. The intent is the objective of the voice interaction and has two approaches. There are local intents and global intents. A local intent is when the user is asked a question in which they respond “Yes” or “No”. A global intent is when a user has a more complex answer. When designing VUI’s, the way different commands can be said need to be taken into consideration in order to recognize the intent and respond correctly. Here is an example of getting directions to a location: “Get directions to 1600 Pennsylvania Avenue”, “Take me to 1600 Pennsylvania Avenue”. Entities are variables within intents. Think of it as the blanks needed to fill into a Mad Libs booklet, such as “ Book a hotel in {location} on {date}” or “Play {song}.” 

VUI vs GUI

User Experience (UX) is the overall experience of an interface product such as a website, application, and more in terms of how aesthetically pleasing it is or how easy it is to navigate for users. Together VUI and GUI play a large role in UX design because they assemble a product for consumers. 

Voice User Interface

As explained earlier, Voice User Interface (VUI) enables users to interact with a device or application using spoken voice commands. VUIs give users complete control of technology hands free, often times without even having to look at the device. 

Graphical User Interface (GUI)

Graphical User Interface (GUI) is graphical layout and design of a device. For example, the screen display and apps on a smartphone or computer is a graphical user interface. GUI can be used to display visuals for VUI, such as a graphic of sound waves when a voice assistant on a smartphone responds to its user. Another real life example can be how Google and Apple Siri use VUI and GUI together.

Apple Siri VUI & GUI

Apple Siri responds to “Hey Siri” using VUI or by pressing down on the home button of the Apple device. Users will know that Siri is active when Siri says “What can I help you with?” through its speaker or on the screen using GUI. While a user speaks to Siri, colorful representational wavelengths move to the sound of speech. This also shows users that Siri is actively listening and processing their question. When a user is quiet, Siri will prompt “Go ahead, I’m listening…” If a user still does not respond, then it will display on the screen “Some things you can ask me:” with a few examples of what it can do, such as calling, face timing, emailing, and more.

This GUI feature is specifically catered to people who are new to Siri and are unsure on what to do. The Apple device will also display what the user has asked and Siri’s response on the screen to show what is being understood from the interaction. Other features that Apple Siri has is the customization of Siri’s gender, accent, and language. 

Google Assistant VUI & GUI

Google Assistant responds to users when it hears “OK Google” or “Hey Google.” At the bottom of the screen, colorful dots will display to let the user know that Google Assistant has been activated and ready to listen. While it waits for the user to ask a question, the dots will move in a wave formation to represent wavelengths until it gets speech. Once a user starts speaking, the dots will transform into bars and move into a wave formation to the sound of speech to let users know it is processing information. Another GUI feature that Google Assistant has is that it will display what the user has asked and Google’s responses. Like Apple Siri, this display is another way of showing users what is being understood by the interaction. Google Assistant is also customizable in language and accent.

VUI vs Voice AI

The term Voice Artificial intelligence (AI) is used with VUI very commonly. Both terms usually get confused to mean the same thing since they are closely connected. VUI is all about the voice user experience on a device. Voice AI is the term for speech recognition technologies. The technologies fall under the Voice AI umbrella and are Automatic Speech Recognition, Name Entity Recognition, and Speech Synthesis. 

Different VUI approaches

Voice command devices also known as voice assistants use VUI and can be auditory, tactile, or visual. Devices can also range from a small sized speaker or to a blue light that blinks in a car’s stereo when it hears a command. More common examples of a voice command device are iPhone Siri, Alexa, and Google Home. These voice assistants are made to help people in daily tasks. There are also device genres for what the VUI is used for. This influences how the interaction between the user and device is set up.

VUI Device Genres

• Smartphones
• Wearables
  • Smart wrist watches
• Stationary Connected Devices 
  • Desktop computers
  • Sound System
  • Smart TV
• Non-Stationary Computing Devices
  • Laptops
  • Speakers
• Internet of Things (IoT)
  • Thermostats
  • Locks 
  • Lights 

Each voice enabled device has a different functionality. A smart tv will respond to changing the channel, but not to sending a text message like a smartphone would. Users can ask for information from the news and weather channel or simply send a voice text with the power of VUI. Not only are there devices, but VUI integrated voice controlled apps that serve the same purpose as well. The VUI will interact with an app in a task-oriented workflow and/or knowledge-oriented workflow. Task-oriented workflows can complete almost anything a user asks it to do, such as setting an alarm or making a phone call. Knowledge-oriented workflows responds to its user by using secondary sources like the internet to complete a task, such as searching for a question about Mt. Everest’s height. 

The Benefits of VUIs

The primary benefit of VUIs is that they allow a hands-free experience that users can interact with while focusing on something else. It can save time in daily routines and improve people’s lives such as, checking the weather or setting an alarm clock the night before work. 

VUI in Workflows & Lifestyles

VUI is beneficial in multitasking productivity in work spaces that range from an office space or outdoor labor. Voice User Interface can actively participate in worker safety by assisting users in hazardous work flows, such as construction sites, oil refineries, driving, and more. Traditional devices like phones and computers aren’t the only devices connected to the internet or VUI. Smart light fixtures, thermostats, smart locks, and other Internet of Things (IoT) are connected as well. These VUI devices are useful in households with travelers and/or busy families from home or a smartphone.

Improving Lives

With individualized experiences, VUI can lead society to a more accessible world and help give a better quality of life. VUI benefits users with disabilities such as the visually impaired or others that cannot adapt to visual UI or keyboards. VUI is also becoming popular with Seniors who are new to technology. Aging has many effects on abilities such as sensory, movement, and memory, which makes VUI an alternative to hands-on assistance. With the assistance of VUI, elders can communicate with loved ones and use devices without the confusion and frustration. 

VUI in Education

Educational strategies are constantly being updated in educational systems for all ages. VUI can be a learning tool where classrooms interact with a voice assistant to create a new experience and cater to all learning styles. Since VUI is very accessible, training isn’t required for using it which makes it very easy to use in any audience. 

Technology Innovation

As VUI grows, it will change the way that products are designed and start a new job demand. VUI design will become a key skill for designers due to the evolving user experience. User Experience (UX) designers are trained in providing experiences for physical input and graphical output. VUI design is different from UX because the design guidelines and principles are different. This will encourage designers to focus more on VUI design. In 2019, it was estimated that 111.8 million people in the US will use a voice assistant at least monthly, up 9.5% from last year. Since users are using voice assistants more than ever, it will eventually become a habit and the new device feature that everyone will own.

It will be easier for users to speak to a device than to physically use a device after the habit has been formed. This will create a high demand for VUI knowledgeable designers and contribute to the change of how devices are designed.

Lastly, another benefit to voice command devices is that they don’t stay stagnant to what they are programmed to do. Over time, the interaction between the user and voice-user interface improves through machine learning as discussed earlier. The user learns how to better utilize the voice command device and the device in return learns how to work with its user. 

Solutions With Alan

With the Alan Platform, it is very simple to create your own voice interface designed for natural communication and conversation. Signing up for an account with Alan Studio gives you access to the complete Alan IDE to create a VUI you can integrate with any pre-existing app. The Alan Platform allows you to create a Voice User Interface completely within your browser and allows you to embed the code into any app, so you only have to write it once and not worry about compatibility issues.

Final Thoughts 

Voice User Interface went from only recognizing numbers 0-9 to more than a million vocabulary words in different styles of speaking. VUI has never stopped progressing and is creating a new job demand and an important focus in User Experience design. As VUI progresses, more voice assistants and solutions are being created to benefit society. Companies and consumers are switching to the new and practical trend of VUI or combining Graphical User Interface with VUI.

Voice assistants come in many shapes, forms, and genres. Each device has its own purpose using VUI, such as assisting in the productivity of workflows, lifestyles, and education. What they all have in common is that their purpose is to help users in their everyday lives with a hands free user experience. This is done by using a range of Artificial Intelligence technologies that are used to create VUIs, including Automatic Speech Recognition, Name Entity Recognition, and Speech Synthesis.

Another reason why VUI never stops growing and improving is because it does not stay stagnant to what it is programmed to do. Over time, the interaction between the user and voice user interface improves through machine learning. The user learns how to better utilize the voice command device and the device in return learns how to work with its user. Together they are working towards a more advanced artificial intelligence and voice user interface. 

This article was reposted at dev.to here:
https://dev.to/alanvoiceai/what-is-voice-ui-2ga7

Have you ever wanted to learn more about adding voice to your app but weren’t sure who to ask? Now is the perfect time!

Join us on Thursday, September 19th at 10AM, PST as we walk YOU through Alan and show you how you can add voice to your app in 10 minutes! Chat with Alan Developers and ask questions live!

Sign up today at:

https://zoom.us/webinar/register/6115686541869/WN_mnocfxtfTRWc9d7y16z7Rg

We are incredibly excited to share with you a brand new episode of This Week in Voice, where our CEO, Ramu Sunkara sits down with Bradley Metrock to discuss the latest in voice. Thank you to Bradley for having us! Check out the episode below:

https://www.thisweekinvoice.com/s4e3-sep-12-2019

The topics discussed in this episode include:

• CNN: How Government Is Using Siri And Alexa To Stop Spread Of Census Misinformation
• Voicebot.AI Story Of The Week: McDonalds Acquires Voice Startup Apprente To Build Drive-Thrus With Voice Assistants
• Fast Company: Amazon Will Let Anyone Answer Your Alexa Questions Now. Check out A Connect Home’s Alexa setup Guide here.
• The Truly Baffling Case Of The Ancient Voynich Manuscript

From their website: This Week In Voice is VoiceFirst.FM’s weekly news podcast, bringing you the most interesting, relevant stories in the rapidly-growing world of voice technology. If you like what you heard, you can check out more episodes on Apple Podcasts, Google Play Music, Stitcher Radio, Soundcloud, TuneIn, and many other preferred podcast providers.

The host, Bradley Metrock, is the CEO of Score Publishing, as well as the Executive Producer of Project Voice, the #1 event for voice tech and AI in America, coming the week after CES. Check out projectvoice.ai for more information about the event.