How Machine Generated Virtual Assistants can 10x Your Productivity in 2022 by@mobidev
370 reads

How Machine Generated Virtual Assistants can 10x Your Productivity in 2022

Read on Terminal Reader

Too Long; Didn't Read

Companies Mentioned

Mention Thumbnail
Mention Thumbnail

Coins Mentioned

Mention Thumbnail
Mention Thumbnail
featured image - How Machine Generated Virtual Assistants can 10x Your Productivity in 2022
MobiDev HackerNoon profile picture



Trusted software development company since 2009. Custom DS/ML, AR, IoT...

react to story with heart

They can help you get an appointment or order a pizza, find the best ticket deals and bring your attention to the fact you are spending a lot on entertainment instead of investments. We are talking about AI virtual assistants, which have already become a familiar part of our daily lives. But what technologies are under the hood of AI assistants and how can you leverage them in your business? Find all the answers in this article.

Intelligent Virtual Assistants Market Insights

Intelligent Virtual Assistants (IVA) also known as Intelligent Personal Assistants (IPA) are AI-powered agents capable of generating personalized responses, pulling from contexts such as customer metadata, prior conversations, knowledge bases, geolocation, and other modular databases and plug-ins. The Intelligent Virtual Assistant market, experiencing rapid growth in the 2020s, is forecasted to reach USD 6.27 billion by 2026, according to Mordor Intelligence.

AI assistant technology is in many ways similar to a traditional chatbot but integrates next-generation analytics, machine learning, AR/VR and data science. While conventional chatbots can generate responses to inquiries based on Markov chains and other similar processes, their static responses pale in comparison to the dynamic insights generated by intelligent virtual assistants.

One of the best-known virtual assistants is Apple’s Siri, a consumer-facing product packaged as a personal assistant. Examples of other IVAs include Amazon’s Alexa, Microsoft’s Cortana, and Google’s Google Assistant. Siri and competitors help customers easily execute commands with voice prompt, automating tasks such as setting alarms on a smartphone, verbally reading out e-mails with text-to-speech technology, playing and searching for music, and sending text messages. The ubiquity and popularity of IVAs in consumer smartphones led to the inclusion of Intelligent personal assistant technology by car manufacturers.

The Asia Pacific region is a critical market to watch when it comes to intelligent virtual assistants, with major growth across the healthcare, technology, and financial sectors. The industry’s heavy hitters include Apple Inc., Inbenta Technologies, IBM Corporation, Avaamo Inc., and Sonos Inc.

The end-users utilizing AI assistant technology can be found in the healthcare, telecommunications, travel and hospitality, retail, and BFSI sectors. Consumer products utilizing IVAs or IPAs include smart speakers, smartphones, cars, commercial vehicles, home computers, home automation appliances, and many more.

Underlying technologies upon which IVAs and IPAs depend include Machine Learning, Cognitive Computing, Text-to-speech, Speech Recognition, Computer Vision, and AR. We will talk about them in more detail later.

Why Do Companies Create AI Assistants?

If you’re an Apple device owner, you probably can’t imagine your life without Siri. Amazon Alexa, Google Assistant, Samsung Bixby – the majority of big brands are investing in the development of AI assistants. So why do companies do this?

The main advantage of using artificial intelligence to create such solutions is that AI can efficiently and quickly process huge amounts of data, find insights and provide smart recommendations. Powered by voice and speech recognition, AI assistants make it much easier to perform many daily tasks such as adding events to your calendar, setting a reminder, or tracking monthly expenses. According to Statista, there will be over 8 billion digital voice assistants in use worldwide by 2024, roughly equal to the world’s population.

The key benefits of building virtual assistants for business include the following:

  • Improved customer support while cutting down on the number of calls and service requests to human agents. With AI assistants you can automate the business flow of interacting with customers. This will allow your employees to focus on more complex tasks and not waste time on requests that can be processed in an automated way.
  • The ease of key data collection. Customer experience data collected by traditional support calls or chats requires analysts to scrub through countless hours of phone calls and information collected and recorded by a live customer support agent. With IVAs, a customer’s queries and the associated metadata can be instantly filed away and categorized for analysis without the need for a customer support agent to take perfect notes.
  • Personalized user experience. AI assistants adapt to the needs of each user, providing the client with a high level of personalization. For example, IPAs can remember not only the user’s name but also their preferences. This helps to increase user engagement, as well as improve customer satisfaction and loyalty.

The ability for companies to piece together customer support and complex parts of their corporate toolchain like Lego bricks is one of the biggest appeals of intelligent virtual assistants. With some modification, a virtual assistant can plug into any database, or any resource to provide critical information and optimize workflow at every level.

Types of AI Virtual Assistants

There are several different types of AI virtual assistants: сhatbots, voice assistants, AI avatars, and domain-specific virtual assistants.

  • Chatbots have been a mainstay of the E-commerce sector since their inception, but modern implementations of chatbots are powered by artificial intelligence, which gives them the ability to think through customer queries rather than push the customer through a chain of static events.
  • Voice assistants use automatic speech recognition and natural language processing to give vocal responses to queries, such as the well-known Siri and Google Assistant products.
  • AI avatars are 3D models designed to look like humans, used for entertainment applications, or to give a human touch to virtual customer support interactions. Cutting-edge technology from companies like Nvidia is capable of producing nearly true-to-life human avatars in real-time.
  • Domain-specific virtual assistants are highly specialized implementations of AI virtual assistants designed for very specific industries, optimized for high performance in travel, finance, engineering, cybersecurity, and other demanding sectors.

Also, we can find virtual assistant technologies created for specific tasks. For example, “Avatar to Person” (ATP) technology based on artificial intelligence and 3D modeling technology allows people with disabilities to perform tasks such as “virtual face reconstruction” and “voice generation simulation” to communicate online freely.

The Technology Behind AI Assistants

Let’s say you want to create your own virtual assistant like Siri. How would you go about making it? Your first and possibly least difficult option would be to integrate Siri into your application directly. Siri, Cortana, and Google Assistant are three well-known examples of AI assistants that many developers integrate into their applications. In 2016, Apple Inc. announced SiriSDK, a development kit that allowed programmers to integrate functions of their own apps as “Tasks” that Siri could perform. SiriSDK uses “Intents” as labels for user intentions and associates Intents with custom classes and properties.

If your company doesn’t want to rely on existing AI assistant options, you’d need an expert team of AI engineers to build your own solution. Let’s dive into the key AI technologies behind intelligent virtual assistants.


If we’re talking about intelligent virtual assistants, they at the very least require Speech-to-text (STT) and Text-to-speech (TTS) capabilities.

Speech-to-text allows apps to convert human speech into digital signals. This is how it works. When you speak, you create a series of vibrations. Using an analog-to-digital converter (ACD) the software converts them into digital signals and extracts sounds, then segments them and matches them to existing phonemes. Phonemes are the smallest unit of a language capable of distinguishing the sound shells of different words. Based on complex mathematical models, the system compares these phonemes with individual words and phrases and creates a text version of what you said.

Text-to-speech does the opposite. This technology translates text into voice output. TTS is a computer simulation of human speech from text using machine learning. The system must go through three steps to convert text to voice. First, the system needs to convert text to words, then perform phonetic transcription and then convert transcription to speech.

Speech-to-text (STT) and Text-to-speech (TTS) are used in virtual assistant technology to ensure smooth and efficient communication between users and applications. To turn a basic voice assistant with static commands into a proper AI assistant, you also need to give the program the ability to interpret user requests with intelligent tagging and heuristics.


Computer vision is an AI technology that extracts meaningful information from visual inputs like digital images or videos. CV is an integral part of creating visual virtual assistants. These assistants can respond with creator-generated videos, not just sounds, which greatly enhances the user experience.

Computer vision allows the system to recognize body language which is a significant part of communication. Visual virtual assistants powered by this technology use a camera that stores data and utilizes real-time face detection to catch when someone is looking at the screen, this sends a signal to the rest of the system, which converts the user’s speech into text.

CV can also greatly increase the accuracy of speech recognition by comparing what the user has said verbally to the movement of the user’s face and mouth.


Noise control is another critical feature for voice assistant accuracy. While many smartphones include software-based noise control and suppression features, you can’t count on this being the case for all of your customers. To compensate for a lack of onboard noise suppression software, top-shelf Bluetooth headsets also include hardware noise suppression, but once again there are no guarantees that your AI assistant is going to be able to detect what your customers are saying in a busy train car. By integrating in-house noise control packages, you minimize the risk of misunderstanding voice queries.


Your AI assistant will also need to at least temporarily store voice information for processing unless you’re going to fill up the customer’s hard drive locally with voice data. Speech compression is critical, but developers toe a fine line with compression. It’s possible to compress an audio file so much that substantial amounts of fidelity are lost, making it difficult or impossible to recover what was said during the processing. Compression technology is rapidly improving, but when developing your voice assistant, audio codecs and compression solutions merit a thorough investigation.


Once you have the voice data, the AI assistant needs to process and interpret the data with Natural Language Processing (NLP) and then execute the requested command. NLP simplifies the speech recognition process. While many AI kits are pre-trained on countless hours of voice samples, you’d still need enough data from customers to adjust for precision for your use cases. If your AI assistant is going to respond verbally, you’ll need speech synthesis such as Google Cloud’s top-of-the-line solution, which produces realistic and clear voices.

However, speech processing is not enough to derive a person’s actual intent and maintain a normal conversation. The request still needs to be interpreted right, and that’s when Natural Language Understanding comes into play.


Natural Language Understanding (NLU) is a different approach to Natural Language Processing and is considered by most computer and data scientists to be a subtopic of NLP. While NLP methods parse, tokenize, and standardize natural language into a standardized structure for command processing, NLU interprets the natural language without standardizing it and derives meaning from queries by identifying the context. In short terms, NLP processes grammar, structure, and compensates for the user’s spelling errors while NLU examines the actual intent behind the query.


Natural language generation produces natural language output. Thanks to this technology, users receive a human-like response from virtual assistants and chatbots. Models and techniques used for NLG can be different and depend on the goals of the project and development approaches. One of the simplest approaches is a template system that can be used for texts that have a predefined structure and require only a small amount of data to be filled in. This approach allows such gaps to be automatically filled in with data retrieved from a row in a spreadsheet, a record in a database table, and so on.

Another approach is dynamic NLG which does not require the developer to write code for each edge case and enables the system to react on its own. This is a more advanced type of natural language generation that relies on machine learning algorithms.


Chatbots that utilize text-based responses only are substantially less complicated than voice assistants. Because you don’t have to then convert speech into text for interpretation, you remove a lot of tooling from the equation when constructing a chatbot. Next-gen text generation such as GPT-3 is capable of producing not only responses to basic queries, but entire news stories from a “seed”. Deep learningmakes it happen.

Virtual assistants and chatbots powered by deep learning algorithms learn from their data and human-to-human dialogue. Chatbots that utilize deep learning examine existing interactions between customers and support staff and create paired messages and responses and compensate for the user’s typos and grammatical errors.


Augmented reality allows you to overlay 3D objects in the real world for an immersive experience. AR-based mobile chatbots and AR avatars are great examples of using this technology. For example, Arcade created a mobile AR Avatar Chatbot called Miss Perkins for the Ragged School Museum of East London. This assistant serves as a guide for museum visitors and quizzes them ensuring an interactive user experience.

Another example of an intelligent AR chatbot was developed for the Vienna Museum of Technology. The creators also used mobile AR. The functionality of the chatbot includes conducting tours and answering user questions about specific display items in the text, images, videos, and audio formats.

The rise of the Metaverse and VR technology leads to the logical conclusion of virtual assistants: 3D AI avatars. Combined with artificial intelligence, AR virtual assistants become more functional, bypassing the limitations of existing AR tools. For example, deep learning allows IVAs to capture user behavior in real-time to drive neural networks that automatically train and improve virtual assistant performance.


Being algorithmic architectures that use neural networks, Generative Adversarial Networks create new instances of synthetic data. GANs consist of real image samples and generators fed into discriminators to generate a realistic 3D face for AI avatars and 3D assistants.

The technology has been utilized in many video games and other products to create true-to-life human figures. GANs can also be utilized to turn still images into full-depth 3D images. Perhaps the most advanced integration of AI avatars so far is Nvidia’s Omniverse Avatar Project Maxine, which creates a photorealistic real-time animation of a human face speaking a text-to-speech sample.


When it comes to AI avatars or 3D virtual assistants, it’s not so much the voice that matters, but the body language and human emotions. Emotional Intelligencepowered with AI helps IPAs track the user’s non-verbal behavior in real-time when communicating and react accordingly. This will make virtual assistants more responsive thanks to Emotion AI that monitors human emotions by tracking facial expressions, body language, or speech.

At the heart of Emotion AI are computer vision and machine learning algorithms. Facial recognition technology analyzes facial expressions using a standard webcam or smartphone camera. Computer vision algorithms identify the main points of a person’s face and track their movement to interpret emotions. Next, the system determines the person’s feelings based on a combination of facial expressions by comparing the collected data with a library of template images. Solutions such as Affectiva or Kairos can measure the following emotional metrics: joy, sadness, anger, contempt, disgust, fear, and surprise.

We should also mention recognizing emotion from speech. Such software analyzes not only what humans say, but also how it was said. To do this, the system extracts paralinguistic features that help to identify changes in tone, volume, tempo in order to interpret them as human emotions.

Challenges and the Future of Virtual AI Assistant Technology

We cannot get around the issue that the adoption of virtual assistant technology is associated with certain challenges. One major obstacle to the future of AI assistant technology is laws concerning data storage and usage. Unchecked use of customer data as training data for AI implementations could easily be challenged by changing data security laws in countries across the world. Controversial data handling policies by companies like Meta (Formerly Facebook) have stoked fears of corporate overreach and privacy concerns after the events of high-profile whistleblower scandals.

Therefore, when developing an AI assistant app, take into account the requirements of privacy and data protection, such as GDPR in the EU legislation. Make sure your app is fully compliant.

In parallel with the first challenge, there is a question of security and protection from security branches. Security mechanisms such as end-to-end encryption, two-factor authentication and biometrics are some of the best features to protect AI assistant apps. In addition, an experienced team of AI engineers will help you implement custom security systems powered by machine learning algorithms.

Despite all the challenges, the future of AI assistant technology looks bright. Advances in technology are also driving the development of smarter virtual assistants. As the NLP process continues to evolve, virtual assistants will be able to perform more complex tasks. In particular, IVAa will be able to make proactive suggestions based on self-learning algorithms and be even more helpful for users.

The development of the metaverses is also closely linked in AI with virtual assistants. Intelligent avatars are the best way to provide a user’s identity in a 3D universe. Artificial intelligence is what will allow us to achieve greater realism of avatars. Based on the study of physical movements, the model learns and can, for example, accurately predict the position of the shoulders and elbows depending on where your headset and controllers are.

Written by Evgeniy Krasnokutsky, AI/ML Team Leader at MobiDev.

The full article was originally published here and is based on MobiDev technology research.


. . . comments & more!
Hackernoon hq - po box 2206, edwards, colorado 81632, usa