LTS QA

The AI-Powered Virtual Assistant Market Size is estimated to be at $3.442 Billion in 2019, and this number is expected to surpass $45.1 Billion by 2027, raising by 37.7% (according to a study by CAGR). And this can all start from the simple voice annotation.

The possibility and utility of AI-Powered Virtual Assistants come from both technical and behavioral aspects. In correlation with the ever-growing demand for on-app assistance, we have the data inputs continuously poured into the AI system for data training. 

To put it another way, one of the most important features to make AI-powered virtual assistants possible is the data inputs, aka voice annotation.

 

The booming industry of AI and virtual assistant

For starters, an intelligent virtual assistant (IVA), or we can call it an AI-powered virtual assistant, is a software technology that is developed to provide responses similar to those of a human. 

With this assistant, we can ask questions, make arrangements or even demand actual human support.

 

Why are virtual assistants on the rise?

Intelligent virtual assistants are widely used, mostly for the reduced cost of customer handling. Also, with quick responses for live chat or any other form of customer engagement, IVA helps boost customer service satisfaction and save time.

Besides external performance as above, IVA also collects customer information and analyzes conversation & customer satisfaction survey responses; thereby, helping organizations improve the customer and company communication.

 

Intelligent virtual assistants can play as the avatars of the enterprises. They can dynamically read, understand and respond to queries from customers, and eventually reduce costs for manpower in different departments. 

We can see many of those IVAs in large enterprises as they can help eliminate the infrastructure setup cost. This is why the revenue for IVA are so high in recent years and perhaps in the years coming.

 

What can virtual assistants do?

The usability and adoption of AI-powered virtual assistance are everywhere. We can see it in our operating systems, mobile applications or even chatbots. With the deployment of machine learning, deep neural networks and other advancements in AI technology, the virtual assistant can easily perform some certain tasks.

 

Virtual assistants are very common in operating systems. These assistants help in setting calendar, making arrangements, setting alarms, asking questions or even writing texts. A multitasking assistant like this is on the large scale, and we might think that these applications are limited within  operating systems only.

However, with the soaring numbers of mobile users and mobile apps, many entrepreneurs and even start-ups are beginning to implement a virtual assistant just within their product apps. This leads to the rising demand for the data input required in different fields.

For example, a healthcare service app requires specific voice annotations regarding medical terms and other healthcare-related matters.

In the report of ResearchAndMarkets.com concerning Global Intelligent Virtual Assistant (IVA) Market 2019-2025: Industry Size, Share & Trends, it is indicated that:

• Smart speakers are developing with the fastest pace and emerging as the major domain for IVA
• Still, Text to speech is the largest segment in IVA. It is estimated to reach a revenue of over $15.37 Billion by 2025
• The country with the dominance in the market of IVA is North America with the main industry of healthcare.
• The key players are Apple Inc., Oracle Corporation, CSS Corporation, WellTok Inc., CodeBaby Corporation, eGain Corporation, MedRespond, Microsoft, Next IT Corporation, Nuance Communications, Inc., and True Image Interactive Inc.

Through the report, we can see that the potential to develop and grow the AI-powered virtual assistant market is on fast-paced growth. For every different domain, we have a different approach for the implementation of IVA.

For better service and business development, enterprises demand effective customer engagement, hence the growing number of virtual assistants to be implemented in different products.

Currently, the intelligent virtual assistant market is majorly driven by the BFSI industry vertical, owing to its higher adoption and increasing IT investment. However, automotive & healthcare are the most lucrative vertical segments and are likely to maintain this trend during the forecast period.

 

How can voice annotation help the IVA?

As Virtual Assistant appears in almost every aspect of life, including calling, shopping, music streaming, consulting, etc., the requirement for voice data processing continues to grow. Besides the speech to text and text to speech annotation, more advanced forms of part of speech tagging or phonetics annotation are also in high demand.

 

For a IVA system to operate properly, the developer has to consider different approaches of interaction methods, including:

• Text-to-text: Text-to-text annotation is not necessarily directly related to the operation of IVA. Nevertheless, labeled texts help the machine understand the natural language of humans. If not done properly, the annotated texts can lead a machine to exhibit grammatical errors or wrongly understand the queries from customers. 
• Speech-to-text: Speech-to-text annotation transcribes audio files into text, usually in a word processor to enable editing and search. Voice-enabled assistants like Siri, Alexa, or Google Assistant are fine examples for this.
• Text-to-speech: Text-to-speech annotation enables the machine to synthesize natural-sounding speech with a wide range of voice (male, female) and accents (Northern, Middle and Southern accent). 
• Speech-to-speech: Speech-to-speech is the most advanced and complicated form of annotation. With the data input of this, the AI can understand the speech of users, and then answer/perform accordingly.

Whichever of the above, we still have to collect data, voices, speeches, conversations, and then annotate them so that machine learning algorithms can understand the input from users.

Voice annotation service requires much effort to deliver understandable and useful datasets. It also takes much time to even recruit and train the annotators, not to mention the on-job time.

If you want to outsource voice annotation, contact LQA now for instant support.

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance

 

One of the most popular use cases of AI and Data Annotation is Autonomous Car. The idea of Autonomous Cars (or Self-Driving Cars) has always been a fascinating field for exploitation, even in entertainment or actual transportation. 

This was once just a fictional outlook, but with the evolution of information technology and the technical knowledge obtained over the years, autonomous cars are now possible.

 

Perhaps the most famous implementation of AI and Data Annotation in Autonomous Cars is Tesla Autopilot, which enables your car to steer, accelerate and brake automatically within its lane under your active supervision, assisting with the most burdensome parts of driving. 

However, Tesla Autopilot has only been confirmed of success in several Western countries. The real question here is that: “Can Tesla Autopilot be used in highly congested roads of South-East Asia countries?”

 

The role of Data Annotation in AI-Powered Autonomous Cars

Artificial Intelligence (AI) is the leading trend of Industry 4.0, there’s no denying that. Big words and the “visionary” outlook of AI in everyday life are really fascinating, but the actual implementation of this is often overlooked. 

In fact, the beginning of AI implementation started off years ago with the foundation of a virtual assistant, which we often see in fictional blockbuster movies. In these movies, the world is dominated by machines and automation. Especially, vehicles such as cars, ships and planes are well taken care of thanks to an AI-Powered Controlling System.

With the innovation of multiple aspects of AI Development, many of the above have become true, including the success in Autonomous/Self-Driving Cars.

 

Training data with high accuracy

The two important features of a self-driving car are hardware and software. For an autonomous car to function properly, it is required to sense the surrounding environment and navigate objects without human intervention.

The hardware keeps the car running on the roads. Besides, the hardware of an autonomous car also contains cameras, heat sensors or anything else that could detect the presence of objects/humans.

The software is perhaps the standing point of this, in which it has machine learning algorithms that have been trained. 

 

Labeled datasets play an important role as the data input for the aforementioned learning algorithms. Once annotated, these datasets will enrich the “learning ability” of AI software, hence improving the adaptability of the vehicles.

 

With high accuracy of the labeled datasets, the algorithm’s performance will be better. The poor-performing data annotation can lead to possible errors during a driving experience, which can be really dangerous.

 

Enhanced Experience for End-users

Who wouldn’t pay for the top-notch experience? Take Tesla as your example. Tesla models are the standard, the benchmark that people unconsciously set for other autonomous vehicle brands. From their designs to how the Autopilot handles self-driving experience, they are combined to create a sense of not only class but also safety.

How Tesla designs their cars is a different story. What really matters for the sake of their customers is safety.

Leaving everything for “the machine” might be frightening at first, but Tesla also guarantees that through many of the experiments and versions of the AI software. In fact, it was proven that Tesla Autopilot can easily run on highway roads of multiple Western countries.

 

We might have seen the footage of how Tesla Autopilot Model X was defeated on the highly congested roads of Vietnam. However, we have to take a look back at the scenario in which we need an autonomous car the most. 

The answer here is the freeway and highway. And Tesla can do very well on these roads.

The role of data annotation in this case is that through the high-quality annotated datasets, the machine is trained with high frequency, therefore securing safety for passengers.

 

The future of autonomous vehicles

We don’t simply jump from No Driving Automation to Full Driving Automation. In fact, we are barely at Level 3, which is Conditional Driving Automation.

• Level 0 (No Driving Automation): The vehicles are manually controlled. Some features are designed to “pop up” automatically whenever problems occur.
• Level 1 (Driver Assistance): The vehicles feature single automated systems for driver assistance, such as steering or accelerating (cruise control). 
• Level 2: (Partial Driving Automation): The vehicles support ADAS (steering and accelerating). Here the automation falls short of self-driving because a human sits in the driver’s seat and can take control of the car at any time. 
• Level 3 (Conditional Driving Automation): The vehicles have “environmental detection” capabilities and can make informed decisions for themselves, such as accelerating past a slow-moving vehicle. But they still require human override. The driver must remain alert and ready to take control if the system is unable to execute the task. Tesla Autopilot is qualified as Level 3.
• Level 4 (High Driving Automation): The vehicles can operate in self-driving mode within a limited area.
• Level 5 (Full Driving Automation): The vehicles do not require human attention. There’s no steering wheel or acceleration/braking pedal. We are far from Level 5.

With Tesla Autopilot qualified as Level 3, we are only halfway through the journey to the full driving automation.

However, we personally think that the matter of these Level 3 vehicles is the training data for the AI system. The datasets that have been poured into this are very limited, possibly can be compared to just a drop in the ocean.

 

To train the AI system is no easy task, as the datasets require not only accuracy but also high quality, not to mention the enormous amount of them.

The speed in which Tesla or any other autonomous vehicle company is going for is quite high in order to be ahead of the competition. Instead of doing it themselves, these companies often seek help at some outsourcing vendor for better management and execution of data processing. These vendors can help with both data collecting and data annotating.

Want to join the autonomous market without worrying about data annotation? Get consults from LQA to come up with the best-fitted data annotation tool for your business. Contact us now for full support from experts.

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance