The essay is going to focus on the application of machine learning (ML) in virtual assistant services. It has been observed that AI-based virtual assistants and chatbot technology are widely used in customer support, HR processes, IoT devices, and computer software. Alexa, SIRI, and Google Assistant are some examples of virtual assistants commonly used by people where different AI (“Artificial intelligence”) technologies are used including machine learning, reinforcement learning, and NLP (“Natural language processing”).

 

Background of Machine Learning Application

Conversational AI technology has been widely used for voice recognition and automated system-generated responses. Chatbot or FAQ bot is argued in terms of having “conversational AI” because it does not include NLP and ML to build knowledge over time. On the other hand, virtual assistant services understand the dialogue due to having NLP, a subset of machine learning. The principle algorithm of NLP allows a virtual assistant to process, interpret, and generate relevant responses in a natural manner (Lalitha et al. 2019). Along with this, different subsets of ML are used in “virtual assistants” such as ASR (“Automatic Speech Recognition”), ADM (“Advanced Dialog Management”), and NLU (“Natural Language Understanding”). The NLP process works along with ML in a continuous feedback loop to sharpen the performance of the AI algorithm.

“Machine learning” technology is made of three basic algorithms including a clustering algorithm, classification algorithm and regression algorithm. This set of algorithms along with AI features and large data sets constantly improves the technology along with experience. The ML gets better at processing and recognizing voice patterns as inputs grow over time to make apposite predictions and decisions based on the situation (Lalitha et al. 2019). NLP (“Natural language processing”) is one of the subsets of “machine learning” which is currently applied for analysing language in the “conversational AI” system. However, the process of utilizing “Deep reinforcement learning” (DRL) in the “virtual assistant” along with NLP and ML has already been initiated on the experimental stage to improve the automation in voice recognition, processing, prediction, and decision-making capacity.    

Application of reinforcement learning

“Reinforcement learning” (RL) is the untrained area of “machine learning” that helps in taking preferable action based on a particular situation  The algorithm of RL is completely different from “supervised machine learning” as it involves a trained model along with specific answer key based on the formulated dataset (Nithuna and Laseena, 2020). Rather in the RL process, the reinforcement agent decides the probable output based on the given task and concerning the environment. “Reinforcement learning” is bound to learn and make decisions from past experience as it does not have any trained dataset. Conversational AI-based virtual assistants go through specific consecutive stages that involve input generation through voice or text. For the analysis of text input NLU (“Natural language Understanding”) processes the input data, while ASR works under NLP for voice recognition, processing and analysis. As opined by Ali and Amin (2019), ML algorithm uses for dialogue management that allows the NLP to respond to users’ queries. In this stage, RL is utilised here to improve the performance of a virtual assistant over time. This function helps in analysing inputs based on the environment and experience to reward the output and make it more accurate and sharpen in response to users’ interaction. This “reinforcement learning” along with NLP and ML completely automates the “virtual assistant” based on the AI algorithm, trained dataset, and past experience.          

 

Current challenges or ethical issues

There are certain challenges and ethical issues related to ML and RL base automated virtual assistants along with technological advances. It has been observed that accents, language mechanics, dialects and background noise create obstacles in understanding and processing raw input. Accordingly, using unscripted language, sabotage, vernacular terms and slang can develop problems in the processing of input (Maria, Drigas, and Skianis, 2022). The “machine learning” and “reinforcement learning” language does not have the ability to interpret the tonality and emotion of the users and respond accordingly.

In addition, data privacy and data security are the biggest concerns in terms of conversational AI-based “virtual assistance”.  “Virtual assistants” needs to aggregate users’ data to process and give answers to queries. As a result, sharing confidential information can be vulnerable to threats and risks due to the possibility of data breaches (Maria, Drigas, and Skianis, 2022). However, software with a “virtual assistant” facility generally uses end-to-end encryptions to protect and secure data from trojans and hacking activities. Still, the standard of documentation safety, reinforcing commitment, and regulatory compliance are the biggest limitations regarding ML-based automated voice assistant services.

 

Opinion on further improvement of the application

There are specific “areas of improvement” that needs to be followed to ensure further improvement in ML-based “virtual assistant service”. The areas of technical improvement that can be improvised in the future are illustrated in the below section:

Application of STT and TTS format: “Speech-to-text” (STT) technology can be improved more to enhance the quality of voice typing for the convenience of the users. The STT technology compromises an “Analog-to-digital” (ACD) converter that matches the “series of vibration” due to the users’ speech with existing phonemes (Jones, 2018). A complex arithmetic algorithm is used to compare the matched phonemes with individual phrases and words to convert the speech into a text version; whereas, the “text-to-speech” (TST) process works completely opposite. This particular section needs further improvement with an advanced ACD converter and mathematical algorithm.

Noise control: Deep learning can be used to control noise and filter the voice of the users to have effective processing with the help of NLP.

“Natural language generation” (NLG): NLG can be applied to improve the quality of output of the “virtual assistants”.

Augmented reality (AR): AR can be used with its 3D feature to ensure users have an immersive experience in the real world.

Emotional intelligence (EI): It is important to utilise EI technology to interpret users’ emotions and tones to respond accurately (Nithuna and Laseena, 2020).

 

Conclusion

The essay has focused on the concept of “machine learning” (ML) and its application in “virtual assistant services”. Besides this, the process of applying “reinforcement learning” (RL) in the voice assistant tools has also been analysed here. The challenges and limitations of these technologies have been evaluated along with plausible scopes of improving the quality of “virtual assistants”.  

 

References

Ali, A. and Amin, M.Z., 2019. Conversational AI Chatbot Based on Encoder-Decoder Architectures with Attention Mechanism. Artificial Intelligence Festival 2.0, pp.7-12.

Jones, V.K., 2018. Voice-activated change: marketing in the age of artificial intelligence and virtual assistants. Journal of Brand Strategy, 7(3), pp.233-245.

Lalitha, V., Dinesh, A., Parameswaran, L. and Kumar, S.D., 2019. ML Based Virtual Personal Assistant. International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) Vol, 6, pp.158-161.

Maria, K., Drigas, A. and Skianis, C., 2022. Chatbots as Cognitive, Educational, Advisory & Coaching Systems. Technium Soc. Sci. J., 30, p.109.

Nithuna, S. and Laseena, C.A., 2020, July. Review on implementation techniques of chatbot. In 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 0157-0161.