February 11, 2021

Conversational AI Examples: How Siri, Alexa & Google Assistant have Human-like Conversations

In today’s day and age, it’s next to impossible to have not completed even a single task without the help of a virtual assistant who adheres to your ‘voice command’. A growing preference for voice interaction with virtual assistants and bots is booming, and it could be because ‘voice’ has always been the most natural way to engage with people and customers too – as the tech giant, Amazon, claims. A voice interface also provides convenience to millions as they save on time and the cognitive burden that goes into typing a query they want answered or a task they want done.

This raises an important question today – what makes this convenience laden technology a reality? 

And is it this the same technology that gave birth to Siri, Alexa and Google Assistant?

Like most revolutionary technologies, this too is driven by AI technology – particularly conversational artificial intelligence. It would be enriching to understand what this technology is and how it has empowered products like Siri, Alexa and Google Assistant to have more human-like capabilities.

What is Conversational Artificial Intelligence?

Conversational artificial intelligence can be defined as an intelligent mechanism of imitating real-life human conversations. This game-changing technology is made possible as it is built on the sturdy foundation of machine learning (ML), and natural language processing (NLP). By feeding in extensive volumes of data, machines can be wired to learn the essence of human interactions, giving them the power to recognise speech and text inputs while also translating its meaning into an array of desired languages.

Are Siri, Alexa and Google Assistant examples of conversational AI?

Yes! Technologies like Siri, Alexa and Google Assistant that are ubiquitous in every household today are excellent examples of conversational AI.  These conversational AI bots are more advanced than regular chatbots that are programmed with answers to certain questions. These virtual assistants are configured to be more human-like, generating responses that are more natural and aligned to real human conversations as compared to chatbot conversations. 

Talking to a virtual assistant is possible through conversational AI

If you have ever asked your virtual assistant, “What’s the weather like today?” Or asked it to, “Play my saturday night music playlist!”, this intelligent machine is able to give you the right answer in a matter of seconds and can perform the task quicker than if you had to lift a finger. All this is made possible with conversational software created with artificial intelligence, which has led to an explosive growth of a number of voice assistants across the world. 

But how are these advanced virtual assistants able to understand human commands and perform a variety of tasks?

Let’s investigate!

How does Siri, Alexa and Google Assistant’s Conversational AI Technology Work?

Machine learning is an integral part of giving Siri, Alexa and Google Assistant the human superpowers they possess today. Machine learning  is an application of Artificial Intelligence that focuses on training systems to improve their ability to learn so they can better perform tasks – in this case interact better with humans. This is done by feeding data to computer systems so that they can automatically analyse patterns to guide decision making in the future. What’s exciting is that machines learn on their own without the need of having to be programmed by a human, allowing them to develop greater capabilities. Other fields of machine learning like natural language processing and deep learning are also integral in helping conversational AI become a reality.

So each time Alexa, Siri or Google assistant make an error while responding to your query, it uses this information to learn and rectify its mistake in the future. Thus by taking in data of when it makes a mistake or when its response was accurate it uses this as a rulebook to guide its behaviour in the future.  

Though this process could be deceptively simple from the outside, it involves the synthesises of a number of complex processes behind the scenes to get the desired result. To help break down these processes into understandable bites of information, we compiled the following steps to explain how conversational AI works:

Step 1 : Automatic Speech Recognition (ASR)

Once a query is shot out, the voice assistant’s artificial intelligence must understand what has been asked through automatic speech recognition (ASR), that converts speech to text. 

Step 2  : Natural Language Understanding (NLU)

Once this is done, natural language processing through natural language understanding (NLU), helps the system interpret the input to understand the sentiment and intent behind the query. It decipers this despite the grammatical errors or shortcuts. 

Step 3 : Machine Learning to Configure a Response

Next, to fabricate an appropriate response, the machine determines the correct answer based on the context of the user’s intent, with the help of machine learning. Over time, by learning different variations of a query guided by the same intent, the machine learns the most appropriate response.

Step 4 : Natural Language Generation (NLG)

After having synthesized all this information, the machine now works to produce a comprehendible response with the help of natural language processing through natural language generation (NLG). Whether it may be completing a task or answering a question, it completes the process in a way that is more akin to human interaction.

Step 5 : Text-to-speech Software

With a response in place, the virtual assistant now translates the text into a voice response with text-to-speech software.

To summarize how conversational AI works:

  1. Automatic speech recognition (ASR) converts speech to text.
  2. Natural language understanding (NLU) interprets the query intent.
  3. Machine learning helps configure a response.
  4. Natural language generation (NLG) helps generate a human-like response.
  5. Text-to-speech software converts the generated response into speech.
How Conversational AI works - A  Diagram with Steps - The case of Siri, Alexa and Google Assistant

Share our infographic to help your network understand how your virtual assistant’s conversational AI works!

Now that we understand the nuts and bolts of what facilitates a conversation with a robot, it would also be worth considering why this technology is useful at all! 

Consumers are always on the hunt for the next best experience or product to make their life easier and this radically changes their behaviour overtime as they adapt to new technologies .

Businesses today are designed with customers taking center stage at every step – from conception to execution. With this as a guiding principle for businesses, when any revolutionary technology props up in the market to bolster that mission, it is impossible to go forward without paying heed to those technological advancements. To paint a clear picture of why conversational AI can help business owners take their offerings up a notch, let’s explore the benefits of conversational AI for businesses.

Why is Conversation AI Important for your Business?

1. Enhancing Customer Experience

With the evolution of myriad businesses missioned to simplify our lives with tailored apps on smart devices, one can often have a disjointed experience toggling from one app to another. This predicament has led to the rise of the voice assistant as a single interface for interaction. Though popularly used as virtual assistants at home to dim the lights or play some music, the possible applications of voice assistants can go well beyond the regular uses we see.

For instance, we now have conversational AI for banking whereby customers can bid adieu to complex navigation menus and could check their account balances by simply asking their smart device. 

2. Optimising Repetitive Tasks

From e-commerce sites to call centres, business employees are often burdened with a continuous flow of repetitive tasks. Though robotic process automation (RPA) is designed to perform recurring tasks, they aren’t always performed with a customer-centric approach in mind. In these cases using conversational AI to generate more intuitive and real human interactions can help fill in the widened chasm today. This will also help automating a high-volume of tasks and interactions in avenue of customer servicing, while giving support staff the bandwidth to deal with more pressing issues.

3. Gaining Insights from a Wealth of Data

Conversational AI, being an unstructured mechanism of communication gathers information during every interaction for algorithmic improvement. This generates rich data that can be utilised to create better customer insights and drive the business forward by closely aligning to user objectives. 

Conclusion

Though today, businesses have not successfully been able to navigate the range of possibilities created by conversational AI, this sector is on track to radically change the way consumers interact with businesses. The conversational AI market size is set to grow from $4.8 Billion in 2020 to a staggering $13.9 Billion by 2025, at a Compounded Annual Growth Rate of 21.9%. The main drivers of this unprecedented growth are increasing demand for conversational AI for customer service, the possibility of omnichannel deployment and a reduction in development costs of these advanced technologies. As organisations realise the true value of conversational AI and grapple for successful execution, they will need to combat the lack of accuracy seen in this technology today and innovate to produce better solutions. As innovation advances, it’s safe to say that the applicability of conversational AI could be endless.


Interested in Learning More about Conversational AI?

Explore CFTE’s short course on Conversational AI in Banking designed with industry experts and stay ahead of the curve with the latest knowledge in digital finance.


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