Honestly, finding an ideal personal virtual assistant is like finding the right husband. We all want it to be customized for our needs. No pun intended.
There’s a reason why every tech company in the world is heavily interested in artificial intelligence. They are all trying to leverage the benefits of AI-powered technologies. Studies have suggested that artificial intelligence based technologies like virtual assistants and augmented reality will drastically increase productivity and economic growth. It could be true as anything that can work effortlessly without human involvement has no scope of error and confirms reliability, security, and speed.
Software that performs all the user based tasks on written or verbal commands. Obviously, this software is highly trained to recognize commands like that and are tested for years before they are brought into action. But we all know how the Siris and Alexas of the world are taking control.
It was back in 2011 when Apple welcomed us to the world of Siri. And now, nine years later, this report by eMarketer can anticipate over 100 million smartphone users to be using voice assistants by the end of 2020. It is truly a revolution. Or should we say a robotic revolution? On this day, there is no industry left that is not making use of AI assistants, from banking to healthcare, they are all working to create their own tech-integrated voice assistants for better customer service.
In fact, in the history of technological advancements, AI is one of the top applications to have undergone mass adoption. Everyone wants a taste. From phones to speakers, everything needs to be smart these days.
As per Forbes, in the next five years, almost every application will integrate voice technology in some way or another. Consumers highly rely on AI personal assistants for multitasking. From texting, calling, to even sharing locations, and making notes. But this can go beyond just taking notes through conversational interactions. These interactions are driven by words in full sentences. It is almost like you are talking to a person.
Software engineers are working round the clock to ensure sophistication of these digital voice assistants for everyday use.
Conversational AI will change the face of interaction between the users and AI personal assistants. Users will be able to hold synchronous conversations with their AI personal assistants. This is honestly a genius way to facilitate customer engagement. Because let’s face it, the days of chatbots are kind of fading away.
In one of the reports from Morgan Stanley, Amazon Echo is the most sold personal assistant to date. About 11 million units since 2017. In fact, about 32% of the purchase orders placed on Amazon Prime are through voice assist.
Therefore, companies like Google, Amazon, Apple, and even Microsoft are working hard towards the advancement of AI. Once your bot is sophisticated enough to solve queries on its own for you, it will be a dream come true for teams that manage customer services and audience engagement.
This is why there is a lot of time and effort invested in AI-powered personal assistants so they can understand elaborate human speeches.
To someone who is not tech-savvy, this could seem a little heavy. But don’t worry, it is pretty straightforward. The use of natural language processing, which is based on machine learning, makes it all possible. NLP is used to program a computer to process and analyze human language input. Just like the networks use JAVA scripts to read the commands, the NLP was formulated to generate and comprehend natural languages. This facilitates users to have regular conversations with a machine. No, this is not a sci-fi movie. We are all living this dream.
The formulation of NLP took place to simplify the human-machine relationship. As a result, the computers can now understand and act on text or vocal commands, translate text, and even perform sentiment analysis. Yes, your Siri will know your mood swings better than your boyfriend.
Just like stages in a conversation, NLP also works in three steps: first comes the speech-to-text process. Then comes part-of-speech tagging or word-category disambiguation. This sounds confusing, but it basically is identifying words in their grammatical forms. The third and final step is the text-to-speech conversion. This is when the computer programming language is converted into an audible or textual format for the user.
While the world is revolving around new sets of technological advancements every day, it is essential to note that a high amount of user data is at risk. The voice recognition systems are receiving inputs all the time. How do these tech companies ensure public safety? This is where the role of Machine Learning and Deep Learning comes in the picture.
A subset of AI that uses algorithms to analyze data, learn from it, and then make informed decisions based on the data received.
A subfield or a subset of machine learning that puts algorithms in layers to create an “artificial neural network” that can learn from the data received and then make informed decisions. This then is subdivided into a broader family, which includes DNN (deep neural networks), DBN (deep belief networks), and RNN (recurrent neural networks).
The point of maintaining all these layers between the user’s command and the computer’s delivery is to ensure relevance and safety. With the growing demand for human-like-machines, industry leaders like Google, Amazon, and Apple are working continuously to aggregate that context into AI applications. The goal here is to generate quicker and more accurate results.
Personal virtual assistants are the best form of communication channels in today’s world: quicker, calmer, and less intrusive than friends and family. Again, no pun intended. It is an exciting time to be in technology, we are literally creating history!
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