“I see a natural progression from knobs and dials to clicks and taps, to swipes and gestures, to voice and emotion”, says Imran Chaudhri, the iPhone’s original UI designer.
The trend charts are now in the favor of Conversational AI and so we know that it’s time to give voice to our chats. We mean like it’s 2021 and still waiting for agents to answer your queries is quite old school. There’s no problem in staying old school, but these advancements in Conversational AI are, in the true sense, irresistible. But we are very curious to know the reason behind this hype and we are sure you’re too. So, let’s read along and get our answers.
Just like chatbots and Intelligent Virtual Assistances (IVAs), Conversational AI is a set of technologies that enables machines and chatbots to understand, process, and answer queries or chat in a super natural, humanized form. At the moment, Conversational AI has proved to be the best propeller across the spectrum of AI in marketing. More and more companies have now targeted these automated buddies and we can already call this the start of a revolution.
COMPONENTS OF CONVERSATIONAL AI
- Machine Learning
- Natural Language Processing
- Deep Learning
Let’s see how these components contribute to Conversational AI and maybe we mind the formula to this automation wonder.
Consisting of data entries, datasets, algorithms and varying features with continuous improvements and updates in them, Machine Learning (ML) can be defined as a sub field of Automation or Artificial Intelligence (AI). With user inputs and experience, these mentioned algorithms and features gets better and better and there is no going back. Betterment in Predictions and customer support is what is targeted and eventually can achieved over the years.
NATURAL LANGUAGE PROCESSING
The current method to analyze language along with machine learning is Natural language processing (NPL). The progress of Machine Learning can be traced back from linguistic processing to computational processing to statistical natural language processing. But the future holds space for deep learning and how it will contribute in the advancement of natural language processing capabilities of Conversational AI
NLP includes 4 steps:
- Input Generation
- Input Analysis
- Output Generation
- Reinforcement Learning
Input generation: Any input from the users like their personal information on websites or for creating an account on any platform is considered under Input Generation. Input can either be voice or text.
Input analysis: All the data gathered from input generation is processed and analyzed, mainly to understand each user and to able to answer their needs. Text-based input will be evaluated using the natural language understanding (NLU) to decode the meaning of the input to our AI buddy and to get what its intention is. However, on the flip side, speech-based input will need a healthy mix of automatic speech recognition (ASR) and NLU to analyze the data.
Dialogue management: Here we discuss the automatic conversation that the chatbots will have with the users. Natural Language Generation (NLG), a component of NLP, helps formulate a response for user’s query, after the analyzing step is done.
Reinforcement learning: Machine learning algorithms refine responses over time to ensure accuracy and clarity.
Deep Learning again targets learning from experience. Every new user is responsible for bettering the bot and ensuring more efficiency. At the time it’s also on AI Experts to keep chatbots and voice bots updated for finer performance.
This all sums up to:
CONVERSATIONAL AI = MACHINE LEARNING + NATURAL LANGUAGE PROCESSING + DEEP LEARNING
We have now shared this magical formula with you. But we don’t stop at this. Implementing this along with our expertise is what we are ready to share with you for automation updates for your company and businesses. Have a look at our services at www.kevit.io or contact us at firstname.lastname@example.org