When Amazon first came out with a smart recommendation algorithm for customers, millions of its customers received their first personalized shopping experience tailored to meet their own interests. This not only introduced us to a whole new era of shopping but was the beginning of using machine learning in a very practical manner. The approach was next adapted by OTT platforms.

The evolution of conversational bots

While chatbots are still evolving to understand different languages, accents and dialects, they have been trusted enough to be put to practice even in sensitive areas like healthcare. 

Conversational AI apps can now tap into medical databases and start patients on diagnosing symptoms based on personal medical history and common medical knowledge or even initiate/book a consultation with a doctor when a patient needs it.

AI as an integral part of our everyday life

Alexa and Siri are very real applications of artificial intelligence that are increasingly integral to our daily life. They can not only remember conversation context, past dialogues and user preferences, but also understand sentiment and mood and respond accordingly.

Gmail is another great example of how AI has improved the everyday productivity of users. It suggests helpful phrases to speed up the email. The more we use, the better it becomes.

How does it achieve advanced natural language processing (NLP)? 

NLP allows the computer algorithm to understand and interpret the user’s request. Enterprise Bot uses a variant of ‘Bidirectional Encoder Representations’ which allows us to truly understand the context of different words. Let’s look at these two simple phrases “Book me a ship” and “Ship me a book”. If we utilize a keyword approach and do not keep the context of how words co-relate we may not be able to do what the user really wants.  

How does the product achieve sentiment analysis?   

To achieve ‘sentiment analysis’ a team of scientists revealed a new algorithm that will teach robots ‘how to behave appropriately in social situations’. The key to this is by allowing them to read and understand ‘children’s stories’ and fiction from ‘Toronto-based Wattpad’ to understand the world.

It also learns from other signals such as ‘capitalization’ or ‘exclamation marks’. The beauty of AI based on ‘sentence structure’ is that it can understand the difference between what a teen-ager would mean by using the word sick, and may not assume directly that it is related to a negative emotion.

Augmented reality and Auto-mobiles headed to AI

Augmented reality is making its way into chat-bots, in instances like to show how a coffee table might look in your living room, or how some new clothes would fit you. Organizations like IDEA, Zara, Loreal, and Amazon are validating its potential. 

Tesla plans machine learning and AI in its driving system and over-the-air update capability such that when one Tesla car learns of a road condition, all Tesla cars learn of that condition simultaneously(co-learning) and adjust their guidance systems as weather, driving, or road conditions change.

Human adaptability

AI is changing every day headed from supervised to unsupervised models. There is a great deal of discussion about the uncertain future that AI can shape. 

The greatest strength of the human mind is its adaptability, just as we adapted to the changes brought by telecommunication, air travel, mobile communication, and smartphones, we have been adapting to AI and will adapt to its future too.