As Satya Nadella mentioned in his keynote at Microsoft Inspire 2019 that AI and Machine learning is being infused into every experience. Chatbots are among the most visible applications of AI technology. As I got an opportunity to work on developing a chatbot in Teams, I realized the importance of Artificial Intelligence in today’s business world. Chatbot evolved in 2018 and are more intelligent as well as human than ever. There is no denying to this fact. The successful adoption of chatbots by end users has led to the use of more and more bots in advanced artificial intelligence technologies and their usage by a custom software development company. Even there are reports that 80–85% of businesses will be deploying advanced chatbots by 2020.
In order to understand the role of Artificial Intelligence in Chatbot development let us understand what a chatbot is, how it works and what it has in store for the future.
What is a Chatbot?
A Chatbot is a computer program or an Artificial Intelligence software that can simulate a real human conversation with real-time responses to users based on reinforced learning. AI Chatbots either use text messages, voice commands, or both. AI robots use a natural language to communicate with Artificial Intelligence features embedded in them.
Most of the chatbots are a kind of messaging interface where instead of humans answering to your messages bots are responding. The conversation humans have with bots is powered by ML algorithms which breaks down your messages into human understandable natural languages using NLP techniques and responds to your queries similar to what you can expect from any human on the other side .
How Chatbots work?
HOW THEY PROCESS HUMAN LANGUAGE?
At a glance, a chatbot can look like a normal app. There’s an application layer, a database, and APIs to call external services. The main thing that’s missing is the UI, which in the case of a bot is replaced by the chat interface. While this setup is convenient for users (that’s why chatbots are on the rise, after all), it does add a layer of complexity for the app to handle. Without the benefit of a rich interface that allows a user to input specific, discrete instructions, it falls on the app to figure out what the user wants and how best to deliver that.
Unlike normal app inputs, human language tends to be messy and imprecise. That’s where the NLP engine comes in. Made up of a number of different libraries, the NLP engine does the work of identifying and extracting entities, which are relevant pieces of information provided by the user, using libraries for common NLP tasks like tokenization and named entity recognition. Tokenization breaks sentences down into discrete words, stripping out punctuation, while named entity recognition looks for words in pre-defined categories (for example, place names or addresses). They might also use a library called a normalizer, which catches common spelling errors, expands contractions and abbreviations, and converts UK English to US English.
For example, let’s take a simple bot, one that only does one thing: Orders takeout. When someone texts the message “order a pizza,” the bot would hopefully recognize the command (“order”) and the request (“pizza”). While these techniques alone might allow a chatbot to understand basic commands, they’re a far cry from actually understanding the structure and purpose of language.
UNDERSTANDING COMPLEX REQUESTS
What if you’re trying to build a bot that’s a more generalized assistant rather than a text-powered version of a simple web app? For that, your bot is going to need to understand context and intent. To establish context and intent, you’ll need some additional NLP tasks that allow the NLP engine to understand the relationships between words. Part-of-speech tagging takes a sentence and identifies nouns, verbs, adjectives, etc. while dependency parsing identifies phrases, subjects, and objects. For example, the sentence “please deliver a large veggie pizza with no mushrooms” might confuse a more basic bot that can only process simple commands, but our dependency parser would hopefully recognize that “no mushrooms” is meant to modify “veggie pizza.” Understanding context and intent allows bots to understand and act upon a much wider array of actions, or even ask the user additional questions until they understand the request. From there, you can add more complex NLP tasks like sentiment analysis, which can identify when a user is becoming frustrated and perhaps escalate the interaction to a human CS rep.
Example of the pizza ordering chatbot illustrates how the context and intent of the conversation is understood in a chatbot.
When it comes to building an NLP engine, there are a lot of options out there, depending on the functionality your bot requires and the language you’re using to build it. Python is often celebrated for its robust machine learning libraries, which include NLTK, SpaCy, and Pattern, all of which provide support for basic NLP tasks, as well as some more advanced applications like deep learning.
Future of Chatbots
Searching for the marketing trends related to the future is easy if you have all the correct data and when it is in front of you. But these trends are a bit obvious and widespread compared to the complex numbers of the demand and supply fluctuations.With chatbots, we know where things are heading to the future. The job for businesses and brands after a certain point is to take the next leap and move forward. For AI and chatbot, the future is coming one way or another, and that can’t be avoided.