Do you remember old chatbots – originally incapable of anything but offering pre-scripted spam ads? The “ask, and you shall receive” concept hasn’t actually worked until recent days.
Chatbots aren’t new in the tech world, though they have hit the mainstream only in 2016. It was the first wave of AI technology introduced to the masses.
In contrast to chatbots of the past, the most sophisticated of today’s bots have the ability to carry on a real organic conversation.
Enterprise chatbot solutions offer rich sources of data for further analysis. With the help of this data, brands become more perceptive to customers needs.
By delivering personalized products and services, businesses optimize engagement, gain better relevance, and higher revenue.
Why are we suddenly seeing so many bots?
Messaging apps simply outstrip other types of applications. The primary driver that stood behind 2016’s chatbot outbreak decreased user interest in social media and messaging apps.
Users don’t need hundreds of apps on their mobile devices to support each separate brand. Messaging applications solve this problem – they are simple, easy, and fast. Thereafter, businesses follow their audience.
At the moment more people are using the main messaging apps than social networks.
Facebook Messenger and WhatsApp are leading the way counting 1 billion users each, QQMobile and WeChat – 800 million, while Skype and Snapchat 300 million each.
Developers have created 100,000 chatbots in the first year of the Messenger Chatbot Platform.
Amazon Alexa has also experienced considerable growth since the beginning of this year when it reached 10,000 chatbots (also known as “skills” there). The chatbot platform passed 23,000 skills by September 2017.
In our recent article, you can learn more about the development technologies, chatbots types and their cost.
Basically, there are two types of chatbots: command-based and AI-powered.
Command-based chatbots operate on a set of rules and are very limited. They only respond to specific commands and can’t become smarter than programmed initially.
However, these bots are one-dimensional, they’re the cheapest to build, easiest to deploy, fastest to develop and integrate.
AI-powered chatbots can understand human language, handle real discussions, and continuously turn more intelligent with each newly held conversation.
Except for the ability to perform everything the command-based bots can do, AI-driven chatbots are able to make real-time decisions, deliver fast customer support, and serve up analytics.
When it comes to building bots, many developers rely on chatbot platforms that allow creating those from scratch.
Intelligent chatbots require an understanding of machine learning, AI, and NLP technologies, as well as back-end development skills and deep familiarity with a variety of languages and technologies.
Though we are still a couple of years away from a completely functional AI performance, machine learning, NLP, and artificial intelligence are getting increasingly powerful.
A bit deeper look at the technologies driving rapid chatbot development
A few years ago, it was hardly within computer power to think the way human brains do. Today artificial intelligence can solve complex problems.
Artificial intelligence (AI), natural language processing (NLP), and machine learning are chatbot underlying technologies. They bring chatbot innovation, hence brand communication, to an entirely new personalized level.
Credit: Slackbot reminder of being “still just a bot”.
Although chatbot solutions for businesses are mainly used in the customer service industry, the tech giants like Microsoft, Google, and IBM suggest the chatbots’ true potential still has to be fully unveiled.
AI opens new opportunities, as this umbrella term includes an array of capabilities that allow software to perform tasks commonly performed only by humans.
Natural language processing is the heart of AI-driven chatbots. With the help of sophisticated NLP algorithms chatbots can process the received text: interpret, infer, and determine what was meant (written or said) and then define a series of appropriate actions.
Without continued developments in NLP, chatbots would remain at the same awkward and spammy stage as they were at the very beginning of their rise.
NLP forms the basis of the language recognition used by famous Apple’s Siri and Google Now. It empowers technology to understand natural language speech and text-based commands and includes two main components: natural language understanding (NLU) and natural language generation (NLG).
NLU is harder than NLG, as natural language has a significantly rich form and structure. It involves mapping the given input and analyzing various aspects of the language.
NLU is aimed at handling and converting unstructured data into a structured form – understandable for the system.
To make the message understandable for a chatbot, NLP proceeds five main steps:
- Lexical analysis
- Syntactic analysis (parsing)
- Semantic analysis
- Discourse integration
- Pragmatic analysis
The first step includes words structure identification and analysis; it divides the text into chapters, sentences, phrases, and words.
Parsing involves analyzing grammar and arranging words, so the relationships among words become clear. Syntactic analyzer would reject the sentence like “the hospital goes to the doctor.”
Semantic Analysis checks the text for meaningfulness and draws its exact meaning by mapping syntactic constructions. The phrase such as “cold fire” would be disregarded.
Discourse integration and pragmatic analysis work on the final interpretation of the text’s real message. As the meaning of any sentence or phrase depends upon the overall context.
Natural language generation (NLG) includes text planning and text realization to produce a meaningful response. Simply put, language generation is the process of creating linguistically correct phrases and sentences.
The primary challenge for NLP is understanding the complexities of human language.
The language structure itself is very ambiguous, regarding lexis, syntax, and other elements of speech like metaphors and similes. The same word can be understood as a verb or a noun; one sentence can be parsed in different ways, one input can have various meanings, etc.
The leaders of modern AI tools and products
Google, IBM, Amazon, and Microsoft are among the niche leaders driving the main advancements in AI technologies.
Using integrated NLP services, developers can build their tools and platforms that make chatbot apps language-intelligent.
One of the most popular language processing technology on our list is Alexa Skills Kit (ASK) by Amazon.
Mainly, it’s a collection of self-service APIs, code samples, tools, and docs that you can use to build skills for Alexa.
Alexa Skills Kit’s considerable benefit is integration with other Amazon Web Services that include API-driven machine learning tools for computer vision, speech, and chatbot functionality.
Amazon offers developers to use sophisticated toolkits like AI-powered Rekognition for image interpretation, AI-driven Polly that automates voice to written text (includes 24 languages), and an open source engine Lex that allows developers to integrate chatbot innovations into mobile apps.
IBM‘s Watson Conversation Service (WCS) provides a few services aimed at language processing. With the help of this service, you can quickly build and deploy chatbots and virtual agents.
Available as a set of open APIs, Watson allows accessing a large scale of sample code for cognitive virtual agents development.
IBM’s product is equipped with tools suitable for both entrepreneurs and developers. Nonetheless, its chatbot platform demands beginners some basic skills in machine learning.
Microsoft has also recently launched three new tools for businesses and developers that allow infusing existing software with AI algorithms. The Azure Machine Learning includes Workbench, Model Management, and Experimentation tools. These services are designed for building new AI agents or build-upon existing models.
The services are ‘packaged up’ with some of the main AI and NLP technologies so that even non-programmers can create simple but useful chatbots.
With Azure Machine Learning Studio, for instance, you don’t need to have complex machine learning experience – simple UI allows to deploy analytics and drag&drop datasets.
The tech giant already houses a number of tools designed for developers that aim to extend their software with language understanding and speech recognition.
As a part of the Microsoft Cognitive Services, its Language Understanding Intelligent Service (LUIS) provides an advanced toolkit that is focused on NLP models. Its machine learning-based platform allows to train new conversation models and build NLP into the apps.
Google‘s TensorFlow literally offers to inject AI into your business. This open source software is specially designed for machine learning projects. TensorFlow includes rich documentation and tutorials to support developers that aren’t yet familiar with the platform.
Google also offers machine learning services with its Cloud AI (actively used by Google Assistant). This deep learning system allows to use pre-trained models or create your own tailored models with its neural net-based ML service.
Google’s Cloud Natural Language API is focused on NLP/NLU capabilities such as “intent-entity detection, sentiment analysis, content classification, and relationship graphs.”
Smaller chatbot platforms worth considering
If you’re looking for a Facebook Messenger platform for developers, consider Wit.ai. Open and extensible, it features natural language capabilities. Its robust management toolkit can be used to build a Siri-like speech interface and train the platform in new conversation models.
Recast.AI is another collaborative platform for developers to implement chatbot solutions for businesses.
You can build, train, deploy and monitor your bots with a sophisticated toolkit based on natural language processing capabilities and user interactions.
With the help of Api.ai, you can design, implement, and integrate conversational interfaces into chatbots. It includes speech recognition, natural language understanding, and a sophisticated toolkit.
Pat Inc. platform is aimed at humanizing interactions between AI system and end users. It takes a linguistic approach to NLP and focuses on leveraging neural network algorithms.
Pat can analyze very complex interferences due to its structure based on linguistics, rather than on statistics and machine learning.
Developing an AI chatbot is quite a complex task; consequently, every case is unique.
The creation process strongly depends on business requirements and performance expectations.
Read more about chatbots: