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The rise of AI chatbots

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Chatbots are here to stay. However, not all chatbots are alike. This blog post explains how AI chatbots are different from messaging apps, why they are so popular, and how you can add AI to your chatbot for creating a better user experience.

Chatbots are software programs that can hold a conversation with a user via written text or speech. Chatbots are designed to give the user the impression of talking to a real human rather than a program. Over the last 2-3 years, chatbot adoption has been massive, and many websites now have a chatbot operating on their homepage. In addition, many frameworks have become available for creating chatbots, which explains the increasing number of chatbots on websites and other digital environments. In this article, AI consultants from Iflexion will discuss a particular kind of chatbots powered by artificial intelligence.

What are AI chatbots?

Chatbots generally come in two different types: messaging apps or virtual assistants. The first category follows pre-programmed rules when interacting with users and is often used by businesses and brands for customer support. In this blog post, we´ll focus more on the second category as they offer more functionality and can handle more complexity, which requires other development skills than the first category.

To be able to offer more human-like conversation, virtual assistants make use of artificial intelligence (AI). AI development aims to imitate human intelligence processes by machines, in this case, computer systems. AI uses machine learning to perform tasks where examples are analyzed to find the meaning. Using AI, virtual assistants can mimic real humans, making online purchases, managing your appointments for the day or searching for information online and reporting it back to you. Every user interaction between the user and the chatbot means a new learning experience so that it can take better decisions for the next iteration. Examples of popular virtual assistants are Alexa by Amazon, Cortana by Microsoft and Siri by Apple.

Why are chatbots so popular?

It’s easy to see why chatbots have become popular in a short time, as they offer large opportunities for businesses to grow, now that people use messenger apps more often than social networks. However, the promise of these apps has not been fulfilled yet for a number of reasons.

Chatbots were hailed as the next big thing by many. User expectations were high, leading many people to believe that chatbots were to replace native and mobile apps in no time. The reality proved different though: oftentimes, chatbots were used for the wrong purposes without even meeting their basic requirement, namely the realization of a human-like conversation through technology.

With regards to virtual assistants, one of the reasons for its relative lack of success is that AI and machine learning still have a long way to go before they can act on the same level as human mind. However, change is on the way as significant progress has been made by major technology companies in the area of voice and image recognition, which enable AI. Now that chatbots have passed the peak of the hype cycle and early adoption phase, more and more people are willing to use virtual assistants. This will likely result in more training data to refine chatbot algorithms, which means a better user experience. More different use cases will result in more different user communities of chatbots.

In other words, the future looks bright for chatbots. Rather than being offered as a single and isolated application, it’s expected that chatbots will extend current internet technology and will be used where it’s most needed. 

Creating a chatbot

Creating a chatbot is similar to building a mobile app or web page: after designing a chatbot, developers create it using one of the many frameworks on the market, choosing between non-coding platforms and coding platforms. The latter requires the use of programming languages, the first doesn’t. This means that you don’t need coding experience to start building your first chatbot.

However, creating more advanced chatbots and adding AI to your chatbot requires programming skills to manage coding libraries for language analysis and machine learning.

Botsify and FloXO are examples of non-coding frameworks. Code-based frameworks enable the integration of a chatbot into a broader tech stack. Google, Facebook, and Microsoft all offer such code-based frameworks. They make it easy to connect to other platforms and devices from different technology providers.

How to add AI to your chatbot

For adding AI to your chatbot, it’s best to choose a coding platform as it allows you to make use of open source libraries for machine learning and AI. Particularly useful in this context are Natural Language Tools that help computers understand human language, allowing chatbots to understand the user’s intent and produce the correct answer based on his or her input.

When designing a chatbot, a flow of possible questions and answers needs to be laid out beforehand. Using AI enables you to match certain words and questions with adequate answers. You are not dependent on your own chat designs: there are natural language plugins available that enable you to query third-party databases. A simple messenger app does not offer this functionality and can only respond to specific commands, following pre-programmed rules.

While multiple programming languages offer AI and machine learning tools, the best choice is Python. It offers open source libraries for chatbots such as TensorFlow and scikit-learn, as well as the Natural Language Toolkit (NLTK) for natural language processing. It can also be used for simpler chatbots instead of advanced ones. Python also offers TextBlob, a framework with a more intuitive interface and gentler learning curve than that of NLTK. SpaCy is another alternative for Python users, but it is only available in English. Only C++, Java, and JavaScript offer machine learning libraries, but they’re not as extensive as the ones for Python.

Conclusion

This blog post covered the rise of the AI chatbot. We explained the difference between messaging apps and virtual assistants. AI and machine learning give virtual assistants their human-like capabilities that messaging apps lack. In general, chatbots have a great future ahead of them. Finally, we explained how to build an AI chatbot and where to turn to for AI and machine learning libraries.

Information about the author

Yaroslav Kuflinski

Yaroslav Kuflinski is an AI/ML Observer at Iflexion. He has profound experience in IT and keeps up to date on the latest AI/ML research. Yaroslav focuses on AI and ML as tools to solve complex business problems and maximize operations.

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