Chatbot learning: everything you need to know about machine learning chatbots

Class has started at chatbot school. Chatbots are attending their AI lessons, and getting smarter each day via the teacher that is machine learning.

And chatbots are proving to be model students. They’re learning to understand human language better than ever before, and they’re getting smarter when it comes to recognising tone, mood and shades of meaning.  (But not to worry – we aren’t at Skynet level just yet.)

These advancements in machine learning chatbots create a huge business communication opportunity. Here’s everything you need to know about machine learning, chatbots, and online customer service.

Types of chatbot

Chatbots are an area of automation that integrate beautifully with live chat software. A chatbot – as you’ve no doubt surmised – is a conversational tool used to automate communications.

Chatbots can have robotic bodies like Sophia, the world’s first robot to gain citizenship, or (more commonly) they can be basic programs that allow a user to interact with a website or app using a chat interface. They can complete a wide variety of services, ranging from fun to functional.

Chatbots come in two main flavours. The first is the standard rule-based bot that completes scripted actions based on keywords. The second is the AI-powered chatbot, which uses machine learning to converse more naturally.

Currently, the rule-based chatbots are a popular ecommerce tool for routine customer service requests. This is because they’re easy to build, and though simplistic, can get basic tasks done. But as AI has advanced significantly, and continues to improve, it’s highly likely that we’ll see a rise of the more complex machine learning chatbots.

Introducing machine learning chatbots

Machine learning refers to the ability of a system (in this case, the chatbot) to learn from the inputs it experiences. One of the ways they achieve this through natural language processing, or NLP, which refers to any interaction between computers and human language.

But NLP is only a start. To achieve true general artificial intelligence, a chatbot or dialogue system needs to be able to do three central things:

          – Offer an informative answer

          –  Maintain the context of the dialogue

          –  Be indistinguishable from the human

When it comes to that last requirement, we’re not quite there yet. Even the best of today’s machine learning chatbots can’t be mistaken for a human. But fortunately for brands, (most) humans are still willing to talk with bots as long as they are helpful, funny, or interesting.

Everyday applications

Consumers are already interacting with machine learning chatbots. Day to day, they’re using two main types of dialogue systems: goal-oriented ones like Siri, and general conversation ones like (now retired) Zo. The former help people solve their everyday problems using natural language, while the latter attempt to converse with people more broadly.

Business chatbots can straddle both areas. They can supply your online customers with the same level of attention they would get in a physical store, guiding them through the entire shopping experience via a live chat interface.

And, the more intelligent the chatbot, the smoother the user experience. Machine learning chatbots can help the visitor find what they’re looking for, answer FAQ, and walk them through checkout – all while maintaining a friendly, conversational tone.

Consumers are already interacting with machine learning chatbots

Chatbot detention

There are pitfalls to be avoided as chatbots evolve. It’s important to remember that chatbots are chatbots. While they can play an increasingly human part, it’s best to call a bot what it is, and not try to pass it off as an actual human.

Machine learning chatbots are in primary school at best. We can’t expect them to have degree-level intelligence for our customer service just yet.

Even with today’s advancements in machine learning, NLP is still in its infancy. It is prone to bias and error, and can be tricked to speak in an offensive manner.

Caution, then, is recommended when implementing machine learning chatbots. While they look to be the future of online customer service, class is still in session for machine learning chatbots. Use due care and attention when deploying. Otherwise, you risk a repeat of Microsoft’s Tay, who was shut down after just 16 hours due to her inappropriate messages.

Stay in school, chatbots

With machine learning chatbots continuing to advance, we are entering an age of intelligent automated shopping assistants. Thanks to more intelligent chatbots integrated with our live chat channels, we can offer increasingly smart customer experiences.

So stay in school, chatbots. By the time you graduate, you’ll be all ready for a lucrative career in AI-assisted customer service.

Useful links

Chatbots: what are they good for

How to explain chatbots to your gran

The pros and cons of full chatbot diclosure

What is an intent-based chatbot?

– The pros and cons of a flow-based chatbot

Could chatbots ever become engaging conversationalists?