The true challenge Conversational AI has. Thinking beyond natural language understanding

Tools
From a business perspective, it is clear what problem Conversational AI solves. Business can benefit greatly by offering a better customer experience and scaling their capability to do so across multiple channels and geographies.

Share This Post

Share on facebook
Share on linkedin
Share on twitter
Share on email

Conversational AI enables them to automate a significant portion of conversations, empowering humans to focus on the conversations that matter.

Take things a level lower though, into technology and service design, and things become less clear. What is the most representative way to describe the problem Conversational AI needs to solve? Is it ensuring that interactions feel as human-like as possible? Is it a natural language understanding (NLU) or machine learning (ML) problem? Is it how to manage conversational state? How you phrase and approach this question will deeply influence what path you take to solve it.

For example, if you view it as primarily an NLP or ML problem – i.e. how do we get machines to understand (classify) what a user said – you will go deeper and deeper into that space. You will end up focussing heavily around intent classification, or conversational structures as patterns that you are trying to match to. Definitely, a crucial part of the problem to solve but, is it the central problem? By phrasing the problem as primarily an NLP problem you risk limiting the scope of action to just one aspect. You are limiting yourself to a hammer and that risks making everything look like a nail.

There is an easy way to test if you are focusing on the right aspect, through a simple thought experiment. For example, if NLU is the central challenge then project yourself into a future where you have a perfect NLU tool at your disposal. As good in language understanding as any human. Are all our interactions now immediately going to be amazing? As most of us experience (often almost daily) even when the counterpart is a human, conversations still break down; things still go wrong. Well, if things still go wrong perhaps the central problem was not language understanding, to begin with.

At OpenDialog we think that the true challenge at the core of Conversational AI is building software that manages to effectively co-operate, co-ordinate and problem solve. The interacting parties, whether humans or humans and machines (or just machines), need to figure out how to work together to achieve their goals. Each brings their own pre-conceptions of how to go about it but they need to come to a common agreement for the “rules of the game” and the expected ways to interact. For example, in a simple question/answer scenario the rules are that one gets to ask a question and then needs to wait for the other to provide an answer. If the one asking the question keeps interrupting, while the question is being answered, or keeps throwing another question on top of the previous one the other party will attempt to establish some guidelines. They might say:

Let me finish answering the first one, and then we can proceed to the second one.

The more complex the setting the more we need to depend on existing prior social knowledge about what the “rules” are (e.g. people generally know that there is a certain process to ordering food, booking a ticket and so on) or the more we need to build instructions in the conversation (we may need to guide the user through a completely novel experience such as setting up a new device in their home or carrying out a new procedure in an industrial setting).

It is the Conversational Designer’s job to design a flexible structure through which this co-ordination and co-operation can happen in the best possible way. There is then the added challenge of having to do that with imperfect NLP tooling that will not always get it right.

This is why at OpenDialog we view Conversational AI as primarily a co-operation and co-ordination problem that then includes an NLP challenge as well. However, by considering the problem with a wider lense we have a broader range of tools at our disposal to solve the problem. We are no longer restricted to only hammers and nails!

Luckily this co-operation and co-ordination problem is one that Artificial Intelligence research has been grappling with for a long time, in a field called multi-agent systems. At OpenDialog we draw from that field relevant ideas and approaches and translate them to easy to use tools that a conversation designer can deploy appropriately as they’re designing the overall system.

Article originally posted on LinkedIn on 23rd February 2021.

More To Explore

OpenDialog overview
Announcement

OpenDialog is in Private Beta!

Conversational applications are going to redefine how we interact with machines and access services. From the essentials, like health, education and social care to the

Ronald Ashri
Blog Post

How OpenDialog came to be

After a couple of years helping clients design and deploy conversational applications (using a variety of existing tools) we realised that something fundamental was missing. Where was the conversational model in conversational design and tooling? So I reached back into my academic AI days and the field of multi-agent research for some help and inspiration. The result is OpenDialog.

Do You Want To Explore what OpenDialog Can do for your business?

drop us a line and we'll be in touch!