So this is a long time pet peeve, but recently I have seen a load of these in succession. I am sure a lot of people who know me are going to read this and think “He’s talking about me”. Truth is there is no one person I am pointing my finger at.
Let me start with what triggered this post. Have a look at this screen shot. There are three things wrong with it, although one of the reasons is not visible, but you can guess.
So disclosure, this is a competitors chat bot, it is also a common pattern I have seen on that chat bot. But I have also seen people do this with Watson Conversation.
Did you guess the issues?
Issue 1: Never ask the end user did you answer them correctly or not. If your system is well trained, and tested then you are going to know if it answered well or not.
Those who think of a rebuttal to this, imagine you rang a customer support person and they asked you “Did I answer you correctly” every time they gave an answer? What would your action be? More than likely you would ask to speak to someone who does know what they are talking about.
If you really need to get feedback, make it subtle, or ask for a survey at the end.
Issue 2: BUTTONS. I don’t know who started this button trend, but it has to die. You are not building a cognitive conversational system. You are building an application. You don’t need an AI for buttons, any average developer can build you a button based “Choose your own adventure“.
Issue 3: Not visible on the image is that you are stuck until you click on yes or no. You couldn’t say yes or no, or “I am not sure”. For that matter I have seen cases where the answer is poorly written and the person would take the wrong answer as right, so what happens then? For that matter selecting yes or no does nothing to progress the conversation.
So what is the root cause in all this? From what I have seen normally it is one thing.
Because older chat bots required a developer to build, it has sort of progressed along those lines for some time. In fact some chat bot companies tout the fact that it is developer orientated, and in some cases only offer code based systems.
I’ve also gotten to listen to some developers tell me how Watson Conversation sucks (because “tensor flow”), or they could write better. I normally tell them to try.
Realistically to make a good chat bot, the developer is generally far down the food chain in that creation. Watson conversation is targeted at your non-technical person.
Heres a little graphic to help.
Now your chances of getting all these is hard, but the people you do get should have some skills in these areas. Let’s expand on each one.
By far the most important, certainly at the start of the project.
Most failed chat bot projects are because someone who knows the business hasn’t objectively looked at what it is you are trying to solve, and if it is even worth the time.
By the same token, I have seen two business analysts create a conversational bot that on the face of it looked simple, but they could show that it saved over a million euros a year. All built in a day and a half. Because they knew the business and where to get the data.
Normally even getting a copywriter makes a huge difference, but one with actual conversational experience makes the solution shine. It’s the difference between something clinical, and something your end user can make an emotional attachment to.
Another thing I see all the time. You get an issue in the chat conversation that requires some complexity to solve. So you have your developer telling you how they can build something custom and complex to solve the issue (probably includes tensor flow somewhere in all of it).
Your behavioural expert on the other hand will suggest changing the message you tell the end user. It’s really that simple, but often missed by people without experience in this area.
Subject Matter Expert (SME)
To be fair, at least on projects I’ve seen there is normally an SME there. But there are still different levels of SMEs. For example your expert in the material, may not be the expert that deals with the customer.
But it is dangerous to think that just because you have a manual you can reference, that you are capable to building a system that can answer questions as if it is an SME.
While you might not need a full blown one, all good conversational solutions are data driven. In what people ask, behaviours exhibited and needs met. Having someone able to sift through the existing data and make sense of it, helps make a good system.
Also almost every engagement I’ve been on, people will tell you what they think the end user will say or do. But often it is never the case, and the data shows this.
What the conversational copywriter does for the engaging conversation, the UI/UX does for the system. If you are using existing channels like Facebook, Skype, Messenger, Slack, etc.. then you probably don’t need to worry as much. But it’s still possible to create something that can upset the user without good UX experience.
It’s also a broad skill area. For example, UX for Web is very different to Mobile, IVR, and Robots.
Watson conversation abstracts the ML layer from the end user. You only need to know how to cluster questions correctly. But knowing how to do K-Fold cross validation, or the importance of blind sets helps in training the system well.
It also helps if your developers have at least a basic understanding of machine learning.
I often see non-ML developers trying to fix clusters with comments like. “It used this keyword 3 times, so that’s why it picked this over that”, which is not how it works at all.
It also prevents your developers (if they code the bot) to create something that is entity heavy. Non-ML Developers seem to like entities, as they can wrap their head around them. Fixed keywords, regex, all makes sense to a developer, but in the long run make the system unmaintainable (basically defeats the purpose of using Watson conversation).
Natural Language Processing (NLP)
I’ve made this the smallest. There was a time, certainly with the early versions of Watson you needed these skills. Not so much anymore. Still, it’s good to understand the basics of NLP, certainly for entities.
In the scheme of things, there will always be a place for the developer.
You have UI development, application layer, back-end and integration, automation, testing, and so on.
Just development skills alone will not help you in building something that the end user can feel a connection to.