Don't Experiment on Your Customers With Machine Learning!

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Don't Experiment on Your Customers With Machine Learning!

Explore the problems surrounding Machine Learning and why a pure ML approach isn't necessarily the right choice for many enterprises.

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Machine Learning experiments

In the third part of our series, we consider the complex issue of Machine Learning. We look at the problems surrounding it, the role that some development tools expect your customers to play, and why a pure Machine Learning approach isn't necessarily the right choice for many enterprises.

Of course, Machine Learning has opened up a world of possibilities — particularly in areas where large sums of data need to be mined and analyzed. Not a day goes by without an article that mentions how Machine Learning has enabled some amazing feat to be achieved.

And it's all true.

For instance, speech recognition — the technology for mapping acoustic sound waves to text characters and the first step in processing speech — successfully uses Machine Learning to learn from the vast amounts of voice inputs and improve levels of recognition.

So Machine Learning most definitely has its place.

Except that's not the whole truth. When it comes to conversational AI there are issues and limitations that enterprises need to be aware of if they are to develop conversational interfaces successfully. And most of them revolve around data, or rather the lack of it.

The Challenges of Machine Learning

One of the key problems is that the only language a machine understands is binary. It has to be taught how humans speak. But Machine Learning algorithms can only discover the many different ways a human might ask a question if they can learn from copious amounts of examples. This training data needs to be classified and cleaned, which can also be a long, laborious process performed, ironically, by humans.

The challenges of language diversity become more apparent in real-world situations. It could be your very first day on the helpdesk for a clothing firm and with no training whatsoever you'd still understand that Where's my delivery? or What's happened to my parcel? and Is my shirt coming today? amount to the same problem.

In fact, you probably wouldn't notice the multitude of ways the same question is asked. Even misspellings wouldn't faze you if it was a live chat environment.

But for a machine, this level of complexity can quickly cause problems, unless it has been taught. And not only that, it needs to learn your ethics, ethos, and brand. Just as you were guided on life's rules and values as a child.

Remember that kid. The one your parents would never let you play with because their folks just let them run wild. Well without control, that's just what a machine will do. Maybe not in a 2001 Space Odyssey way, or at least not yet, but enough to make your brand famous for the wrong reasons!

The need for data to train Machine Learning systems has created a dilemma for many enterprises. How do you collect conversational data without a conversational system?

Experimenting on Your Customers Is Not the Answer

Few enterprises have available the vast resources required to curate and train the data. Even then, it takes time, a precious commodity that most businesses can't afford in a competitive marketplace.

So they make a fatal mistake.

They develop an app and expect their customers to train it for them.

Typically, the interface will only be able to recognize questions asked in a specific way, anything else will be an anomaly thrown out to a generic 'safety-net' answer or the customer immediately transferred to a live agent. These errors are then flagged to a human to correct.

Depending on the development tool used, it may mean additional work adjusting the system by a specialist linguist technician or hours of work coding in the different ways a customer might ask just one question. All of this takes additional resources and time. Meanwhile, customers are still using the app and receiving the same unsatisfactory experience.

Or the app may answer the question, but it won't necessarily be the one your customer asked. In which case, unless the customer helpfully told you No this didn't answer my question, you may never know it's answering with the wrong response.

Either way, though the app might learn a little more with each interaction, at what cost is this to the business?

Don't get me wrong. We all experiment on our customers. Searching for the little things that make a big difference to the bottom line. It might be a rejig of the website, or perhaps a new internal process. But these are typically minor changes. In the big scheme of things, if something doesn't work out, it's no deal breaker.

But launching a conversational app that doesn't have the conversational skills to understand your customer's questions, let alone answer them, is a recipe for disaster. Particularly when speed and convenience are winning over brand loyalty. Customers will stop returning if the experience is less than satisfactory. And worse? They'll tell their friends too.

ai, challenges of machine learning, machine learning, machine learning experiments, training data, what is machine learning

Published at DZone with permission of Andy Peart , DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

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