I was speaking at a quarterly kickoff for a company in Australia two years or so ago, and I was asked: ‘Is Conversational AI really AI, and if so, why?’ It was a question that I contemplated when I first transitioned into the industry. Rather than give a technical breakdown of the language being the data model and what NLP was, I decided to try an analogy that gave a basis for artificial intelligence, and then drew the similarities and distinctions. Here is roughly how it went:
For those of you who have children and are past the toddler stage, you know that a child copies everything you do from the moment they develop the motor skills to do it. That is the point where you start having to watch what you say and how you react to others because you are an example. You teach your children different skills and behaviors directly or by imitation, and they repeat and get better and better at those skills on their own. Eventually, they start coupling those skills together to form new behaviors on their own. The one moment that every parent has when they look at their child doing something and they ask themselves, ‘How did they learn that?’—you realize that they formed a completely new behavior based on the ones they already knew. At some point in your parenthood, you will reach the moment where regardless of the many skills and behaviors your child has learned, they make a choice based on the consequences and the past outcomes of different choices, and it is evident that this choice was well thought out. (By the way, that is the holy grail of parenthood!)
In terms of computing, the imitating child is not necessarily artificial intelligence, it could merely be a great algorithm for tackling a task. The child that cobbles a couple of skills together to make a new one is not necessarily AI either—it is more likened to ‘ML’ or machine learning, but the last example, where the child makes a decision based upon an analysis of consequences or desired outcomes and executes on that, well, that is really worthy of being called ‘AI.’
At that moment, the small crowd seemed to have an ‘a-ha’ moment so I continued:
What does that look like in Conversational AI? Spoiler alert: we haven’t yet reached the ‘teenage’ years! If you consider an intent to be a skill or behavior and the utterances, along with their variances, to be the improvements of those skills, we are largely still at the mimicking baby stage. There are some companies and universities that are really pushing the limits of commercially viable Conversational AI with features such as intent discovery, which is more squarely in the ML category, but those are not widely available and standard fare yet.
This analogy, in various forms and lengths, seemed to be popular in the interviews, podcasts, webinars, etc., in which it appeared in the following months. I bring it up here because it still seems to be the best explanation I have seen. AI has become an overused term for anything that falls into slightly cutting edge these days. It brings me back to the dawn of terms such as ‘cloud computing’ and ‘Service Oriented Architecture,’ which are similarly nondescript but heavily overused. With that said, there are several technologies in addition to Conversational AI that are commonly thought of as AI. Let’s take a peek at a few of them and see how they relate to Conversational AI.
This is really the next level of the classic Enterprise Search, or the one search for everything in the enterprise. I have personally been involved in many ES projects as they are usually adjacent to enterprise portal or intranet projects that used to be my bread and butter. So, what makes Cognitive Search part of the artificial intelligence bandwagon? Again, it comes down to understanding user intent, similar to Conversational AI. If you can better understand intent, you can dial in relevancy, which is the primary stat of Cognitive Search. It also, more readily, ‘blends’ the search results together into a highly relevant soup of results where the user doesn’t have to care where the results necessarily come from.
Cognitive Search is an excellent companion to Conversational AI because in many cases it leverages the search algorithms to derive an answer. Remember, in a previous post, our distinction of Conversational AI primarily being a difference in the user interface? You could slap a CUI on top of a cognitive search for a different experience, which could have both advantages and disadvantages, but that is a topic for another time.
This area of consumer-grade AI is typically where the most cutting-edge work and feature-set lies. Simply put, predictive analysis takes into consideration the past (aka reports) and the present (aka dashboards), and predicts through patterns and potential outcomes, based on similar inputs. Sounds like our mature child example above, doesn’t it? Where predictive analysis really gets fun is when you apply it to Conversational AI in the capacity of predicting what a user might say next! This can greatly improve the accuracy of intents when less-than-stellar utterances are given.
Robotic Process Automation (RPA)
I left robotic process automation for the last because it is probably the least understood albeit most widely used AI technology out there, and it requires a bit more in terms of explanation than the others. RPA ‘bots’ can replace mundane and repeatable tasks that humans traditionally execute such as opening and routing emails, data entry, moving files around, processing and scraping forms, interacting with websites, etc. They will also typically have some sort of orchestration functionality in the product, which is basically the workflow for machines. All of this allows for more business functions to occur with less human interaction. Many of these super-bots can also learn how to do their jobs more efficiently and adapt to different conditions—such as scraping data from unstructured documents. If the algorithm is adept in identifying the first party on a mortgage document, it can most likely figure out how to do the same with other document types or from the same type of document from another vendor or state. This combination of human substitution and ML capability is what puts RPA into the AI technology category.
The imitating child is not necessarily AI. Here's the Part-IV to know why...
Now, you might say that some of these tasks can be completed by a chatbot—and you would be right. Remember, anything is possible with enough lines of code! However, if it is a common task that is performed under the guise of self-service or something similar, it may be perfectly carried out by an RPA or a chatbot. A chatbot can also leverage robotic process automation if needed, and many vendors boast compatibility or integration with one of the major RPA vendors out there. Here is my advice on this:
If you have already adopted an RPA technology and it is embedded in your business:
- Ask your RPA vendor which chatbot vendor they recommend and have worked with regarding your specific use case. Or,-
- Ask your prospective chatbot vendor if they have any referenceable use cases with your in-house RPA vendor that they can share, thus demonstrating your use case.
If you do not have an RPA technology selected yet, but are likely to do so along with a chatbot:
- Consider if your RPA decision is just to automate something a chatbot can already do with a boxed ability or simple integration. If it is, don’t waste your money on RPA for now.
- If you have broader use cases for RPA, select your RPA vendor first, and then refer to the previous bullet point above and select a course of action.
In summary, Conversational AI is in good company, and the reality is that it is just fine to call all of these great technologies ‘AI’ regardless of which baby step they are at! I would remiss not to say that there are a few unique vendors out there that have all-encompassing intelligent platforms. They might have all or most of these technologies wrapped together, and may even gear the platform toward a particular industry vertical, or a horizontal function. Many of these might be worth a look because you may find that deployed in your organization, the total sum is worth more than the value of its individual parts.
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