The Curious Case of AI Technology

sasha uritsky

Apr 13, 2016

The notion of Artificial Intelligence has been around for a while.

Yet, unlike other prominent technological innovations such as electric cars or the processor speed, its progress has not been linear.

In fact, as far as industrial impact is concerned, there were times when allegedly there was no progress at all.

The widespread fascination with AI started several generations ago, in 80-s of the last century. This is when a pioneering work of Noam Chomsky on computational grammar led to a belief that human language capabilities in particular, and human intelligence in general, can be straightforwardly algorithmized. The expectation was that the AI-based programs will have a significant and lasting industrial impact.

But despite unabridged enthusiasm and significant amount of effort the practical results were minuscule. The main outcome was disappointment and AI become somewhat of a dirty word for the next 20 years. The research became mostly confined to scientific labs, and although some notable results have been achieved, such as development of neural networks and Deep Blue machine beating acting world champion in chess, the general community was largely unaffected.

The situation started to change about 5-10 years ago with a new wave of industrial research and development.

We now experience somewhat of a renaissance of AI with bots, semantic search, self-service systems, intelligent assistant programs like Siri are taking over. In addition, optimists of science are bragging confidently about reaching singularity during our lifetime.

The progress this time seems to be genuine indeed. There are indisputable breakthroughs, but even more impressive is the width of industries adopting AI solutions, from social networks to government services to robotics to consumer apps.

For the first time AI is expected to have a huge impact on the community in general.

There is this vibe around AI which hasn’t been felt in years. And with power comes responsibility, as they say, - prominent thinkers such as Stephen Hawking raised their voice against the dangers of powerful AI for humanity. Still, as far as current topic is concerned, this is all part of the vibe.

Despite all the plethora of upcoming opportunities, it is important to observe that we are yet to advance from anticipation stage. AI has not became a major industrial asset, an AI firm has not reached a unicorn status, and despite the fact that major industrial players such as IBM are pivoting towards fully-fledged AI-based model it has not manifested itself in business results.

We are still waiting for AI-based technology to disrupt the global community.

The overall expectation is that it is about to happen. But it hasn’t happened yet.

Interested in reading more? Check out our other blogs:

Artificial Intelligence of Chatbots: What Do You Need to Know.

While Chatbots have been around for a little while now, their presence is more noticeable thanks to Facebook and Microsoft’s recent advancements.

Initially customers complained about the robot-like experience and the limited functionality of first generation bots and rarely found them useful. The customers were skeptical about how valuable in practice chatbots actually are, which has left recent AI vendors like nmodes with the task to combat the leftover stigma from the poor customer experiences and shortcomings of these initial offerings.

Chatbots, like an IVR?

We’re all used to calling into a contact center and punching numbers into a menu to be routed to the correct agent or service to address our needs. Interactive Voice Response solutions (IVRs) drive this interaction and are basically If/then routing trees that “listen” to the digit entered and “transfer” the user to the appropriate next step. While tremendous advancements in technology have brought voice recognition capabilities, those first generation IVRs were all about automated actions based on prompts. Enter your account number, press 1 to speak to an agent, etc…

The first generation Chatbots are just like an IVR. They can respond to prompts to progress through a predetermined process or display some canned information like pricing, a contact number, route to an agent, etc., but that was about the extent of it. Still 1^stgeneration Chatbots came with 4^thgeneration expectations. While these basic functions have tremendous value to a business, the customer expectation is very different when dealing with a phone call vs. a chat session. Consumers have experienced IVR routing for decades whereas chat is still relatively new and is perceived as a conversation with a person, rather than interacting with a machine. Add on the fact that many vendors and consumers mislabeled Chatbots as Artificial Intelligence in the beginning and the expectation of a dynamic, responsive customer experience is even greater.

So it’s no surprise that customers were less than impressed with “Artificial Intelligence” that could only display simple answers and basic information. We were expecting Hal from 2001: A Space Odyssey or KIT from Knight Rider, and we got a pixelated PONG instead.

Let’s talk…

Now, Artificial Intelligence has evolved to be integrated into Chatbots to deliver a more powerful user experience. While these new versions of Chatbots coming out are powered by Artificial Intelligence, AI powered chat also exists independent of bots in some instances. Confusing? Yeah, I was too.

The beauty behind true Artificial Intelligence is its ability to recognize the context of a conversation and respond with relevant, contextual information dynamically. A customer can now “speak” to technology the same way they would hold a conversation and the AI has the ability to “read” the customer’s intent to provide information quickly and efficiently. No more are you limited to a set of canned responses. The AI can reach in to a wider array of relevant information to craft unique responses based on any number of criteria. While in most cases AI is still limited to a few topics per use case, the technology is growing quickly, making almost daily improvements in functionality and customer experience.

What is even cooler is that the longer the AI is deployed, the more it “learns” and improves the speed and quality of responses. So while the scope of AI interactions is limited at first, the maturity curve is quick, delivering an ever-improving customer experience without having to invest in additional people, processes, or technology. It really is like a “growing up” of technology, right before your eyes.

What Is AI Engine and Do I Need It?

Chatbots and assistant programs designed to support conversations with human users rely on natural language processing (NLP). This is a field of scientific research that aims at making computers understand the meaning of sentences in natural language. The algorithms developed by NLP researchers helped power first generation of virtual assistants such as Siri or Cortana. Now the same algorithms are made available to the developer community to help companies build their own specialized virtual assistants. Industry products that offer NLP capabilities based on these algorithms are often called AI engines.

The most powerful and advanced AI engines currently available on the market are (in no particular order): IBM Watson, Google DialogFlow, Microsoft LUIS, Amazon Lex.

All these engines use intents and entities as primary pnguistic identifies to convey the meaning of incoming sentences. All of them offer conversation flow capability. In other words, intents and entities help to understand what the incoming sentence is about. Once the incoming sentence is correctly identified you can use the engine to provide a reply. You can repeat these two steps a large number of times, thus creating a conversation, or dialog.

In terms of language processing ability and simplicity of user experience IBM Watson and Google DialogFlow are currently above the pack. Microsoft LUIS is okay too; still, keeping in mind that Microsoft are aggressively territorial and like when users stay within their ecosystem, it is most efficient to use LUIS together with other Microsoft products such as MS Bot Framework.

Using AI engine conversation flow to create dialogs makes building conversations a simple, almost intuitive, task, with no coding involved. On the flip side, using AI engine conversation flow limits your natural tendency to make conversations natural. The alternative, delegating the conversation flow to the business layer of your chatbot, adds richness and flexibility to your dialog but makes the process more comppcated as it now requires coding. Cannot sell a cow and drink the milk at the same time, can you?

Amazon Lex lacks the semantic sophistication of their competitors. One can say (somewhat metaphorically) that IBM Watson was created by linguists and computer scientists while Amazon Lex was created by sales people. As a product it is well packaged and initially looks pleasing on the eye, but once you start digging deeper you notice the limitations. Also, Amazon traditionally excelled in voice recognition component (Amazon Alexa) and not necessarily in actual language processing.

The space of conversational AI is fluid and changes happen rapidly. The existing products are evolving continuously and a new generation of AI engines is in the process of being developed.