AI unmasked: How is nmodes different

A close look at the history of humanity leaves us with no choice but to admit that the majority of jobs as we know them now will be transferred to automated systems. This is part of the technological and scientific progress our civilization is undertaking and it is irreversible.

Artificial intelligence became mainstream in 2016. For the first time artificial intelligence is not only available to big companies like Google, Amazon or Apple, but to the majority of businesses worldwide.  Startups have started building products and services using artificial intelligence en masse.

The essence of artificial intelligence is massive, intuitive computing power: machines so smart that they can learn and become even smarter.  The machines are becoming quicker and more nimble. They cover wider range of conversation topics. They now connect to robotic systems and online interactive systems. There is literally very little they cannot, or will not be able to, do as applied to industrial workforce.

With all the good that’s going to come with automation, we are suddenly faced with a new problem: the elimination of many low and middle class jobs. Many jobs that have already been severely impacted by computers (manufacturing, administrative support, retail, and transportation) will continue to diminish. In the nearest future routine-based jobs (telemarketing, sewing) and work that can be solved by smart algorithms (tax preparation, data entry keyers and insurance underwriters) are most likely to be eliminated.

What to do? It is fruitless to fight automation, we need to find ways to work with automation rather than against it.

The solution is to become more creative as species. Creativity is the natural advantage of humans over machines. Automation is about to change the course of the world, it’s going to be a great disruptor and impact the workforce like nothing we’ve seen before. We can sit around and gradually become obsolete, or accept the challenge and use the tool of creativity, which we are in unique possession of, to maintain our superiority.

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.