Cognitive computing is a term that probably goes over the head of most of the general public. IBM defines it as the ability of automated systems to learn and interact naturally with people to extend what either man or machine could do on their own, thereby helping human experts drill through big data rapidly to make better decisions.
One way I like to describe cognitive computing is as the engine behind “conversational optimization.” In this context, the “cognition” that drives the “conversation” is powered by big data, advanced analytics, machine learning and agile systems of engagement. Rather than rely on programs that predetermine every answer or action needed to perform a function or set of tasks, cognitive computing leverages artificial intelligence and machine learning algorithms that sense, predict, infer and, if they drive machine-to-human dialogues, converse.
Cognitive computing performance improves over time as systems build knowledge and learn a domain’s language and terminology, its processes and its preferred methods of interacting. This is why it’s such a powerful conversation optimizer. The best conversations are deep in give and take, questioning and answering, tackling topics of keenest interest to the conversants. When one or more parties has deep knowledge and can retrieve it instantaneously within the stream of the moment, the conversation quickly blossoms into a more perfect melding of minds. That’s why it has been deployed into applications in healthcare, banking, education and retail that build domain expertise and require human-friendly interaction models.
IBM Watson is one of the most famous exemplars of the power of cognitive computing driving agile human-machine conversations. In its famous “Jeopardy!” appearance, Watson illustrated how its Deep Question and Answer technology—which is cognitive computing to the core—can revolutionize the sort of highly patterned “conversation” characteristic of a TV quiz show. By having its Deep Q&A results rendered (for the sake of that broadcast) in a synthesized human voice, Watson demonstrated how it could pass (and surpass) any Turing test that tried to tell whether it was a computer rather than, say, Ken Jennings. After all, the Turing test is conversational at its very core, as this recent article on Watson’s genesis makes abundantly clearly.
What’s powering Watson’s Deep Q&A technology is an architecture that supports an intelligent system of engagement. Such an architecture is able to mimic real human conversation, in which the dialogue spans a broad, open domain of subject matter; uses natural human language; is able to process complex language with a high degree of accuracy, precision and nuance; and operates with speed-of-thought fluidity.
Where the “Jeopardy!” conversational test was concerned (and where the other participants were humans literally at the top of that game), Watson was super-optimized. However, in the real-world of natural human conversation, the notion of “conversation optimization” might seem, at first glance, like a pointy-headed pipedream par excellence. However, you don’t have to be an academic sociologist to realize that society, cultures and situational contexts impose many expectations, constraints and other rules to which our conversations and actions must conform (or face disapproval, ostracism, or worse). Optimizing our conversations is critical to surviving and thriving in human society.
Wouldn’t it be great to have a Watson-like Deep Q&A adviser to help us understand the devastating faux pas to avoid and the right bon mot to drop into any conversation while we’re in the thick of it? That’s my personal dream and I’ll bet that before long, with mobile and social coming into everything, it will be quite feasible (no, this is not a product announcement—just the dream of one IBMer). But what excites me even more (and is definitely not a personal pipedream), is IBM Watson Engagement Advisor, which we unveiled earlier this year. It is a cognitive-computing assistant that revolutionizes what’s possible in multichannel B2C conversations. The solution’s “Ask Watson” feature uses Deep Q&A to greet customers, conduct contextual conversations on diverse topics, and ensure that the overall engagement is rich with answers, guidance and assistance.
In a blog a year ago, I related cognitive/conversational computing to “next best action,” which is one of today’s hottest new focus areas in intelligent systems. At its heart, next best action refers to an intelligent infrastructure that optimizes agile engagements across many customer-facing channels, including portal, call center, point of sales, e-mail and social. Your customers (all of whom are human, I suppose) engage in a never-ending whirligig of conversations with humans and, increasingly, with automated bots, recommendation engines and other non-human components that, to varying degrees, mimic real-human conversation.
Essentially, the Turing test of multichannel engagement is whether you can continuously orchestrate conversations across all channels so that they appear to embody a single human-feeling corporate-brand persona.
No, we don’t propose that “Watson” be that persona for your company. That’s us, obviously, in service to you.
Let’s carry on this conversation. What do you think?