IBM this week announced that the ANZ bank's wealth management division will adopt its Watson technology for customer service and engagement.
The announcement is significant, not necessarily because of the sophistication of the Watson technology, but because of IBM's ability to successfully market the Watson concept.
To take us all back a little, the term ‘cognitive computing’ emerged in response to the failings of what was once termed ‘artificial intelligence’ (AI).
Although the underlying concepts have been around for 50 years or more, AI remains a niche and specialist market with limited applications and a significant trail of failed or aborted projects.
That’s not to say that we haven’t seen some sophisticated algorithmic-based systems evolve. There’s already a good portfolio of large scale, deep analytic systems developed in the areas of fraud, risk, forensics, medicine, physics and more.
Compared to cognitive computing though, artificial intelligence is actually a very specific - and more accurate - term. It’s not real intelligence. It’s artificial.
Real intelligence is not just about being able to quickly recall some facts or identify patterns in information at blazingly fast speeds. It’s as much about understanding context, intuition and peripheral vision as it is about intelligently handling what data you already have.
After all, it wasn’t until the very last part of last century before we identified that personal relationship intelligence (EQ) was just as important to human existence as knowledge-based intelligence (IQ).
So let’s get real. Despite the fact that Watson was trained to successfully win a game show (Jeopardy), IBM’s technology — and others, to be fair — are not cognitive computing systems at all.
That’s not to say they aren’t valuable - just that we shouldn’t overstate their value or capabilities.
Here are a few personal takes on IBM’s Watson technology.
- Watson provides the “illusion of cognitive computing”.
Just as magicians have repositioned themselves to become “masters of illusion”, Watson’s perceived magic qualities mask the underlying complexity required to achieve seemingly simple and intelligent output.
Magicians need the right environment to make their magic work and so too does this type of technology. Apply it to a different problem or domain and you’re likely to get some very different results (hint: we haven’t seen Watson winning any other game shows yet…)
- Watson’s data set was large, but form still followed function.
Many people’s amazement with Watson was that it took an incredibly large, "unstructured" data set and could still quickly and accurately come to the right answer.
When you dig deeper though, you begin to realise that, while the input data may have been seemingly unstructured, Jeopardy questions themselves do in fact follow a format.
The data set it used might not be structured (in the traditional sense), but it still does have form. Jeopardy questions are based on identifying facts. If you don’t have a fact, you don’t have a hope of answering a Jeopardy question.
- Self-learning technologies (like people) don’t always learn the right things.
My 25+ years of experience in the IT industry has taught me that all forms of learning technologies have remarkably similar traits to humans – that the skills and qualities that initially work for you, often end up working against you.
I’ve seen numerous self-learning technologies over the years that have eventually required “un-training”.
- Watson is a collection of both IBM technologies and capabilities.
Watson is not something you buy. It’s still something you, and/or IBM, build.
Watson’s power is more in the fact that it’s marketed as a "thing”, not just an overarching concept. The power of Watson is not so much in the technology (though this is admittedly still impressive) but the fact that its power exists in the simplicity of it having a name.
IBM’s Watson is an evolved collection of technologies wrapped with some deep human expertise.
If you’re trying to sell the message of deploying more advanced technologies within your organisation, then “buying Watson” might just be an easy sell.
If you’re expecting an easy implementation of some very sophisticated technology though, you may just be a little disappointed.
What’s your view on Watson and how likely is it that your organisation will soon go down the "cognitive computing" path?