Picture AI falling helplessly into the morass that sucked in "big data," "smart" anything, and "Internet of" you name it. That rumble that you would feel might be Alan Turing fidgeting in his grave.
I can hear some marketing newbie now, "Well, our product is made of plastic. That's artificial, right? And, we think it's really intelligent. Artificial intelligence!"
Or, machine learning. "It's really smart!"
For more than 60 years hundreds of very bright and accomplished computer scientists, from Turing to today's doctoral students, have researched and debated what AI is, and what it isn't. At what point is a computer actually thinking? The answers aren't easy.
Then, we have machine learning as a subset of or precurser to AI. Feed a neural network with enough examples -- such as text and images -- and it advances to the point where it can translate English into another language, recognize faces of people, or identify the most successful treatments for diseases.
I suppose AI is destined to be cast into Buzzword Hades once everyone from that marketing newbie to the CEO desperate for something innovative hear more about the real-world successes of AI and machine learning. Memos and meetings will be punctuated with shouts of, "We need to be doing that."
We already are seeing examples of niche applications utilizing techniques such as image recognition in anti-terrorism initiatives and pattern recognition in cybersecurity. Applications in the commercial space seem to be ready to pop up in the public view.
A Forbes article cites three industry sectors -- healthcare, finance, and insurance -- as prime candidates for AI and machine learning applications.
The article notes, "Sequencing of individual genomes and then comparing them to a vast database will allow doctors -- and/or AI bots -- to predict the probability that you will contract a particular disease and the best ways to treat those diseases when they appear. Companies including Google, Apple, Samsung, and others are investing billions in developing new biometric sensors. Combined with big data, the information from these sensors could help prevent disease and extend lifespans."
In finance, writer Bernard Marr says, AI-based systems will replace human financial advisors, analyzing thousands more investment possibilities and big data drawn from our own social media and other activities to help shape our financial strategies.
In insurance, systems are utilizing AI already to identify who should get discounts on their life, health, or car insurance, based on their life styles and past activities.
Incalculable numbers of applications in every industry will tag along, whether they use AI or not.
I guess it's inevitable that any success stories in AI will turn it into a "me too" buzzword. We have a tendency in the tech and business sectors to get bored with tech terms, perhaps frustrated when the concepts they represent aren't being adopted fast enough. Just yesterday, Gartner released data showing that enterprise investments in big data are up but that fewer companies plan future investments in the concept. Of course, that news was greeted by blogs effectively saying, "Alas, poor Big Data, I knew him..."
Over the decades I've come to the conclusion that our rush to adopt new buzzwords is based on the fact that we have a two or three year attention span when it comes to tech initiatives that have timelines to maturity that are two or three times longer. If a concept isn't broadly adopted and providing ROI after those first two or three years we declare it a failure and move on to the next buzzword. Patience is not in our dictionary.
We have to remember that testing technologies, conceiving and building out corporate applications, and implementing them across dozens of departments with thousands of employees isn't a snap-your-fingers type of thing. And, some situations don't justify a move to the new idea at all, as we are sure to learn with big data, IoT, and AI after that.