We all have those friends or colleagues who are the “go to” person for tough problems, whether it’s car repairs or career advice. The difference between the “go to” person and everyone else is experience and a kind of raw talent. In fact, the very best – the experts – leverage those qualities to innovate. In Malcolm Gladwell’s Blink, he recites example after example of experts at the top of their field making genius decisions in an instant.
Despite constant advances in technology, the idea that a machine could ever achieve that expert level seems unfathomable. Even technologists, who are accustomed to accepting everything new, find themselves doubting the ultimate potential of artificial intelligence (AI).
Wired magazine lamented the apparently impossible gap in an article titled The Best AI Still Flunks 8th Grade Science. And where are those flying cars I was promised? What about my robotic butler?
Technology has delivered so much that we feel entitled to all of yesteryear’s promises.
That sense of entitlement becomes a constantly rising bar that, ironically, makes us forget just how advanced AI is becoming. A machine playing tic-tac-toe would have been considered AI 100 years back. Fifty years ago, chess was only human – but now automated opponents are routine. Recently, Google’s AlphaGo software defeated the world’s best player of Go. This game was thought to be beyond the reach of computers, as success has historically been tied to human intuition and complex pattern recognition.
But artificial intelligence extends far beyond board games. In the past, we would have scoffed at AI choosing wine or music based on our tastes, but now we assume computers will do that quickly, reliably, and seamlessly for us.
So here’s the revelation: AI is whatever computers can’t do already. It’s a moving target.
Advancements in AI will continue to snowball, especially as people become more familiar with open source AI frameworks and machine learning libraries. These platforms allow anyone from anywhere to build algorithms. The more this technology becomes democratized, the more we’ll see AI develop; and in turn, the possibilities of its implementation will grow and change.
What about AI in business?
Billions have been saved by automated algorithms that optimize impossibly complex schedules and logistics. Productivity is amplified by connectivity and intuitive interfaces that understand their users and their needs. Consumers are now routinely categorized in complex ways that enable more efficient, less annoying CRM.
You know where this is going. AI is all around us; we just call it “productivity software.” We don’t need Wired’s 8th grade scientists; what we do need is focused, targeted expert help. And we’re getting it.
For problems where machines have a special talent (big data, number crunching, high performance), software can encode the “experience” requirement, resulting in, for all practical purposes, true genius performance.
The latest example is Search-Powered Analytics™. You ask a business question as if you were talking to a colleague, but a machine provides you the answer. It appears magical at first, but the facts support answers. So you learn to trust the magic the same way you rely upon power outlets or cell phones or Pandora’s choice of a playlist. It makes data fun again.
Jeopardy showed us that IBM’s Watson knows a very small amount about a very large number of things. Jack of all trades, master of none. The real AI for business is just the opposite: an expert with a sharp focus and no distractions. It never gets tired, always stays current, does not play politics, and stays up all night helping you research when your competitors come knocking. Two years from now, you won’t remember how you got by without your “go to” business AI.
Additional Reading About AI for Business
- Machine Learning in Business Intelligence Solves the Puzzle — This article explores the current and future of impact AI and machine learning algorithms on the business intelligence space.
- Citizen Data Scientist: What It Means in 2019 — For the average business user, AI and machine learning are immensely powerful for understanding and diagnosing performance without the help of a tech-savvy analyst, empowering average business people to become citizen data scientists. This article breaks down the rise of the citizen data scientist and what that means in practical terms.
- Natural Language & Analytics: A Cheat Sheet for Business People — Natural language acts as bridge between the complex algorithms that make sense of data and the people who need to leverage that data to be competitive in their industries. This educational webpage gives necessary context to the many roles natural language plays in the AI analytics space.
