There’s so much hype around artificial intelligence (AI) that it can be hard for people to get their brains around how it will help them in their daily lives or on the job. In simplest terms, think of AI as what happens when a machine performs a task that human beings find interesting, useful or difficult to do.
Today’s AI works by using computer models to simulate intelligent behavior. It can sift through hundreds of resumes to help recruiters find the best candidate, give doctors more insight into how tumors function and assist engineers building autonomous cars.
The potential of AI can vary by country, affected by factors such as the flow of information, communications infrastructure, regulatory frameworks, and public and private investment in the digital economy. Positive benefits also vary by industry, depending on factors such as the adoption rate of AI and the industry’s investment in AI.
Overall, researchers say AI has the potential to increase economic growth rates and boost profitability for much of the world’s economy. In a paper published by the McKinsey Global Institute in September 2018, McKinsey forecast that AI could add nearly $13 trillion worth of global economic activity by 2030, or about 16 percent higher cumulative gross domestic product (GDP) than last year. This amounts to 1.2 percent additional growth in GDP per year.
AI vs. machine learning
Too often, people use AI and machine learning interchangeably. While these are related, they are not the same. The application itself defines AI; machine learning uses experience (rather than programming) to improve the application’s performance. For example, in Artificial Intelligence: The Simplest Way, I prepared a chart that shows how machine learning would predict an equipment breakdown in a plant, while AI schedules the repair. We also showed how machine learning predicts the likelihood of a disease, while AI develops new treatments. And finally, machine learning predicts the performance of a system, while AI develops a new system to meet performance goals.
For people who worry that life as we know it will cease to exist, it’s too early to worry about a sentient AI apocalypse. Today, we still know very little about how the human brain works — which means we know even less about how to build a computer that works just like the human brain. Despite fears of an impending AI dystopia, its capabilities are still very limited compared to human intelligence.
However, AI has many useful applications. It can drive competitive advantage. For companies just starting, find an area of the business that you can make as smart as possible, as quickly as possible. Identify the data that you think might make a real difference. Then test your ideas and learn and adjust as you go.
The current wave of AI works by using computer models to simulate intelligent behavior. Machine learning algorithms are good at learning new behaviors, but bad at identifying when those behaviors are harmful or don’t make sense. Companies deploying AI will need a highly skilled workforce that’s trained to ensure the technology remains both useful and safe.
And please don’t think of AI as a way to automate the core system and eliminate people. It’s more constructive to think of most companies as networks of people interacting with various, loosely connected digital systems. Realistically, AI helps connect disparate systems and improves the way we interact with both machines and one another. Rather than displacing people, I believe that AI will make the modern workplace smarter and more meaningful.