Artificial Intelligence holds a lot of promises, yes, but it has turned into a buzzword that is too often misunderstood and misapplied. This noise around it has bred a long list of myths and misconceptions. To help set the record straight on the current state of AI, we’ve put together a list of the top 10 myths we hear most frequently.
One: Automation will take away more jobs than it creates
In fact, industry experts claim that the opposite will happen—more jobs will rise from AI than those displaced. The adoption of AI will primarily replace repetitive, lower-skilled jobs that don’t require human creativity or empathy.
However, it won’t mean the end of human workforce; on the contrary, it will provide an opportunity for jobs to become less menial and more thoughtful. Some call this workforce transformation “super jobs”.
Two: AI will completely displace humans
Contrary to popular belief, although AI is highly equipped to process complex data and complete tasks efficiently, it still lacks cognitive thought and needs to follow the logic and datasets human have created for it.
The best AI strategy is therefore one that uses an augmented workforce, in which “super job” employees will work closely with AI to make technical and interpretive, problem-solving decisions to optimize the process.
Three: AI is just a way to cut costs down
While it’s true that some organizations may turn to AI as a way to cut costs, there are so many other benefits it can bring. It can help improve efficiency, reduce risk, improve employee satisfaction and retention, and so more.
Four: Higher productivity equals to higher profits and less employment
AI and automation will undoubtedly raise productivity growth. But that doesn’t necessary mean that profits will only go to executives and shareholders. Higher productivity will also translate to lower prices, higher wages, higher demand, and employment growth.
Five: AI, machine learning and deep-learning are the same thing
These are buzzwords often used interchangeably. AI is a broad, general term used to describe machines that perform tasks characteristic of human intelligence. Machine-learning is a subset of AI that enables ‘intelligence’ by training algorithms through data.
Deep-learning is an even narrower subset of machine-learning, based on learning from unstructured data as opposed to task-specific algorithms.