Artificial intelligence (AI) has the power to disrupt many industries, and it’s no surprise that it is among the most buzzworthy topics in business today. However, AI is also very misunderstood: In addition to being touted as the next big thing for cutting-edge companies, AI is also supposedly going to replace human workers and dismantle industry as we know it. Should we all be concerned? What is AI, really?

Simply put, the phrase “artificial intelligence” describes machines that use decision-making or computing processes that mimic human cognition. In practice, this requires that a machine understands information about its environment, whether that’s a physical space, the status of a game, or relevant information from a database. An artificially intelligent system can then use this data to optimize actions that help achieve a specific goal, like winning a game of chess.

The possible applications of AI are incredibly broad. Strategic games are popular challenges for programmers today, but AI is also widely used to optimize financial investments, identify genetic mutations linked to diseases, understand language and power customer support chat bots, recognize features of images, predict the weather, and so much more. It’s understandable that many people find the idea of AI excelling across so many areas unsettling — but there’s one type of reasoning that AI lacks, a shortcoming that means machine learning engineers and data scientists aren’t going to be automating their way out of working any time soon.

General intelligence remains AI’s greatest weakness. While artificially intelligent machines are collectively capable of achieving everything listed above and more, any individual algorithm is typically only able to optimize its decisions and actions to achieve a singular goal. The human element still plays a key role in understanding the context of this goal, prioritizing work across different goals, and communicating the outcomes of AI-powered models effectively.

To learn more about how artificially intelligent machines are trained, check out our earlier posts on the two major types of machine learning and some of the most popular machine learning algorithms.

Nikki Castle
Nikki Castle

Marketing operations specialist at DataScience.com.