4 things you need to know about machine learning in engineering

Machine learning is being used in a number of industries, including engineering. If you’re unaware, machine learning is defined as a type of artificial intelligence that allows computers to be more accurate in predicting outcomes without being programmed to do so.

Overall, machine learning is meant to make our jobs and lives easier by streamlining everyday processes. When we make data-driven decisions as a society, we’re more productive and efficient. Let’s take a closer look at several areas where machine learning is taking off:

1) Robots

Yes, you read that right. Robots. And we’re not talking about the robots in Star Wars.

Real-life robots are used by automobile manufacturers on the production line to complete both simple and intricate tasks. The incorporation of these robots decreases costs and human error. Many of these robots use AI and machine learning to help better the process and discover patterns in the data.

There’s an argument that these robots are replacing jobs due to the rapid normalization of new processes and technologies. The best way to increase job security is to stay up to date with the latest practices and learn how to work alongside robots and direct them.

2) The Internet of Things (IoT)

The Internet of Things has increased in popularity over the past decade in an effort to keep everyone connected. IoT has a constant working relationship with open source, big data, and software-defined networking. Smartphones and other devices easily connect to the IoT and have for decades. Engineers use IoT sensors to achieve “connected intelligence” that allow prescriptive, predictive, and adaptive analyses for their projects.

3) Big Data

Engineers heavily on big data, more so than other industries. However, no data is useful without the right systems in place to analyze it. Machine learning produces systems that allow engineers to give the data context. Once the data has context, engineers can analyze the success of projects and make plans to improve our communities through infrastructure, technology, and more.

4) Predictive Modeling

Predictive modeling is a best practice when it comes to predicting likely outcomes for events or processes. Predictive modeling is essential to thousands of businesses in marketing, manufacturing, healthcare, software testing, and more. Predictive modeling is necessary to improve supply chains and also helps businesses detect fraud, reduce risk, and improve operations.

Are you utilizing machine learning in any of your current projects? If so, we’d love to hear about your experience. Please visit the ACEC Utah Facebook page and leave a comment.