Digital Solutions

Big Data describes the huge amount of information we’re able to collect, analyze and use to find trends and associations in the way we live. Big Data is often characterized by the way data is used, its ability to determine cause and effect and its implications for decision-making. For example, analyzing the health records of thousands of people with the same diet might tell you that certain food makes people more prone to heart disease. With this knowledge, people can choose to stop eating this food to be healthier. The conclusions made from Big Data come from large sample sizes and are therefore more accurate and more valuable. When manufacturers are armed with insights from Big Data, they can identify the root causes of inefficiency and waste to reduce costs and streamline processes.

The Industrial IoT connects machines, data, and people. First, it takes a network of industrial devices, like sensors and maintenance software, and allows them to share information with each other. This provides a platform to track, collect, exchange, access and analyze large chunks of data more efficiently. The insights obtained from this data are then used to improve manufacturing procedures. Instead of collecting data from several sources separately and trying to connect the dots, IIoT does this for you. Imagine all the assets and software programs in your facility speaking to each other, sharing information and spitting out numbers that give you deeper insight into your operation. This is the power of IIoT.

Machine learning is teaching a computer to learn on its own by finding patterns in a large amount of data and making conclusions based on these patterns. It’s a quicker way to parse information and uncover new insights that can be used to improve processes. It’s like how Netflix learns from all the previous movies and tv shows you have watched and uses this knowledge to suggest more viewing material, or how doctors can introduce a computer program to a series of x-ray images and corresponding symptoms so they can find common patterns and better diagnose illness or injury. Don’t worry if you’re a little puzzled about the difference between AI and machine learning. Although the two are very similar, there are key differences. AI is a culmination of different technologies to help computers achieve a higher level of thinking and reasoning. Machine learning is one of these technologies with a singular program and a specific goal. In this way, AI is like a bridge and machine learning is one of its pillars. Another pillar might be the Internet of Things or Big Data. All these technologies come together to bridge the gap between what’s possible for humans and what’s possible for computers.

The definition of artificial intelligence is a moving target. More generally, AI is when a computer gains the ability to think and reason like a human and, in doing so, is capable of doing uniquely human tasks, such as speech recognition or decision-making. The way this definition translates to the real world is also constantly changing. A calculator was once considered AI, since math was something only the human brain could perform. Today, we have digital assistants, like Siri or Alexa, or generative design programs that solve complex engineering problems in manufacturing.