Setting New Goals for Machine Learning & Artificial Intelligence for 2022
2021 has been a remarkable year for AI and ML and 2022 will not be any different. There are numerous industries that are benefiting from machine learning and artificial intelligence implementation, such as manufacturing, transportation, energy, healthcare, etc. This year will be exciting to see how AI will begin to infuse itself into the Internet of Things and setting the stage for significant improvements in productivity, improved uptime, and reduced costs -- regardless of the market segment.
One of machine learning and artificial intelligence's biggest impacts in the future will be within manufacturing. Machine learning is already being utilized to optimize processes, improve safety inspections, drive down costs, increase productivity, reduce warranty costs while improving customer satisfaction.
When it comes to improving manufacturing efficiency and reliability, traditional approaches are far from optimal.
The traditional approach to improving manufacturing equipment reliability and efficiency is regular scheduled maintenance. While that is an improvement over just fixing or replacing equipment when it breaks, it's not how we should be operating when there is so much technology out there that can improve this process significantly. Even with periodic maintenance, equipment can suddenly break down, idling workers, delaying shipments, and disappointing customers. This is where machine learning and artificial intelligence can help. As we begin the new year, it's important to understand how emerging technologies can help shape the way you do business. Many companies have already taken the leap into their AI journey, which means their machines are getting smarter and more efficient every second. The longer you wait, the harder it will be to catch up. This is the time to start learning about Artificial Intelligence, Machine Learning, and all of the terms you will need to know to become confident in selecting the right partner.
Taking a Deeper Look into Machine Learning
Machine learning, is comprised of several models such as Anomaly Detection, Classification, Regression, Digital Twin, Optimization, and Forecasting. Any of these models have the potential to learn from the equipment usage patterns in an industrial environment, allowing it to predict when maintenance should be performed long before something actually breaks down or deteriorates beyond a safe operating level. You can also optimize your operations with a digital twin environment so once you have reached the "optimal" process, you can then implement this into your plant floor.
This technology might seem "too good to be true" or "the future", but in fact -- it'a already here! Industry 4.0 — Smart Manufacturing is setting the stage for 2022.
Although these technologies are still in their early stages, they are gradually maturing. Much of this can be traced back to the expansion of "The Edge", increases in cloud performance, and evolutionary enhancements in algorithm training and inference. Collectively, they form what is being referred to as the Artificial Intelligence of Things, or AIoT.
Some terms are also coming into better focus. AI refers to machine intelligence, while machine learning — a subset of AI — is being defined as the process of acquiring knowledge essential to AI. So machine learning is used to collect, gather, and train a system to make the right decision or selection, and AI is the system that makes those decisions.
Machines may initially make mistakes. However, with time and many tries and errors, they can "learn" to make the correct selections or infer the correct answers. An AI system might be given one million images to learn what a cat looks like, for example. It will begin by learning that a cat has two ears, two eyes, a nose, and a mouth, but so do other animals. After many samples, the AI system can learn the subtle differences. This works the same with finding bottle cap dents in a pharmaceutical facility, mold in a food and beverage facility, or even an oil leak in a machine.
AIoT allows devices and software to communicate with one another as well as smart sensors, smart gateways, and local servers in a manufacturing setting. Add AI data analytics into the mix, and you have actionable intelligence, which is frequently referred to as intelligent automation.
AIoT, in combination with IoT, extends the potential of IoT by providing more accurate real-time information for improved problem solving. Improved data analytics also lead to scalable lower-cost solutions as a consequence of better data analysis. Better customer experience, superior product development, and the promise of greater money are all possible outcomes as a result of AIoT.
In the past, all of these decisions were made in the cloud. Sensors gathered data and sent it back to the cloud, where it was stored and analyzed later. This method was proved to be ineffective and costly, slowing down networks with data traffic. With edge computing, AIoT decision-making happens at the equipment level. That reduces latency, bandwidth requirements, and results in much greater efficiency in manufacturing operations.
Preventive maintenance is an important part of smart manufacturing, but this is just the beginning. AIoT can be deployed in many different areas in a factory to further increase productivity. Smart manufacturing no doubt will increase productivity and profitability, but be careful planning on how everything should work, including machine learning requirements and implementation. This is the most important step to future success. Realistic ROI expectations of investment should be included in the planning, which is why partnering with the right company is essential in getting started with your AI Journey. Schedule a call with a solution architect at SORBA.ai to learn how we have worked with other Fortune 500 companies and have shown them REAL results.