Artificial intelligence (AI) and machine learning show vast potential for how industrial companies compete in the nest era of production and manufacturing. With significant labor and skills gaps on hand, automation technologies may provide the tools needed to keep the global economy humming.

This year's Hannover Messe tradeshow is showcasing many companies who are strong in the fields of automation and digital intelligence. Here are some of the most notable.

Monolith AI strives to halve industry processes

As every manufacturing engineer is aware, automation is everywhere in modern production environments. Complex robotics partners with programmed assembly/production operations to make a diverse range of products. All of the infrastructure is already there, but what if it was run by AI? What if a deep learning core utilized an internet of things (IoT) framework to leverage the full power of an already efficient automated production line?

With Monolith AI, machine learning tools integrate a new breed of automotive, aerospace and industrial manufacturing systems, that will process and predict repetitive tasks with far greater understanding. The goal is to intelligently optimize common and unique tasks through the use of self-learning models, via the integration of Monoliths’ AI software. Monolith AI will be presenting their unique take on self-learning models in Hall 3.

Siemens employs a holistic approach to AI

Some exhibitors focus on straightforward automation systems. Others, those looking to the future, are working with the IoT or focusing on data acquisition and processing through deep learning. Siemens engineers are striking a course for tomorrow by employing deep learning tools as whole system solutions.

Essentially, this is how Siemens perceives their answer to the “Industry 4.0” paradigm. By integrating their AI systems in the design stage, in robot-run manufacturing lines and as IoT control nodes, predictive learning algorithms realize a slew of industry benefits. From cost-cutting to product quality improvements, the possibilities of this holistic approach are endless.

Here, artificially accelerated training is a major focal point. Instead of taking weeks or months gathering raw data from a multi-stage production process, industry-trained machine intelligences are taught to recognize and optimize laborious tasks in a matter of hours. And, because every single stage of manufacturing is part of Siemens’ vision, the digitally trained models impact everything, from initial design to final quality assurance. Siemens as a globally invested leader in AI innovations can be seen in Hall 9.

Beckhoff gives machines the gift of sight

While it’s true that the current generation of machine learning tools can analyze sensor data and use it to streamline industrial processes via numerous predictive algorithms and deep learning models, they can’t actually “see” the real world. Beckhoff vision solutions are changing that disconnect in the data acquisition chain by giving AI machines the power to see their industry subsystems at work.

As a key Hannover Messe exhibitor, their TwinCAT 3.0 machine learning platform will be demonstrated as an easy to deploy software solution that functions as the building blocks of a PC powered industrial processing hub that could just transform the way in which businesses view the manufacturing sector.

This means that a software framework, one that comprises of any number of CPUs and GPUs, can easily communicate with IoT systems, PLC machinery, image sensors, deep learning networks and numerous AI building blocks to construct the required adaptive industrial manufacturing facility. In this example, the exhibitor is clearly aiming for an open-source platform that’s scalable and affordable.

Bosch invests heavily in AI innovations

A multinational company capable of directing huge amounts of investment capital, Bosch is building a better future by constructing AI research and development facilities. This has led to the birth of the Bosch Center for Artificial Intelligence (BCAI), a center that’s dedicated its resources towards advances in AI technologies.

Self-driving automobiles are one of the more futuristic efforts coming out of these R&D facilities, but the machine learning experience isn’t limited to autonomous cars. Quite the contrary, Bosch has had a huge part in the creation of thousands of manufacturing plants. Real-time analysis of factory data is cited as the crux of a Bosch innovated AI manufacturing future. This will be achieved through low latency 5G network usage and IoT interoperability.

Essentially, macro scale data chains in massive manufacturing plants require real time data analysis optimization, and that’s not a feasible proposition without access to a superfast communications network. Bosch has an exhibit in Hall 13, but their industrial presentations will be digitally hosted.

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A few final thoughts of a grander manufacturer’s future

Finally, as the manufacturing sector evolves, the pain of the evolutionary process can be greatly mitigated by embracing AI technologies. That’s an action that needs to happen today, not on some vague future date. If that’s the case, other manufacturers will already have infused their production facilities with time and cost saving machine learning intelligence.

The technology is available now, as made so abundantly clear by the showcase carried out at the AI-centric Hannover Messe. To take advantage of that technology, the infrastructure needs to be established now. If a client remains inflexible at this critical time, they’ll be left behind.

The more logical path forward is a collaborative one, a future that embraces AI technologies and all the benefits they have to offer. As with all new developments, however, data security issues and the knowledge that AI integration represents a huge shift in cultural consciousness might cause a business some concerns.

Even with this potential stumbling block, the gains are too great to ignore. If manufacturers enter this scenario with enough knowledge to make an informed decision, there’s no reason AI shouldn’t be seamlessly integrated into a production cycle.