Artificial Intelligence - Quarterly Insights Q3/21
Five minutes to go… 10 minutes to go… 20 minutes to go. Everyone who regularly waits for their train is familiar with this reverse countdown. Even in Germany, the country of punctuality, it is not unusual to bridge longer waiting times with podcasts. The statistics confirm the feeling of every train rider: in an EU comparison, Germany ranks third to last in terms of the punctuality of its trains. Deutsche Bahn is therefore turning to Industrie 4.0, using algorithms and artificial intelligence (AI) to tackle the problem. Especially with a complex system like rail traffic, planning is not easy. Hundreds of trains must be scheduled to the minute in a huge rail network. Delays and rescheduling of a train in Berlin can have effects as far away as Freiburg. In Stuttgart, Deutsche Bahn is therefore testing a system developed by the group’s own “House of AI”. An AI application determines the consequences of several decisions in the event of disruptions in the network. From this, a sort of video with recommendations for action is created. The dispatcher can fast-forward through the video and use it to make decisions.
Data-based decision support up to the takeover of decisions by AI are a pillar of the data-driven company of tomorrow. Not only the railroad but also every manufacturing company must ask itself how it can provide the user with optimal decision support in complex situations like planning, quality assurance or plant control. AI is therefore not a talking robot, but an algorithm in an application that, using trained data, calculates probabilities for events and provides the user with help in decision-making.
Manufacturing companies need to build significantly more competencies in the field through staff acquisition and a strong partner network. But available experts in the field are scarce and in demand. However, the right experts are not enough. The success factors of a sustainable implementation also include consideration of the company’s digital status quo: Where does my company stand on the path to Industrie 4.0? In which use cases is the greatest need and benefit? Because defining fancy AI use cases that do not consider the technical, organizational and cultural basis of the company leads to frustration and failure. Therefore, the current I.40 maturity of the plant should be assessed. The assessment focus must not only be on technical aspects but must also consider the organization (processes and workflows) and the culture (mindset and digital skills of the staff). In companies with multiple, sometimes globally distributed, plants, it is also necessary to set global standards that result from local needs and are implemented locally.
In our current issue of Quarterly Insights, we take a closer look at the application of artificial intelligence in manufacturing companies. Amongst others we have a podcast for you with Dr. Johannes Winter, CEO of Plattform Lernende Systeme – Germany’s AI platform, a book recommendation, and a special tip from the team for anyone who wants to understand how machine learning works in a practical way. Enjoy.
Whitepaper | The increasing digitalization of the economy leads to shorter innovation cycles and faster market penetration of products. In this fast-moving environment, a gradual adaptation of technologies and business models is causing companies to fall behind. It is therefore essential for companies to master the organizational ability to both increase efficiency in their core business and to innovate exploratively using new technologies. Only this ensures long-term success. This ability is called ambidexterity. Our whitepaper presents proven practices how this can be introduced in organizations.
Whitepaper | In the future todays and even tomorrows isolated solutions will be transferred to a semantic interoperability of AI solutions. This will enable industrial AI applications that can be used across entire departments, divisions or even companies, not only for specific uses cases. These AI applications are portable, technology-neutral and not tied to a particular platform provider. In order to do so a number of challenges need to be addressed e.g., efficient modelling and continuous updating. Next to this hot topic, in the 2020 acatech Cooperation “Using the Industrie 4.0 Maturity Index in Industry” practical examples show how the Industrie 4.0 Maturity Index has been used to add value in a variety of different businesses, from automotive suppliers to chemical companies.
For our German readers: In our podcast series, we had the pleasure to welcome Dr. Johannes Winter. He is the managing director of the German AI thinktank “Plattform Lernende Systeme”. This platform brings together leading experts in Artificial Intelligence from science, industry, politics and civic organizations. Together with our Dr. Felix Optehostert, he talked about the benefits this platform is providing to companies and society, what AI is already accomplishing in the production industry, and how Europe can compete in the world against superpowers like the US and China.
This New York Times Bestseller from 2018 is written by Dr. Kai-Fu Lee. He is one of the world’s most respected experts on AI and China and a former executive at Apple, SGI, Microsoft, and Google. In his book Lee reveals the pace China has suddenly created in applying AI in business. The US and Europe are leading the research and development of AI, but China is taking a rapid pace in monetizing it. Looking at the US-Sino AI competition, Lee states that there are great responsibilities coming with this technological power and changes will come to blue-collar and white-collar jobs. He provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon. This book tries not to give the reader a deep understanding of how AI works, but it depicts what political and economic changes and challenges will come in an AI-driven world, dominated by the US and China.
Artificial intelligence and machine learning are trendy topics, not only in the industry. However, I found that most people were not aware what they are really talking about. For me I wanted to change that, so this summer, I set myself the goal of understanding what Machine Learning really is. I decided to take an online course held by the Stanford professor and founder of deeplearning.ai Andrew Ng. As an engineer, it was great to dust off the math skills (machine learning is pure math) in one’s head and refresh them. At the end it doesn’t matter if one takes this course or another. The point is to understand the things you’re dealing with and not just shine with half-knowledge. And this course is great to understand machine learning. One not only learns different mathematical models and to which cases they can be applied, but also learn to program them and with which tricks the accuracy of the predictions can be optimized. New for me: more data is not always the key to a better prediction. So definitely a recommendation for everyone who wants to understand things.
“It is difficult to think of a major industry that AI will not transform. This includes healthcare, education, transportation, retail, communications, and agriculture. There are surprisingly clear paths for AI to make a big difference in all of these industries.”
– Andrew Ng
Computer Scientist and Professor at Stanford University
Global Leader in AI