In many industries, advocates of artificial intelligence and autonomous technology are quick to promise sweeping transformation and fully autonomous solutions. However, the optimists usually promise more than they can deliver and soon find the engineering challenges are greater than they first realised. In this article, Zohar Kantor, vice president of sales at artificial intelligence start-up Lean.AI, asks whether we celebrated the arrival of autonomous quality inspection too soon. In 2013, Elon Musk said, "it's a bridge too far to go to fully autonomous cars." Although the world of driverless vehicles has moved on significantly since this admission, it was a belated recognition that the Tesla CEO had under-estimated the challenges of operating a vehicle without a human being in the driver's seat.

Identified as the key for further enhancing competitiveness and workforce reinforcement, AI has the potential to be included in various operation processes. One such field, AI in visual inspection – with computer vision and machine learning on the rise – has been becoming more popular thanks to the engagement of top players across industries. In the recent online sharing, experts from FPT Corporation, FPT Software, Landing AI, and Schaeffler discussed their visions for the future of'AI in Real-time Quality Inspection'. All parties emphasised on the use of data-centric approach to shorten AI training duration in machine learning and addressed critical issues faced by brownfields factories. Until just recently, factory owners were equipped with rule-based vision inspection, which required IT experts to write pages of rules for the algorithm to detect product defects.

Quality has always been part of Audi's DNA, and it's also the aim of a pilot project launched last year at the Neckarsulm site to check the correct execution of spot welds in high-volume production. The partners in this project, which is part of the Volkswagen Group's Industrial Cloud which is to be implemented in other plants, are Siemens and Amazon Web Services. In the body of a car like the Audi A6, around 5,300 spot welds are required. Up until now, the quality of the welds has been monitored manually by technicians using ultrasound on the basis of random analyses. In the WPS Analytics pilot project, in which specialists from the fields of manufacturing, innovation management, planning, digitization and IT are involved, artificial intelligence is used to detect quality anomalies automatically and in real time.

In the production work of the future, autonomous systems will support people through data-based analyzes and intelligent solution patterns for value-adding activities. Experts from the two Fraunhofer institutes IPA and IAO in Stuttgart will show their guests at the "International Open Lab Day" on April 16, 2021 the benefits that artificial intelligence (AI) can bring. In the future, AI could identify potential for optimization, make fitters' work easier or set up personnel deployment plans: These are just a few examples of how AI can help make production more resilient and flexible. However, this does not mean that the human being becomes an assistant to the algorithms, but remains the director of the processes, works efficiently in an adaptive environment and no longer has to take on so many control tasks. Digital technologies help to make production work more human-centered.

The use of artificial intelligence (AI) is a central element of the digital transformation process at the BMW Group. The BMW Group already uses AI throughout the value chain to generate added value for customers, products, employees and processes. Michael Würtenberger, Head of'Project AI: "Artificial intelligence is the key technology in the process of digital transformation. But for us the focus remains on people. AI supports our employees and improves the customer experience. We are proceeding purposefully and with caution in the expansion of AI applications within the company. The seven principles for AI at the BMW Group provide the basis for our approach."

In today's world, artificial intelligence (AI) is transforming several industries, and many of us interact with AI on regular basis in some form or another. From banking and manufacturing to e-commerce and personalized advertisements, AI is becoming imperative in almost every business. Business experts believe that it is an essential tool for data analytics, predictive suggestions, chatbots, and so on. AI can be defined as any software algorithm which possesses human-like features, such as an ability to learn, plan, and solve problems. These attributes can be groomed, and the system made more intelligent depending upon the type of industry where it is going to be used.

Digitization has been as revolutionary for industry as the introduction of electricity. Now artificial intelligence (AI) and big data are generating new opportunities – and a number of challenges. In the "Artificial Intelligence in Europe" report published by consultancy firm EY, some 57 percent of the companies surveyed expect AI to have a significant or very significant impact on business areas that are "entirely unknown to the company today," and 65 percent expect AI to have a significant or very significant impact on their core business. "The opportunities generated by digitization in general are game changers in most sectors, which is one reason why digital solutions have gained a strong foothold in different parts of society," says researcher Daniel Langkilde, who develops AI solutions for Scania, Volvo and other major companies. Digitization lays the foundation for entirely new business models, sometimes simultaneously throwing out the old models.