Quality control is a crucial activity performed by manufacturing companies to ensure their products conform to the requirements and specifications. The introduction of artificial intelligence models enables to automate the visual quality inspection, speeding up the inspection process and ensuring all products are evaluated under the same criteria. In this research, we compare supervised and unsupervised defect detection techniques and explore data augmentation techniques to mitigate the data imbalance in the context of automated visual inspection. Furthermore, we use Generative Adversarial Networks for data augmentation to enhance the classifiers' discriminative performance. Our results show that state-of-the-art unsupervised defect detection does not match the performance of supervised models but can be used to reduce the labeling workload by more than 50%. Furthermore, the best classification performance was achieved considering GAN-based data generation with AUC ROC scores equal to or higher than 0,9898, even when increasing the dataset imbalance by leaving only 25\% of the images denoting defective products. We performed the research with real-world data provided by Philips Consumer Lifestyle BV.

With the advent of technology, it has become easy for insurance companies to carry out insurance renewal and other processes. Over the years, artificial intelligence has developed to a great extent, making it possible for companies to carry out tech driven operations in an easy way, including the insurance industry. With the impact of AI, application of machine learning, data modeling, the entire insurance process has been smooth, thereby increasing customer satisfaction to a great level. AI has played a major role in the motor insurance industry, making it easy for companies to carry out car inspection processes, thereby automating purchase, claims and renewal processes to a great extent. The Motor Vehicles Act, 1988 makes it mandatory for a vehicle owner to drive with a valid car insurance policy.

We've previously written about the use of Ai to automate vehicle inspections, how it works and its advantages. Now, we will delve into some areas where it can be used and how it benefits said businesses. Many businesses can be improved and optimized with the use of automated vehicle inspection, all this through AI and machine learning. It will cut costs, vastly reduce time consumption, and increase reliability by eliminating human error. This are just a few examples of businesses that can be improved by this technology.

In recent years, quantum computers have become one of most attractive machines based on the principles of quantum mechanics. We make use of quantum superposition, quantum entanglement and so on in the calculation process. Expectations have increased enormously since the announcement of Quantum supremacy1, 2 in October 2019 and December 2020 by superconducting and photonic quantum computers. There are three types of quantum computing devices: quantum annealing3, quantum gates and optical Continuous-Variable2, 4. Quantum gate quantum computing devices include the superconducting type5, 6, trapped ion type7, 8, semiconductor quantum type9, 10, diamond NV centre type11 and Rydberg atom type12. The performance of quantum devices has been rapidly improving day by day in recent years because of severe development competition.

Foodborne illness afflicts 48 million people annually in the U.S. alone. Over 128,000 are hospitalized and 3,000 die from the infection. While preventable with proper food safety practices, the traditional restaurant inspection process has limited impact given the predictability and low frequency of inspections, and the dynamic nature of the kitchen environment. Despite this reality, the inspection process has remained largely unchanged for decades. CDC has even identified food safety as one of seven "winnable battles"; however, progress to date has been limited.

It's not an easy time to be in the automotive manufacturing business. A global pandemic has upended supply chains and wreaked havoc on auto sales in general. In parallel, automotive factories are increasingly connected through Industry 4.0 – the melding of the physical and digital worlds – and solutions such as robotics, remote sensors and digital control systems, which produce seemingly unending (and overwhelming) new streams of data to be processed and interpreted. But – pandemic notwithstanding – perhaps no challenge is more pressing than the skilled labour shortage in the Canadian automotive sector. As the baby boomer workforce retires, it's getting more and more difficult for manufacturers to find experienced workers with the technical craftsmanship, which is difficult to teach and transfer, to replace their predecessors and build vehicles with increasingly integrated computer technology.

An Israel start-up has created an inspection technology based on artificial intelligence to check cars for problems and defects. FREMONT, CA: The Tel Aviv-based Israeli company, UVeye provides high-end solutions for automatic external inspection of vehicles, using advanced technologies. Recently they have introduced a smart-machine inspection service driven by AI. The company raises 31 million USD to expand AI-driven inspection systems since 2017. The new artificial intelligence-based system will thoroughly inspect a moving car in three to four seconds. It will end those nasty damage arguments at the rental-car return counter by checking any kind of fluid leak and for dents or defects as small as 0.08 inch.

Visual Inspection is routinely carried out across industry to determine whether a structure, product, component or process meets the specified requirements. Typical examples include the detection of product defects in-service or during maintenance and as point of manufacturing in-process monitoring. Such inspection is usually carried out by a trained individual who has sufficient knowledge and experience to visually identify faults and non-conformant quality and performance. Artificial Intelligence (AI) provides an opportunity to introduce innovation and new technology to the visual inspection process, offering a solution to challenges and requirements. The Centre for Modelling & Simulation (CFMS) has produced a demonstrator that uses a combination of computer vision and AI technologies to automate the manual inspection process.

Inspection of overhead (OH) power lines and their subsequent maintenance is one of the major activities of electric utilities. Patrolling OH lines, which includes both distribution and transmission lines, is still an old-fashioned job and is treated as a tedious work. The traditional visual inspection of OH assets is highly error prone and costly. All the different types of insulators on OH lines may appear perfectly ok to the naked eye, but the presence of cracks and dirt can lead to flashover and subsequent tripping of the OH circuits. Proper detection of cracks and the amount of dust and dirt on insulators is still a challenging task.

The future of AI manufacturing is now as the Advanced Manufacturing Research Centre (AMRC) – part of High Value Manufacturing Catapult – introduces cutting edge IBM AI hardware to Factory 2050. "This is the first industry-focused AI system of its kind in the UK. It's the result of the very close relationship with IBM we have developed over recent years, enabling the AMRC to open up another dimension of Industry 4.0 for our partners, and the wider supply chain." The hardware enables AMRC researcher engineers to shred the time it takes to develop an algorithmic model from weeks down to a few hours. As backbone to this capacity and some of the world's largest supercomputers, the IBM Power9 AC922 server is a game changer.