Unsupervised artificial intelligence (AI) is used to carry out quality control inspections in the manufacturing industry from Maurice Sidney's blog

Even though the application of artificial intelligence to improve quality inspection is not a new concept, a joint venture between two major players in the manufacturing industry has the potential to significantly improve outcomes while also lowering entry barriers into the industry.

The new venture will be known as 'Lean AI,' which is an abbreviation for Lean Artificial Intelligence, and it will be based in the San Francisco Bay Area. The solution has emerged as a result of recent technological advancements in the form of unsupervised AI, a cutting-edge deep learning technology that does not require massive datasets, months of setup time, or well-established inspection paradigms in order to function properly. Joint venture between Johnson Electric, which has extensive knowledge and experience in the manufacturing industry, and Cortica Group, which has developed a ground-breaking autonomous artificial intelligence technology for visual inspection, has resulted in the establishment of the new company. Having extensive knowledge and experience in the manufacturing industry, Johnson Electric is a great choice.

As a result of the combined power of Cortica's Autonomous AI technology and JE's extensive market knowledge, Karina Odinaev, CEO of Lean AI, believes that the company will deliver a product that reduces the cost of human error when it comes to quality inspection in manufacturing, as well as address the vulnerabilities in the current market.

In order to overcome the difficulties it is currently experiencing, Lean AI is implementing a newer generation of unsupervised deep learning-based quality assurance technology to help it overcome these difficulties. Cortica's technology, according to Igal Raichelgauz, the company's founder and chairman, requires significantly less computing power than traditional methods, can be deployed at a fraction of the cost of traditional methods, and produces significantly better performance outcomes than traditional methods. Besides being robust and generic, our technology can be applied to a variety of signal domains, including but not limited to the following: visual signals, audio signals, time series signals, and other signal domains. In terms of capabilities, visual inspection is only the beginning; audio and time series analysis are also possibilities. Technology based on intelligent automation, also known as autonomous artificial intelligence (AI), is quickly becoming the de facto standard in the world of business.