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Deep learning is one of the most dominant software breakthroughs. It is sweeping across many industries around the world. What makes deep learning unique is that it uses data to automate the programming process end-to-end. Deep learning takes the idea of machine learning to its logical conclusion and replaces the entire program with stages that can be trained with data. The result is that programs can be far more capable and accurate while requiring less human effort to create. In particular, the revival of deep learning methods improved the performance on many basic level tasks by leveraging large amounts of data in a learning framework. Three factors have contributed to the recent breakthroughs in deep learning are faster processor performance, larger data sets, and more complex neural networks.

The most impressive accomplishments of deep learning have been in the field of computer vision. Prior to modern deep learning, computer vision had limited commercial development. Deep learning has dramatically reduced error rates in computer vision, especially in image classification, localization, object detection, object segmentation, image colorization, and image reconstruction.

AI-innovate applies deep learning technology in a several image processing and video analytics applications, from assisting medical practitioners with computer-aided diagnosis, to detecting defects in manufacturing lines, to recognizing safety measures in jobsites.

A digital twin is a virtual model of a process, product, or service. In manufacturing, the digital twin is a virtual representation of the as-designed, as-built, and as-maintained physical product, augmented by real-time process data and analytics based on accurate configurations of the physical product, production systems, or equipment. This pairing of the virtual and physical worlds, in essence, allows analysis of data and monitoring of systems to head off problems before they even occur, prevent downtime, optimize performance, and even plan for the future by using simulations.

Thus, the digital twin is a bridge between the physical and digital world. While virtual models are conceptual in nature, real-time and operational data is a digital representation of real physical events. CAD models represent the virtual fit, form, and function of the digital twin’s physical counterpart.  However, real-time operational and asset data are required to execute analytics applications that define the state and behavior of the performance-based digital twin and allow optimization and process improvement.

Digital twin includes partial or entire components of the IoT: smart components that use sensors to gather data about real-time status, working condition, or position are integrated with a physical item. The components are connected to a cloud-based system that receives and processes all the data the sensors monitor. This input is analyzed against business and other contextual data.

Today’s advanced virtual simulation technology is an integral component of the digital twin. Comprehensive simulation platforms can simulate and validate the functionality of product design concurrently, enabling the designers to validate their designs as they go. In the context of the digital twin, real-time sensor data can be used to populate simulation applications that then emulate the physical product and enable design improvements. Customers are offered the use of the product/equipment along with complete maintenance and operational optimization based on the predictive/prescriptive capabilities of the digital twin. The manufacturer maintains ownership of the equipment while providing the service of maintenance based on a digital twin as a more manageable and profitable business model.

Digital twins are powerful masterminds to drive innovation and performance, and this aspect makes it very important to businesses. It is equivalent to the most talented product technicians with the most advanced monitoring, analytical, and predictive capabilities at their fingertips, but significantly faster and more accurate. Increasing performance efficiency, decreasing the cost of production, and almost eliminating the probability of error are the immediate advantages of digital twin.

Data analysis is the ability to take raw data and turn it into meaningful insights.The demand for this super power is skyrocketing. Data analysts find, collect, capture, inspect, and transform data. They add functions and formulas, and then they merge data sets so that they can further analyze it with pivots, charts and other visualizations.

All data has a purpose and a data analyst seeks methods to achieve that purpose by providing insight and informed decision-making. Learning about data from existing reports can help data analysts get ahead of the learning curve of the data. Data analysts typically start analyzing the reports that are currently used. They monitor the way a business is using data to make their decisions, determine categories, and establish rules.

Data analysis workflow is made up of raw data collection, data cleaning, data interpratation, and data modeling. The model can be used to make reports, describe behavior, predict results, find root causes, and evaluate action impacts.

AI-innovate experts take advantage of data analysis techniques to make the best use of data in various industries including manufactruing, infracstructure, healthcare, and agriculture.

Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and take actions or make recommendations. Once the data is transferred to a computer, sophisticated computer vision software is used to analyze and manipulate the image, extract information, and generate decisions based on information.

Vision systems include PC-based application-specific systems, fully integrated systems such as smart cameras, embedded vision processors, and vision sensors. Advanced vision systems have the capability to deliver accurate information based on the optimized technology, even at high rates of throughput and with short cycle times.

Embedded vision is the integration of visual means and processing boards, instead of computers. These systems are economical, easy to use, and require minimum maintenance. Embedded vision systems are used in applications such as robotics, industrial vision, security, autonomous vehicles in agriculture, and digital dermatoscopes.

Machine vision is a highly specialized technology that ensures the products quality. Machine vision systems are capable of dealing with complex repetitive activities with improved and advanced accuracy and consistency. Although machine vision systems are flexible and have versatile features, a machine vision system designed for one task may not be suited for any other. Application-specific machine vision systems address particular challenges across industries. Applications such as production line inspection and motion control still face a few obstacles, especially on real-time image processing. That being said, Advances in artificial intelligence, innovative machine learning, and deep learning processes can ensure higher degree of automation and more reliable identification.

AI-innovate team seeks for approaches that suit the unique requirements of each challenge of each customer in each industry.