This commentary tests a methodology proposed by Munk et al. (2022) for using failed predictions in machine learning as a method to identify ambiguous and rich cases for qualitative analysis. Using a dataset describing actions performed by fictional characters interacting with machine vision technologies in 500 artworks, movies, novels and videogames, I trained a simple machine learning algorithm (using the kNN algorithm in R) to predict whether or not an action was active or passive using only information about the fictional characters. Predictable actions were generally unemotional and unambiguous activities where machine vision technologies were treated as simple tools. Unpredictable actions, that is, actions that the algorithm could not correctly predict, were more ambivalent and emotionally loaded, with more complex power relationships between characters and technologies. The results thus support Munk et al.'s theory that failed predictions can be productively used to identify rich cases for qualitative analysis. This test goes beyond simply replicating Munk et al.'s results by demonstrating that the method can be applied to a broader humanities domain, and that it does not require complex neural networks but can also work with a simpler machine learning algorithm. Further research is needed to develop an understanding of what kinds of data the method is useful for and which kinds of machine learning are most generative. To support this, the R code required to produce the results is included so the test can be replicated. The code can also be reused or adapted to test the method on other datasets.

RAILWAY AGE, SEPTEMBER 2020 ISSUE: Whether it's the track structure or the equipment that operates on it, there are many things that the naked eye cannot readily see. Increasingly, machine vision technology is becoming the best way to identify potential flaws before they lead to failures. "The various machine vision technologies deployed detect thousands of conditions each year that could potentially lead to accidents," says Robert Coakley, Director of Business Development, ENSCO Rail. Compared to manual visual inspections, he says, autonomous machine vision offers advantages of speed, reduced track occupancy, inspection frequency and consistency. The equipment is installed on revenue service trains, can perform inspections at track speed and does not require the additional occupancy of a hi-rail vehicle.

Last week we took a look at computer vision; what it is, how it works, and some of the applications for computer vision in agtech. In case you missed last week's article, computer vision or machine vision typically refers to the use of machine learning or deep learning algorithms in image processing to allow a machine to "see" and identify objects around it. Different computer vision technologies may use a variety of camera types to act as the machine's "eyes" depending on the imaging requirements. In the case of fully autonomous vehicles, an accurate computer vision system is essential. In typical vehicles, hazard detection, navigation, and object avoidance all depend on a human operator.

The implementation of machine vision in finance is proving to be beneficial not only to businesses but also to consumers in general. Although it seemed far-fetched five years ago, financial institutions have been using advanced technologies in their operations. These technologies enable organizations to improve the customer experience and lighten employees' workloads. The global machine vision market is expected to grow at a CAGR of 7.7% to reach USD 18.24 billion by 2025. There, however, remains a certain level of skepticism amongst the institutes and consumers in the adoption of machine vision in the finance sector. Machine learning is slowly but surely making inroads in the finance sector with major banks already leveraging its ease-of-use mechanism.

The implementation of machine vision in finance is proving to be beneficial not only to businesses but also to consumers in general. Although it seemed far-fetched five years ago, financial institutions have been using advanced technologies in their operations. These technologies enable organizations to improve the customer experience and lighten employees' workloads. The global machine vision market is expected to grow at a CAGR of 7.7% to reach USD 18.24 billion by 2025. There, however, remains a certain level of skepticism amongst the institutes and consumers in the adoption of machine vision in the finance sector.

Advances in 3D imaging have allowed vision users to overcome some challenging inspection tasks. In the machine vision marketplace, 3D imaging continues to mature, tackling applications 2D imaging cannot. "In a manufacturing setting, the fusion of 2D with 3D is necessary to measure how well components go together into an assembly and assess the product for final fit, finish, and packaging," says Terry Arden, CEO of LMI Technologies. According to David Dechow, Principal Vision Systems Architect at Integro Technologies, a systems integrator specializing in machine vision technologies with broad experience in helping companies implement 3D and 2D imaging for industrial automation, accuracy has improved as well. And with inspection tasks in 3D space, which may include measurement or reconstruction, precision is even more essential than with most tasks in robotic guidance or bin picking.

Niccolo Mejia covers AI applications across industries at Emerj. He holds a bachelor's degree in Writing, Literature, and Publishing from Emerson College. In this article, we explore the applications of AI software within the automotive industry from production and manufacturing to insurance and transportation. We will discuss the equipment involved in collecting and analyzing data along with the potential value they offer to manufacturers, shared mobility companies, insurers, and drivers. We begin our overview of AI in the automotive industry with how machine vision technology could improve the robots that car manufacturers use to build vehicles and maintain quality control.

Rather, this new reality applies to the cameras enabled with machine vision capabilities that are increasingly being placed inside advanced production facilities. They are radically transforming manufacturing processes, while simultaneously boosting productivity, efficiency and quality. Machine vision refers to the process of using an image to extract information, then leveraging the information to confirm presence or absence, check position or orientation and to spot patterns or exceptions resulting from the analysis of multiple sequential images over time. These capabilities are being applied in a variety of ways to revolutionize manufacturing and production processes. For example, machine vision can serve as a basis for automation and collaboration with robots deployed for manufacturing processes.

Despite cultural and religious exclusions, pork is the most widely eaten meat in the world, accounting for 36% of all meat consumed. Asia is the biggest consumer; China alone accounts for one quarter of global pig trade, and its middle class is expected to double in the next 15 years, increasing even more the market for pork. While trade will certainly be a consideration among top producing countries, such as China itself, the US, Brazil, Spain and Russia, there are challenges for the industry that could be reduced, or even eliminated, with the implementation of digital and other data driven technologies. Processors focus on employee safety and welfare priorities even while trying to improve meat production efficiencies. For producers and farmers disease mitigation, animal health and performance are top of mind.