NOTE: Controls wasn't the only area of interest at PACK EXPO. CONTROLS INNOVATIONS Two PACK EXPO Las Vegas exhibitors a few aisles apart in the Lower South Hall featured analytics platforms that provide better real-time visibility into the manufacturing process. From Oden Technologies comes The Oden Platform (1). It's a comprehensive industrial Internet of Things analytics platform that provides employees at each level of a manufacturing plant with clear visibility into multiple data sets pertaining to the manufacturing process. Oden helps manufacturers monitor their production process and improve operational efficiency in real time by diagnosing problems that otherwise would have been missed. Oden helps users track performance metrics of multiple assets and accurately predict downtime based on historical data. In addition, by utilizing the platform, manufacturers can reduce bottlenecks at each stage of production and can save costs by eliminating quality issues, waste, and downtime.Photo 1 This platform is designed to help manufacturing units attain the best performance out of their manufacturing assets and leverage artificial intelligence (AI) and machine learning (ML) algorithms to empower prescriptive analytics. This allows employees on the floor to diagnose and mitigate issues as soon as they arise or offer alerts to avoid issues. In a connected manufacturing environment, companies need real-time accurate insights to improve the productivity and efficiency of their production lines. While manufacturers are increasingly willing to adopt manufacturing analytics practices, legacy equipment and limited technical know-how among machine operators are holding them back. In addition, employees on the floor are unable to make the most of the analytics tools at their disposal and are simply not achieving the expected impact on their profitability.

Nick Bostrom is a philosopher at University of Oxford and the director of the Future of Humanity Institute. He has worked on fascinating and important ideas in existential risks, simulation hypothesis, human enhancement ethics, and the risks of superintelligent AI systems, including in his book Superintelligence. I can see talking to Nick multiple times on this podcast, many hours each time, but we have to start somewhere. This conversation is part of the Artificial Intelligence podcast.

World leaders have woken up to the potential of artificial intelligence (AI) over the past year. Billions of dollars in governmental funding have been announced, dozens of hearings have been held, and nearly 20 national plans have been adopted. In the past couple of months alone, Canada and France launched the International Panel on AI (IPAI), modeled on the Intergovernmental Panel on Climate Change, to examine the global impacts of AI; the U.S. Congress created a National Security Commission on the subject; and the Pentagon tasked one of its top advisory bodies with devising ethical principles for its use of AI. "AI" broadly refers to the science and technology of machines capable of sophisticated information processing. Current applications include face recognition, image analysis, language translation and processing, autonomous vehicles, robotics, game-playing, and recommendation engines. Many more applications are likely to emerge in the coming years and decades.

Most structures and materials have defects, and if the conditions are right, these defects can lead to the initiation and propagation of cracks. Finding out where and with what orientation a surface crack is most likely to initiate is a critical part of analyzing and designing a structure. An important quantity to compute in this type of analysis is the energy release rate, which is the energy available for crack propagation. The energy release rate is compared to the fracture toughness, a material property that describes the energy required for a crack to propagate. Calculating the energy release rate for the infinite potential locations and orientations of a surface crack in a 3-D structure using conventional methods is an exhaustive task because a detailed analysis needs to be performed for every crack location and orientation. A new method developed by researchers at the University of Illinois at Urbana-Champaign can pinpoint the location and direction of a critical crack in a structure with a single analysis.

The U.S. Postal Service (USPS) said on Nov. 7 that it would average 20.5 million packages per day through the remainder of the year. That adds up to a projected 800 million package deliveries between Thanksgiving and New Year's Day. The USPS is making an investment in new artificial intelligence technology to make the processing of those millions of packages more efficient. Although it will not impact this holiday season's shipments, the USPS is testing a range of hardware and software solutions from Nvidia to speed up the processing of packages, according to a November statement. Engineering teams from the Postal Service and Nvidia have been collaborating for several months on the project.

CAMPBELL, Calif., March 26, 2020 – WekaIO (Weka) announced that The Business Intelligence Group has named Weka a winner in its Artificial Intelligence Excellence Awards program. The Weka File System (WekaFS), Weka's flagship product that is uniquely built to solve big problems, delivers the industry's best performance at any scale. WekaFS has a clean sheet design that handles the demands of new emerging and converging workloads, including artificial intelligence (AI) and machine/deep learning (ML/DL), high-performance data analytics (HPDA), and high-performance computing (HPC). The file system can deliver 80 GB/sec of bandwidth to a single GPU server, scale to Exabytes in a single namespace, and support an entire pipeline for edge-to-core-to-cloud workflows. The system also delivers operational agility with versioning, explainability, and reproducibility along with governance and compliance with in-line encryption and data protection.

That said, technologies--such as big data, cloud computing, supercomputers, artificial intelligence (AI), robotics, 3D printing, thermal imaging and 5G--are being used to effectively complement the traditional methods of increased hygiene, self- and forced quarantines, and enforced global travel bans. Having enforced traditional measures in place, for instance, police officers in China now wear AI-powered helmets that can automatically record the temperatures of pedestrians. The high-tech headgear has an infrared camera, and sounds an alarm if anyone in a radius of 16ft has fever. Equipped with the facial-recognition technology, it can also display the pedestrian's personal information, such as their name on a virtual screen. Officials at railway stations, airports and in other public areas in India, too, are using smart thermal scanners to record temperatures from a distance, thus helping in identifying potential coronavirus carriers.

According to a Brookings Institution report, "Automation and Artificial Intelligence: How machines are affecting people and places," roughly 25 percent of U.S. jobs are at a high risk of automation. Among the most vulnerable jobs are those with routine physical and cognitive tasks such as office administration, production, transportation and food preparation. The jobs that are the least vulnerable to automation are generally classified as abstract and manual occupations -- "those that involve tasks that are … difficult to codify or take place in physical environments that are difficult to control." According to the report, "abstract roles -- typically in management, technology or finance -- tend to require more formal education and skills such as creativity, persuasion, intuition and problem solving." The report predicts what automation does not replace, it will complement -- as will be the case with many technology workers.

This post describes our recent work on unsupervised domain adaptation for semantic segmentation presented at CVPR 2019. ADVENT is a flexible technique for bridging the gap between two different domains through entropy minimization. Our work builds upon a simple observation: models trained only on source domain tend to produce over-confident, i.e., low-entropy, predictions on source-like images and under-confident, i.e., high-entropy, predictions on target-like ones. Consequently by minimizing the entropy on the target domain, we make the feature distributions from the two domains more similar. We show that our approach achieves competitive performances on standard semantic segmentation benchmarks and that it can be successfully extended to other tasks such as object detection.

AI is fast becoming an amazing asset, having achieved superhuman levels of performance in domains such as image recognition, Go, and even poker. Many are excited about the future of AI and humanity. At the same time, there is a general sense that AI does suffer from one pesky flaw: AI in its current state can be unpredictably unreliable. The classical example is the Jeopardy! IBM Challenge, during which Watson, the IBM AI, cleaned the board with ease, only to miss the "Final Jeopardy!" question, which was under the category of US Cities: "Its largest airport is named for a World War II hero; its second largest for a World War II battle."