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Secret code or foreign language? For machines, it might not matter. Without any prior knowledge, artificial intelligence algorithms have cracked two classic forms of encryption: the Caesar cipher and Vigenère cipher. As translating languages is similar to decoding a cipher, the approach may improve translation software.
Robotics and AI technology is rapidly changing the way businesses operate. From manufacturing to healthcare, AI and robotics transform how businesses produce, deliver, and manage products and services. In the Highway Maintenance industry, robotics and AI technology can help to improve safety, reduce labor costs, and increase efficiency. Like many industries, robotics and AI technology can improve safety in the Highway Maintenance industry by reducing the need for human operators to perform dangerous, dirty, and disengaging tasks. By using robots to perform hazardous jobs such as crack sealing, the number of accidents and fatalities among drivers and maintenance crews can be reduced.
While the RMV® utilizes AI vision, it holds the capability of scanning and reading cracks in the road that need improvements. In this case, the AI vision can be used to quickly and accurately identify, measure, map out, and assess road conditions. This information can then be used to prioritize the repair and maintenance of roads as it is communicated to the robot. AI can also detect other deformations in the road surface, such as potholes, that may require further repair or attention from a manual crew. AI vision is an integral part of the Robotic Crack Sealer and helps to enhance the future of crack sealing, and road maintenance as a whole. As AI and robotics work together, RMV utilizes both to identify the best points for applying the sealant for repairs. The AI vision system can also be used to assess the condition of the road surface to determine the most suitable option for distress. The vision system can identify the best points to start and end the sealing process, ensuring a more accurate and efficient sealing job. It can also be used to monitor and adjust the sealant application rate, allowing for a more consistent and effective seal. This helps to reduce the risk of over-application or under-application of sealant, resulting in a longer-lasting and more effective seal. With RMV integrating AI vision into the process of crack sealing, the Robotic Crack Sealer is able to provide a safer, more accurate, efficient, and cost-effective solution for keeping roads in top condition.
Back in 1975, Cher had an eponymous TV music-variety show. It only lasted one season but in its short time it had some big stars as guests, including the Jackson 5. In this clip, watch Michael start off the robot dance with Cher, with his brothers soon joining in. So smooth! The first video below includes a 2003 audio interview with Cher about the episode: "I think of how hard it was for me to learn to do that. The guys just knew how to do it." (via Historic Vids)
For new technology to have a positive impact transparency and cooperation matter. Back in 2013, we started with Poppy, the first 3D printed open-source humanoid robot and since then we have been dedicated to creating open source, open science and open data products.
SealMaster Pavement Products & Equipment and Pioneer Industrial Systems (PIS) have introduced the CrackPro Robotic Maintenance Vehicle (RMV). Minimizing labor and keeping crew members off the road, it requires a driver and a person who stays on the back of the unit to monitor application and replenish the crack sealant.
Canon is currently developing an AI technology capable of detecting cracks and other defects, which will enhance the efficiency of inspection work related to the maintenance and management of social infrastructure (bridges, tunnels, etc).
As a leading manufacturer in the field of imaging, Canon is applying the image-related AI technologies it has cultivated over the years to develop technology capable of detecting cracks in concrete structures. The image-based inspection system that Canon is developing comprises three processes:1. Image capture2. Image processing3. Defect detectionEach process helps to enhance the precision and work efficiency of inspections.
In this phase, tilt correction is performed to correct the angle of the captured image so as to obtain a front-facing view of the structure. The camera image is then precisely overlaid onto the drawing of the structure to enable accurate evaluation of cracks. Pillars and other objects obstructing the wall can be removed from the final image by merging multiples images captured from different angles.
When developing the defect detection AI system, it was necessary for the system to learn from data that correctly indicated the position of a crack. However, unfamiliar with the field, Canon developers lacked a basic understanding of the nature of these cracks. To that end, joint research was conducted with Tosetsu Civil Engineering Consultant Inc., who have a wealth of experience image-based inspection. This collaboration led to the development of a defect detection AI system suitable for such practical applications as merging defect detection results.
Importing assets into robotics simulators is critically important and oftentimes a significant challenge when setting up a training or testing scenario. Using the powerful connector capabilities built into Omniverse, Isaac Sim has built-in support for popular product design formats. The advanced URDF importer has been tested on multiple robot models. Additionally, CAD files can be imported directly from Onshape and from STEP files with minimal post-processing. To make it easier to add assets to different environments, Isaac Sim supports Shapenet. The Shapenet importer provides access to a massive amount of 3D assets.
The United States should develop a data strategy that promotes innovation and consumer protection. Right now, there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design. AI requires data to test and improve its learning capacity.50 Without structured and unstructured data sets, it will be nearly impossible to gain the full benefits of artificial intelligence.
For these reasons, both state and federal governments have been investing in AI human capital. For example, in 2017, the National Science Foundation funded over 6,500 graduate students in computer-related fields and has launched several new initiatives designed to encourage data and computer science at all levels from pre-K to higher and continuing education.57 The goal is to build a larger pipeline of AI and data analytic personnel so that the United States can reap the full advantages of the knowledge revolution.
Federal officials need to think about how they deal with artificial intelligence. As noted previously, there are many issues ranging from the need for improved data access to addressing issues of bias and discrimination. It is vital that these and other concerns be considered so we gain the full benefits of this emerging technology.
If interpreted stringently, these rules will make it difficult for European software designers (and American designers who work with European counterparts) to incorporate artificial intelligence and high-definition mapping in autonomous vehicles. Central to navigation in these cars and trucks is tracking location and movements. Without high-definition maps containing geo-coded data and the deep learning that makes use of this information, fully autonomous driving will stagnate in Europe. Through this and other data protection actions, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.
But so far, anything close to a robot president was limited to those kinds of stories. Maybe not for much longer. In fact, true believers like Istvan say our computer leader could be here in less than 30 years.
If you are invited to a pre-recorded, video interview through a platform, such as HireVue, pay attention. This is likely a robot interview. AI, or algorithm-based interviews, are used by many large employers. These interviews allow companies such as Hilton, Under Armor, BASF, UnitedHealth Group, and the U.S. Postal Service to interview large numbers of potential candidates more efficiently than they otherwise could. Instead of scheduling phone screens with 10 potential candidates, for example, AI-enabled hiring software can quickly scan interviews with hundreds of applicants.
Engineering inspection and maintenance technologies play an important role in safety, operation, maintenance and management of buildings. In project construction control, supervision of engineering quality is a difficult task. To address such inspection and maintenance issues, this study presents a computer-vision-guided semi-autonomous robotic system for identification and repair of concrete cracks, and humans can make repair plans for this system. Concrete cracks are characterized through computer vision, and a crack feature database is established. Furthermore, a trajectory generation and coordinate transformation method is designed to determine the robotic execution coordinates. In addition, a knowledge base repair method is examined to make appropriate decisions on repair technology for concrete cracks, and a robotic arm is designed for crack repair. Finally, simulations and experiments are conducted, proving the feasibility of the repair method proposed. The result of this study can potentially improve the performance of on-site automatic concrete crack repair, while addressing such issues as high accident rate, low efficiency, and big loss of skilled workers.
Cracks on the surfaces of concrete engineering structures are among the earliest indicators of structural deterioration. Structures suffer from fatigue stress and cyclic loading (Tedeschi & Benedetto, 2017). As a result of external loads, minute cracks on concrete surfaces may produce interconnected passageways, which will worsen the safety of structures (Algaifi et al., 2018). Thus, civil engineers face the challenge of reducing the harm caused by deteriorating structures. In this regard, intelligent technology for unmanned detection and repair is necessary. 2b1af7f3a8