A new AI coding tool enters the race: let’s take a closer look at Devin, a new AI coding assistant (currently in early access).
Category: robotics/AI – Page 330
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“Compared with other traditional methods, the proposed has lower computational complexity, faster operation speed, weak influence of light, and strong ability to locate dirt,” the research group said. “The improved path planning algorithm used in this study greatly improves the efficiency of UAV inspection, saves time and resources, reduces operation and maintenance costs, and improves the corresponding operation and maintenance level of photovoltaic power generation.”
The novel approach uses mathematical morphologies for image processing, such as image enhancement, sharpening, filtering, and closing operations. It also uses image histogram equalization and edge detection, among other methods, to find the dusted spot. For path optimization, it uses an improved version of the A (A-star) algorithm.
Recent advances in the field of artificial intelligence (AI) and computing have enabled the development of new tools for creating highly realistic media, virtual reality (VR) environments and video games. Many of these tools are now widely used by graphics designers, animated film creators and videogame developers worldwide.
One aspect of virtual and digitally created environments that can be difficult to realistically reproduce is fabrics. While there are already various computational tools for digitally designing realistic fabric-based items (e.g., scarves, blankets, pillows, clothes, etc.), creating and editing realistic renderings of these fabrics in real-time can be challenging.
Researchers at Shandong University and Nanjing University recently introduced a new lightweight artificial neural network for the real-time rendering of woven fabrics. Their proposed network, introduced in a paper published as part of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ‘24, works by encoding the patterns and parameters of fabrics as a small latent vector, which can later be interpreted by a decoder to produce realistic representations of various fabrics.
Carbon nanotube networks made with high purity and ultraclean interfaces can be used to make a tensor processing unit that contains 3,000 transistors in a systolic array architecture to improve energy efficiency in accelerating neural network tasks.
Constructing spatial maps from sensory inputs is challenging in both neuroscience and artificial intelligence. Gornet and Thomson show that as an agent navigates an environment, a self-attention neural network using predictive coding can recover the environment’s map in its latent space.
The “neural processing unit” is being pushed as the next big thing for “AI PCs” and “AI smartphones,” but they won’t eliminate the need for cloud-based AI.
Nvidia, the world’s largest company by value, is reportedly developing a new artificial intelligence (AI) chip based on its flagship product B200 for the China market.
The mass production of the new chip, which may be called B20, will commence later this year while shipments will start in the second quarter of next year, Reuters reported, citing sources familiar with the matter.
The report said Nvidia will work with Inspur, one of its distributors in mainland China. However, Inspur said it has not started any business and cooperation related to B20 as of now. It said the Reuters report is not true.
Propulsive motion in soft robotic systems requires the power amplification of stored energy. An accumulated strain energy-fracture power-amplification method is used to create light-driven soft robotic systems with a controlled launching ability.