US election results may influence infrastructure regulation and artificial intelligence development
Read more
Olhar Digital
olhardigital.com.br

US election results may influence infrastructure regulation and artificial intelligence development

The outcome of the mid-term elections in the United States, scheduled for this Tuesday (the 3rd), could potentially affect the advancement of a bill that proposes to change federal environmental licensing rules for energy infrastructure projects. This occurs against the backdrop of growing electricity demand needed to power data centers supporting artificial intelligence expansion.

The Bipartisan American Accessibility to Economy and Employment Act (BAAJA), developed by Republican and Democratic lawmakers, aims to alter environmental assessment and authorization procedures for large enterprises. This initiative combines changes across various legislative acts and seeks to reduce the time required for infrastructure projects to move from planning to implementation.

The text of the bill is the result of negotiations between four senators holding leadership positions in the committees on environment and public works, as well as energy and natural resources: Shelley Moore Capito and Mike Lee, who are Republicans, and Sheldon Whitehouse and Martin Heinrich, representing Democrats.

One of the key goals of BAAJA is to establish deadlines for federal environmental reviews. According to the text, the analysis of major projects should generally be completed within one to two years. The proposal also provides for closer coordination between government agencies, limits certain avenues for judicial challenge, and sets conditions for increasing the reliability of issued licenses.

The bill amends provisions in three federal environmental laws: the National Environmental Policy Act (NEPA), which regulates the assessment of impacts from work and projects; the Clean Water Act, which protects water resources; and the Endangered Species Act, aimed at conserving endangered animals and plants.

According to analysis, this reform could change the approach to analyzing projects affecting natural areas, waterways, and animal habitats. The stated goal of proponents is to prevent the endless delay in obtaining construction permits due to successive administrative stages and legal disputes. However, environmental organizations question whether reducing these stages will lead to diminished opportunities for public participation and oversight.

The proposal also concerns electricity transmission. BAAJA provides for changes to the operations of the Federal Energy Regulatory Commission (FERC), expanding its authority to authorize certain interstate lines if governmental processes stall. The initiative also adjusts criteria for such enterprises and aims to facilitate the connection between producing and consuming regions.

This aspect is particularly important because expanding electricity generation itself does not solve supply problems. Energy produced at a power plant must reach consumers through a grid capable of transporting large volumes over long distances. Without transmission lines, new generation projects may face difficulties connecting to the system, even if they have production capacity.

According to Thiago Maia, a physicist with master's and doctoral degrees in nuclear physics, an oil engineer, and an MBA holder in project management, simplifying licensing could help overcome some of these obstacles. In an interview with Olhar Digital, he explained that the construction of power plants, substations, and transmission lines could accelerate due to reduced approval times.

Similar stories

Six humanoid AgiBot G2 robots operated in a Shanghai store for 24 hours, completing 1730 tasks
Read more
pandaily.com

Six humanoid AgiBot G2 robots operated in a Shanghai store for 24 hours, completing 1730 tasks

Six humanoid robots of the AgiBot G2 model completed a 24-hour shift in a convenience store located in the Xuhui district of Shanghai. This live-streamed event ran from 10:00 on November 2nd to 10:00 on November 3rd, and according to AgiBot, online viewership exceeded two million people.

Each robot performed a strictly defined function: one greeted customers at the entrance, two handled stocking, inventory replenishment, and shelf patrolling, one prepared coffee, one managed the checkout and hot food service, and the sixth was responsible for table cleaning. According to AgiBot's final count, the six robots completed 1730 tasks, which included 683 customer receptions, 369 deliveries and goods pickups, 147 coffees prepared, 126 breakfast orders, 277 inventory replenishment and shelf checking operations, and 128 instances of table cleaning.

Four out of the six robots passed the event certification, which assessed task completion speed, reaction time, ability to handle exceptions, and customer feedback. Invited testers attempted to create difficulties for the robots by mixing regular and diet cola and requesting only the latter, creating disorder on a full pack of chewing gum shelf, as well as standing in aisles or dropping boxes in the robots' paths.

Reports indicated that the robots confirmed beverages by combining voice requests with visual verification; they restocked shelves by visually counting items instead of relying on price tag placement; and they stopped to navigate around obstacles. Minor manipulations, such as hanging candy bags on hooks or picking up a soft cake without damaging it, remained among the most challenging tasks, and the robots occasionally made errors.

All six robots were coordinated using Genie Master, an embodied agent framework from AgiBot, which links task decisions, movement execution, and feedback through its Embodied Execution Protocol. The system analyzes successful and unsuccessful runs daily, stores useful experience in long-term memory, and filters corrected attempts for use in training data, while the robots practiced during periods of lowest activity. AgiBot emphasized that the test was conducted without human involvement in the loop, meaning no human demonstrations or on-site manual adjustments.

Ren Guanghui, Vice President of AgiBot's Embodied Research Center, told the media that the company would track metrics such as task success rate, first-attempt success, response time, total cycle time, qualified deliveries per unit of time, frequency of human intervention, recovery time, and system availability. However, the figures published upon completion only concerned the number of tasks completed, and AgiBot has not yet released data on success rates or the number of interventions.

This test launch marked the conclusion of AgiBot's AI WAVE series of releases, which began on October 22nd and integrated the company's datasets, simulation platform, models, and robotic hardware onto one live platform.

Xiaomi humanoid robot completes first eight-hour shift at auto plant and begins apartment trials
Read more
pandaily.com

Xiaomi humanoid robot completes first eight-hour shift at auto plant and begins apartment trials

Xiaomi has announced that its humanoid robot has completed its first eight-hour working cycle at an automotive plant. During this shift, the robot ensured full daily production demand on the central console side cover sorting station.

This significant milestone was presented by the company during the World Internet Conference Summit in Uzhgen in 2026. There, the robotics team received the 'Leading Technology' award for the base model technology designed for large-scale robotic manipulation.

According to Xiaomi, the robot independently resolved detected anomalies during the shift and interacted with production line systems, which the company views as a preliminary check of capabilities at the line level. This launch is part of an internship at the factory that began in March. At that time, the robot worked autonomously for three hours at the self-tapping screw loading station, achieving a two-sided installation success rate of 90.2% while maintaining a 76-second line cycle.

By July, the success rate increased to 98%, and the robot took over work at two additional stations: side cover sorting and container stacking. The overall success rate across these three stations ranges from 94% to 98%.

In a domestic setting, Xiaomi has begun trial operations in the Xiaomi Youth Apartment—the company's residential complex for employees. Engineers developed an agent system built upon the pre-trained Xiaomi-Robotics-1 model. This system covers data collection, model output, and task execution, and was refined after several rounds of additional training.

During the test run, the system functioned continuously for over 30 minutes across three rooms, completing more than 50 tasks. These included tool preparation, tidying up the sofa and tables, cleaning up trash, placing items back in place, inspecting the premises, re-checking the entire area, and exiting.

A video published by Xiaomi demonstrates this launch as a single 32-minute segment. Xiaomi-Robotics-1 was introduced on July 16th and made public in August. The company reports that the model was pre-trained on over 100,000 hours of manipulation trajectories in real-world conditions, covering more than 1700 scenes and 260 types of tasks. It was then further trained on over 10,000 hours of cross-embodiment data, allowing it to adapt to mobile manipulators and two-armed robots.

The company also noted that in July, the model ranked first in the overall RoboCasa 365 and RoboDojo rankings. A separate vision, language, and action model, Xiaomi-Robotics-0, uses a hybrid architecture that, according to Xiaomi, provides an output latency of 80 milliseconds on a consumer graphics card.

Xiaomi presents both applications—industrial and domestic—as a unified model stack. The company did not disclose how many robots are participating in the apartment trials, which specific apartment is being used for testing, or how long the trial period will last.

Huawei Cloud discontinues support for openPangu-2.0 models on MaaS platform and recommends alternatives
Read more
pandaily.com

Huawei Cloud discontinues support for openPangu-2.0 models on MaaS platform and recommends alternatives

Huawei Cloud has announced plans to decommission the openPangu-2.0-Flash and openPangu-2.0-Pro model services on its MaaS (Model as a Service) platform. According to the model deprecation notice, these services will stop being sold on November 6th Beijing time and will be completely disabled on November 30th.

IT Home reported this notice, which was published on the Huawei Cloud support website on November 3rd. As part of the schedule, new users will be unable to purchase or activate the specified services after November 6th, but existing customers will retain the ability to use them until the end of the month. After November 30th, access to both models will be closed for both new and existing users. The notice lists these services in the CN Southwest-Guiyang1 Huawei Cloud region.

The company offers recommended replacements for each model. Users of openPangu-2.0-Flash version 20260630 are advised to switch to DeepSeek-V4.1-Flash-1017, while users of openPangu-2.0-Pro version 20260730 are directed to Zhipu's GLM-5.3. Huawei Cloud emphasizes that the models differ in capabilities, API parameters, pricing, and real-world performance, and advises customers to review the documentation and conduct their own assessment before switching.

The migration guide indicates that customers using the standard MaaS V1 API will need to switch to an OpenAI-compatible endpoint, whereas those already using API V2 or an OpenAI-compatible interface only need to change the model parameter.

According to IT Home, Huawei Cloud plans to release a new generation of openPangu models incrementally but has not yet disclosed their names or timelines. These two models were among Huawei's largest open releases this year: openPangu-2.0-Flash with 92 billion parameters was released at the end of June, and the sparse openPangu-2.0-Pro model with 505 billion parameters, trained on Ascend NPUs, was released at the end of July. In September, Huawei also released code for pre-training, controlled fine-tuning, and reinforcement learning for this series. However, the deprecation notice applies only to hosted MaaS services and does not affect open releases.

Model changes on the platform happen frequently. In the same bulletin, Huawei Cloud scheduled the decommissioning of GLM-5.1 on November 16th, replacing it with GLM-5.3, and is also migrating users from DeepSeek-V4-Flash to DeepSeek-V4.1-Flash and decommissioning Kimi-K2.6 on November 8th, while also listing several Qwen and Wan models for retirement.

Previously, DeepSeek-V4-Pro was decommissioned in September, and several versions of DeepSeek, Qwen, and GLM were retired in the summer. For MaaS customers, this means that workloads built on openPangu-2.0 must be migrated to other hosted platform models, at least until the next generation of openPangu is available.

Popular