News
Google’s DeepMind unit develops AI that predicts 3D layouts from partial images
Google’s DeepMind unit, the same division that created AlphaGo, an AI that outplayed the best Go player in the world, has created a neural network capable of rendering an accurate 3D environment from just a few still images, filling in the gaps with an AI form of perceptual intuition.
According to Google’s official DeepMind blog, the goal of its recent AI project is to make neural networks easier and simpler to train. Today’s most advanced AI-powered visual recognition systems are trained through the use of large datasets comprised of images that are human-annotated. This makes training a very tedious, lengthy, and expensive process, as every aspect of every object in each scene in the dataset has to be labeled by a person.
The DeepMind team’s new AI, dubbed the Generative Query Network (GQN) is designed to remove this dependency on human-annotated data, as the GQN is designed to infer a space’s three-dimensional layout and features despite being provided with only partial images of a space.
Similar to babies and animals, DeepMind’s GQN learns by making observations of the world around it. By doing so, DeepMind’s new AI learns about plausible scenes and their geometrical properties even without human labeling. The GQN is comprised of two parts — a representation network that produces a vector describing a scene and a generation network that “imagines” the scene from a previously unobserved viewpoint. So far, the results of DeepMind’s training for the AI have been encouraging, with the GQN being able to create representations of objects and rooms based on just a single image.
As noted by the DeepMind team, however, the training methods that have been used for the development of the GQN are still limited compared to traditional computer vision techniques. The AI creators, however, remain optimistic that as new sources of data become available and as improvements in hardware get introduced, the applications for the GQN framework could move over to higher-resolution images of real-world scenes. Ultimately, the DeepMind team believes that the GQN could be a useful system in technologies such as augmented reality and self-driving vehicles by giving them a form of perceptual intuition – extremely desirable for companies focused on autonomy, like Tesla.
-->
Google DeepMind’s GQN AI in action. [Credit: Google DeepMind]
In a talk at Train AI 2018 last May, Tesla’s head of AI Andrej Karpathy discussed the challenges involved in training the company’s Autopilot system. Tesla trains Autopilot by feeding the system with massive data sets from the company’s fleet of vehicles. This data is collected through means such as Shadow Mode, which allows the company to gather statistical data to show false positives and false negatives of Autopilot software.
During his talk, Karpathy discussed how features such as blinker detection become challenging for Tesla’s neural network to learn, considering that vehicles on the road have their turn signals off most of the time and blinkers have a high variability from one car brand to another. Karpathy also discussed how Tesla has transitioned a huge portion of its AI team to labeling roles, doing the human annotation that Google DeepMind explicitly wants to avoid with the GQN.
Musk also mentioned that its upcoming all-electric supercar — the next-generation Tesla Roadster — would feature an “Augmented Mode” that would enhance drivers’ capability to operate the high-performance vehicle. With Tesla’s flagship supercar seemingly set on embracing AR technology, the emergence of new techniques for training AI such as Google DeepMind’s GQN would be a perfect fit for the next generation of vehicles about to enter the automotive market.
News
Elon Musk makes a key Tesla Optimus detail official
“Since we are naming the singular, we will also name the plural, so Optimi it is,” Musk wrote on X.
Tesla CEO Elon Musk just made a key detail about Optimus official. In a post on X, the CEO clarified some key wording about Optimus, which should help the media and the public become more familiar with the humanoid robot.
Elon Musk makes Optimus’ plural term official
Elon Musk posted a number of Optimus-related posts on X this weekend. On Saturday, he stated that Optimus would be the Von Neumann probe, a machine that could eventually be capable of replicating itself. This capability, it seems, would be the key to Tesla achieving Elon Musk’s ambitious Optimus production targets.
Amidst the conversations about Optimus on X, a user of the social media platform asked the CEO what the plural term for the humanoid robot will be. As per Musk, Tesla will be setting the plural term for Optimus since the company also decided on the robot’s singular term. “Since we are naming the singular, we will also name the plural, so Optimi it is,” Musk wrote in his reply on X.
This makes it official. For media outlets such as Teslarati, numerous Optimus bots are now called Optimi. It rolls off the tongue pretty well, too.
Optimi will be a common sight worldwide
While Musk’s comment may seem pretty mundane to some, it is actually very important. Optimus is intended to be Tesla’s highest volume product, with the CEO estimating that the humanoid robot could eventually see annual production rates in the hundreds of millions, perhaps even more. Since Optimi will be a very common sight worldwide, it is good that people can now get used to terms describing the humanoid robot.
-->During the Tesla 2025 Annual Shareholder Meeting, Musk stated that the humanoid robot will see “the fastest production ramp of any product of any large complex manufactured product ever,” starting with a one-million-Optimi-per-year production line at the Fremont Factory. Giga Texas would get an even bigger Optimus production line, which should be capable of producing tens of millions of Optimi per year.
News
Tesla is improving Giga Berlin’s free “Giga Train” service for employees
With this initiative, Tesla aims to boost the number of Gigafactory Berlin employees commuting by rail while keeping the shuttle free for all riders.
Tesla will expand its factory shuttle service in Germany beginning January 4, adding direct rail trips from Berlin Ostbahnhof to Giga Berlin-Brandenburg in Grünheide.
With this initiative, Tesla aims to boost the number of Gigafactory Berlin employees commuting by rail while keeping the shuttle free for all riders.
New shuttle route
As noted in a report from rbb24, the updated service, which will start January 4, will run between the Berlin Ostbahnhof East Station and the Erkner Station at the Gigafactory Berlin complex. Tesla stated that the timetable mirrors shift changes for the facility’s employees, and similar to before, the service will be completely free. The train will offer six direct trips per day as well.
“The service includes six daily trips, which also cover our shift times. The trains will run between Berlin Ostbahnhof (with a stop at Ostkreuz) and Erkner station to the Gigafactory,” Tesla Germany stated.
Even with construction continuing at Fangschleuse and Köpenick stations, the company said the route has been optimized to maintain a predictable 35-minute travel time. The update follows earlier phases of Tesla’s “Giga Train” program, which initially connected Erkner to the factory grounds before expanding to Berlin-Lichtenberg.
-->Tesla pushes for majority rail commuting
Tesla began production at Grünheide in March 2022, and the factory’s workforce has since grown to around 11,500 employees, with an estimated 60% commuting from Berlin. The facility produces the Model Y, Tesla’s best-selling vehicle, for both Germany and other territories.
The company has repeatedly emphasized its goal of having more than half its staff use public transportation rather than cars, positioning the shuttle as a key part of that initiative. In keeping with the factory’s sustainability focus, Tesla continues to allow even non-employees to ride the shuttle free of charge, making it a broader mobility option for the area.
News
Tesla Model 3 and Model Y dominate China’s real-world efficiency tests
The Tesla Model 3 posted 20.8 kWh/100 km while the Model Y followed closely at 21.8 kWh/100 km.
Tesla’s Model 3 and Model Y once again led the field in a new real-world energy-consumption test conducted by China’s Autohome, outperforming numerous rival electric vehicles in controlled conditions.
The results, which placed both Teslas in the top two spots, prompted Xiaomi CEO Lei Jun to acknowledge Tesla’s efficiency advantage while noting that his company’s vehicles will continue refining its own models to close the gap.
Tesla secures top efficiency results
Autohome’s evaluation placed all vehicles under identical conditions, such as a full 375-kg load, cabin temperature fixed at 24°C on automatic climate control, and a steady cruising speed of 120 km/h. In this environment, the Tesla Model 3 posted 20.8 kWh/100 km while the Model Y followed closely at 21.8 kWh/100 km, as noted in a Sina News report.
These figures positioned Tesla’s vehicles firmly at the top of the ranking and highlighted their continued leadership in long-range efficiency. The test also highlighted how drivetrain optimization, software management, and aerodynamic profiles remain key differentiators in high-speed, cold-weather scenarios where many electric cars struggle to maintain low consumption.
Xiaomi’s Lei Jun pledges to continue learning from Tesla
Following the results, Xiaomi CEO Lei Jun noted that the Xiaomi SU7 actually performed well overall but naturally consumed more energy due to its larger C-segment footprint and higher specification. He reiterated that factors such as size and weight contributed to the difference in real-world consumption compared to Tesla. Still, the executive noted that Xiaomi will continue to learn from the veteran EV maker.
“The Xiaomi SU7’s energy consumption performance is also very good; you can take a closer look. The fact that its test results are weaker than Tesla’s is partly due to objective reasons: the Xiaomi SU7 is a C-segment car, larger and with higher specifications, making it heavier and naturally increasing energy consumption. Of course, we will continue to learn from Tesla and further optimize its energy consumption performance!” Lei Jun wrote in a post on Weibo.
-->Lei Jun has repeatedly described Tesla as the global benchmark for EV efficiency, previously stating that Xiaomi may require three to five years to match its leadership. He has also been very supportive of FSD, even testing the system in the United States.
Elon Musk makes a key Tesla Optimus detail official
Tesla is improving Giga Berlin’s free “Giga Train” service for employees
