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.
Tesla has spent years trying to engineer its way out of the automotive paint shop, one of the most expensive, space-consuming, and environmentally costly steps in vehicle manufacturing. With the Cybercab, Tesla confirmed on X this week that a new reaction injection molding process will embed color directly into the panel itself during production.
“Our new reaction injection molding (RIM) process shrinks Cybercab paint cycles from hours to minutes. This cuts those parts’ manufacturing and supply chain emissions by 35% and eliminating 100% of paint volatile organic compounds (VOCs) emitted in traditional paint methods.” noted Tesla.
While the RIM process isn’t necessarily new and has existed since the 1960s, what makes Tesla’s application notable is how it is being used specifically for exterior body panels that traditionally required a separate paint process after forming.
Tesla’s RIM approach integrates the color directly into the panel material during the molding process itself. The pigment is part of the polymer mix injected into the mold, meaning the panel comes out of the mold already colored, with no separate paint application required. The clear coat or protective layer can be applied at the mold stage or through a much faster post-process than traditional multi-stage painting. Tesla claims this compresses what was a multi-hour paint cycle into minutes per panel.
Tesla’s obsession with killing the paint shop is one of the most consistent threads running through the company’s manufacturing philosophy going back years. As far back as 2018, Musk was trimming paint color options to simplify production, tweeting at the time: “Moving 2 of 7 Tesla colors off menu on Wednesday to simplify manufacturing.” Two years later, in a 2020 Automotive News interview, Musk laid out his broader vision, saying he believed Tesla factories could one day be 1,000 times more efficient than conventional plants, and pointing to the paint shop as one of the biggest sources of waste, cost, and complexity. The Cybertruck was the most extreme expression of that thinking. Tesla chose an unpainted stainless steel exterior partly because it would eliminate the need for a $200 million paint facility at Gigafactory Texas. The stainless approach proved harder and more expensive than anticipated, but the underlying ambition never changed. The Cybercab is what happens when that same ambition meets a manufacturing process that delivers on it.
Lifestyle
Tesla app update makes Robotaxi ownership make a lot more sense
Tesla’s app now shows a live indicator when your car is actively driving itself.
A recent Tesla app update, released last week (4.58.5), gives visibility on whether a vehicle is navigating in its semi-autonomous mode or being drive by a human driver. The updated app now displays a live “Self-Driving” indicator in bright blue text directly beneath the vehicle’s speed readout whenever Full Self-Driving is actively engaged, along with the signature glowing blue navigation path that FSD users see on the main touchscreen. It is a small visual update with meaningful implications for how Tesla owners monitor their vehicles remotely.
The feature was first spotted in the wild by X user Jordan Camina, who shared video of a Hardware 3 Model S displaying the new animation through the app while driving. That detail is significant because it confirms the update is not limited to newer HW4 vehicles. It works across hardware generations, and Tesla confirmed it will eventually support all vehicles regardless of chip platform once both the app and vehicle software are updated. The vehicle side requires software version 2026.20.6.1, which has reached nearly 40% of the fleet so far, as monitored by NotaTeslaApp.
The feature makes the most practical sense when viewed through the lens of Tesla’s expanding robotaxi operation. In a robotaxi context, the owner of a vehicle generating ride revenue has a direct financial and safety interest in knowing whether their car is operating under autonomous control at any given moment. The app’s new FSD indicator gives fleet owners exactly that visibility, the same way a logistics company monitors whether a delivery driver is following the planned route. It also carries implications for Tesla’s insurance model. Tesla’s own insurance product prices premiums in part based on FSD engagement rates, and real-time visibility into when FSD is active creates a feedback loop that could eventually tie directly into policy pricing. For individual owners who have opted their personal vehicles into the robotaxi network, the update effectively turns the Tesla app into a fleet management dashboard, one that tells you whether your car is earning money, whether it is driving itself to do it, and whether everything is operating the way it should from wherever you happen to be.
Tesla expands Robotaxi to Florida, marking its third state for autonomy
As Teslarati has reported, Tesla launched unsupervised robotaxi rides in Miami this summer, a milestone that makes a remote FSD status indicator significantly more practical than a cosmetic feature. When a vehicle is operating as a robotaxi without a driver present, the owner or fleet operator needs a reliable way to confirm autonomy is engaged. The app now provides exactly that.
As noted by NotATeslaApp, The update also arrived alongside a hint buried in the same app version that Tesla plans to use the cabin camera to verify driver identity before FSD can be activated. Pairing identity verification with a live autonomy status indicator points toward the infrastructure Tesla is building for a fleet of driverless vehicles that owners can monitor the way you would track a package delivery.
Elon Musk
California snubs Tesla in its newly passed EV incentive that favors Rivian and Lucid
California passed a $135 million EV incentive that rewards Rivian and Lucid while sidelining Tesla
California just drew a line in the EV incentive sand to put Tesla on the wrong side of it. The state recently passed a $135 million program offering first-time electric vehicle buyers a direct incentive with no application required, but the rules were written in a way that leaves Tesla at a structural disadvantage compared to Rivian and Lucid.
The program caps eligible vehicles at $50,000 for new EVs and $25,000 for used ones. That pricing threshold rules out a significant portion of Tesla’s lineup, though some lower-priced Model 3 and Model Y configurations would still qualify. California-based automakers are exempt from the price cap entirely, regardless of what their vehicles cost. Rivian, headquartered in Irvine, and Lucid, based in the San Francisco Bay Area, both benefit from that exemption. Rivian’s R2 starts at roughly $45,000 but has versions above the cap. Lucid’s Air and Gravity start at $70,990 and $79,990 respectively, well above any threshold a non-California company would face.
California hits Tesla Cybercab and Robotaxi driverless cars with new law
Tesla built its reputation and a significant portion of its early market share in California, where EV adoption has consistently led the nation. The company operates its original factory in Fremont, California, and the state was home to Tesla’s headquarters for most of its existence. That changed in 2021 when Tesla moved its corporate headquarters to Austin, Texas. Since then, the relationship between the company and California Governor Gavin Newsom has been openly adversarial, with Musk and Newsom trading public criticism on multiple occasions.
California’s EV incentive landscape has shifted repeatedly in recent years, and Tesla has previously lost eligibility for state-level programs as its vehicles exceeded income-adjusted price thresholds. The federal $7,500 EV tax credit, which Tesla models have qualified for and lost depending on policy cycles, is no longer available after it expired without renewal, making state-level programs more meaningful to buyers than they have been in years.
The practical impact for buyers is more nuanced than the headline suggests. California residents purchasing a Tesla under $50,000 for the first time can still access the incentive. But the exemption written for California-based manufacturers is a structural advantage that rewards where a company plants its headquarters flag rather than where it builds its products, and Tesla moved that flag to Texas.
The secret behind Tesla’s Cybercab Gold goes well beyond just the color
Tesla app update makes Robotaxi ownership make a lot more sense
