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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
Tesla wins FCC approval for wireless Cybercab charging system
The decision grants Tesla a waiver that allows the Cybercab’s wireless charging system to be installed on fixed outdoor equipment.
Tesla has received approval from the Federal Communications Commission (FCC) to use Ultra-Wideband (UWB) radio technology in its wireless EV charging system.
The decision grants Tesla a waiver that allows the Cybercab’s wireless charging system to be installed on fixed outdoor equipment. This effectively clears a regulatory hurdle for the company’s planned wireless charging pad for the autonomous two-seater.
Tesla’s wireless charging system is described as follows in the document: “The Tesla positioning system is an impulse UWB radio system that enables peer-to-peer communications between a UWB transceiver installed on an electric vehicle (EV) and a second UWB transceiver installed on a ground-level pad, which could be located outdoors, to achieve optimal positioning for the EV to charge wirelessly.”
The company explained that Bluetooth is first used to locate the charging pad. “Prior to the UWB operation, the vehicular system uses Bluetooth technology for the vehicle to discover the location of the ground pad and engage in data exchange activities (which is not subject to the waiver).”
Once the vehicle approaches the pad, the UWB system briefly activates. “When the vehicle approaches the ground pad, the UWB transceivers will operate to track the position of the vehicle to determine when the optimal position has been achieved over the pad before enabling wireless power charging.”
Tesla also emphasized that “the UWB signals occur only briefly when the vehicle approaches the ground pad; and mostly at ground level between the vehicle and the pad,” and that the signals are “significantly attenuated by the body of the vehicle positioned over the pad.”
As noted by Tesla watcher Sawyer Merritt, the FCC ultimately granted Tesla’s proposal since the Cybercab’s wireless charging system’s signal is very low power, it only turns on briefly while parking, it works only at very short range, and it won’t interfere with other systems.
While the approval clears the way for Tesla’s wireless charging plans, the Cybercab does not appear to depend solely on the new system.
Cybercab prototypes have frequently been spotted charging at standard Tesla Superchargers across the United States. This suggests the vehicle can easily operate within Tesla’s existing charging network even as the wireless system is developed and deployed. With this in mind, it would not be surprising if the first batches of the Cybercab that are deployed and delivered to consumers end up being charged by regular Superchargers.
Elon Musk
Tesla posts updated FSD safety stats as owners surpass 8 billion miles
Tesla shared the milestone as adoption of the system accelerates across several markets.
Tesla has posted updated safety stats for Full Self-Driving Supervised. The results were shared by the electric vehicle maker as FSD Supervised users passed more than 8 billion cumulative miles.
Tesla shared the milestone in a post on its official X account.
“Tesla owners have now driven >8 billion miles on FSD Supervised,” the company wrote in its post on X. Tesla also included a graphic showing FSD Supervised’s miles driven before a collision, which far exceeds that of the United States average.
The growth curve of FSD Supervised’s cumulative miles over the past five years has been notable. As noted in data shared by Tesla watcher Sawyer Merritt, annual FSD (Supervised) miles have increased from roughly 6 million in 2021 to 80 million in 2022, 670 million in 2023, 2.25 billion in 2024, and 4.25 billion in 2025. In just the first 50 days of 2026, Tesla owners logged another 1 billion miles.
At the current pace, the fleet is trending towards hitting about 10 billion FSD Supervised miles this year. The increase has been driven by Tesla’s growing vehicle fleet, periodic free trials, and expanding Robotaxi operations, among others.
Tesla also recently updated the safety data for FSD Supervised on its website, covering North America across all road types over the latest 12-month period.
As per Tesla’s figures, vehicles operating with FSD Supervised engaged recorded one major collision every 5,300,676 miles. In comparison, Teslas driven manually with Active Safety systems recorded one major collision every 2,175,763 miles, while Teslas driven manually without Active Safety recorded one major collision every 855,132 miles. The U.S. average during the same period was one major collision every 660,164 miles.
During the measured period, Tesla reported 830 total major collisions with FSD (Supervised) engaged, compared to 16,131 collisions for Teslas driven manually with Active Safety and 250 collisions for Teslas driven manually without Active Safety. Total miles logged exceeded 4.39 billion miles for FSD (Supervised) during the same timeframe.
Elon Musk
The Boring Company’s Music City Loop gains unanimous approval
After eight months of negotiations, MNAA board members voted unanimously on Feb. 18 to move forward with the project.
The Metro Nashville Airport Authority (MNAA) has approved a 40-year agreement with Elon Musk’s The Boring Company to build the Music City Loop, a tunnel system linking Nashville International Airport to downtown.
After eight months of negotiations, MNAA board members voted unanimously on Feb. 18 to move forward with the project. Under the terms, The Boring Company will pay the airport authority an annual $300,000 licensing fee for the use of roughly 933,000 square feet of airport property, with a 3% annual increase.
Over 40 years, that totals to approximately $34 million, with two optional five-year extensions that could extend the term to 50 years, as per a report from The Tennesean.
The Boring Company celebrated the Music City Loop’s approval in a post on its official X account. “The Metropolitan Nashville Airport Authority has unanimously (7-0) approved a Music City Loop connection/station. Thanks so much to @Fly_Nashville for the great partnership,” the tunneling startup wrote in its post.
Once operational, the Music City Loop is expected to generate a $5 fee per airport pickup and drop-off, similar to rideshare charges. Airport officials estimate more than $300 million in operational revenue over the agreement’s duration, though this projection is deemed conservative.
“This is a significant benefit to the airport authority because we’re receiving a new way for our passengers to arrive downtown at zero capital investment from us. We don’t have to fund the operations and maintenance of that. TBC, The Boring Co., will do that for us,” MNAA President and CEO Doug Kreulen said.
The project has drawn both backing and criticism. Business leaders cited economic benefits and improved mobility between downtown and the airport. “Hospitality isn’t just an amenity. It’s an economic engine,” Strategic Hospitality’s Max Goldberg said.
Opponents, including state lawmakers, raised questions about environmental impacts, worker safety, and long-term risks. Sen. Heidi Campbell said, “Safety depends on rules applied evenly without exception… You’re not just evaluating a tunnel. You’re evaluating a risk, structural risk, legal risk, reputational risk and financial risk.”
Tesla wins FCC approval for wireless Cybercab charging system
Tesla posts updated FSD safety stats as owners surpass 8 billion miles
