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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.
Tesla is bolstering its Cybercab fleet with the State of Texas’s regulatory bodies ahead of the planned launch of the all-electric ride-hailing vehicle this Thursday.
Seven purpose-built Tesla Cybercabs have been added to Texas’s official automated vehicle registry, appearing in the Texas Motor Carrier Credentialing System (TxMCCS) public lookup just three days before Tesla’s invite-only Cybercab launch event in Austin on September 3.
The records, visible through TxDMV’s Motor Carrier and Automated Motor Vehicle Operator Lookup, list seven 2026 Tesla Cybercabs under Tesla Robotaxi, LLC. Their VINs begin with the 5YJA prefix, distinct from the 7SAYG Model Y robotaxis that already dominate Tesla’s Texas fleet.
Community trackers that scrape the same public database recorded the new entries on August 31, bringing Tesla’s authorized Texas robotaxi total to 276 vehicles: 269 Model Ys and the seven Cybercabs:
🚨 There are now SEVEN Tesla Cybercabs registered with the state of Texas
We are just three days from Cybercab! pic.twitter.com/ndwhuFYsep
— TESLARATI (@Teslarati) August 31, 2026
Texas Senate Bill 2807, which took effect in late May 2026, created a self-certification framework for commercial Level 4 operations. Operators file through TxMCCS, attest to SAE Level 4 capability, maintain insurance, and keep an active vehicle list.
Tesla completed that process months earlier for its existing Model Y Robotaxi service, which has carried paying passengers in Austin, Dallas, Houston and other markets. Adding the Cybercabs to the same authorization means the new two-seat, steering-wheel-free vehicles are now legally recognized for commercial use on Texas roads.
The timing is deliberate as Tesla scheduled the September 3 event at its Austin campus after sending invitations to selected Robotaxi riders and other guests. The company has described the evening as a chance to “experience the future of full autonomy” and plans to livestream it.
Production Cybercabs, which lack pedals and a steering wheel, have been rolling off the Giga Texas line for months; some earlier examples still carried temporary driver controls for data collection. Registering a small fleet of the finished design immediately before the public event signals that Tesla intends to move the purpose-built vehicle from factory and test tracks into the same Robotaxi app already used by Model Y passengers.
The seven units remain a tiny fraction of Tesla’s overall Texas authorization and far smaller than competing fleets. Registration does not automatically equal unsupervised public rides; it is the legal prerequisite.
Still, the sudden appearance of Cybercab VINs in the state’s lookup system, after a year of Model Y-only listings, is the clearest official confirmation yet that Tesla’s dedicated robotaxi hardware is entering the regulatory pipeline at the same moment the company is preparing to show it to invited guests and a global livestream audience.
Whether those seven vehicles appear at the September 3 event or begin carrying passengers shortly afterward, their presence in TxMCCS marks a concrete regulatory step that has been anticipated since the Cybercab concept was first revealed.
Tesla has expanded the operational geofence for its driverless Robotaxi service in Austin, Texas, marking the first such increase in some time. The updated Service Area for Robotaxi in Austin now spans about 288 square miles and is roughly 9 percent larger than the previous boundary.
This incremental growth adds approximately 24 square miles of coverage, bringing the prior zone of roughly 264 square miles into a broader footprint that better serves northern suburbs.
The expansion extends the geofence northward toward Pflugerville along the US 183 corridor, incorporating additional neighborhoods north of the Domain and areas such as Mesa Park. These additions include higher-end residential and commercial districts that previously sat just outside the allowed operating zone.
Riders can now request unsupervised trips that begin or end in these newly included locations, provided the entire route remains inside the digital boundary:
Tesla has just expanded its Robotaxi service area in Austin, Texas for the first time in 10 months.
The new service area is roughly 9% larger than the old one, and is about 288 square miles in total. pic.twitter.com/i4oBM1KfWS
— Sawyer Merritt (@SawyerMerritt) August 31, 2026
Tesla first launched public Robotaxi operations in Austin in mid-2025 with a modest initial zone of about 20 square miles. Subsequent enlargements in 2025 and early 2026 steadily grew the map until it covered much of the metropolitan area.
After the last major update roughly ten months earlier, the company held the boundary steady while it collected additional miles and refined the FSD suite.
The modest nine percent increase still matters for daily utility. Longer trips become possible, more residents gain access, and the fleet can accumulate more diverse real-world data across new road types and traffic patterns. Observers note that the added territory aligns with existing Tesla service infrastructure, which could support more efficient vehicle staging in the North end of Austin.
Although the geofence has grown, Tesla continues to operate a relatively small unsupervised fleet in the city. The company has emphasized safety and software readiness over rapid geographic scaling. This latest map update signals that Tesla remains committed to expanding Robotaxi availability in its home market as it prepares for further software improvements and potential Cybercab deployments.
The 288-square-mile zone now gives Austin riders one of the larger driverless service areas currently available in the United States.
Tesla’s 2026 Summer Update turns Grok from a talking companion into a true in-car controller, with considerably expanded capabilities. Where the assistant once answered questions and handled navigation, it can now operate hardware and software through ordinary speech, including several requests at the same time.
Grok has become incredibly robust over the past few quarters, and this new ability that Tesla has included with its Summer Update is perhaps the clearest sign of just how capable it has become.
You can issue many commands to Grok at once, and each will be taken care of: anything from headlight control to climate adjustments to mirror folding can now all be taken care of with a simple sentence spoken toward Grok:
This is cool, you can now give Grok a ton of commands at once in your Tesla and it’ll do them all together.
The commands I gave:
• Fold mirrors
• Turn on wipers to fastest setting
• Change temp to 65°
• Open self-driving app
• Open gloveboxComes with Tesla’s 2026 Summer… pic.twitter.com/drZxoHrknv
— Sawyer Merritt (@SawyerMerritt) August 26, 2026
Here’s another example I used in my Model Y:
🚨 Using Grok to control the cabin temperature and turn on headlights in 2026 Model Y on FSD v14.3.8 and 2026.26.6.5 pic.twitter.com/7clgQkGABk
— TESLARATI (@Teslarati) August 27, 2026
Tesla’s official Release Notes list the core powers of Grok’s new capabilities: Grok can place phone calls from a paired phone, search a streaming service and queue music, adjust climate, and search the Controls panel. Drivers can also ask it questions about the vehicle or the latest features included in a recent software update.
It can also take care of driving settings, dynamics, and other relevant preferences that deal with how the car operates and handles.
Tesla Summer Update begins rolling out: a look at the new features
It’s also more of a use case than a novelty feature. Grok was great for learning about something that was being pondered while Full Self-Driving was taking care of the major automotive responsibilities, but it did have some useful functionality when it came to navigation.
Another less-noted improvement is the swift transition that Grok has from thinking of its answer to giving you one. In the past, it would take a few seconds for Grok to truly come up with a valuable response. It now seems that it is improving, at least slightly.
Tesla Cybercab fleet grows in Austin ahead of launch event
Tesla expands driverless Robotaxi geofence in Austin
