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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.
Elon Musk
Elon Musk handed Grok something no other AI company can get their hands on
Elon Musk says SpaceX will feed engineering data into Grok’s next model, avoiding restricted material.
Elon Musk said Tuesday that SpaceX will feed its internal engineering data into the next major training run for Grok, the AI model now folded into SpaceX following February’s merger. In a post on X, Musk wrote that SpaceX’s “massive corpus of world-class engineering data,” excluding anything restricted under U.S. arms export law, will be added during supplemental training of what he called the “2T run,” a reference to a roughly two trillion parameter model that would nearly double the parameters behind the latest Grok 4.5 that’s rolling out.
SpaceX’s massive corpus of world-class engineering data (excluding material blocked by ITAR) will be added during supplemental training of the 2T run.
This will dramatically improve Grok’s engineering capabilities. https://t.co/BbQEViFByn
— Elon Musk (@elonmusk) July 21, 2026
The excluded material that Musk is referring to would fall under the International Traffic in Arms Regulations (ITAR), which restricts export of technical data tied to defense and space hardware. That likely rules out propulsion specifics for Merlin and Raptor engines along with guidance and control details for SpaceX’s launch vehicles, but leaves manufacturing knowledge, materials science, and Starlink hardware design on the table.
The announcement extends a pattern that has been building since SpaceX’s Nasdaq debut in June, when the company went public with Grok and xAI’s Colossus supercomputer folded into the pitch to investors.
Days after that listing, SpaceX closed its $60 billion all stock acquisition of coding startup Cursor, giving xAI both enterprise software distribution and a stream of real world developer data to train on. Grok 4.5 launched July 8 running partly on that Cursor training data, with Musk describing it as roughly comparable to Anthropic’s Opus 4.7 but faster and cheaper to run.
Feeding SpaceX’s own engineering data into the next AI model follows the same logic Musk has applied across xAI’s sister companies. Tesla supplies real world driving data and manufacturing expertise, X supplies conversational data, and now SpaceX supplies aerospace engineering data built up since 2002.
Musk did not give a release date for the upcoming AI model, referred to elsewhere as Grok 4.6. He has said the two trillion parameter run is in its final training phase and expected to wrap this week.
Tesla has extended its Bay Area ride-hailing service to include pickups and drop-offs at San Francisco International Airport (SFO). The update, shared via the company’s official channels on July 21, allows users in the region to request rides directly to and from one of California’s busiest airports.
The expansion builds on Tesla’s secured limousine permit for SFO operations. Public records show the permit became effective March 20, 2026, and remains active through January 31, 2027. Tesla vehicles operating the service now display authorized limousine permits issued by the City and County of San Francisco.
Our Bay Area rideshare service now goes to SFO ✈️
— Tesla AI (@Tesla_AI) July 21, 2026
Tesla’s ride-hailing program in California relies on Model Y vehicles equipped with Full Self-Driving (Supervised) technology. Human safety drivers remain present in compliance with state regulations, distinguishing the service from fully driverless operations.
The Bay Area geofence covers a broad area spanning north of San Francisco to south of San Jose, offering extensive connectivity across the region.
UPDATE: Elon Musk reveals why Tesla didn’t say ‘Robotaxi’ upon California launch
This SFO addition follows earlier progress at other Bay Area airports. Tesla previously expanded service to San Jose Mineta International Airport (SJC) in late 2025. The company had engaged with SFO, SJC, and Oakland International Airport officials as early as September 2025 to secure necessary approvals for passenger transport.
The service provides a new option for travelers seeking electric, app-based transportation integrated with Tesla’s ecosystem. Rides are booked through Tesla’s dedicated ride-hailing application, which handles matching, routing, and payments. Pricing follows standard ride-hailing models, with potential adjustments based on distance, time, and demand.
Tesla’s California ride-hailing program launched in July 2025 with an initial invite-only rollout in the Bay Area. It started alongside operations in Austin, Texas, marking the company’s second major U.S. market.
The Bay Area remains a primary focus in California, with service centered on high-demand corridors connecting residential, commercial, and now major transportation hubs. This latest airport integration represents a practical step in Tesla’s broader mobility ambitions within the state.
Tesla has officially revealed its 2026 Summer Update, which comes with a variety of crazy new features, including Navigation fixes that owners have been wanting for months.
Tesla routinely releases a larger update with the Spring, Summer, Fall, and Winter updates, where it ships a variety of new features, bug fixes, and other additions to customer cars.
The 2026 Spring Update featured things like “Hey Grok” voice assistance, a redesigned self-driving app, Unreal Engine visual upgrades, and more.
🚨 TESLA’S SUMMER UPDATE FOR 2026 IS HERE:
Featuring:
✅ Self-Driving Stats in Mobile App
✅ Caraoke with Scoring
✅ Automatic Navigation
✅ Preferred Routes
✅ Set Arrival Energy from Mobile App
✅ Send Custom Wraps from Mobile App
✅ Rear Display Lock
✅ Other Improvements
🔌… https://t.co/C9IW3egEhH— TESLARATI (@Teslarati) July 21, 2026
Tesla’s Summer Release has about ten new features; we’ll show you each and detail them below:
New Grok Voice Commands
“Grok can now make phone calls, search and play music, adjust climate, open the glovebox, and answer questions about your Tesla.”
Self-Driving Stats in Mobile App
“View and share self-driving stats from the mobile app.”
Caraoke With Scoring
“Caraoke now scores your singing while in Park. High scores are saved to your Tesla profile.”
Automatic Navigation
“Automatic Navigation now adapts to your routine.
In addition to Home, Work, and upcoming calendar events, your vehicle can now suggest and route to places you visit regularly – like a school drop-off on the way to work, or the gym on the way home.”
Preferred Routes
“For a more personalized experience, navigation now prioritizes routes that you’ve taken before”
Set Arrival Energy from Mobile App
“Set your desired Arrival Energy from your phone.”
Send Custom Wraps from Mobile App
“Skip the USB drive and upload a custom wrap of your car from the mobile app. Instructions for creating a custom wrap here: https://github.com/teslamotors/custom-wraps.”
Rear Display Lock
“Kids can watch content on the rear screen, but only the front row can control it through the rear screen app.”
Other Improvements
- Find Superchargers by name when searching for a destination
- Add Apple Music songs to queue from search and artist page
- Set your preferred zoom level for the Self-Driving visualization
- Intro animations for new Model 3 and Y
Elon Musk handed Grok something no other AI company can get their hands on
Tesla expands ridesharing service in California to new hotspot
