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Google’s DeepMind unit develops AI that predicts 3D layouts from partial images

[Credit: Google DeepMind]

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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. 

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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.

Simon is an experienced automotive reporter with a passion for electric cars and clean energy. Fascinated by the world envisioned by Elon Musk, he hopes to make it to Mars (at least as a tourist) someday. For stories or tips--or even to just say a simple hello--send a message to his email, simon@teslarati.com or his handle on X, @ResidentSponge.

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Tesla Cybercab and Robotaxi’s real advantage won’t be the ride service

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Concept art of a Tesla Cybercab in Las Vegas Strip as rendered via Grok

For a century, the car has asked the same thing of its owner, which is basically to pay attention. Even the best ride-hailing app like Uber or Lyft only moved the problem into someone else’s hands. You stopped driving, but you still sat in a stranger’s car, while half listening, and wondering whether to take a phone call in the car without being rude. The Cybercab has no wheel, no pedals and no driver. With Grok @bot now live inside it, it becomes something else entirely, and I think that matters more than the fleet count that everyone is tracking.

Grok @bot is an agent, not a voice assistant. A rider signs in once, and from then on can say what they need and watch it happen. Order dinner from a favorite restaurant. Fill the grocery cart. Move money, answer email, adjust a calendar, push a work project forward. Tesla engineer Yun-Ta Tsai confirmed it is enabled across every Cybercab and the rest of the Model Y fleet, and Tesla’s own pitch is that Full Self-Driving handles the commute while Grok handles the errands.

Think about what that does to a thirty minute ride. Uber sells you a trip, while Waymo sells you the same trip in a more private setting. Tesla is selling the thirty minutes themselves, returned to you with an assistant attached. Errands that used to eat a Saturday get done between pickup and drop off. The ride stops being dead time between two places and becomes a place of its own.

Tesla integrates Grok Bot into its vehicles for the ultimate personal assistant

Best of all, your Bots follow you into any Cybercab, and presumably your own Tesla, then logs you out automatically when you step out. Anyone can bolt a chatbot onto a screen, but much harder to own the vehicle, the autonomy stack and the assistant at once. It also quietly ties SpaceX’s Super Intelligence business to Tesla’s fleet, after Tesla converted its xAI stake into SpaceX equity. The relationship between those two companies is getting harder to describe as separate.

This is also where the hard questions start, because an agent that can spend your money in a vehicle other people also ride in needs some answers. How does it handle a voice it shouldn’t trust? What gets recorded, who can see it, and for how long? What happens when a rider leaves halfway through a task?

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If Tesla gets those answers right, the scoreboard changes. We have been counting Cybercabs and wait times. The more telling number may be what riders accomplish per trip. That is data almost no one else will ever have, and it is the real reason to watch Grok @bot.

What do you think? Leave me a comment.

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Why Elon Musk’s target date for buying an Optimus robot is harder than it sounds

Musk says China’s magnet export ban cost Optimus months, and robot hands remain shockingly hard.

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Concept rendering of Tesla Optimus in mass production

Elon Musk says China’s export curbs on magnets used in electric motors set back Tesla’s Optimus program by two to three months, and that the robot’s hands remain the hardest part of the project. He made the comments Saturday during a live interview with Tesla Owners Silicon Valley at the X Takeover event at Giga Texas, in front of roughly 2,000 attendees. The Optimus discussion begins around the 49 minute mark of the stream.

On the hands, Musk was blunt and noted that a general purpose humanoid needs fine motor skills that are “incredibly difficult to get right. Shockingly difficult,” he said in the interview. He pointed to a problem he says no one has demonstrated yet: “One of the hardest things also generally is to do precision and force at the same time,” such as driving a small self tapping screw into wood. Touch sensors add another layer, since it is “actually kind of hard to get touch sensors that elastically deform and can handle high cycle life and it won’t just break after a few thousand cycles.”

He also took aim at rival robot videos. “Any complicated looking robot demo, it’s a canned demo. It’s not real,” Musk said, adding that real AI means showing a robot something it has never seen and watching it still cope, the way FSD handles a city it has never driven.

Musk expects the Optimus ramp to be slower than a new vehicle production ramp. “The initial part of that S curve of production ramp is going to be very slow at the beginning,” Musk said, partly because “unlike cars, there’s no pre existing supply chain for robot stuff.” That echoes his July reply on X that Optimus production “will be extremely slow at first, as everything is new.”

The magnet disclosure puts a number on a problem Musk first raised in 2025. He said China “prevented the export of magnets for use in electric motors” and that it “set back the Optimus program by like two or three months.” Then he added: “We had robots with no arms.” Tesla’s fix is to bring the supply chain home. “You’ve got to be able to make all the parts domestically,” he said. Teslarati reported in April 2025 that Musk tied the issue to permanent magnets in the arm actuators, where motors have to be as small as possible.

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Tesla Optimus V3 hand and arm details revealed in new patents

Production is moving regardless, with a reported internal goal of more than 1,000 a week by the end of 2026. Teslarati recently covered how Tesla is certifying supply partners in China for Optimus parts, while the company’s hand design has been the subject of new patent filings.

Musk has said he wants Optimus available to the public by late 2027, though Tesla has not confirmed that date or published updated production figures.

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Investor's Corner

Elon Musk drops a Thanksgiving hint about Starship’s biggest test yet

Elon Musk says Starship Flight 15 will try to catch the ship itself, a first.

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Elon Musk says SpaceX’s next Starship flight will try something nobody has done before, by catching the ship itself. Speaking to roughly 2,000 people at the X Takeover event at Giga Texas on Saturday, Musk said Flight 15 is about four to six weeks away and “probably launches a little bit before Thanksgiving, is my guess.”

That is a slip from earlier reporting, which had pointed to a launch as early as October 19. Musk explained why the date keeps moving. “We’re making a bunch of improvements to the rocket between every flight,” he said, adding that there are often hundreds, sometimes thousands, of small changes. This time the list is short, “but some of them take a fair bit of work and we’ve got to go inside the tank to make some of these changes.”

“We’re going to try to catch the ship, which is a super big deal,” Musk said. “If we do so, that’ll be the first time that there’s been full recovery of an orbital class rocket.” SpaceX has already caught the Super Heavy booster with the tower’s mechanical arms, and has talked about catching the ship since late 2025, but the upper stage has never been caught.

Musk also laid out what comes after. “The next big milestone would be to refly both the booster and the ship,” he said, describing a Flight 16 where “all parts of an orbital rocket are reflown.”

The reason SpaceX wants this comes down to cost and cadence. Every Starship ship so far has ended its flight in the ocean, and SpaceX expects most of those water landings to end in fireballs. A ship that returns to Starbase intact can be inspected and flown again. SpaceX’s IPO filing, says Starship V3 is designed to deliver 100 metric tons to orbit in a fully reusable configuration, that SpaceX aims to cut the cost to reach orbit by 99% or more versus the historical average, and that tower catches should allow “multiple launches per day.”

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The payoff goes beyond cheaper launches. The next generation Starlink V3 satellites are too large for Falcon 9 and can only fly on Starship, which can carry up to 60 per flight. Flight 14 on September 28 deployed the first 26 of them after Starship reached orbit despite an early engine shutdown, as Teslarati reported. Musk blamed engine computer software, and said the heat shield “was extremely robust,” which he claimed in August had solved Starship’s biggest challenge.

NASA’s Moon plans depend on the same capability. NASA’s inspector general has described a lander mission that needs a propellant depot in low Earth orbit and more than 10 tanker flights, with SpaceX targeting one tanker launch every six days. That schedule only works if ships and boosters come back fast and fly again. Also, ship to ship propellant transfer has not been attempted yet.

One interesting hurdle yet to be explored is bringing the ship back to the launch site, with reentry over land, which will undoubtedly come with a lot of aviation safety reviews.

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