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.
SpaceX is preparing for the 13th integrated flight test of its Starship system, with a targeted launch as early as Thursday, July 16. The 90-minute launch window opens at 5:45 p.m. CT from Starbase in South Texas.
This comes roughly seven weeks after Flight 12 on May 22, underscoring the company’s accelerating pace in its rapid development campaign. The mission will use the latest Starship and Super Heavy V3 vehicles equipped with Raptor 3 engines. Booster 20 will attempt a controlled boostback burn, followed by a splashdown in the Gulf of Mexico, while Ship 40 will follow a suborbital trajectory.
Starship’s thirteenth flight test is preparing to launch as early as Thursday, July 16 → https://t.co/Rp7VwBzpWx pic.twitter.com/jdpFlQUEpF
— SpaceX (@SpaceX) July 11, 2026
Key objectives for Flight 13 will include demonstrating reliable stage separation, engine performance under various conditions, and controlled reentry.
A major milestone for Flight 13 is the first deployment of 20 next-generation Starlink V3 satellites. These satellites feature advanced laser links for inter-satellite communication, deployable solar arrays, and onboard cameras, six of which will capture imagery of Starship’s heat shield during flight.
Several heat shield tiles on Ship 40 will be painted white to serve as imaging targets, while additional experiments test upgraded tiles on aft flaps, modified attachments on the aft skirt, and load-sensing tiles to measure stresses. The upper stage will also attempt a single Raptor engine relight in space before a targeted splashdown in the Indian Ocean.
These tests build directly on lessons from Flight 12, which introduced the V3 configuration but encountered issues including a booster flip anomaly during boostback and an engine-out event on the ship. Hardware and software modifications on Booster 20 and Ship 40 aim to improve engine relight reliability, startup sequencing, and overall robustness.
Next Starship launch aiming for Thursday https://t.co/SajPPd4pdb
— Elon Musk (@elonmusk) July 12, 2026
The short interval between Flights 12 and 13 highlights SpaceX’s iterative approach. Elon Musk has repeatedly emphasized that Starship launches will become “incredibly common” in the coming years.
The company envisions scaling to rates as high as one launch per hour within 4-5 years, potentially enabling thousands of flights annually. Such cadence is essential for Starship’s goals: establishing orbital refueling for lunar and Mars missions, deploying massive satellite constellations, and making life multiplanetary.
With each flight, Starship edges closer to full reusability and operational maturity. Success on July 16 would mark another step toward routine access to space and the ambitious vision of humanity becoming a spacefaring civilization.
Tesla shared a striking video showcasing the decommissioning of the original Model S and Model X assembly line at its Fremont Factory in Northern California. Completed in just 46 days, the teardown involved heavy machinery dismantling concrete pits, removing robotic arms and conveyors, and clearing the space for new production.
The post, captioned “End of an era,” captured both the end of a historic chapter and Tesla’s aggressive pivot toward its next major initiative, Optimus.
End of an era: Decommissioning the original Model S & X assembly line in just 46 days pic.twitter.com/kGEdfhl62h
— Tesla Manufacturing (@gigafactories) July 10, 2026
The decision to retire the Model S and Model X originated during Tesla’s Q4 2025 Earnings Call in late January 2026. CEO Elon Musk announced that production of the company’s flagship sedan and SUV would wind down by the end of Q2 2026, describing it as bringing the programs to an “honorable discharge.”
Custom orders ceased around early April 2026, with the final vehicles rolling off the line in early May. A special signature delivery ceremony on May 20 marked the emotional close for these vehicles, which had defined Tesla’s early success and luxury EV segment since the Model S launch in 2012.
The primary reason for tearing down the lines was to repurpose the valuable factory floor space for high-volume production of Tesla’s Optimus humanoid robot. Musk had indicated on Earnings Calls that the Fremont S/X line would be replaced by a dedicated Optimus manufacturing line targeting a capacity of one million units per year.
This move aligns with Tesla’s broader strategic shift from traditional vehicle manufacturing toward robotics and artificial intelligence, leveraging the company’s expertise in autonomy, AI training, and high-volume production.
Optimus, Tesla’s general-purpose humanoid robot, is designed to perform repetitive or dangerous tasks in factories, warehouses, and eventually homes. Powered by Tesla’s AI and Neural Networks, it aims to be a versatile, affordable platform. Production of Optimus Gen 3 is already underway in limited form at Fremont, with full-scale output on the converted line expected to begin in late July or August.
Tesla is targeting rapid scaling, with internal ambitions pointing toward tens or even hundreds of thousands of units annually by the end of 2026.
Longer-term, Tesla is constructing a much larger second-generation Optimus facility at Giga Texas, with potential capacity reaching millions of units per year. The company views Optimus as a transformative product that could eventually surpass its automotive business in scale and value, enabling widespread deployment of useful robots across industries. CEO Elon Musk has even predicted it would be the most popular product of all-time.
As one era closes at Fremont, another is rapidly taking shape.
Elon Musk posted a candid admission on his social media platform X on June 9, declaring that he had been “clearly wrong” about Anthropic. The statement marked a notable reversal from his earlier skepticism toward the AI company.
In September, Musk had written, “Winning was never in the set of possible outcomes for Anthropic,” reflecting his view at the time that the startup had lacked the foundation or even the trajectory to succeed in what is an incredibly intense race for advanced artificial intelligence.
Musk’s latest post came amid discussion of Anthropic’s reliance on external compute resources. He praised the company’s progress, stating that Anthropic is “obviously currently the leader in AI” and that “no company has released a model as good as Mythos/Fable,” with expectations of a strong follow-up in Mythos 2.
The tone shifted dramatically from dismissal to acknowledgement of superior performance.
I was clearly wrong about Anthropic. They are obviously currently the leader in AI. No company has released a model as good as Mythos/Fable and they will undoubtedly have Mythos 2 ready soon.
And I would never cut them off in a way that hurt them badly, even as a competitor.…
— Elon Musk (@elonmusk) July 9, 2026
The context of Musk’s comments added significance. Anthropic has been operating under a recent compute deal with SpaceXAI, Musk’s AI infrastructure-focused venture. The pair entered a short-term GPU lease agreement initiated in May, providing Anthropic access to critical computing power for training and deploying its frontier models.
SpaceXAI signs agreement with Anthropic for massive AI supercomputer access
Some observers had speculated that Musk could leverage this dependency to disadvantage a rival. Musk directly addressed the possibility, writing, “I would never cut them off in a way that hurt them badly, even as a competitor. That’s not my style.”
To support his commitment to ethical competition, Musk referenced concrete examples from his other companies. Tesla famously open-sourced its entire portfolio of electric vehicle patents in 2014. The move was designed to accelerate the global adoption of sustainable transportation technology rather than protect proprietary advantages.
Tesla also made its Supercharger network available to competing electric vehicle manufacturers, transforming what could have remained an exclusive charging ecosystem into a shared infrastructure that benefits the broader industry and reduces barriers for EV adoption.
Musk further pointed to SpaceX’s practices, noting that the company launches satellites for competing commercial systems “with no increase in price or use of unfair terms.” He extended the principle to his social platform, observing that “even my worst enemies attack me on this platform,” underscoring preference for open discourse over retaliation.
These examples have illustrated Musk’s long-standing philosophy that long-term technological progress is best served by open competition and infrastructure sharing rather than leveraging market power to stifle rivals. In the fast-evolving AI sector, where compute resources and model capabilities determine leadership, Musk’s stance suggests a willingness to compete on innovation and performance alone.
Musk’s admission arrives as SpaceXAI itself advances its own frontier models while maintaining business relationships across the ecosystem. By publicly correcting his earlier assessment and reaffirming principles of fair play, Musk highlights a model of competition that prioritizes advancement of the field over short-term tactical advantages.
SpaceX reveals Starship Flight 13 launch date
Tesla shows rapid teardown of Model S and X lines, paving the way for Optimus at Fremont
