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.
News
SpaceX Starship Flight 13 faces wrath of the Texas skies
SpaceX pushed Starship Flight 13 to Friday, blaming weather instead of the previous engine issues.
SpaceX called off Thursday’s launch attempt of Starship Flight 13, pushing the mission to Friday because of weather tied to Tropical Storm Bertha. The company confirmed the delay on X, noting “Now targeting Friday, July 24 for Starship’s thirteenth flight test, due to weather. A key objective for the flight test is to get clear imagery from the ground of Starship’s heatshield as it flies at a higher dynamic pressure during ascent, which won’t be possible with today’s weather conditions.”
This is the second delay for Flight 13 in two weeks. SpaceX first tried to launch the mission on July 16, but the countdown ended in an automated abort at T-0 when four of Super Heavy Booster 20’s 33 Raptor engines failed to ignite. Musk said at the time that two Raptors would need to be removed and replaced, as Teslarati reported. The company spent the following week destacking Ship 40 and Booster 20, swapping engines, and running leak checks before restacking the vehicle on Pad 2 Wednesday night, according to Spaceflight Now’s live coverage.
Unlike the engine problem, Thursday’s delay has nothing to do with the hardware. SpaceX wants clean footage of Starship’s heat shield captured from the ground as the vehicle flies through max dynamic pressure, something the storm’s cloud cover over South Texas would not allow. The company said visibility should improve for Friday’s attempt, with the same 90 minute window opening at 5:45 p.m. CT.
Flight 13 will be the second outing for the V3 versions of Starship and Super Heavy, following their debut on Flight 12 in May. The mission carries 20 production Starlink V3 satellites, the first time SpaceX has flown operational satellites rather than mass simulators on Starship. Six of those satellites are fitted with cameras to inspect the heat shield from a different angle during ascent, giving engineers a second data source beyond the ground imagery the weather is currently blocking.
Booster 20 will attempt a boostback burn and a splashdown landing burn in the Gulf of America, while Ship 40 follows a suborbital trajectory toward a landing in the Indian Ocean. The flight plan largely mirrors Flight 12, though the booster will run a more aggressive ascent burn after max Q this time, and the ship’s heat shield includes load sensing tiles meant to measure stress at the higher dynamic pressure SpaceX is targeting.
If Friday’s attempt succeeds, Flight 13 could be the last suborbital test in the program. SpaceX is already looking to push for an orbital flight on Flight 14.
Tesla stock (NASDAQ: TSLA) endured one of its sharpest single-day declines in years on July 23, tumbling approximately 14.5 percent and closing near $320 after opening the session around $374. The drop erased more than $140 billion in market value amid heavy trading volume and left the shares at multi-week lows.
The sell-off followed the company’s second-quarter 2026 results, released the previous evening. Tesla reported record revenue of $28.2 billion, up 26 percent year over year, driven by a Q2-record 480,126 vehicle deliveries. Energy storage deployments also rose strongly.
Tesla (TSLA) Q2 2026 earnings results: miss on EPS, beat on revenue
Yet profitability disappointed sharply. Operating income fell 57 percent to $398 million, compressing the operating margin to just 1.4 percent. Non-GAAP earnings per share came in at $0.33, well below the roughly $0.53 analysts had expected. Free cash flow turned negative by $1.1 billion as capital expenditures surged 142 percent to $5.8 billion, largely tied to accelerated spending on artificial intelligence, robotics, and autonomous systems.
The losses on capex were expected, as Tesla said it would be spending heavily in 2026.
Investors also reacted to lingering uncertainty surrounding key product timelines. During the Earnings Call, management reiterated ambitions for Robotaxi deployment and the Optimus humanoid robot, but offered limited new concrete milestones, renewing questions about execution pace that have long accompanied Tesla’s ambitious roadmap.
The magnitude of the decline places it among Tesla’s more severe one-day percentage losses since its 2010 initial public offering. Historically, the two largest single-day drops (split-adjusted) remain September 8, 2020, when shares fell 21.1 percent amid broader market volatility and valuation concerns, and January 13, 2012, with a 19.3 percent plunge during the company’s early growth struggles.
Other notable declines include an 18.6 percent drop on March 16, 2020, at the onset of pandemic-related market turmoil. Thursday’s move ranks roughly ninth on the all-time list but stands out as the steepest in more than a year.
Despite the short-term pain, Tesla’s long-term trajectory has repeatedly recovered from such volatility. The latest results underscore both the strength of its core automotive and energy businesses and the near-term costs of heavy investment in next-generation technologies.
Elon Musk clapped back at France’s decision to withhold the approval for Tesla’s Full Self-Driving (FSD) Supervised system, projecting a clear and blunt message to French Transport Minister Phillippe Tabarot, after he publicly rejected the technology in its current form.
Tabarot outlines several concerns with Tesla Full Self-Driving in a detailed video statement, where he said, “The safety trade-offs are not yet sufficient to authorize it as it currently stands,” he said. He emphasized that FSD is not a true self-driving system and that the driver remains fully responsible.
Key issues Tabarot also brought up included allowing speeding when surrounding traffic exceeds limits and what he believes are insufficient guarantees of driver attention during complex urban maneuvers such as lane changes, intersections, and roundabouts.
Delaying the approval of FSD in France will cost lives
— Elon Musk (@elonmusk) July 22, 2026
While acknowledging technological progress and France’s support for autonomous innovation, Tabarot stressed that deployment must prioritize road safety. He noted ongoing technical discussions with Tesla, the Netherlands, and other European partners, with further ecosystem meetings planned for the fall.
Musk’s rebuke highlights the human cost of regulatory caution. Tesla’s latest safety reports provide compelling data supporting accelerated adoption. In the most recent 12-month period, vehicles using FSD (Supervised) recorded one major collision per approximately 5.1 million miles driven, dramatically better than the U.S. national average of one crash per 698,000 miles.
Even Tesla vehicles driven manually with active safety features outperform the average by a wide margin. These figures come from billions of real-world miles of telemetry, showing FSD vehicles involved in far fewer incidents than both manual Teslas and the broader U.S. fleet.
Critics argue Tesla’s comparisons require careful scrutiny regarding reporting thresholds and fleet demographics, yet the data consistently positions FSD as a potential lifesaver. With road fatalities remaining a leading cause of death worldwide, Musk contends that proven safer technology should not face prolonged bureaucratic hurdles.
France’s measured approach reflects the broader European regulatory caution, which many, especially Musk, have been critical of in the past. However, as autonomous systems from Tesla and competitors like Waymo demonstrate superior safety in independent studies, pressure is mounting for harmonized approvals.
Musk’s warning carries the belief that every month of delay may equate to avoidable tragedies on European roads.
SpaceX Starship Flight 13 faces wrath of the Texas skies
Tesla stock tumbles after earnings, one of its sharpest single-day declines
