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 readies Starship Flight 14 for a historic journey into uncharted territory
SpaceX finished Starship’s Flight 14 rehearsal, clearing the way for its first orbital flight Monday.
SpaceX has cleared one of the last hurdles before Starship’s first trip to orbit. The company posted on X Thursday afternoon that its launch rehearsal for Flight 14 was complete, keeping the mission on track for Monday, September 28. The launch window opens at 7:15 a.m. CT at Starbase, Texas, and runs for 75 minutes.
A wet dress rehearsal is essentially launch day without the launch. Crews fill Booster 21 and Ship 41 with thousands of tons of extremely cold propellant, run the countdown nearly to ignition, then drain everything back out. It lets engineers catch leaks or equipment problems before anything leaves the pad. SpaceX still needs a launch license from the FAA before the stack, which stands 407 feet tall, can fly.
Flight 14 matters because of where it is going. All 13 previous Starship flights followed a suborbital path, which works like throwing a ball extremely high and far: the vehicle reaches space, but it is always on a course that brings it back down within about an hour. This time, Ship 41 will perform a short engine firing called an orbital insertion burn roughly 25 minutes after liftoff, giving it enough speed to keep falling around Earth instead of back into it. SpaceX plans about six laps at an altitude near 275 kilometers (171 miles) over nearly 10 hours, as Teslarati detailed when the mission was first announced.
Launch rehearsal complete ahead of Starship Flight 14 pic.twitter.com/h5LBYyBqi4
— SpaceX (@SpaceX) September 24, 2026
Getting into orbit also means Starship has to prove it can get back out. The ship must relight a single Raptor engine in space to slow down for reentry. SpaceX says it will only attempt the orbital insertion burn after flight controllers confirm the hardware needed for that return burn has enough backup, and its flight plan includes health checks that could shorten the mission to two or five orbits.
Flight 14 is also the first to put working satellites into service. Flight 13 carried 20 Starlink V3 satellites in July, but they came back down with the ship because that mission never reached orbit. This time, 26 V3 satellites are meant to stay up and join the constellation within a few weeks. Together they add about 26 terabits per second of network capacity, which SpaceX says is roughly 10 times what a single Falcon 9 launch of older V2 Mini satellites adds. Three of them carry cameras that will photograph Starship’s heat shield in orbit to check for tile damage before reentry.
The hardware has changed too. Ship 41 flies with extra fasteners on tiles in the most vulnerable areas, fixes for gaps where superheated plasma slipped behind tiles, and curved tiles designed to reduce heating between them. Two tiles recovered from Ship 40 will fly again, the first reuse of any part of a Starship heat shield. Booster 21 carries better engine filtering and new relight software after ice clogged three center engines on the previous booster, leaving only eight of 13 engines to restart for its landing burn.
Ship 41 is targeting a splashdown in the Pacific Ocean west of Chile, a new recovery zone after several Indian Ocean landings, while Booster 21 aims for the Gulf. Neither will be caught by the tower on this flight. Elon Musk said in August that a ship catch was likely “in a few months.”
Elon Musk
Google just picked SpaceX for its first step into orbital AI
Google will launch its first Project Suncatcher AI satellite on SpaceX’s Transporter-18 rideshare next week.
Google is about to put its own AI chips into orbit for the first time, and it is paying SpaceX to get them there.
The company said Thursday that the first in-orbit test of Project Suncatcher, its research effort to find out whether space can host large-scale AI computing, will fly next week on SpaceX’s Transporter-18 rideshare mission.
The satellite, called MVP, is about the size of a refrigerator and carries four of Google’s Tensor Processing Units, the same chips Google runs in its ground data centers. Google originally planned to launch two custom satellites in 2027, but chose to move faster by integrating its chips into a satellite.
MVP’s solar panels supply about one kilowatt of power, and Google will run Gemini models on the TPUs only in bursts of roughly 15 minutes before the chips shut down so the radiators can shed heat. In a blog post, Google said its Trillium TPUs survived vibration testing that mimicked sustained launch loads of up to 10g, with individual components seeing 50 to 100g, and handled a radiation dose greater than a five year mission would deliver.
SpaceX and Google mull massive partnership on Musk’s orbital data dream: report
Next week’s flight, slated for October 1, follows a relationship that became public in May, when Teslarati reported that Google was in talks with SpaceX for a launch deal tied to orbital data centers. Google also holds a stake of roughly 6% in SpaceX.
The two companies are chasing the same idea from very different starting points. SpaceX’s own orbital compute program is built around the AI1 satellite, a roughly 70 meter structure derived from Starlink V3 hardware that is designed for 150 kW of peak compute, about 150 times the power MVP will draw. Elon Musk has brushed off concerns about crowding orbit with those satellites, and SpaceX is building its Gigasat factory in Bastrop, Texas, to produce them, targeting an annualized rate of about 1 GW of space compute by the end of 2027.
Musk also posted on X on Thursday that “the amount of compute in space will obviously round up to 100% of all compute.”
Google has been more cautious in public. Its research estimates that launch prices need to fall below about $200 per kilogram before an orbital data center can compete with a ground facility on energy cost, a threshold the company believes could be reached around the mid 2030s. The Suncatcher team has said it expects the effort to remain a project rather than a product for years, which leaves the first real test of its hardware riding on a rocket from the company with the most aggressive timeline in the field.
Tesla has taken another concrete step toward owning its battery supply chain, and it’s doing so with what is perhaps the most important vehicle in its short-but-storied history.
On September 23, Tesla announced that it has officially built the first Cybercab with cathode material produced in-house at the company’s first cathode plant in the U.S., and the first in the U.S. overall.
First Cybercab made using our in-house cathode material – from the first cathode plant in the Americas pic.twitter.com/X95aVXsT9H
— Robotaxi (@robotaxi) September 23, 2026
Active cathode material is the most expensive piece of a lithium-ion battery cell, and it often accounts for more than a third of cell cost. For years, the industry sourced a majority of it from Asia, but Tesla’s decision to make it in the United States bodes well for the Cybercab project. This is the latest chapter in Tesla’s vertical integration strategy, which began in public at Battery Day in 2020.
At the Battery Day Event, Elon Musk said the company would build a North American cathode plant and overhaul the process to cut costs and waste, while also making some of the most powerful and long-lasting cells in the industry.
The Austin facility took years to appear. Tesla filed permits for “Project Cathode” in 2022 on land near Giga Texas. By mid-2022, the building frame was up and Tesla later invested hundreds of millions of dollars as part of a larger expansion of the Giga Texas plant. The company stated it was operating the first large-scale cathode production facility in North America to supplement 4680 cell production.
One month later, that material reached a finished Cybercab.
Made with nickel cathode manufactured locally at Gigafactory Texas! https://t.co/DqMm5fZV3n
— Elon Musk (@elonmusk) September 24, 2026
The timing of this breakthrough is monumental for the Cybercab program. As Tesla officially launched the first Cybercab rides to the public earlier this month, production of the ride-hailing-geared vehicle is moving forward on the planned S-curve that CEO Elon Musk told everyone to expect.
Nevertheless, packs of Cybercab units have been spotted throughout the United States, in an effort to potentially activate the fleet as soon as the company gains regulatory approval in various geographic areas.
On top of that, Tesla owning the cathode step and pairing it with its own in-house lithium from the Gulf Coast refinery shortens the supply chain that once stretched thousands of miles and subjects every pack to fewer external price shocks and geopolitical risks.
Tesla is not yet independent of all of its foreign suppliers, as some precursor metals come from mines and chemical plants. But the first in-house cathode Cybercab shows the company is closing the most expensive and most concentrated gap in its battery production efforts. For a vehicle like Cybercab to operate at a high utilization within the Robotaxi network, that control over cost is so crucial.
It is arguably as important as the software that drives it.
SpaceX readies Starship Flight 14 for a historic journey into uncharted territory
Google just picked SpaceX for its first step into orbital AI
