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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 highlights one of Tesla FSD Supervised’s most underrated features
In his post on X, Musk wrote, “Tesla self-driving now recognizes hand signals.”
Tesla’s Full Self-Driving (Supervised) is able to recognize and respond to hand signals, as highlighted recently by CEO Elon Musk.
In his post on X, Musk wrote, “Tesla self-driving now recognizes hand signals.”
Musk shared the update in a quote reply to a video posted by Tesla Europe, which showed a vehicle operating with Full Self-Driving (Supervised) navigating a tight lane in the Netherlands while responding to hand gestures from a person directing traffic.
Hand signal recognition is an important capability for advanced driver-assistance and autonomous systems. In real-world driving, pedestrians, construction workers, parking attendants, and other drivers frequently use hand gestures to direct traffic, yield right of way, or indicate when it is safe to proceed. For a self-driving system operating in mixed environments, interpreting these non-verbal cues is critical.
Musk’s post comes as Tesla owners have surpassed 8 billion cumulative miles driven with FSD (Supervised) engaged. “Tesla owners have now driven >8 billion miles on FSD Supervised,” the company wrote in a post on X.
Annual FSD (Supervised) miles have increased sharply over the past five years. Roughly 6 million miles were logged in 2021, followed by 80 million in 2022, 670 million in 2023, 2.25 billion in 2024, and 4.25 billion in 2025.
In the first 50 days of 2026 alone, Tesla owners logged another 1 billion miles. At the current pace, the fleet is trending toward approximately 10 billion FSD (Supervised) miles this year.
Tesla’s latest North America safety data, covering all road types over a 12-month period, also indicates that vehicles operating with FSD (Supervised) were recorded one major collision every 5,300,676 miles. By comparison, the U.S. average during the same period was one major collision every 660,164 miles.
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Tesla hiring for Commercial Charging role hints at Semi push in Europe
The job opening was highlighted by David Forer, Senior Project Developer for Charging at Tesla, on LinkedIn.
Tesla appears to be expanding its Commercial Charging efforts in Central Europe. The job opening was highlighted by David Forer, Senior Project Developer for Charging at Tesla, on LinkedIn.
In a post on LinkedIn, Forer stated that Tesla is looking for a “high-energy executer to own Commercial Charging Sales in Central Europe.” He added that the role will involve closing commercial deals across Tesla’s “entire product range (Supercharging & Megacharging).”
The job listing specifies that the hire will lead the sale of Tesla’s high-power charging products, including Supercharger and Heavy Duty Charging, to major partners such as charge point operators, real estate owners, and retail companies. The role requires fluency in German and English and is based onsite in Munich.
Tesla already operates more than 75,000 Superchargers globally, though the Semi’s Megacharger network is still in its early stages. The inclusion of Heavy Duty Charging in the job description is notable, then, as it aligns with Tesla’s Megacharger infrastructure, which is designed to support the Tesla Semi.
Tesla CEO Elon Musk recently confirmed that the Tesla Semi is moving into high-volume production this 2026. In a post on X, Musk noted that “Tesla Semi starts high volume production this year.”
Aerial footage of the Tesla Semi Factory near Giga Nevada also shows that the facility looks nearly complete, with work now underway inside the facility.
Tesla has also refreshed the Semi lineup on its official website, listing two variants: Standard and Long Range. The Standard trim offers up to 325 miles of range with an energy consumption rating of 1.7 kWh per mile, while the Long Range version provides up to 500 miles.
Both variants support fast charging and can recover up to 60% of range in 30 minutes using compatible infrastructure such as the Megacharger Network.
The presence of Heavy Duty Charging in a Central Europe-focused sales role could indicate that Tesla is preparing charging infrastructure ahead of wider Semi deployment in the region. While Tesla has not formally announced a European launch timeline for the Semi, the vehicle, particularly its range, makes it an ideal fit for the area.
Tesla Full Self-Driving is set to get an awesome new feature in the near future, CEO Elon Musk confirmed on X.
Full Self-Driving is the company’s semi-autonomous driving program, which is among the best available to the general public. It still relies on the driver to ultimately remain in control and pay attention, but it truly does make traveling less stressful and easier.
However, Tesla still continuously refines the software through Over-the-Air updates, which are meant to resolve shortcomings in the performance of the FSD suite. Generally, Tesla does a great job of this, but some updates are definitely regressions, at least with some of the features.
Tesla Cybertruck owner credits FSD for saving life after freeway medical emergency
Tesla and Musk are always trying to improve the suite’s performance by fixing features that are presently available, but they also try to add new things that would be beneficial to owners. One of those things, which is coming soon, is giving the driver the ability to prompt FSD with voice demands.
For example, asking the car to park close to the front door of your destination, or further away in an empty portion of the parking lot, would be an extremely beneficial feature. Adjusting navigation is possible through Grok integration, but it is not always effective.
Musk confirmed that voice prompts for FSD would be possible:
Coming
— Elon Musk (@elonmusk) February 21, 2026
Tesla Full Self-Driving is a really great thing, but it definitely has its shortcomings. Navigation is among the biggest complaints that owners have, and it is easily my biggest frustration with using it. Some of the routes it chooses to take are truly mind-boggling.
Another thing it has had issues with is being situated in the correct lane at confusing intersections or even managing to properly navigate through local traffic signs. For example, in Pennsylvania, there are a lot of stop signs with “Except Right Turn” signs directly under.
This gives those turning right at a stop sign the opportunity to travel through it. FSD has had issues with this on several occasions.
Parking preferences would be highly beneficial and something that could be resolved with this voice prompt program. Grocery stores are full of carts not taken back by customers, and many people choose to park far away. Advising FSD of this preference would be a great advantage to owners.
Elon Musk highlights one of Tesla FSD Supervised’s most underrated features
Tesla hiring for Commercial Charging role hints at Semi push in Europe
