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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.
News
Tesla confirms that it finally solved its 4680 battery’s dry cathode process
The suggests the company has finally resolved one of the most challenging aspects of its next-generation battery cells.
Tesla has confirmed that it is now producing both the anode and cathode of its 4680 battery cells using a dry-electrode process, marking a key breakthrough in a technology the company has been working to industrialize for years.
The update, disclosed in Tesla’s Q4 and FY 2025 update letter, suggests the company has finally resolved one of the most challenging aspects of its next-generation battery cells.
Dry cathode 4680 cells
In its Q4 and FY 2025 update letter, Tesla stated that it is now producing 4680 cells whose anode and cathode were produced during the dry electrode process. The confirmation addresses long-standing questions around whether Tesla could bring its dry cathode process into sustained production.
The disclosure was highlighted on X by Bonne Eggleston, Tesla’s Vice President of 4680 batteries, who wrote that “both electrodes use our dry process.”
Tesla first introduced the dry-electrode concept during its Battery Day presentation in 2020, pitching it as a way to simplify production, reduce factory footprint, lower costs, and improve energy density. While Tesla has been producing 4680 cells for some time, the company had previously relied on more conventional approaches for parts of the process, leading to questions about whether a full dry-electrode process could even be achieved.
4680 packs for Model Y
Tesla also revealed in its Q4 and FY 2025 Update Letter that it has begun producing battery packs for certain Model Y vehicles using its in-house 4680 cells. As per Tesla:
“We have begun to produce battery packs for certain Model Ys with our 4680 cells, unlocking an additional vector of supply to help navigate increasingly complex supply chain challenges caused by trade barriers and tariff risks.”
The timing is notable. With Tesla preparing to wind down Model S and Model X production, the Model Y and Model 3 are expected to account for an even larger share of the company’s vehicle output. Ensuring that the Model Y can be equipped with domestically produced 4680 battery packs gives Tesla greater flexibility to maintain production volumes in the United States, even as global battery supply chains face increasing complexity.
Elon Musk
Tesla Giga Texas to feature massive Optimus V4 production line
This suggests that while the first Optimus line will be set up in the Fremont Factory, the real ramp of Optimus’ production will happen in Giga Texas.
Tesla will build Optimus 4 in Giga Texas, and its production line will be massive. This was, at least, as per recent comments by CEO Elon Musk on social media platform X.
Optimus 4 production
In response to a post on X which expressed surprise that Optimus will be produced in California, Musk stated that “Optimus 4 will be built in Texas at much higher volume.” This suggests that while the first Optimus line will be set up in the Fremont Factory, and while the line itself will be capable of producing 1 million humanoid robots per year, the real ramp of Optimus’ production will happen in Giga Texas.
This was not the first time that Elon Musk shared his plans for Optimus’ production at Gigafactory Texas. During the 2025 Annual Shareholder Meeting, he stated that Giga Texas’ Optimus line will produce 10 million units of the humanoid robot per year. He did not, however, state at the time that Giga Texas would produce Optimus V4.
“So we’re going to launch on the fastest production ramp of any product of any large complex manufactured product ever, starting with building a one-million-unit production line in Fremont. And that’s Line one. And then a ten million unit per year production line here,” Musk stated.
How big Optimus could become
During Tesla’s Q4 and FY 2025 earnings call, Musk offered additional context on the potential of Optimus. While he stated that the ramp of Optimus’ production will be deliberate at first, the humanoid robot itself will have the potential to change the world.
“Optimus really will be a general-purpose robot that can learn by observing human behavior. You can demonstrate a task or verbally describe a task or show it a task. Even show it a video, it will be able to do that task. It’s going to be a very capable robot. I think long-term Optimus will have a very significant impact on the US GDP.
“It will actually move the needle on US GDP significantly. In conclusion, there are still many who doubt our ambitions for creating amazing abundance. We are confident it can be done, and we are making the right moves technologically to ensure that it does. Tesla, Inc. has never been a company to shy away from solving the hardest problems,” Musk stated.
Elon Musk
Rumored SpaceX-xAI merger gets apparent confirmation from Elon Musk
The comment follows reports that the rocket maker is weighing a transaction that could further consolidate Musk’s space and AI ventures.
Elon Musk appeared to confirm reports that SpaceX is exploring a potential merger with artificial intelligence startup xAI by responding positively to a post about the reported transaction on X.
Musk’s comment follows reports that the rocket maker is weighing a transaction that could further consolidate his space and AI ventures.
SpaceX xAI merger
As per a recent Reuters report, SpaceX has held discussions about merging with xAI, with the proposed structure potentially involving an exchange of xAI shares for SpaceX stock. The value, structure, and timing of any deal have not been finalized, and no agreement has been signed.
Musk appeared to acknowledge the report in a brief reply on X, responding “Yeah” to a post that described SpaceX as a future “Dyson Swarm company.” The comment references a Dyson Swarm, a sci-fi megastructure concept that consists of a massive network of satellites or structures that orbit a celestial body to harness its energy.
Reuters noted that two entities were formed in Nevada on January 21 to facilitate a potential transaction for the possible SpaceX-xAI merger. The discussions remain ongoing, and a transaction is not yet guaranteed, however.
AI and space infrastructure
A potential merger with xAI would align with Musk’s stated strategy of integrating artificial intelligence development with space-based systems. Musk has previously said that space-based infrastructure could support large-scale computing by leveraging continuous solar energy, an approach he has framed as economically scalable over time.
xAI already has operational ties to Musk’s other companies. The startup develops Grok, a large language model that holds a U.S. Department of Defense contract valued at up to $200 million. AI also plays a central role in SpaceX’s Starlink and Starshield satellite programs, which rely on automation and machine learning for network management and national security applications.
Musk has previously consolidated his businesses through share-based transactions, including Tesla’s acquisition of SolarCity in 2016 and xAI’s acquisition of X last year. Bloomberg has also claimed that Musk is considering a merger between SpaceX and Tesla in the future.
Tesla confirms that it finally solved its 4680 battery’s dry cathode process
Tesla Giga Texas to feature massive Optimus V4 production line
